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Mechanics of abrasion emissions of particulate matter and microplastics

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Mechanics of abrasion emissions of particulate matter and microplastics

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Content MECHANICS OF ABRASION EMISSIONS OF PARTICULATE MATTER AND MICROPLASTICS by Ketian Li A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirement for the Degree DOCTOR OF PHILOSOPHY (CIVIL ENGINEERING) August 2024 Copyright [2024] Ketian Li ii Acknowledgements First and foremost, I would like to express my deepest gratitude to my advisor, Dr. Qiming Wang. His unwavering commitment to academic excellence and genuine dedication to his students’ success has been nothing short of inspirational. His meticulous guidance, coupled with his ability to challenge and stimulate my thoughts, has not only shaped this research but also profoundly impacted my growth as a scholar. Dr. Wang’s deep reservoir of knowledge, his patience in answering even the most minute queries, and his encouragement during the challenging times of this research journey have been the cornerstone of my academic pursuits. Under his mentorship, I have not just learned the intricacies of my subject but also imbibed a philosophy of perseverance, diligence, and a never-ending quest for knowledge. Additionally, I extend heartfelt appreciation to my esteemed committee members. A special acknowledgment is due to Professor Constantinos Sioutas. His mentorship expands my horizons and honing my analytical skills. His meticulous attention to detail, coupled with an astoundingly comprehensive understanding of the subject, has been invaluable. I am also immensely grateful to Professor Hangbo Zhao, a distinguished expert in the realm of advanced manufacturing. From his research, I always draw a great deal of inspiration for my own scientific inquiries. Next, I would like to express my gratitude to my senior lab mates Kunhao, Justin, An Xin, and Audie. They have always been exemplary figures in my academic journey. In particular, I owe a great deal to Kunhao, whose selfless sharing has greatly benefited me. Additionally, I would like to thank my close friend Qipan whose support has been a crucial guarantee for my academic success. Last but not the least, I would like to express my gratitude to my lab mates Yanchu, Yuyan and Haixu. They have been not only valuable partners in my research endeavors but also cherished companions in my life. Finally, I would like to extend my heartfelt gratitude to my parents. Their unwavering support has been the foundation of my academic achievements, and their constant encouragement has been the driving force behind my perseverance. iii Table of Contents Acknowledgements·······················································································ii List of Tables··························································································· viii List of Figures···························································································· ix Abstract ··································································································xiv Chapter 1 : Introduction··················································································1 1.1 Particulate matter: non-tailpipe and tailpipe emissions ···································1 1.2 Airborne particles: PM2.5 and PM10 ·······················································2 1.3 Microplastic pollution ·········································································5 1.3.1 Source of microplastic: primary microplastic and secondary microplastic. ···5 1.3.2 Harmful effect of Microplastics.·····················································9 1.3.3 Current microplastic removal method in wastewater treatment plant ········ 13 1.4 Laundry microfiber ·········································································· 16 1.4.1 Definition and Characteristics of Laundry Microfibers························· 16 1.4.2 Growing Laundry Microfiber Pollution··········································· 17 1.4.3 Harmful Effects of Laundry Microfibers ········································· 18 1.4.4 Current Wastewater Treatment and Challenges ································· 19 1.5 Engineering living material································································· 20 1.5.1 Cell production of substances to modify material properties ·················· 21 1.5.2 Cell delivery of motion to impart mobility ······································· 22 1.5.3 Cell production of bonding between engineering host·························· 24 1.5.4 Cell delivery of charges for energy generation ·································· 26 1.6 Overview of Dissertation···································································· 27 Chapter 2 : Mechanics of Abrasion Emissions of Particulate Matter and Microplastics ····· 30 iv 2.1 Objective ······················································································ 30 2.2 Significance statement······································································· 30 2.3 Introduction ··················································································· 31 2.4 Experimental setting and mechanism hypotheses ······································· 34 2.5 Hypothesis validation with polyurethanes of various fatigue toughnesses··········· 36 2.6 Verification with polyurethanes under various loading conditions···················· 40 2.7 Verification with source materials for daily-life abrasion emissions ················· 42 2.8 Guidance for mitigating abrasion emissions·············································· 43 2.9 Outlook ························································································ 45 2.10 Materials and methods ····································································· 45 2.10.1 Materials. ············································································ 45 2.10.2 Preparation of polyurethane samples. ··········································· 46 2.10.3 Measurement of polyurethane crystallinities. ·································· 46 2.10.4 Measurement of Young’s modulus � and Poisson’s ratio �.················ 47 2.10.5 Measurement of fatigue properties. ·············································· 47 2.10.6 Characterization of abrasion emissions.········································· 48 2.10.7 Characterization of sandpaper grits. ············································· 49 2.10.8 Measurement of the coefficient of friction of the sandpapers. ··············· 49 2.10.9 Study of distance of measurement point for PM10 ···························· 49 2.11 Supplementary Information ······························································· 51 Chapter 3 : Mechanics of Abrasion-Induced Particulate Matter Emission ····················· 57 3.1 Objective ······················································································ 57 3.2 Introduction ··················································································· 57 3.3 Experimental·················································································· 61 3.4 Theoretical framework ······································································ 67 v 3.4.1. Problem statement··············································································· 67 3.4.2 Boundary Value Problem of Macroscopic Fracture····························· 68 3.4.3. Effective moduli of a cracked body··············································· 75 3.4.4. Additional energy release rate model············································· 81 3.4.5 Overall problem-solving process ·················································· 83 3.5 Results ························································································· 88 3.5.1 Theoretical results for micromechanical model of cracked body ············· 88 3.5.2. Effects of variables on energy release rates ····································· 89 3.5.3 On the critical point of �� = ��··································· 93 3.5.4 Calibration of dispersion rate � ··················································· 94 3.5.5 Effect of material toughness on particulate emission ··························· 95 3.5.6. Effect of normal force on particulate emission·································· 97 3.5.7 Effect of surface roughness on particulate emission ···························100 3.6 Conclusions ··················································································102 Chapter 4 : Harnessing microorganisms to upcycle plastic waste to multifunctional engineered living materials···········································································105 4.1 Objective ·····················································································105 4.2 Introduction ··················································································105 4.3 Results and discussions·····································································108 4.3.1 Manufacturing concept·····························································108 4.3.2 Living bacterial binding enables exceptional mechanical properties ········111 4.3.3. Living bacteria enable solids capable of giant energy generation ···········115 4.3.4 Living bacteria enable self-healing and re-processing ·························118 4.4 Discussions···················································································121 4.5 Material and Methods ······································································123 vi 4.5.1 Materials.·························································································123 4.5.2 Preparation of plastic powder in various diameter.·····························123 4.5.3 Cultivation of S.P. bacteria (ATCC 11859) on agar gel. ······················123 4.5.4 Preparation of samples. ····························································124 4.5.5 Characterization of the static mechanical properties. ··························125 4.5.6 Characterization of the dynamical mechanical properties.····················126 4.5.7 Preparation of the PET microbial fuel cells. ····································126 4.5.8 Characterization of the PET microbial fuel cells power output.··············127 4.5.9 Characterization of the PET microbial fuel cells stiffness.····················127 4.5.10 Characterization of the healing process.········································128 4.5.11 Characterization of the reprocessing (method 1: adding biofilm).··········128 4.5.12 Characterization of the reprocessing (method 2: adding growth medium).128 4.5.13 Characterization of the self-strengthening. ····································129 4.6 Supplementary Information································································130 Chapter 5 : Upcycling Laundry Fibers into Multifunctional Engineered Living Materials ·158 5.1 Objective ·····················································································158 5.2 Introduction ··················································································158 5.3 Paradigm concept ···········································································161 5.4 Mechanical properties ······································································165 5.5 Crack-healing property ·····································································169 5.6 Energy generation property ································································172 5.7 Potential applications·······································································175 5.8 Conclusion ···················································································178 5.9 Materials and Methods ·····································································179 5.9.1 Materials.·························································································179 vii 5.9.2 Preparation of microfibers. ····································································179 5.9.3 Cultivation of S.P. bacteria (ATCC 11859) on agar gel. ······················180 5.9.4 Preparation of samples for mechanical testing.·································181 5.9.5 Preparation of bacterial sample for SEM imaging.·····························181 5.9.6 Characterization of the static mechanical properties. ··························182 5.9.7 Characterization of the fracture toughness.······································182 5.9.8 Characterization of the healing process. ·········································183 5.9.9 Preparation of the microfiber-microbial fuel cells.·····························184 5.9.10 Characterization of the microfiber-microbial fuel cells power output.·····184 5.9.11 Characterization of the impact resistance property ···························185 5.9.12 Characterization of the chemical resistance····································186 5.10 Supplementary Information ······························································186 Chapter 6 : Research summery and outlook ·······················································195 References ······························································································198 viii List of Tables 3.1. Employed parameters for the calculation for Figs. 3.8-3.9.……………………………………………90 3.2. The mean diameter and standard deviation of the grit diameter of various types of sandpaper, and the measured kinetic friction coefficients between these sandpaper and various polyurethane samples.……………………………………………………………………………………………91 ix List of Figures 2.1 Daily-life abrasion emission examples and the proposed setup for abrasion emission experiments.……………………………………………………………………………………………………………………33 2.2 Mechanistically relating the abrasion emission of polyurethanes to their fatigue fracture properties.……………………………………………………………………………………………………………39 2.3 Mechanism verification with polyurethanes under various loading conditions.………41 2.4 Mechanism verification with source materials for daily-life abrasion emissions.……………43 2.S1 Characterization of crystallinities of polyurethane samples.…………………………………51 2.S2. Measurement of the fatigue property of an organic sample.……………………………………52 2.S3. The fatigue toughness thresholds �! in a function of the crystallinities of polyurethane samples PU-A to PU-E. ………………………………………………………………………………53 2.S4. The PM10 concentrations in a function of the fatigue toughness thresholds �! for the abrasion emission results shown in Fig. 2B.…………………………………………………………………53 2.S5. Visualization of the emitted particulate matter.………………………………………………………54 2.S6. Characterization of sandpaper grits.…………………………………………………………………55 2.S7. The PM10 concentration in a function of measurement point distance.………………………55 3.1. Overall impact of abrasion emission of tire particles.……………………………………………………61 3.2. Experimental setup.…………………………………………………………………………………………………63 3.3. Size distribution of the abrasion-emitted particles from five polyurethan elastomer samples.……………………………………………………………………………………………………………………………64 3.4. Measurement of the fatigue property of polymer samples.……………………………………………66 3.5. The overall structure of the problem-solving framework.……………………………………………84 x 3.6. A 2D rectangular topology model to illustrate the relationship between the particle number and the crack number.…………………………………………………………………………………………85 3.7. Mechanical properties of cracked body. ……………………………………………………………………89 3.8. Parameter study for the total energy release rate and the additional energy release rate. 92 3.9. Density plot of energy release rate � under various parameters �", �#, and 〈�〉.………94 3.10. Calibration of the dispersion rate with experimental results of Material C.…………………95 3.11. Effect of material toughness on the particulate emission. Particle concentrations of PM10 particles emitted from various materials.…………………………………………………………………97 3.12. Effect of normal forces on the particulate emission. Particle concentrations of PM10 particles emitted from Material B under various compression forces.………………………99 3.13. Effect of surface roughness on the particulate emissions. Particle concentrations of PM10 particles emitted from Material B with various sandpapers.……………………………………101 4.1. The manufacturing concept of upcycling waste plastics into engineering living materials.…………………………………………………………………………………………………………………………110 4.2. Mechanical properties of the engineering living material consisting of plastic powder and the biofilm.………………………………………………………………………………………………………………114 4.3. Energy generation property of the living material consisting of plastic powder and the bacteria.……………………………………………………………………………………………………………………117 4.4. The schematic displaying the self-healing and re-processing process of the living material.…………………………………………………………………………………………………………………………120 4.S1. Grinding process and characterization of plastic powder.…………………………………………130 4.S2. Bacteria cultivation process.……………………………………………………………………………………131 4.S3. Stress-strain curves, Young’s modulus, flexural strength, and toughness of control xi groups.……………………………………………………………………………………………………………………………132 4.S4. Stress-strain curves, Young’s modulus, flexural strength, and toughness of PET-bacteria composites with different mass ratios.…………………………………………………………133 4.S5. Stress-strain curves, Young’s modulus, flexural strength, and toughness of PET-bacteria composites with different mass ratios.…………………………………………………………134 4.S6. Stress-strain curves, Young’s modulus, flexural strength, and toughness of PET-bacteria composites with different average PET diameters.………………………………………135 4.S7. Frequency response of storage modulus �′, loss modulus �′′, and phase shift tan � with different mass ratios.………………………………………………………………………………………………136 4.S8. Frequency response of storage modulus �′, loss modulus �′′, and phase shift tan � with different average PET diameters.………………………………………………………………………………137 4.S9. Drop test on a cushion substrate.………………………………………………………………………………138 4.S10. Schematic illustration of PET microbial fuel cell.…………………………………………………139 4.S11. Hacker board setup to measure power.……………………………………………………………………140 4.S12. Schematic illustration of indentation test on a PET microbial fuel cell.……………………141 4.S13. Mechanical test data of self-healing for 1 hour.………………………………………………………142 4.S14. Mechanical test data of self-healing for 2 hours.……………………………………………………143 4.S15. Mechanical test data of self-healing for 4 hours.……………………………………………………144 4.S16. Mechanical test data of self-healing for 6 hours.……………………………………………………145 4.S17. Mechanical test data of self-healing for 12 hours.…………………………………………………146 4.S18. Mechanical test data of self-healing for 18 hours.…………………………………………………147 4.S19. Mechanical test data of self-healing for 24 hours.…………………………………………………148 xii 4.S20. Mechanical test data of self-healing for 48 hours.…………………………………………………149 4.S21. Mechanical test data of reprocessing, cycle 0, method 1.………………………………………150 4.S22. Mechanical test data of reprocessing, cycle 1, method 1.………………………………………151 4.S23. Mechanical test data of reprocessing, cycle 2, method 1.………………………………………152 4.S24. Mechanical test data of reprocessing, cycle 3, method 1.………………………………………153 4.S25. Mechanical test data of reprocessing, cycle 4, method 1.………………………………………154 4.S26. Reprocessing samples by method 2.……………………………………………………………………155 4.S27. Stress-strain curves, Young’s modulus, flexural strength, and toughness of reprocessed samples in different cycles, method 2.……………………………………………………………156 4.S28. Self-strengthening of samples.……………………………………………………………………………157 5.1. The sources of microfibers, the microfiber living composite materials made by 3D printing and their superior properties.………………………………………………………………………………164 5.2. The mechanical property of the microfiber living composite materials.………………………168 5.3. The self-healing property of the microfiber living composite materials.………………………171 5.4. The self-powering property of the microfiber living composite materials.…………………174 5.5. The potential applications of the microfiber living composite materials.……………………177 5.S1. Laundry microfibers collected from the washing machine filter screen.……………………186 5.S2. Bacteria cultivation process.……………………………………………………………………………………187 5.S3. Optical microscope image (a) and SEM image (b) of the bacterial Sporosarcina Pasteurii.…………………………………………………………………………………………………………………………188 5.S4. Characterization of the fracture toughness (critical energy release rate �) using pure shear test.………………………………………………………………………………………………………………189 xiii 5.S5. Preparation of the microfiber-microbial fuel cells.……………………………………………………190 5.S6. Hacker board setup for LED blinking.……………………………………………………………………191 5.S7. The simulation results of the heavy object suspension test using finite element method.…………………………………………………………………………………………………………………………192 5.S8. Screw spikes drop test on the material.……………………………………………………………………193 5.S9. Characterization of the chemical resistance of the microfiber living material.……………194 xiv Abstract Microplastic pollution constitutes a substantially detrimental type of environmental contamination and poses threats to human health. Among the sources of airborne and marine microplastics, evidence indicates that non-exhaust emissions resulting from tire abrasion and other organic materials have emerged as a notable contributor. However, the mechanistic comprehension of organic material abrasion emissions has thus far remained obscure. We hypothesize the abrasion emission of organic materials as a fatigue fracture process and discover that abrasion emissions start to drastically increase only when the abrasion-induced effective energy release rate exceeds the fatigue toughness threshold of the organic material, and that the corresponding surface area density of the emitted particulate matter scales with the crack propagation area rate of the fatigue fracture. To enhance our comprehension of this kind of emission, we developed a multi-scale scratching model using the principles of linear elastic fracture mechanics. Macroscopically, material wear and tear can be viewed as a process of macro-crack propagation associated with the fatigue fracture. Microscopically, we take into account the effect of microcracks propagating under cyclic loading on the material’s modulus and energy release rate during fatigue fracture. This framework enables a more accurate calculation of the energy release rate, effectively simulates material abrasion and fragmentation, and elucidates the emission mechanism of finer particles. Furthermore, addressing the issue of microplastic recycling, we propose a solution to upcycle microplastic fibers into engineering living materials harnessing microorganisms. The microplastics recovered from washing machines and dryers can be restructured and rebuilt using bacterial adhesion and then shaped into complex components with 3D printing technology. This material not only exhibits excellent mechanical properties but also possesses outstanding self-healing and self-powering xv capabilities. These attributes make the material potentially applicable in flexible armor, robotic skin, and self-powering infrastructure. 1 Chapter 1 : Introduction 1.1 Particulate matter: non-tailpipe and tailpipe emissions Particulate matter (PM) is a critical environmental pollutant that significantly impacts human health. PM can be classified by its size and composition, with varying effects on health outcomes [1-3]. Long-term exposure to ambient PM is linked to cardiovascular, respiratory, and carcinogenic diseases, as well as premature death [4-8]. PM is released into the atmosphere either as primary aerosols or through secondary chemical processes, originating from both natural and anthropogenic sources [9, 10]. Among these, traffic emissions are a primary source of ambient PM in urban areas [11]. Traffic emissions are broadly categorized into tailpipe and non-tailpipe emissions. Tailpipe emissions result from fuel combustion and lubricant volatilization during the engine’s operation. These emissions primarily include fine particles (PM2.5) and ultrafine particles (PM0.1), which are predominantly composed of hydrocarbons [12]. On the other hand, non-tailpipe emissions encompass road dust resuspension, tire wear, brake wear, clutch wear, and road surface wear [13]. Non-tailpipe particles are generally larger and contain significant amounts of redox-active and toxic metals, contributing to their high oxidative potential and associated health risks [14-16]. Over the past two decades, due to stringent environmental regulations, the relative contribution of exhaust and non-exhaust emissions has changed. Regulatory measures have successfully reduced exhaust emissions through advanced emission control technologies [17, 18]. However, this reduction has led to an increased proportion of non-exhaust emissions, primarily due to increased traffic volume and the proliferation of electric vehicles [19-22]. Although electric vehicles do not produce exhaust emissions, they significantly contribute to road dust [23]. 2 Traffic-related non-exhaust emissions now constitute a large portion of total nonexhaust emissions [18]. These emissions are particularly challenging to control because of their diverse sources and complex nature. Factors such as wear and tear of vehicle components (like tires and brakes), road surface conditions, and local meteorological conditions all affect the generation of non-exhaust emissions [24, 25]. Despite efforts to reduce these emissions through measures such as the use of dust suppressants and street cleaning, effective management remains exceedingly difficult. In summary, while legislative measures have successfully reduced exhaust emissions, the significance of non-exhaust emissions, especially those from tire wear and brake wear, has been steadily increasing. This presents an increasingly serious environmental and public health challenge. Addressing this issue requires a comprehensive understanding of the sources and mechanisms of non-exhaust emissions and the development of targeted strategies to mitigate their impact. 1.2 Airborne particles: PM2.5 and PM10 As a form of pollution with significant detrimental impacts on the ecological environment and human health, air pollutants have been extensively studied over the years. Among the various types of air pollution, particulate matter (PM) pollution is one of the most significant [26-29]. Particulate matter in the air is typically denoted as PM, referring to Particulate Matter. Utilizing a nationwide network of monitoring sites, the EPA has developed ambient air quality trends for Particulate Matter (PM) [30-32]. Particulate matter (PM) is a complex mixture of tiny particles and liquid droplets suspended in the air. According to statistics, globally, approximately 2.5 to 3 billion tons of particulate matter are emitted into the atmosphere annually through both natural and anthropogenic activities [33]. Larger particles tend to settle to the Earth’s surface within a short period, while smaller particles can remain suspended in the air for extended durations. It is estimated that around 100 million tons of particulate matter 3 remain in the atmosphere at any given time [34]. The concentration of particulate matter in the air can also be expressed in terms of the number of particles per unit volume of air. In relatively clean air, the number concentration of particulate matter typically ranges from a few hundred to about 1,000 particles per cubic centimeter. In polluted urban air, the number of particulate matter particles per cubic centimeter averages around 100,000 [35]. During severe air pollution episodes, the number of particulate matter particles in the air can exceed 1,000,000 or even more [36]. Larger particulate matter suspended in the air tends to settle to the Earth’s surface within a short period due to gravity, whereas smaller particles can remain suspended in the air for extended durations. It is generally considered that particles with a diameter of less than 100 micrometers have the potential to become air pollutants [36]. Among these particles, PM2.5 and PM10 are the most representative. PM2.5 and PM10 are classified based on the aerodynamic diameter of the particulate matter: particles with an aerodynamic diameter less than 2.5 micrometers are categorized as PM2.5, while those with an aerodynamic diameter of 10 micrometers or less are categorized as PM10. This classification considers only the size and mass concentration without regard to the chemical composition and toxicity of the particles. The diameter of PM10 is approximately one-fifth the thickness of a human hair and can enter the human nasal cavity and mouth; hence, it is also referred to as inhalable particulate matter [8]. The diameter of PM2.5 is about one-twentieth the thickness of a human hair. PM2.5 is a subset of PM10, but due to its smaller size, PM2.5 can penetrate the respiratory tract, reach the lungs, and enter the alveoli directly. Therefore, PM2.5 is also known as respirable particulate matter [36, 37]. Particulate Matter (PM) can be classified into primary particles and secondary particles based on their formation mechanisms. Primary particles are those directly emitted from sources, while secondary particles refer to those formed through physicochemical processes from 4 gaseous substances such as sulfur dioxide and nitrogen oxides that are emitted into the air [38]. Due to the differences in sources and formation mechanisms, the composition of particulate matter in the air also varies significantly [39]. Moreover, particles that remain suspended in the air for extended periods can adsorb various substances during their drift and undergo complex physicochemical reactions on their surfaces, making the composition of particulate matter even more complex, with dozens of different metal and non-metal elements [39]. Studies have shown that the major components of PM2.5 include elemental carbon, organic carbon compounds, sulfates, nitrates, ammonium salts, among others, which account for approximately 70-80%. Other various metal elements present in PM2.5 include sodium, magnesium, calcium, aluminum, iron, lead, zinc, arsenic, cadmium, and copper [40]. Many of these metal elements are toxic heavy metals. Additionally, because PM2.5 can migrate extensively in the atmosphere, it can become a carrier for various toxic and harmful substances in the air, including numerous microorganisms, bacteria, viruses, and strong carcinogenic organic compounds such as polycyclic aromatic hydrocarbons [38, 40]. Due to the minute size and complex composition of PM2.5 and PM10, their adverse effects on human health are well-documented and concerning. Larger particles in PM10 are generally intercepted by the respiratory system, with most particles remaining in the mouth and nasal cavities or primarily in the throat. In contrast, PM2.5 can bypass the body’s natural defense mechanisms, entering the respiratory and circulatory systems [37]. Once in the respiratory system, PM2.5 can reach the lungs, directly impacting pulmonary ventilation, interfering with gas exchange, and potentially inducing pulmonary fibrosis, asthma, bronchitis, and even lung cancer [41, 42]. When PM2.5 enters the circulatory system, it can travel along the blood vessels to the heart, causing cardiovascular diseases. The World Health Organization (WHO) estimates that millions of premature deaths each year are attributable to particulate matter exposure [43]. Furthermore, the impact of PM2.5 and PM10 on the ecological 5 environment is significant and cannot be overlooked. PM2.5, in particular, has a strong scattering effect on sunlight, leading to reduced atmospheric visibility and air turbidity. The high concentration of suspended PM2.5 and PM10 in the air also provides ideal condensation nuclei for the formation of haze, which is considered the primary cause of haze formation[44]. The sources of PM2.5 and PM10 are diverse. In urban areas, traffic-related emissions constitute a significant proportion [45]. Traffic-related emissions can be divided into tailpipe emissions and non-tailpipe emissions. As tailpipe emissions are strictly regulated by laws, reducing non-tailpipe emissions, such as tire wear and brake wear, has become a new research focus [46]. The particulate matter generated from tire wear and brake wear is predominantly composed of microplastics, which are hard to naturally degrade and can persist in the atmosphere for extended periods. This persistence makes the study of the mechanisms behind tire wear and brake wear abrasion emissions critically important. 1.3 Microplastic pollution 1.3.1 Source of microplastic: primary microplastic and secondary microplastic. Microplastic pollution has emerged as a significant environmental issue due to its widespread presence in terrestrial and marine ecosystems [47, 48]. Initially, the focus was on larger plastic debris [49], but recent studies have highlighted the hazards posed by tiny plastic particles known as microplastics [50-52]. The concept of microplastics was first introduced by Thompson in 2004 [53], referring to fibers, particles, and fragments with diameters less than 5 mm. Due to their small size, microplastics have more severe impacts on ecological environments, marine organisms, and human health compared to larger plastic debris [47, 54, 55]. Based on their sources, microplastics can be classified into two major categories: primary microplastics and secondary microplastics [56]. l Primary Microplastics 6 Primary microplastics are manufactured as small plastic fragments and directly released into the environment. Their production pathways are diverse, primarily encompassing household activities, industrial production, and transportation. In household activities, laundering and drying clothes are the main sources of microplastic generation and release. Microplastics produced and released through laundering and drying clothes account for approximately 35% of microplastic emissions in the marine environment [57]. The mechanism behind this is that synthetic fiber clothing releases microplastic fibers due to mechanical abrasion and agitation during washing and drying [58-60]. These fibers detach from the fabric and enter wastewater. Given their small size, wastewater treatment plants cannot effectively capture all these microplastic fibers, which are likely to eventually enter aquatic ecosystems and oceans. Additionally, microplastics are frequently used in facial cleansers, hand soaps, cosmetics, and even pharmaceuticals [61-65]. For example, in cosmetics, the primary ingredients of exfoliants and skin cleansers include polyethylene (PE) and polypropylene (PP) particles (<5 mm), polystyrene (PS) beads (<2 mm), and polyolefin granules (74-420 microns) [66]. During the regular use of these products, microplastics are also released into the environment [67]. In industrial production, microplastic particles serve as raw materials for manufacturing plastic products such as plastic bags. At the same time, microplastics are often generated and released into the environment as by-products, for example, during the maintenance of plastic or plastic-based materials and as a result of the degradation process of plastic products [68, 69]. Additionally, some microplastics are specifically produced for industrial applications, such as abrasives [70]. For instance, microplastics can be used in drilling fluids for oil and gas exploration and as industrial abrasives [63]. They can also be used as abrasive agents for removing rust and paint. In the transportation sector, the wear and tear of vehicle tires on roads generates and releases microplastics, which are a major source of marine microplastic pollution, accounting for approximately 28% of microplastic pollution in the oceans [57, 66, 71]. With 7 the continuous increase in the number of vehicles, this proportion shows a trend of annual growth [72]. l Secondary Microplastics The formation of secondary microplastics can be understood as a process in which various environmental factors and the properties of polymers collectively act to reduce the structural integrity of larger plastic debris, resulting in the creation of microplastics. Physical, chemical, and biological processes all contribute to this breakdown [73-75]. When large plastic fragments are exposed to sunlight, they become brittle, yellowed, and cracked due to intense UV radiation, strong oxidation, and physical abrasion from waves and turbulence, eventually breaking down into smaller microplastic particles [76]. These microplastics often become submerged in surface water or deeper environments, and over time, they continue to degrade into even smaller particles. A significant source of secondary microplastics is the degradation of consumer products such as plastic bags, bottles, and other packaging materials. As the quantity of microplastics increases, the abundance of plastics in marine environments also rises, thereby amplifying their potential impact on the ecological environment. Both primary and secondary microplastics can enter the marine environment through various pathways, including maritime activities and land-based activities. Microplastics generated and released from the laundering of synthetic fiber clothing can enter aquatic environments through industrial or domestic wastewater systems [77]. Even if these microplastic-containing waters pass through wastewater treatment plants, a significant proportion of microplastics, due to their density, size, and abundance, are not captured by the treatment systems and ultimately enter the marine environment through runoff [78]. Microplastics can also directly enter the marine environment via stormwater, sewers, wind, and water currents. Sludge is another potential source of microplastic pollution. Microplastics captured and separated by wastewater treatment systems are not fully degraded but remain in 8 the sludge, which is then widely used as fertilizer for crops, leading to secondary entry into the ecological environment and subsequent pollution [77, 79]. Additionally, microplastics can enter the ocean through the feces of zooplankton. Organisms exposed to microplastic environments readily ingest microplastics, which are then encased in feces and excreted. These feces can be ingested by other marine organisms after excretion. The size (< 5 mm) and associated low density of microplastics facilitate their long-distance transport and widespread distribution via water currents. Consequently, the ultimate fate of microplastics is the marine environment: they either accumulate in the marine water column, settle in seabed sediments, or are ingested by organisms and transferred through the food chain [77]. To date, plastic debris has been found in the Pacific Ocean, Indian Ocean, Atlantic Ocean, polar regions, and the deep sea [50-52, 80]. Some plastics undergo physical, chemical, and biological processes that cause them to fragment and reduce in size, forming microplastic particles. Browne et al. discovered marine microplastic pollution along the shorelines of 18 locations worldwide, spanning six continents from the equator to the poles [51]. Large quantities of microplastic particles have been found in Africa, Asia, Southeast Asia, India, South Africa, North America, and Europe [81]. Beyond the oceans, microplastics have also been detected in estuaries, rivers, straits, inland lakes, urban outskirts, and other locations. The widespread distribution of industrial resin pellets has been documented globally, with significant concentrations found on beaches in New Zealand, Canada, Bermuda, Lebanon, and Spain[82]. In Japan, high levels of polychlorinated biphenyls (PCBs) have been detected in polypropylene pellets collected from beaches [83]. Similar findings in Singapore, India, and Belgium further illustrate the ubiquity of these pollutants [84-86]. Currently, scientists employ various microscopy and spectroscopy techniques to monitor and analyze microplastic pollution, such as optical microscopy, electron microscopy, Raman spectroscopy, nuclear magnetic resonance (NMR), and Fourier-transform infrared 9 spectroscopy (FTIR) [67, 87, 88]. Numerous studies have confirmed that various marine organisms, from plankton to fish, can ingest significant amounts of microplastics. Their widespread distribution poses severe impacts on ecological environments, the health of marine organisms, and human health [47, 89]. 1.3.2 Harmful effect of Microplastics. Due to their widespread presence and resistance to degradation, microplastics have become ubiquitous pollutants in both terrestrial and aquatic ecosystems [90]. A substantial body of research has confirmed that microplastic pollution poses significant threats to ecosystems and human health [54]. The following will provide a comprehensive overview of the dangers posed by microplastics, with a focus on their adverse impacts on the natural environment, their transfer through the food chain, and their effects on human health. l Environmental Impact of Microplastics Large quantities of microplastic pollutants can alter the physical and chemical characteristics of marine habitats [91]. For instance, coral reefs, which provide habitat for one-third of marine fish species and thousands of other organisms, are particularly susceptible to microplastic ingestion [92]. Corals often mistake microplastics for food, leading to their accumulation in the digestive system [93]. Over time, this can significantly impair coral health, reduce coral species diversity, and diminish habitat heterogeneity [94, 95]. Moreover, the extensive presence of microplastics in the ocean greatly affects plankton [96, 97], which play a crucial role in marine ecosystems as key members of the marine food web [98]. Phytoplankton, in particular, are essential for producing organic matter through photosynthesis, providing energy for other marine organisms. However, microplastics can penetrate the cell walls and membranes of phytoplankton, reducing their chlorophyll concentration and impairing their photosynthetic efficiency. This process can severely disrupt carbon fixation in marine ecosystems [99]. 10 Additionally, microplastic pollution also affects benthic communities, thereby impacting the entire marine ecosystem. Benthic communities are a crucial component of marine ecosystems, comprising approximately 98% of overall marine life. Benthic organisms such as oysters, mussels, and lobsters are particularly prone to microplastic ingestion due to their feeding mechanisms [99]. Suspension feeders like blue mussels and oysters filter large volumes of water to extract food particles, inevitably ingesting microplastics in the process. Studies have found microplastics in the digestive systems of blue mussels and oysters from various coastal regions, indicating the widespread nature of this pollution [100]. The ingestion of microplastics by benthic organisms can severely impair their feeding capacity and health, reducing their adaptability and survival. Moreover, microplastics can accumulate in the sediments where these organisms live, posing long-term risks to benthic communities and the overall health of marine ecosystems [99]. Over time, microplastic pollution will affect the functionality of benthic habitats and marine ecosystems as a whole. l Transfer of Microplastics to the Food Chain The transfer of microplastics through the food chain poses significant threats to both ecological and human health. Due to their small size and widespread presence in aquatic systems and oceans, these tiny particles are ingested by a range of marine organisms, from plankton to large fish and marine mammals [47, 101-103]. Microplastics have a high surface area-to-volume ratio, enabling them to effectively adsorb hydrophobic pollutants from the water. Some microplastic particles either absorb persistent organic pollutants (POPs) from seawater or contain them inherently, including substances like dichlorodiphenyltrichloroethane (DDT), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs) [83]. These pollutants are released into organisms as microplastics move up the food chain. Research has shown that microplastics have the ability to absorb heavy metals from aquatic environments, which can be transported along the food 11 chain, ingested by marine organisms, and penetrate their tissues, posing significant risks to higher trophic level species [99]. For example, harmful algal species exposed to microplastics can produce algal toxins that accumulate in bivalves and crustaceans, continuously concentrating through the food chain and ultimately leading to human poisoning [52, 104]. Laboratory studies have shown that zooplankton, a key component of the marine food web, can ingest microplastics, which can then transfer to higher trophic-level organisms such as fish and marine mammals [96, 105]. The transfer of microplastics and their associated pollutants through the food web results in bioaccumulation and biomagnification, increasing the risk of top predators, including humans, being exposed to harmful substances. Fish, species of both ecological and economic importance, are not exempt from the impacts of microplastics. Fish that ingest microplastics may suffer from physical harm, such as intestinal blockage and tissue damage, as well as chemical harm from toxic additives and adsorbed pollutants leaching out [91]. Studies have reported the presence of microplastics in various fish species, including those consumed by humans. These microplastics, as polymers, attach to the external surfaces of the digestive tract, obstructing and blocking the digestive tract, and can lead to chemical effects such as inflammation, liver stress, and reduced growth [106, 107]. The transfer of microplastics through the food web leads to bioaccumulation and biomagnification, increasing the risk of top predators, including humans, being exposed to harmful substances. l Impact on Human Health The presence of microplastics in the environment and their transfer through the food chain have significant implications for human health. Humans are exposed to microplastics through various pathways, with the consumption of contaminated seafood and marine products being a major route. Studies have shown that certain fish entering the food market contain varying levels of plastic. In the Mediterranean region, approximately 18% of top carnivorous fish, such 12 as swordfish (Xiphias gladius), bluefin tuna (Thunnus thymus), and albacore tuna (Thunnus alalunga), are subject to different degrees of microplastic contamination [108]. Non-biotic marine products can also be contaminated with microplastics, potentially transferring these particles to humans. Yang et al. demonstrated that even table salt is contaminated with microplastics [109]. Moreover, as toxic substances carried by microplastics accumulate and transfer, they can ultimately affect human health and survival. The physical presence of microplastics in the gastrointestinal tract can lead to inflammation, cellular damage, and alterations in the gut microbiome, which are associated with various health conditions such as obesity, metabolic disorders, and gastrointestinal diseases[110]. Additionally, microplastics can carry toxic additives and adsorb harmful environmental pollutants, such as phthalates, polychlorinated biphenyls (PCBs), and heavy metals, which are known to be highly toxic and resistant to degradation [110]. Ingesting microplastics can result in the accumulation of these toxic substances in human tissues, potentially causing a range of health issues, including endocrine disruption, carcinogenic effects, and reproductive toxicity [111]. For example, phthalates and PCBs have been linked to hormonal imbalances and an increased risk of breast cancer in women [112, 113]. Moreover, microplastics often contain additives such as plasticizers, flame retardants, and stabilizers, which can leach out and exert toxic effects on organisms [110]. These additives are known to disrupt endocrine function, leading to hormonal imbalances and reproductive issues. In humans, exposure to endocrine-disrupting chemicals (EDCs) has been associated with infertility, developmental disorders, and certain cancers [111]. Research also indicates that microplastics frequently act as carriers for pathogenic microorganisms, meaning that when humans ingest microplastics, they may also ingest these pathogens, further exacerbating the impact on human health and increasing the risk of infections and diseases. 13 1.3.3 Current microplastic removal method in wastewater treatment plant The widespread presence of microplastics (MPs) in municipal wastewater has become an increasingly serious environmental issue. This is primarily due to the extensive use of plastic microbeads in personal care products and the release of microfibers during the laundering of synthetic textiles. These microplastics, typically less than 5 µm in size, infiltrate wastewater systems and pose significant threats to aquatic ecosystems and human health. Municipal wastewater treatment plants (WWTPs) play a critical role in mitigating these pollutants [78]. However, existing wastewater treatment technologies are not fully effective in removing microplastics, leading to their discharge into natural water bodies. l Stages of Wastewater Treatment and Their Efficacy in MP Removal A typical wastewater treatment plant process includes four stages: primary treatment, primary settling, secondary treatment, and tertiary treatment. Each stage plays a specific role in the overall reduction of microplastics, but no single stage can ensure complete removal of microplastics [114]. Understanding the function and limitations of each stage is crucial for developing comprehensive strategies to address microplastic pollution. l Preliminary Treatment Primary treatment involves the initial removal of large debris and insoluble impurities from wastewater. This step includes coarse screening (6-150 millimeters), fine screening (less than 6 millimeters), and grit removal. The presence of large fats, oils, and grease in wastewater can help capture microplastics, with removal efficiencies ranging from 35% to 59% [114, 115]. However, efficiency varies depending on the physical characteristics of the microplastics, such as size, density, and morphology. The main processes and features are as follows: Ø Coarse Screening: Removes large items, such as rags and branches, to prevent damage to pumps and membranes. 14 Ø Fine Screening: Captures smaller debris, further reducing the load of solid impurities. Ø Grit Removal: Separates heavier particles, such as sand and gravel, to prevent wear on downstream equipment. The removal efficiency during primary treatment is influenced by factors such as the size, chemical composition, and morphology of the microplastics [116]. For example, larger microplastics (1-5 µm) are more easily removed at this stage, while smaller microplastics may pass through the screening process [117]. Additionally, the shape of the microplastics (e.g., fibrous, or spherical) affects their likelihood of being captured by fats or sludge flocs [118]. l Primary Treatment Primary settling involves gravity separation and surface skimming operations to further remove suspended solids and some microplastics. Heavier microplastics settle with the sludge, while lighter microplastics are captured by floating fats. The removal efficiency at this stage ranges from 50% to 98%, significantly influenced by the characteristics of the microplastics and environmental conditions. The main processes include: Ø Gravity Separation: Utilizes sedimentation tanks to allow heavier particles to settle at the bottom, forming sludge. Ø Surface Skimming: Removes lighter particles and floating debris, including fats and oils, which can capture microplastics. In primary settling, the density and buoyancy of microplastics play a crucial role in their removal. For instance, low-density polyethylene (PE) microplastics tend to float and are removed during skimming, while denser microplastics such as polyvinyl chloride (PVC) and polyethylene terephthalate (PET) settle with the sludge [119]. l Secondary Treatment Secondary treatment focuses on the biological degradation of organic pollutants using the activated sludge process. Biofilms formed on microplastic surfaces can alter their buoyancy, 15 enhancing sedimentation and separation [120]. However, the extent of microbial degradation of microplastics is minimal due to short contact times and low degradation rates [121]. Overall, the removal efficiency at this stage ranges from 86% to 99.8%, significantly reducing the size of residual microplastics [122]. The main processes include: Ø Activated Sludge: Involves a mixture of microorganisms used to degrade organic substances in the wastewater. Ø Biofilm Formation: Microorganisms form biofilms on microplastic surfaces, altering their density and aiding in sedimentation. Despite high removal efficiencies, the fragmentation of microplastics during secondary treatment can produce smaller particles that are more challenging to capture. Studies have shown that microplastics larger than 500 micrometers are almost entirely removed, while those smaller than 190 micrometers remain in the effluent [115]. l Tertiary Treatment Tertiary or advanced treatment aims to produce high-quality effluent using techniques such as gravity filtration, sand filtration, disc filtration, dissolved air flotation, biofiltration, and membrane bioreactors (MBR) [118]. These methods collectively provide a removal efficiency of 98% to 99.9% for microplastics. Nevertheless, fibrous microplastics remain difficult to eliminate because they can pass longitudinally through filtration systems [123]. The main processes include: Ø Gravity and Sand Filtration: Remove remaining suspended solids and some microplastics. Ø Disc Filtration and Dissolved Air Flotation: Enhance the removal of fine particles. Ø Biofiltration and MBR: Provide additional treatment, reducing organic and microbial contaminants. 16 Research indicates that fibrous microplastics are the predominant residual type in tertiary treatment effluent, with studies showing that these high aspect ratio particles can pass through filtration pores [123]. This highlights the need for more effective filtration technologies to capture fibrous microplastics. l Challenges in MP Removal and Environmental Implications Despite reports of high removal efficiencies, significant amounts of microplastics remain in treated effluent. In particular, microplastics smaller than 190 micrometers and fibrous microplastics are likely to escape from wastewater treatment plants (WWTPs) and re-enter the ecological environment, continuing to pose environmental threats. Besides, microplastic fibers extracted from wastewater treatment plants (WWTPs) are predominantly retained in sludge. Most of this sludge is either directly landfilled or processed into agricultural fertilizers, without undergoing actual degradation. Consequently, a significant portion of microplastic fibers in sludge re-enters the environment through soil erosion or surface runoff. Additionally, the generation of smaller microplastic particles during treatment complicates the management of microplastic pollution. 1.4 Laundry microfiber 1.4.1 Definition and Characteristics of Laundry Microfibers Microfibers (MFs) from laundry are an emerging environmental issue of global significance. As a type of microplastic, microfibers are typically plastic particles smaller than 5 millimeters. Although some textile-derived microfibers exceed this length, they are still classified as microplastics due to their diameter [124-126]. These longer fibers, once introduced into the environment, can break down into smaller fragments [127]. Microfibers are primary microplastics released from the fashion industry’s supply chain, spanning from textile manufacturing to washing processes and waste management [128]. Among microplastics, 17 microfibers are notable for their high mobility in aquatic environments [129]. Specifically, synthetic textiles such as nylon, wool, and polyester release microfibers during laundry processes. Through mechanical friction and turbulent water flow, these fibers are stripped and carried into wastewater systems. Studies have shown that a 5-kilogram polyester fabric wash load can release between 6 million to 17.7 million microfibers [130], contributing approximately 2.2 million tons of microfibers annually to the marine environment through this mechanism [131]. 1.4.2 Growing Laundry Microfiber Pollution The increasing demand and consumption of synthetic fabrics have exacerbated microfiber pollution. Initially, synthetic fabrics such as nylon, polyester, spandex, and acrylic were produced as low-cost alternatives to natural fabrics [132]. Advances in materials science and engineering have improved the texture of these fabrics, leading to their growing popularity. Today, synthetic fabrics are gradually replacing natural fabrics in the textile industry. Over the past decade, the demand for synthetic fabrics has surged, with a compound annual growth rate (CAGR) of 4% from 2010 to 2019, and it is expected to exceed 7% in the next decade[133]. Specifically, polyester accounts for 52% of synthetic fabric usage, followed by polyamide (5%) and other synthetic fibers (6%) [58, 134]. Polyester is widely used globally due to its mechanical strength, aesthetic appeal, lightweight, and low cost. In 2018, polyester accounted for more than half of the global production of microfiber fabrics in the textile industry, approximately 55 million tons [135]. However, unlike natural fabrics, the microfibers released from synthetic fabrics during washing are very difficult to degrade naturally in the environment. The most common synthetic fabric, polyester, takes over 200 years to degrade [90]. Studies have shown that regenerated cellulose fibers significantly degrade within 90 days in the marine environment, and animal fibers like wool and silk also degrade, albeit more slowly than plant fibers, taking two to four 18 years to fully degrade. However, polyester, which accounts for more than half of the global textile industry’s production volume, shows no signs of degradation[136]. The microfibers generated during the washing of these synthetic fibers are discharged into the ecological environment and accumulate over time. As time progresses, the microplastic pollution caused by washing synthetic garments has become increasingly severe [128]. 1.4.3 Harmful Effects of Laundry Microfibers Microfibers (MFs) are a significant and emerging environmental pollutant that have extensive harmful effects on aquatic habitats and pose potential risks to human health. These synthetic fibers are typically made from non-biodegradable materials such as polyester, acrylic, and nylon. Microplastic pollutants can have severe impacts on the ecological environment, marine life, and human health. The main reasons are as follows: Microfibers have small sizes and vast quantities: Due to their small size, a significant number of microfibers are not captured by wastewater treatment systems and thus smoothly enter the ecological environment. Additionally, because microfibers are difficult to degrade naturally, their continuous accumulation over time has led to a terrifying buildup of microfibers in marine and other ecological environments. Microfibers are almost ubiquitous, contaminating everything from coastlines to the deep sea [137]. Microfibers have strong mobility and can easily bioaccumulate through the food chain. In aquatic environments, microfibers can migrate over large areas through water flow and eventually deposit in the ocean. Even if microfibers are captured by wastewater treatment systems and enter sludge, a considerable proportion of these non-degradable microfibers can re-enter the natural environment through various pathways. When present in aquatic environments, microfibers are easily ingested by aquatic organisms as food. Subsequently, through the biological magnification effect of the food chain, microfibers are transferred to higher-level organisms, posing a significant risk of ultimately harming humans. 19 Microfibers have strong harmfulness and toxicity: Due to their small size, microfibers can penetrate the human cardiovascular and pulmonary systems when ingested, posing serious health risks. Microfibers contain toxic chemicals such as plasticizers, added during textile manufacturing [138], which may be released upon ingestion, harming human health. Additionally, microfibers’ high adsorption capacity for hydrophobic organic chemicals and their large surface area-to-volume ratio allow them to form toxic aggregates by attaching to copollutants [139, 140]. Ingesting microplastics may lead to the accumulation of these toxic substances in human tissues, potentially causing various health issues, including endocrine disruption, carcinogenic effects, and reproductive toxicity [141]. For instance, phthalates and PCBs have been linked to hormonal imbalances and an increased risk of female breast cancer [113]. Moreover, microfibers can serve as hosts for pathogenic bacteria and other microorganisms, which, when ingested by humans, increase the risk of infections and diseases [142, 143]. Due to these characteristics of microfibers and their significant threat to human health, there is an urgent need to develop effective methods for recycling microfibers. 1.4.4 Current Wastewater Treatment and Challenges In many developed countries, laundry wastewater is treated in centralized wastewater treatment plants (WWTPs), where multiple treatment stages achieve high microfiber removal efficiency [115, 144]. Primary treatment includes screening and flotation, which can remove 40-65% of microfibers [115]. Secondary treatment involves the adsorption and flocculation of activated sludge, adding a removal rate of 20-60%. Tertiary treatments, such as sand filtration and microfiltration, further remove the remaining microfibers [125]. Membrane bioreactors, as an alternative tertiary treatment method, can achieve removal rates of 90-99% [145, 146]. Currently, not all WWTPs include tertiary treatment, which is typically reserved for water reuse or sensitive environments [125]. However, microplastic fibers separated from WWTPs are 20 primarily retained in sludge, most of which is either directly landfilled or further processed into agricultural fertilizers without actual degradation. A large portion of the microplastic fibers in sludge re-enters the environment through soil erosion or surface runoff. For example, in North America, 44,000 to 300,000 tons of microplastic fibers return to the environment in this manner [147]. Some studies also indicate that crops can absorb microfibers from sludge used as agricultural fertilizers, allowing these microplastics to continue accumulating through the food chain and ultimately reaching humans, posing a threat to human health. Therefore, there is an urgent need in the industry for a method to recover and upcycle laundry microfibers. 1.5 Engineering living material Engineered Living Composites (ELCs), which integrate living cells into engineered materials, represent a novel class of materials with significant potential for sustainable construction, biomaterials, and eco-friendly manufacturing. Over billions of years of evolution, nature has developed a remarkable ability to create materials with exceptional performance and multifunctionality. Examples include materials with high mechanical strength, such as nacres, bones, teeth, and wood,[148-150] remarkable stretchability, such as spider silk and muscles [151, 152], the ability to change colors, such as the skins of chameleons and cuttlefish, [153, 154]and the capacity to generate electricity, such as electric eels[155]. These natural materials are also notable for their efficiency and sustainability. Inspired by these natural examples, researchers are increasingly focusing on integrating living organisms into synthetic materials to endow them with novel functionalities. This innovative approach aims to harness the inherent capabilities of biological systems, such as self-healing, self-adapting, and environmental responsiveness, to create a new class of materials known as Engineered Living Materials (ELMs). The ELCs could be systematically categorized by exploring the interactions between living cells and their host materials. It delves into various mechanisms such as the Cell production of substances to modify material properties, Cell delivery of motion to impart 21 mobility, Cell production of bonding between engineering host, and Cell delivery of charges for energy generation. 1.5.1 Cell production of substances to modify material properties Living cells function as bio-factories, producing substances like enzymes, cellulose, and glucose that enhance the properties and functionalities of engineered hosts [156]. l Cellular Production of Enzymes for Biomineralization: Microorganism-generated enzymes act as biological catalysts, facilitating reactions under mild conditions and reducing energy consumption and waste. Specific enzymes produced by bacteria like Bacillus subtilis enable biomineralization, enhancing the mechanical properties of materials [157]. Biomineralization is a process where living cells produce enzymes that facilitate the hardening or stiffening of tissue through mineralization [158-160]. This natural phenomenon can be harnessed to create Engineered Living Cells (ELCs) with enhanced mechanical strength, wear resistance, and bioactivity. This is achieved through the precise deposition of minerals such as calcium carbonate or silica by the embedded living cells [161, 162]. This process has been harnessed for applications such as self-healing concrete and the creation of strong, organic/inorganic composites. l Cells Produce Cellulose: Cellulose synthesized by bacteria and plant cells is usually used as primary component of cell walls, providing structural support [163]. Bacterial cellulose, noted for its mechanical strength and biodegradability, is utilized in applications ranging from bio-composite materials to medical wound dressings [34, 164-171]. Research highlights include the development of cellulose nanofiber plates with high mechanical strength and the fabrication of 3D structures capable of regeneration and repair. The Ellis group utilized Komagataeibacter rhaeticus to produce bacterial cellulose (BC) spheroids designed for regenerative and repairable 3D 22 structures [172]. Ruhs and Studart created a 3D printing platform that embeds bacteria in a hydrogel, which is beneficial for bioremediation and biomedical uses [173]. Lastly, the Lu and Ellis group engineered functional bacterial cellulose-based materials through a yeast and bacteria co-culture, facilitating autonomous generation and DNA-encoded modifications for biosensing applications [174]. l Chloroplasts Produce Glucose: Photosynthesis in cyanobacteria and plants converts carbon dioxide and water into glucose and oxygen, providing a sustainable source of glucose. This glucose can be integrated into biohybrid systems for biofuels, chemical synthesis, or biomedical scaffolds. Research has explored embedding chloroplasts into 3D-printable polymers to enhance mechanical strength and self-healing capabilities through glucose-mediated cross-linking [175, 176]. The Wang group embedded living chloroplasts in 3D-printable polymers, enhancing mechanical strength and crack healing through glucose-induced cross-linking [175]. Similarly, Strano’s team developed a gel matrix with chloroplasts that produce glucose for polymerization, thereby improving the gel’s strength and self-repair abilities through photosynthesis and carbon dioxide fixation [177]. 1.5.2 Cell delivery of motion to impart mobility Incorporating living cells into engineering materials enables motion by leveraging cellular motility and responsiveness to environmental stimuli [178]. This allows materials to change shape, move, or self-assemble in response to signals, making them ideal for soft robotics applications. Biohybrid materials, integrating muscle cells or bacteria, can harness cellular contraction or expansion to produce macroscopic movements. Key challenges include maintaining cell viability and functionality within the material over time and achieving precise motion control [179-181]. 23 Recent advances illustrate the potential of biohybrid materials. The Dabiri and Parker group created a swimming jellyfish by seeding neonatal rat cardiomyocytes onto PDMS films, where the cardiomyocytes provided contraction force and the PDMS returned the structure to its shape [182]. Similarly, the Sanchez and Schmidt group developed a micro-bio-robot combining a motile sperm cell with a magnetic microtube for movement, studying factors affecting the robot’s speed and ensuring secure cell encapsulation [183]. The Park group developed a biohybrid robotic ray mimicking batoid fish swimming by integrating rat cardiomyocytes onto an elastomeric body, achieving controlled swimming through phototactic guidance [184]. The Zhao group created a cardiomyocytes-driven soft robot inspired by snakes and caterpillars, using asymmetric claws and CNT-assisted myocardial tissue for crawling motion, with isoproterenol increasing crawling speeds [185]. The Saif group fabricated a microscale flagellated swimmer propelled by cardiomyocyte-induced deformations in a PDMS filament [186]. The Bashir group demonstrated light-controlled skeletal muscle actuators for bio-bots with directional and rotational locomotion. The Zhao group designed biohybrid actuators with cardiomyocytes in an anisotropic inverse opal structure, inducing shape changes and modulating structural colors in response to cardiomyocyte activity [187, 188]. The Leonardo group developed tunable bio-hybrid micromotors using genetically engineered E. coli bacteria, modulating swimming speeds with light intensities [189]. The Tanaka group introduced a bio-micropump powered by earthworm muscle, integrated with microchips, and triggered by an external power supply [190]. These studies highlight the transformative potential of biohybrid materials, combining biological cells with engineered materials to enhance dynamic functionalities for applications in soft robotics, biomedical devices, and beyond. 24 1.5.3 Cell production of bonding between engineering host The integration of biological cells with engineering materials is a rapidly evolving field poised to transform the creation of composite materials. By combining the structural integrity of traditional engineering substances with the dynamic functionality of biological organisms, this research holds significant promise. At the molecular level, this integration relies on complex adhesion mechanisms, including chemical covalent bonds and physical interactions such as van der Waals forces and electrostatic interactions [191-194]. These mechanisms are carefully designed to ensure the viability of organisms and their functional integration with engineered scaffolds. l Bacteria as Bio-Glues Bacteria act as innovative bio-glues, opening new avenues for the fabrication of bio-hybrid materials that dynamically respond to their environment. These microorganisms can adhere to a variety of surfaces and interfaces, making them ideal for integrating biological functions with non-living materials[195]. Through biofilm formation, bacteria create robust, self-regenerating matrices that bind cells to engineering scaffolds, imparting living characteristics such as selfhealing and adaptive responses to external stimuli. The Zhong group introduced engineered Bacillus subtilis biofilms as "living glues" that mimicked natural underwater adhesion systems [196]. These biofilms demonstrated enhanced adhesion capabilities and tunable performance through enzymatic modification and metal ionassisted curing. The Schmit-Dannert group engineered Bacillus subtilis to express selfassembling protein scaffolds capable of silica biomineralization and cell cross-linking, enhancing the mechanical properties of biocomposite materials [197]. They also developed a dual-strain glue system capable of autonomously performing repair tasks by localizing to damage sites and forming physical plugs with amyloid glue components. 25 Liu and Herrmann introduced a novel bio-glue composed of genetically engineered supercharged polypeptides (SUPs) and anionic aromatic surfactants, achieving ultra-strong adhesion on hard substrates [198]. This strong adhesion, including on soft tissues, was accomplished without covalent bond formation, using a balance of non-covalent interactions such as electrostatic complexation, cation-π interactions, hydrophobic interactions, and possibly hydrogen bonding. The Riedel-Kruse group developed a genetically encoded platform in Escherichia coli for controlled multicellular assembly through cell-cell adhesion [199]. This platform utilized a library of surface-displayed nanobodies and antigens, enabling selective and adjustable adhesion among bacterial cells. They used this bacterial adhesive toolbox to create programmable, self-healing living sensors capable of detecting bioelectrical and biomechanical signals [200]. Bacteria can also serve as anchor points for further polymer synthesis. For example, Soh and Hawker’s cytocompatibility-controlled radical polymerization (CRP) techniques generated structurally defined synthetic polymers on live cell surfaces [201]. This method allowed for the efficient addition of synthetic polymers to the surfaces of yeast and mammalian cells with high grafting efficiency and cell viability. l Mycelium-Based Engineered Living Composites Mycelium, the root-like structure of fungi, is notable for its sustainability, versatility, and potential in construction applications [202]. Mycelium-based materials exhibit biodegradation [203], self-repairing capabilities, thermal insulation [204, 205], acoustic dampening [206, 207], and strong mechanical strength, which can be tailored to specific applications through controlled growth conditions [208-211]. The Cao group proposed mechanically strong fungal mycelium composites with chemical adhesion and mechanical interlocking, utilizing extracellular polymeric substances and glycosylated proteins in the fungal outer cell walls [212]. The Schaak and Wang groups 26 developed a fungi-bacterial composite to create moldable, foldable, and regenerative living structures by introducing engineered Pantoea agglomerans into feedstocks [213]. The bonding mechanism relied on fungal mycelium growth on feedstock, forming a dense network of hyphal mycelium that bonded particles and fibers together, creating cohesive biocomposite materials. These materials demonstrated unique properties such as healing, recyclability, and scalable manufacturing due to the dynamic nature of non-covalent interactions. The bonding between cells and engineering materials represents a pivotal advancement in creating multifunctional composites. The integration of biological organisms with engineered scaffolds through sophisticated adhesion mechanisms and innovative bio-glue strategies offers promising potential for the development of self-healing, adaptive, and environmentally responsive materials. 1.5.4 Cell delivery of charges for energy generation Microbial fuel cells (MFCs) utilize the metabolic processes of microorganisms to convert organic and inorganic substrates directly into electrical energy, offering a sustainable alternative to traditional power sources [214, 215]. These cells generate electric current by leveraging specific bacteria to oxidize substrates, releasing electrons captured by an electrode. MFCs’ versatility allows them to use a wide range of substrates for energy production and pollution reduction [216, 217]. However, challenges remain in enhancing their efficiency and longevity, scaling the technology for practical applications, and improving our understanding of microbial-electrode interactions. Current research focuses on optimizing electrode materials and configurations and genetically engineering microorganisms for better performance [218]. Advancements in cell-material bonding are crucial for improving MFCs’ performance and scalability. The Choi group presented power electronic skins (e-skins) that utilize MFCs with sweat-eating bacteria, converting sweat’s chemical energy into electrical energy for sustainable, self-sufficient power [219]. The Etienne group fabricated a living bio-composite 27 for electroactive biofilms using bacterial cells with carbon nanotubes (CNTs) and cytochrome c, achieving high current density [220]. The Etienne group fabricated a living bio-composite for electroactive biofilms using bacterial cells with carbon nanotubes (CNTs) and cytochrome c, achieving high current density. The Zhang group developed an aerosol jet printing method to create hierarchical indium tin oxide electrode structures, enhancing cell loading and light transmission, and capturing electricity from cyanobacterium Synechocystis sp. PCC 6803 [221]. The Nixon group used an inkjet printing approach to fabricate cyanobacterial and electrode components for biophotovoltaic cells, maintaining the viability and photosynthetic capacity of cyanobacteria and addressing scale-up issues [222]. The Zhong group developed TcReciver/CsgAA7 biofilms for CdS NPs mineralization, integrating biological and engineering materials for enhanced performance and scalability of MFCs [223]. The Adir group used live cyanobacteria, specifically Synechocystis sp. PCC 6803, to generate photocurrent and hydrogen in a bio-photoelectrochemical cell (BPEC) [224]. This process utilized light-driven electron transfer from both respiratory and photosynthetic systems to a graphite electrode without external electron donors or acceptors. 1.6 Overview of Dissertation This report presents the findings from four distinct research projects, each contributing to two primary areas of focus. The first area involves the elucidation of the mechanisms behind particle generation resulting from organic material abrasion emissions, approached from the perspective of fatigue fracture mechanics. Utilizing these mechanistic insights, we propose a series of effective strategies to mitigate the production of abrasion emissions. The second area addresses the persistent issue of plastic and microplastic waste, which are notoriously challenging to recover. We introduce an innovative approach that harnesses biotechnological advancements to upcycle these wastes into engineered living materials. These engineered 28 living materials demonstrate not only superior mechanical properties but also self-healing and self-powering capabilities. In Chapter 2, this study elucidates the mechanistic relationship between the abrasion emissions of organic materials and their mechanical properties. Through a mechanics model, it is proposed that abrasion emissions stem from a fatigue fracture process under cyclic loading. Emissions significantly increase when the abrasion-induced effective energy release rate surpasses the material’s fatigue toughness threshold. Beyond this threshold, the surface area density of particulate matter correlates with the crack propagation area rate. These findings offer guidance for reducing the abrasion emissions of airborne particulate matter and marine microplastics. In Chapter 3, this study introduces a multi-scale fatigue fracture model to understand the abrasion emission mechanism of organic materials. On a macro scale, the original materials form macroscopic cracks on the scratched surface as hard objects cut and scoop the material, leading to material detachment. Concurrently, numerous microcracks develop within the matrix of the original material, propagating under cyclic loads and causing the material to fragment into smaller particles. Using the multi-scale fatigue fracture framework, a predictive model was developed that accurately forecasts the size distribution and quantity of abrasion particles. These findings establish a quantitative link between fracture mechanics and PM10 emissions, providing insights into particulate formation and potential pollution reduction. In Chapter 4, this study presents a microbiological method for upcycling plastic wastes into functional load-bearing structures. By leveraging bacterial adhesion on plastics (such as PE, PS, PP, PET), this eco-friendly process spontaneously forms composites with superior mechanical properties. Optimal bacterial concentration and smaller plastic particle size enhance stiffness, strength, and toughness. These composites also exhibit superior damping and unique living properties, including self-powering, self-healing, and reprocessing 29 capabilities. This method not only recycles plastics but also offers sustainable solutions for future infrastructure, energy, and pollution challenges. In Chapter 5, this study proposes an innovative method for upcycling microfibers using living microorganisms. By utilizing bacterial biofilms, laundry microfibers can be transformed into composite materials with exceptional mechanical properties and superior fracture toughness. This method bypasses the need for chemical bond disruption, making it more energy-efficient and cost-effective compared to chemical degradation approaches. Additionally, the resulting biogenic materials are suitable for 3D printing and exhibit selfhealing and self-powering functionalities, making them promising for applications in biorobotics, flexible living armor, and self-powered infrastructure. This strategy provides a novel perspective on environmental protection and the management of pollutants by converting waste into advanced engineered living materials. In Chapter 6, a conclusion that summarizes all projects is provided with the outlook of future research. 30 Chapter 2 : Mechanics of Abrasion Emissions of Particulate Matter and Microplastics 2.1 Objective Data show that non-exhaust emissions from the abrasion of tires and other organic materials have become a significant contributor to airborne particulate matter and marine microplastics. However, the mechanistic understanding of abrasion emissions of organic materials has remained elusive to date. Here, we hypothesize the abrasion emission of organic materials as a fatigue fracture process and validate the hypothesis with different organic materials under various cyclic loading conditions. We discover that abrasion emissions start to drastically increase only when the abrasion-induced effective energy release rate exceeds the fatigue toughness threshold of the organic material, and that the corresponding surface area density of the emitted particulate matter scales with the crack propagation area rate of the fatigue fracture. This work not only opens the door for mechanistically understanding particulate matter pollution from a solid mechanics perspective, but also provides rational guidance for modern society to mitigate abrasion emissions of airborne particulate matter and marine microplastics. 2.2 Significance statement While controlled abrasion has been long employed to shape objects, until recent decades unintentional abrasion of organics had been identified as a key process for emitting non-exhaust airborne particulate matter and marine microplastics that result in extremely high health and ecological risks. Modern society is urgently calling for effective mitigation strategies, but the bottleneck is the lack of a mechanistic understanding of abrasion emissions. In this study, we attempt to understand the abrasion emission by mechanistically relating it to a fatigue fracture process. This study may serve as the foundation for future rational strategies for abrasion emission mitigation. 31 2.3 Introduction When an organic material undergoes cyclic friction with an engineering object with a higher hardness, the organic material will be abraded to emit particulate matter. Such an abrasion emission phenomenon can be widely found in daily life, such as tire particle emissions from automobiles (Fig. 2.1A), organic braking-pad particle emissions from vehicles or motors (Fig. 2.1B), and shoe sole particle emissions from footwear (Fig. 2.1C). As exhaust emissions of particulate matter have been significantly reduced by tight regulations, non-exhaust emissions, such as tire and brake emissions, have become the major source of airborne particulate matter in the urban society [225-227]. According to a report from the United Kingdom Government’s Air Quality Expert Group in 2019 [228], non-exhaust emissions constitute 60% PM2.5 and 73% PM10 from road transport. In addition, researchers have revealed that particles released from vehicle tires are previously unrecorded but indeed significant sources of microplastics in the marine environment [71, 72, 229, 230]. For example, it has been estimated that 100,000 metric tons of microplastics are shed from tires, transported through the air, and dumped in the ocean every year [231]. Worse still, a great proportion of particles emitted from abrasion emissions are microplastic particles, tiny plastic pieces less than 5 mm long, which are challenging to naturally degrade in the environment and tend to accumulate over time [11, 232, 233]. Larger particles could gradually break down into smaller particles that can potentially be inhaled by humans [4-6, 234-237]. Since airborne particulate matter poses a high health risk to human beings [238-241] and marine microplastics detrimentally affect the marine ecological metabolism [71, 72, 229, 230], modern society is calling for effective strategies for mitigating their emissions from the abrasion of tires and organic materials. However, without understanding the fundamental mechanism of the abrasion emission, any randomly proposed strategy may become irrational. 32 To uncover the mechanism secret of abrasion emissions from tires and other organic materials, previous studies primarily focused on the experimental characterization of abrasion emissions of particulate matter in the lab [242, 243] or the field [227, 244]. Despite the enormous effort, the fundamental mechanism of abrasion emissions of organic materials has remained largely elusive to date [225, 226, 245]. Some researchers believe abrasion should be a mechanics problem that is associated with material fracture [246-252], while others believe abrasion emissions may be associated with thermally induced burning of organic materials [225, 226]. Even for those who consider abrasion emission as a fracture mechanics problem [246- 252], a mechanistic understanding of the relationship between abrasion emissions and the mechanical properties of organic materials remains unknown. A mechanistic understanding framework, if successfully established, would provide rational and quantitative guidance for mitigating abrasion emissions of airborne particulate matter [225-227] and marine microplastics [71, 72, 229, 230]. Here, we uncover the secret of abrasion emissions by establishing a mechanistic linkage between the abrasion emissions of organic materials and their mechanical properties. By employing a mechanics model, we hypothesize that the abrasion emission is a fatigue fracture process that organic particles are abraded and detached from the material bulk under cyclic loading. Thus, the emission of the particulate matter is closely related to the effective energy release rate applied by the cyclic abrasion loading. We discover that abrasion emissions start to drastically increase only when the abrasion-induced effective energy release rate exceeds the fatigue toughness threshold of the organic material. Above the fatigue toughness threshold, the surface area density of the emitted particulate matter scales with the crack propagation area rate of the fatigue fracture. These discoveries have been experimentally verified with synthetic polyurethane polymers that feature various crystallinities and are loaded with kinetic frictions with various normal loads and surface roughness. We also verify the discoveries with various 33 abrasion emission sources in daily life: a tire casing from an automobile, a braking pad from a bicycle, and a rubber sole from footwear. The discoveries in this report not only dispute the previously believed thermal-burning hypothesis and verify a mechanics mechanism, but also derive rational guidance for the modern society for mitigating the abrasion emissions of airborne particulate matter and marine microplastics. Figure 2.1 Daily-life abrasion emission examples and the proposed setup for abrasion emission experiments. (A) Tire particle emissions from an automobile. (B) Organic brake pad particle emissions from a bicycle. (C) Shoe sole particle emissions from footwear. (D) The proposed setup for abrasion emission experiments. A motor-controlled rotating wheel with a polymer sample firmly attached to the wheel is housed in a closed chamber. Sandpaper was forced to abrade the moving polymer sample to emit particles that are detected by a particle counter. (E) A zoom-in schematic to illustrate the contact between the sandpaper and the polymer sample. (F) A further zoom-in schematic to illustrate the abrasion fracture induced by a sandpaper grit. C Pavement Pushing force on ground Shoe sole particles Rotating direction B Force Brake pad particles Pavement Tire particles A Rotating direction D Rotating direction Polymer sample Chamber Particle counter Emitted particles Normal load Rotating wheel Sandpaper E F Normal load Sandpaper Polymer sample Sample moving direction Sandpaper grit 34 2.4 Experimental setting and mechanism hypotheses To mimic the abrasion emission induced by cyclic friction in daily life (Fig. 2.1A-C), we constructed an experimental system as shown in Fig. 2.1D. A motor-controlled rotating wheel with a polymer sample firmly attached to the wheel was housed in a closed chamber. Sandpaper was forced to contact the polymer sample with a prescribed normal force that was controlled by a force gauge. When the motor was turned on, the polymer particles were emitted from the abrasion between the polymer sample and the sandpaper. A particle counter at 20 cm to the abrasion location was used to measure the concentration of the emitted particles with sizes ranging from 0.3 µm to 10 µm. We choose particles below 10 µm (PM10) for the measurement because PM10 is the primary harmful airborne particulate matter widely existing in daily life [225-227]. To understand the abrasion emission in the experimental setting shown in Fig. 2.1D, we here propose a mechanics hypothesis. Similar to the tire abrasion of a moving automobile (Fig. 2.1A), the polymer sample is abraded by the sandpaper cyclically. Considering the nature of cyclic loading, we hypothesize that the emission of the polymer particles is due to a fatigue fracture process [253-255], in that polymer materials are fractured and detached from the bulk under cyclic abrasion loading. During the abrasion loading, sandpaper with �$ grits (each grit with a circular cross-section with a mean diameter of �) is forced by a normal load �% to indent into the polymer sample by a depth of � (Figs. 2.1EF). The indentation depth � of each grit can be estimated as � ≈ �%(1 − �&)FC2��$E , where � and � are Young’s modulus and Poisson’s ratio of the polymer sample, respectively [256]. As the polymer sample is moving with the rotating wheel, the sandpaper abrades the polymer by using a friction force �" to fracture and detach the polymer materials in front of the sandpaper grits (Fig. 2.1F). During this process, there are two main processes that take place simultaneously. On the macro level, the original materials would form a macroscopic crack on the scratched surface as the 35 hard object cuts and scoops the original material. The macrocrack are the primary cause of material detachment. Meanwhile, the original material will generate numerous microcracks within the matrix. These microcracks will also continue to propagate under cyclic loads and become the reason for the material to split into smaller particles after detaching from the matrix. The applied enegegy release rate of the macrocrack could be a dominant factor for the abrasion emissions. Such an abrasion mechanics problem has been solved by Akono et al. [249, 250] who approximated the applied energy release rate as �' = �" &(1 − �&)/H2��&�$ &�(1 + 2�/�)J, where the friction force �" = ��% and � is the kinetic friction coefficient that is affected by the surface roughness. Plugging the indentation depth � into �', we can estimate the applied energy release rate to fracture and abrade the polymer as �' = �&�%�� &��$ & + �%(1 − �&) . (1) According to the theory of fatigue fracture proposed by Paris et al. [253] and the later studies that applied the fatigue fracture theory to rubbers [254, 255], when a cyclic load is applied to a solid with a pre-existing crack, there is a material property called fatigue fracture toughness threshold �! that determines whether the fatigue crack will propagate or not. Based on this concept, we propose the following mechanics hypothesis: when �' < �! , the preexisting crack is expected not to propagate under infinite cycles of loads, which means that the polymer materials will ideally not be abraded and detached from the material bulk, thus corresponding to a very low concentration of abrasion emissions of particulate matter in the experiment. However, when �' > �!, the pre-existing crack is expected to propagate under cyclic loads, leading to a relatively high concentration of abrasion emissions of particulate matter, which should be quantitatively relevant to the crack propagation rate of the fatigue fracture under the applied loads. 36 2.5 Hypothesis validation with polyurethanes of various fatigue toughnesses To validate the above mechanism hypothesis, we selected a class of polyurethanes as a model material system, because the fatigue fracture toughness of polyurethane rubbers can be widely tuned by simply varying the polymer chain length (Fig. 2.S1) [257]. The polyurethanes with shorter chain lengths feature higher crystallinities that are expected to result in higher fatigue fracture toughness (Fig. 2.S1), because the energy per unit area required to fracture crystalline domains in a polymer is much higher than that required to fracture the amorphous domain [258, 259]. We here denote these polyurethane samples as PU-A to PU-E for crystallinities of 53.3, 42.7, 29.8, 20.6, and 9.9, respectively (Fig. 2.S1). To measure the fatigue property of the polyurethanes, we employ the single-notched method that has been widely used in fatigue tests of polymer materials (Fig. 2.S2) [254, 255]. Specifically, we use an unnotched sample under a cyclic stretch �( (with a frequency of 5 Hz) to estimate the energy release rate � and use a single-notched sample under the same cyclic stretch �( to observe the crack propagation per loading cycle ��⁄��, namely, crack propagation area rate, where � is the crack surface area (i.e., crack length times sample thickness) and � is the cycle number. For each material shown in Fig. 2.2A, the crack propagation area rate ��⁄�� first remains to be close to zero with increasing energy release rate at the beginning and starts to increase only after the applied energy release rate is above the fatigue toughness threshold �!. The fatigue toughness threshold �! indicates a critical point where the crack begins to propagate under the applied cyclic load. As expected, �! increases with increasing crystallinity within the polyurethane (Figs. 2.2A and 2.S3). Using the testing chamber shown in Fig. 2.1D, we carried out the particulate matter emission tests for polyurethanes that were bonded on a rotating wheel for 2 min (the same rotating frequency of 5 Hz and the same normal force of 3 N, Materials and Methods). For each polyurethane sample, the measured particle concentration � (number/L) decreases with 37 increasing particle sizes � from 0.3 µm to 10 µm (Fig. 2.2B). Compared among polyurethane samples, the overall concentrations (i.e., ∑ �) of the emitted PM10 increase as the fracture toughness of polyurethanes decreases (Fig. 2.S4). It means that it becomes more difficult to emit PM10 particles for tougher polyurethane. It can be visually verified by imaging contaminated glass slides (initially clean) within the experimental chamber (Fig. 2.S5). The microscopic images of these glass slides show that fewer particles are attached to the slides if the toughness of the polyurethane is higher (Fig. 2.S5B). In addition, we find that although the concentrations of emitted PM10 decrease with increasing fatigue threshold �! , such a decreasing relationship (i.e., ∑ � versus �!) does not follow a linear relationship (Fig. 2.S4). To obtain a deterministic scaling law for the PM emissions, we apply one experimental condition (i.e., the same normal load �% = 3 N, the same sandpaper, and the same wheel rotation frequency of 5 Hz) to five types of polyurethane samples. Under this experimental condition, the applied energy release rate �' (Equation 1) for PU-A is below the fatigue threshold �! (Fig. 2.2C); while �' for others (PU-B to PU-E) are all above the corresponding fatigue threshold �! (Fig. 2.2D). We find that the PM10 concentration emitted from PU-A is drastically lower than those from the other four polyurethane samples (Fig. 2.2E). For example, the PM10 concentration emitted from PU-A is only 29.5% of that from PU-B, 27.5% of that from PU-C, 26% of that from PU-D, and 15.8% of that from PU-E. Such an ultra-low PM10 concentration can be attributed to the close-to-zero crack propagation rate when the applied cyclic load only provides a very small energy release rate, i.e., �' < �! (Fig. 2.2C). When the crack propagation rate is non-zero (i.e., �' > �! ), a crack is required to propagate approximately through half the surface area of a particle to be able to fully detach the material to become an isolated particle. Then, the total crack propagation area should scale with the half particle surface area density (per unit volume), i.e., ∑(��&⁄2), where � is the particle diameter. Besides, the total crack propagation area should scale with (��⁄��)�)*)'+, 38 where �)*)'+ is the total loading cycle number. Since we employ the same total loading cycle number �)*)'+ (with a fixed loading time and frequency), we can write a scaling law between the total particle surface area density (per unit volume) � and the crack propagation area rate as � = Z(��&) ∝ �� . (2) To estimate ��⁄�� for each material, we need to first calculate the applied energy release rate �' from Equation 1 and then harness ��⁄�� versus � relationship (see black dash lines in Fig. 2.2D) to estimate the corresponding ��⁄��. As shown in Fig. 2.2F, for those materials with �' > �! (PU-B to PU-E), � nicely displays a linear relationship with the corresponding ��⁄��, illustrated by the red dashed line. However, for PU-A with �' < �!, the data point is much below the red dashed line; such a different behavior is because the crack is not supposed to propagate when the applied energy release rate is below the fatigue toughness threshold. Based on the experimental results from Fig. 2.2F, we reach a tentative conclusion: The abrasion emission rate begins to drastically increase only when the abrasioninduced energy release rate exceeds the fatigue toughness threshold; above the fatigue toughness threshold, the surface area density of the emitted PM10 is linearly proportional to the crack propagation area rate. 39 Figure 2.2 Mechanistically relating the abrasion emission of polyurethanes to their fatigue fracture properties. (A) Fatigue properties of 5 types of polyurethane samples (PUA to PU-E): the crack propagation area rates ��⁄�� for cyclic loads in functions of the corresponding energy release rate �. The dashed lines indicate the fatigue toughness threshold �!. (B) The concentrations of emitted particles with size from 0.3 µm to 10 µm for polyurethane samples PU-A to PU-E. (C) Crack propagation area rates ��⁄�� in a function of energy release rates � for PU-A. The applied energy release rate �' is smaller than the fatigue toughness threshold �! of PU-A. (D) Crack propagation area rates ��⁄�� in a function of energy release rates � for PU-E. The applied energy release rate �' is larger than the fatigue toughness threshold �! of PU-E. (E) PM10 concentrations of polyurethane samples PU-A to PU-E under the same applied energy release rate �'. Two shaded areas indicate two regions 0 4 8 12 16 20 PM10 concentration (103/L) D PU-A C �" > �$ �" < �$ 101 102 10-5 10-4 10-3 10-2 10-1 dc/dN (mm2/cycle) G (J/m2 ) PU-A �" �$ PU-B PU-C PU-D PU-E E 0.0 0.1 0.2 0.3 0.4 0.5 0 10 20 30 40 Particle surface area density S (103 m2/L) Crack propagation area rate dc/dN (mm2 /cycle) F PU-A PU-B PU-C PU-D PU-E 100 101 102 10-5 10-4 10-3 10-2 10-1 100 101 dc/dN (mm2/cycle) G (J/m2 ) PU-E �" �$ 0 3 6 9 12 Particle concentration (103/L) 0 3 6 9 12 Particle concentration (103/L) 0 3 6 9 12 Particle concentration (103/L) 0 3 6 9 12 Particle concentration (103/L) 0 3 6 9 12 Particle concentration (103/L) 100 101 102 10-5 10-4 10-3 10-2 10-1 100 101 dc/dN (mm2/cycle) G (J/m2 ) 100 101 102 103 10-5 10-4 10-3 10-2 10-1 100 101 dc/dN (mm2/cycle) G (J/m2 ) 100 101 102 103 10-5 10-4 10-3 10-2 10-1 100 101 dc/dN (mm2/cycle) G (J/m2 ) 101 102 10-5 10-4 10-3 10-2 10-1 dc/dN (mm2/cycle) G (J/m2 ) 101 102 10-5 10-4 10-3 10-2 10-1 dc/dN (mm2/cycle) G (J/m2 ) B A PU-A PU-B PU-C PU-D PU-E �" = 83.42 �" = 40.20 �" = 17.24 �" = 12.59 �" = 9.60 PU-A PU-B PU-C PU-D PU-E 0.3 0.5 1 2.5 5 10 0.3 0.5 1 2.5 5 10 0.3 0.5 1 2.5 5 10 0.3 0.5 1 2.5 5 10 0.3 0.5 1 2.5 5 10 Particle size (µm) Particle size (µm) Particle size (µm) Particle size (µm) Particle size (µm) 40 corresponding to �! > �' and �! < �', respectively. (F) Particle surface area densities � in a function of crack propagation area rates ��⁄�� for polyurethane samples PU-A to PU-E. 2.6 Verification with polyurethanes under various loading conditions To verify the above tentative conclusion, we carried out abrasion emission experiments with the same polyurethane (PU-B) under various loading conditions (Fig. 2.3). First, since the normal force is a significant factor affecting the abrasion process, we examine the effect of the normal force on the abrasion emission (Figs. 2.3AB). As shown in Fig. 2.3A, the measured PM10 concentration does not simply follow a linear relationship with the applied normal force, but apparently falls into two groups: When the normal force is below a threshold, the PM10 concentration remains relatively small and is almost independent of the applied normal force; once the normal force is above the threshold, the PM10 concentration drastically bumps up and then increases with increasing normal forces. After converting the normal force to the applied energy release rate using Equation 1, we find that such a threshold is corresponding to the fatigue toughness threshold �! (i.e., 40.2 J/m2 ): Only when �' > �!, the PM10 concentration starts to drastically increase (Fig. 2.3A). As shown in Fig. 2.3B, the particle surface area densities � for different normal forces are plotted in a function of the corresponding estimated crack propagation area rates ��⁄��. When �' > �!, � displays nicely a linear relationship with ��⁄��, indicated by a red dashed line, while those � for lower normal forces sit far below the red dashed line. Next, since surface roughness is another important factor for the abrasion emission, we carried out the PM emission experiments using different types of sandpaper with varied surface roughness (Figs. 2.3CD). We selected 7 types of sandpaper and analyzed their grit size distributions using a scanning electron microscope (Fig. 2.S6). The mean diameters of the sandpaper grits � vary from 62 µm to 380 µm. With experimental measurements (see Materials and methods section: Measurement of the coefficient of friction of the sandpapers), 41 we find that the kinetic friction coefficients � increase with increasing mean diameters of the sandpaper grits � (Fig. 2.S6C). According to Equation 1, the applied energy release rates increase with decreasing grit sizes � (horizontal axis of Fig. 2.3C). Then, we plot the PM10 concentration in a function of the applied energy release rates for various grit sizes � (Fig. 2.3C). As expected, the PM10 concentration remains relatively low when �' < �! and suddenly bumps up when �' > �!. Besides, the particle surface area densities � show a linear scaling with the crack propagation area rate ��⁄�� only for the cases with �' > �! (Fig. 2.3D). Figure 2.3 Mechanism verification with polyurethanes under various loading conditions. (A) PM10 concentrations of polyurethane samples PU-B in a function of the applied energy release rates �' induced by varied normal forces �%. Two shaded areas indicate two regions corresponding to �! > �' and �! < �', respectively. (B) Particle surface area densities � in a function of crack propagation area rates ��⁄�� for polyurethane samples PU-B under varied normal forces �% . (C) PM10 concentrations of polyurethane samples PU-B loaded by sandpaper with grits of different sizes in a function of the applied energy release rates �'. (D) 0 20 40 60 80 100 120 0 2 4 6 8 10 Mean diameter of grit r 380 µm 100 µm 250 µm 80 µm 180 µm 62 µm PM10 concentration (10 120 µm 3/L) Applied energy release rate Ga (J/m2 ) 0 50 100 150 200 250 0 3 6 9 12 0.5 N 4 N 1 N 5 N 2 N 6 N 3 N PM10 concentration (103/L) Applied energy release rate Ga (J/m2 ) Normal force FN �" > �$ �" < �$ �" > �$ �" < �$ 0.00 0.05 0.10 0.15 0.20 10 20 30 40 0.5 N 4 N 1 N 5 N 2 N 6 N 3 N Particle surface area density S (103 m2/L) Crack propagation area rate dc/dN (mm2 /cycle) Normal force FN B 0.00 0.01 0.02 0.03 0.04 10 20 30 380 µm 100 µm 250 µm 80 µm 180 µm 62 µm 120 µm Particle surface area density S (103 m2/L) Crack propagation area rate dc/dN (mm2 /cycle) Mean diameter of grit r D A C �$ < �" �$ > �" �$ < �" �$ > �" 42 Particle surface area densities � in a function of crack propagation area rates ��⁄�� for polyurethane samples PU-B loaded by sandpaper with grits of different sizes. 2.7 Verification with source materials for daily-life abrasion emissions To further verify the generality of the proposed mechanism for abrasion emissions, we next carried out experiments with representative source materials for daily-life abrasion emissions. For example, we obtained the source materials for automobile tires (Fig. 2.4A), bicycle brake pads (Fig. 2.4B), and shoe soles (Fig. 2.4C). Fatigue tests were employed by using cyclic loads of 5 Hz to determine the relationships between the energy release rate � and the crack propagation area rate ��⁄�� (Figs. 2.4A-C). The fatigue toughness thresholds �! can be determined from the ��⁄�� versus � relationships. Next, we used the source materials to carry out the abrasion emission experiments to measure the emitted PM10 concentrations under sandpaper frictions with a 5 Hz angular frequency of rotating wheel (Figs. 2.4D-F). The experimental results of the abrasion emissions nicely echo the proposed mechanism: For each source material, when the normal force is large enough to enable the applied energy release rate �' > �!, the particle surface area densities � linearly scale with the corresponding crack propagation area rates ��⁄��, indicated by the red dashed lines (Figs. 2.4D-F). However, when �' > �!, the particle surface area densities � are well below the red dashed lines. 43 Figure 2.4 Mechanism verification with source materials for daily-life abrasion emissions. (A-C) Fatigue properties of an automobile tire (A), a bicycle brake (B), and a shoe sole (C): the crack propagation area rates ��⁄�� for cyclic loads in functions of the corresponding energy release rate �. The dashed lines indicate the fatigue toughness threshold �!. (D-F) Particle surface area densities � in functions of crack propagation area rates ��⁄�� for organic samples from an automobile tire (D), a bicycle brake (E), and a shoe sole (F). 2.8 Guidance for mitigating abrasion emissions The results from this report clearly derive two strategic recommendations for the whole society to mitigate the abrasion emissions of airborne particulate matter and marine microplastics. First, it is recommended that the fatigue toughness threshold of the organic materials that are frequently used in abrasion settings, such as tires, brake pads, and shoe soles, should be designed to be well above the applied energy release rates in the possible loading settings. Taking vehicle tires as an example, for a vehicle with a certain weight �", having tour tires. The kinetic friction coefficient between the tire and the road is �". The roughness of the road is �', meaning average, or arithmetic average of profile height deviations from the mean line. 0.000 0.005 0.010 0 40 80 120 4N 6N 8N 10N 12N 14N Particle surface area density S (103 m2/L) Crack propagation area rate dc/dN(mm2 /cycle) Normal force FN E 102 103 104 10-5 10-4 10-3 10-2 10-1 100 101 dc/dN (mm2/circle) G (J/m2 ) B �" = 1924.23 0.000 0.005 0.010 0.015 0 40 80 120 160 6N 8N 10N 12N 14N 16N Particle surface area density S (103 m2/L) Crack propagation area rate dc/dN (mm2 /cycle) Normal force FN F 102 103 104 10-5 10-4 10-3 10-2 10-1 100 101 dc/dN (mm2/cycle) G (J/m2 ) C �" = 1540.98 0.00 0.01 0.02 0 40 80 120 160 4N 6N 8N 10N 12N 14N Particle surface area density S (103 m2/L) Crack propagation area rate dc/dN(mm2 /cycle) Normal force FN D 102 103 104 105 10-5 10-4 10-3 10-2 10-1 100 101 dc/dN (mm2/cycle) G (J/m2 ) A �" = 2574.13 �. < �" �. > �" �. < �" �. > �" �. < �" �. > �" Automobile tire Automobile tire Bicycle brake Bicycle brake Shoe sole Shoe sole Sample Sample Sample 44 From the definition, it can be understood that the road surface roughness �' can be analogously compared to the size of the grit on sandpaper. Substituting �% = �"�/4 and � = �' into the Equation 1, the applied energy release rate �' can be roughly estimated as Equation 3 �' = ,! "-!$.#/ 0.#"/%$ "1-!$(345") . (3) The tire material should be designed to feature a fatigue toughness threshold �! well above �', for example, �! = ��', where � is a safety factor with a typical value of 3-5. Note that heavier vehicles should use tire materials with higher fatigue toughness thresholds �!. Regulations should be established to forbid the usage of commercial tires with �! ≤ �' for different vehicle weights. The industrial standards for selecting tire materials for vehicles with different weights should be significantly reformed to involve the consideration of the impact of the fatigue toughness property on the abrasion emissions. Second, it is recommended that the government should establish annual abrasion emission tests for the organic materials that are frequently used in abrasion settings. It is because the fatigue toughness of organic materials would degrade over long-term service time [260], and the abrasion emission may drastically increase if the material is degraded. Taking vehicle tires as an example, during the abrasion emission tests, varied weight loadings within the normal service weight range should be applied to the vehicle to measure the concentration of the emitted particulate matter (i.e., PM10) around the tires. If the PM10 concentration does not vary too much over various weight loading, the vehicle tires are proven to be in a good condition. However, if the PM10 concentration evidently increases with increasing weight loadings, the applied energy release rate may already exceed the fatigue toughness, and therefore, the vehicle tires should be replaced. In summary, we hypothesize the abrasion emission of organic materials as a fatigue fracture process and validate the hypothesis with various organic materials under various cyclic 45 loading conditions. We find that the fatigue toughness threshold of the organic material governs the onset of abrasion emissions, and that after onset, the surface area density of the emitted particulate matter is proportional to the crack propagation area rate of the fatigue fracture. We expect that our discoveries may fundamentally reform strategies that the government may take to mitigate abrasion emissions, for example, reforming industrial standards for selecting tire materials and installing abrasion emission tests for vehicle tires to ensure low abrasion emissions. The results of this report dismiss the previously-believed thermal-burning hypothesis [225, 226] but verify a mechanism of fatigue fracture mechanics. The mechanism framework of this work opens new and promising venues for understanding abrasion emissions of particulate matter and microplastics by constructing the linkage between solid mechanics and air pollution. 2.9 Outlook Due to the more severe adverse effects of particles with diameters less than 0.3 µm on human health, there is significant research potential in developing more accurate measurement and predictive methods for particles below this size. Additionally, the current experimental conditions were maintained under standard temperature and humidity. The impact of varying temperature and humidity conditions warrants further investigation. Long-term impact and environmental accumulation effects of abrasion emissions could also be further studied. 2.10 Materials and methods 2.10.1 Materials. Poly(tetrahydrofuran) (PolyTHF, average Mn ~250, 650, 1000, 2000 g/mol), isophorone diisocyanate (IPDI), Ethyl acetate, dibutyltin dilaurate (DBTDL). All chemicals were purchased from Sigma-Aldrich, USA, and were used without further purification. The automobile tires were obtained from the Continental company. The size is 215/55R18. Section 46 Width is 215 Millimeters. Season is non-winter. Load Index is 94.0. Load Capacity is 2000 Pounds. The shoe sole (Brand: MIIDII) was purchased from Amazon. Product Dimensions is 12.5 x 5 x 0.15 inches with 11.99 Ounces. Bicycle brake (Brand: Lomodo) was purchased from Amazon. Size of rubber pad is 1.57 inch/ 4 cm in length, 0.55 inch/ 1.4 cm in height, 0.4 inch/ 1 cm in thickness, the total height is 0.866 inch/ 2.2 cm. Bike Type is Mountain Bike, Road Bike Mountain, BMX Bike. 2.9.2 Preparation of polyurethane samples. 2.10.2 Preparation of polyurethane samples. To prepare the polyurethane samples, we first preheated a total of 0.025 mol of PolyTHF with various molecular weights (for Material A: 0.025 mol of PolyTHF (Mn ~250); for Material B: 0.015 mol of PolyTHF (Mn ~250) and 0.01 mol of PolyTHF (Mn ~650); for Material C: 0.015 mol of PolyTHF (Mn ~250) and 0.01 mol of PolyTHF (Mn ~1000); for Material D: 0.015 mol of PolyTHF (Mn ~650) and 0.01 mol of PolyTHF (Mn ~1000); for Material E: 0.015 mol of PolyTHF (Mn ~250) and 0.01 mol of PolyTHF (Mn ~2000) at 90°C and bubbled with nitrogen for 1 h to remove water and oxygen. After being cooled to room temperature, 200 wt.% of Ethyl acetate was mixed with the PolyTHF with magnetic stirring for 1 h. 0.025 mol of IPDI was then added to the mixture with magnetic stirring for another 1 h. To complete the synthesis, 2 wt.% of DBTDL was added to the mixture with magnetic stirring for 24 h. During the synthesis process, nitrogen was continuously bubbled in the solution to prevent a reaction between the mixture and the oxygen. After the synthesis process, the obtained solution was poured into a glass mold and placed into a vacuum chamber for 72 h to evaporate the solvent. 2.10.3 Measurement of polyurethane crystallinities. The crystallinities of polyurethane samples were measured by differential scanning calorimetry (DSC/cell: RCS1-3277, cooling system: DSC1-0107). For each material, the sample was cut into pieces and weighed as �7'89+:. The sample was placed into the DSC and heated up from 47 50°C to 200°C at the rate of 20°C/min. During the process, the sample was under a nitrogen atmosphere with a flow rate of 30 mL/min. There is an endothermic transition ranging from 120°C to 160°C in the curve of heat flow, indicating the melting of the crystalline domains. The fusion enthalpy for the crystalline domains �!;<7)'++=>: can be calculated by integrating the narrow peak ranging from 120°C to 160°C. Therefore, the crystallinities can be calculated as � = �!;<7)'++=>:/(�7'89+: ∙ �!;<7)'++=>: ? ) where �!;<7)'++=>: ? = 167 J/g is the fusion enthalpy of 100 wt.% crystalline measured at the equilibrium melting point [261, 262]. 2.10.4 Measurement of Young’s modulus � and Poisson’s ratio �. The Dynamic mechanical analyzer (DMA 850, TA instrument) was used to measure the Young’s modulus � of materials at 5 Hz and the amplitude was set as 5 �m. The Poisson’s ratio � was obtained by measuring the lateral strain upon an axial compression. 2.10.5 Measurement of fatigue properties. To measure the fatigue properties of the prepared materials, the single-notch method was adopted [259]. The notched and unnotched dog-bone shaped samples were prepared with 5 mm in width, 10 mm in length, and 1mm in thickness in their testing region. The notched sample was given a 1 mm initial crack on the edge by a sharp blade (Fig. 2.S2A). The Dynamic mechanical analyzer (DMA850, TA instrument) was used to conduct cyclic tensile tests on the samples. For all the samples, the frequency was set as 5 Hz. In each experiment, we choose a maximum applied stretch �8'@ and conducted cyclic tensile tests on unnotched samples. The curves of nominal stress � versus stretch � of the unnotched samples were obtained. After several cycles, the curves of � versus � in the process of loading and unloading tend to converge gradually. The strain energy density under the �)A cycle can be calculated as �(�8'@, �) = ∫ � B%#& 3 �� from the � versus � curve of unnotched samples (Fig. 2.S2B). Then, the cyclic tensile test with the same �8'@ was applied to the notched sample. The digital 48 microscope (AM4815ZT, Dino-Lite; resolution, 20 mm/pixel) was used to record the crack length �(�) and crack growth rate ��/�� on the notched sample over the �)A cycles. The applied energy release rate � in the notched sample with the maximum applied stretch of �8'@ can be calculated as �(�8'@, �) = 2�(�8'@) ∙ �(�) ∙ �(�8'@, �) , where � = 3/ p�8'@ [259]. For each material, the curve of crack growth rate ��/�� versus the applied energy release rate � can be acquired by doing tests with different �8'@. The applied energy release rate � when the crack growth rate begins to increase significantly can be regarded as the fatigue threshold �! (Fig. 2.S2C). Note that the resolution of ��/�� is 0.002 µm/cycle because the resolution of the digital camera is about 0.02 mm (20 µm/pixel). Then the crack propagation area rate can be calculated as ��/�� = ��/�� ⋅ �, where � is the thickness of sample. 2.10.6 Characterization of abrasion emissions. To characterize abrasion emissions, a motor-controlled rotating wheel (diameter 20 cm) with organic samples firmly bonded to the wheel by a superglue is housed in a closed chamber. The chamber was made of acrylic plastics (McMaster-Carr). The rotating speed of the wheel is 300 rpm that is corresponding to 5 Hz. Sandpaper (15 mm by 15 mm) with different grit sizes was glued on a rigid plastic plate and forced to contact the organic samples with normal forces controlled by a force gauge (LANDTEK, FM-204-100K). A particle counter (TENMA, 72- 10190) was fixed at 20 cm from the abrasion region to record the concentration of the emitted particles with different sizes (0.3 µm, 1 µm, 2.5 µm, 5 µm, 10 µm). The particle counter (TENMA, 72-10190) is an optical particle sizer with 6 channels: 0.3 µm, 0.5 µm, 1.0 µm, 2.5 µm, 5.0 µm, and 10 µm, whose counting efficiency is 50% at 0.3 µm and 100% for particles larger than 0.45 µm. Coincidence loss is 5%, 2000000 particles per ft3. The concentration of PM10 is calculated by summing up the concentration of particles with sizes of 0.3 µm to 10 µm. 49 A microscope slide attached with double-sided carbon tape was fixed next to the particle counter to collect the emitted particles for the purpose of imaging. The microscope images were taken by an optical microscope (Nikon ECLIPSE LV100ND). 2.10.7 Characterization of sandpaper grits. Grits of each sandpaper (type 40, 60, 80, 120, 150, 180, 220 from 3M company) were imaged by a scanning electron microscope (Nova NanoSEM 450) and processed with ImageJ software. To obtain the grit size distribution for each sandpaper, 100 grits on the sandpaper were randomly picked to measure the grit diameter. The average diameter and the standard deviation were calculated by assuming the particle size distribution follows the normal distribution and fitting the statistical data. 2.10.8 Measurement of the coefficient of friction of the sandpapers. To measure the kinetic friction of each material on the sandpaper, we carried out sliding friction tests under the ASTM D1894 standard with a mechanical tester (Instron, Model 5942). 2.10.9 Study of distance of measurement point for PM10 The authors did PM10 measurement experiments for different distances and we found the measured PM10 concentration was decreasing as the measurement distance increasing and followed the diffusion equations. The diffusion equation has the following form as: C!(�,)) C) = ∇ ∙ [�(�, �)∇�(�,�)] (4) where �(�,�) is the density of the diffusing material at location � and time �, and �(�, �) is the collective diffusion coefficient for density � at location � . If � is constant, then the equation reduces to the following linear differential equation C!(�,)) C) = �∇&�(�,�)]. (5) 50 According to the symmetry of the rotation wheel in the experiment setting, the problem can be simplified as 1-D diffusion problem as C!(;,)) C) = �[ C" C;" + 3 ; C C;]�(�,�)]. (6) The radius of the rotating wheel is 10 cm, and the assumption can be made that the flux at the wheel surface is constant as C!' C; . Thus, the boundary condition and the initial condition can be defined as C!(;,)) C; = C!' C; |� = 10, �(�,�) = 0| � = ∞, �(�,�) = 0|� = 0. �(�,�) can be expressed as �(�,�) = �(�)�(�) using variables separation. Then the Equation 5 can be expressed as ��F = �[�FF� + 3 ; �F �]. (7) Rearrange the above equation so that after transposition, the left side of the equation is solely a function of �, and the right side is solely a function of �. Therefore, both sides can only equal a constant, which we designate as −� "( " = � { .(( . + 3 ; .( . | = −�. (8) The solution of origin problem can be obtained by solving the following ODE } �F + �� = 0, �FF + 3 ; �F + 3 G �� = 0. (9) The solution of � can be easily obtained as �F (�) = �>�4H) and the solution of � is the linear combination of the solutions of 0 order Bessel function which can be written as �(�) = �3�?(�) + �&�?(�), (10) where �?(�) is solution of the first kind 0 order Bessel function and �?(�) is the solution of second kind 0 order Bessel function. Since the original function is not defined at the origin, it is not possible to discard �?(�) through the finite boundary conditions at the origin as is usually done in solving thus it is difficult to obtain an analytical solution. Here, we provided the numerical solution for �(�,�) and found it fitted well with the experimental results with 51 � = 8 cm&/s and C!' C; = 1.8 × 100L43cm43. Thus, we can get a tentative conclusion that the PM10 concentration emission follows the diffusion equation. The authors chose 20cm distance for measurement point because of the size of the closed chamber (see Figure 1D). The width of the chamber is 40cm. A small hole was drilled on the chamber wall, allowing the probe of the particle counter to precisely fit and extend into it for sampling. As the abrasion device is positioned at the center, the distance from the abrasion point to the particle counter probe is 20 cm. In experiments, the authors always fixed the measurement point to ensure the reproducibility of the experiment. 2.11 Supplementary Information Figure 2.S1 Characterization of crystallinities of polyurethane samples. (A) Differential scanning calorimetry thermographs of polyurethane samples PU-A to PU-B. (B) Calculated crystallinities of various polyurethane samples. 52 Figure 2.S2. Measurement of the fatigue property of an organic sample. (A) Schematics to illustrate the tests on unnotched and notched samples. (B) A schematic to illustrate the stressstretch behavior of the organic sample under cyclic loadings. (C) A schematic to illustrate a representative relationship between the crack propagation area rate and the energy release rate. The deflection point indicates the fatigue toughness threshold �!. 53 Figure 2.S3. The fatigue toughness thresholds �! in a function of the crystallinities of polyurethane samples PU-A to PU-E. Figure 2.S4. The PM10 concentrations in a function of the fatigue toughness thresholds �! for the abrasion emission results shown in Fig. 2B. 54 Figure 2.S5. Visualization of the emitted particulate matter. (A) A schematic to illustrate the experimental setting with a glass slide to collect the emitted particulate matter. (B) Optical microscope images of the contaminated glass slides with emitted particulate matter for the experiments with various polyurethane samples PU-A to PU-E. 55 Figure 2.S6. Characterization of sandpaper grits. (A) Scanning electron microscope images of various types of sandpaper. (B) The grit diameter distributions. (C) Mean diameter and standard deviation of the grit diameter of various types of sandpaper, and the measured kinetic friction coefficients between these sandpaper and various polyurethane samples. 56 Figure 2.S7. The PM10 concentration in a function of measurement point distance. (A) The PM10 concentration in a function of measurement point distance. The blue points are experimental results measuring at distance of 5 cm, 10 cm, 20 cm, and 30 cm. The red line is the theoretical result of the diffusion equation. Making � = 120 s, � = 8 cm&/s and C!' C; = 1.8 × 100 L43cm43 at � = 10 cm where �(�, �) is the diffusion coefficient, �? is the initial PM10 concentration in the diffusion equation. (B) The schematic diagram of experimental setup for PM10 measurement with the 20 cm measurement point distance. 57 Chapter 3 : Mechanics of Abrasion-Induced Particulate Matter Emission 3.1 Objective Microplastic pollution constitutes a substantially detrimental type of environmental contamination and poses threats to human health. Among the sources of airborne and marine microplastics, evidence indicates that non-exhaust emissions resulting from tire abrasion and other organic materials have emerged as a notable contributor. However, the mechanistic understanding of abrasion emission of organic materials has remained elusive. To fill the gap, we here develop a multi-scale abrasion mechanics model using the principles of linear elastic fracture mechanics. Macroscopically, material wear and tear can be viewed as a process of macro-crack propagation associated with the fatigue fracture. Microscopically, we consider the effect of microcracks propagating under cyclic loading on the material modulus and energy release rate during fatigue fracture. This framework leads to an evaluation of the effective energy release rate for the abrasion-induced emission of particulate matter, thus leading to a calculation of the concentration of the emitted particulate matter with varied sizes. The theory is validated by corresponding experiments and high consistency is exhibited between the theoretical and experimental results. This research constructs a quantitative relationship between fracture mechanics and abrasion emissions. This research not only paves the way for a mechanistic understanding of particulate matter pollution from a solid mechanics perspective but also offers rational guidance for modern society to alleviate airborne particulate matter and marine microplastic abrasion emissions. 3.2 Introduction The prevalence of microplastic pollution has been widely studied and represents a significantly harmful form of environmental contamination [263-265]. After the generation of microplastic particles through various human activities, those with diameters less than 10 micrometers tend 58 to remain suspended in the air for extended periods, contributing to air pollution [266], while others tend to deposit in the freshwater ecosystem from which a substantial proportion of microplastics are transported over great distances and eventually deposited in oceans [264, 267]. Microplastic particles suspended in the air pose a strong threat to human health [268- 270]. Fine particles can enter the human respiratory system and even the cardiovascular system to cause severe diseases and shorten human lifespan [241, 270, 271]. Microplastic particles deposited on freshwater ecosystems or deposited in the ocean will greatly damage the ecological environment [272, 273], and even endanger human health again through Bioaccumulation [274-276]. Due to the difficulty of recycling microplastics, reducing the generation of microplastics from the source has become the focus of attention. Studies have shown that as exhaust emissions of particulate matter have been significantly reduced by tight regulations, traffic-related non-exhaust emissions, such as tire wear (Fig. 3.1a) and brake wear have become the major source of airborne particulate matter in urban society (Fig. 3.1b) [71, 72, 277-279]. Evidence showed that the tire wear emits significant amounts of microplastic particles over their lifetime (approximately 40,000 to 50,000 km), with about 10%-50% of the tire weight worn and discharged into the environment (Fig. 3.1c), equating to 0.8 kg/year per capita globally [266, 280]. The significance of this becomes clear when considering the global vehicle amount, which is estimated to exceed 1 billion currently and predicted to exceed 2 billion by 2040. Particles emitted from brake wear also reached 500,000 tons per year and are expected to continually increase [280]. The development of efficient methods for reducing emissions from tire and organic material abrasion is highly desired for modern society. However, without a thorough understanding of the underlying mechanisms of abrasion emissions, any indiscriminately proposed strategy may lack a rational basis. To unveil the mechanism of abrasion emissions from tires and other organic materials, previous studies have primarily focused on the experimental characterization of particulate 59 matter produced through abrasion [279], both in the laboratory [243, 281] and in field conditions [11, 280, 282]. Despite extensive experiments have been carried out in the laboratory and the field, the particle size distribution and physical and chemical properties of the released particles have been unexplored, and the mechanism of the abrasive emission of such organic materials is still elusive to date [277, 278, 283]. Some scholars postulated that the microplastic generation is related to the thermally induced shedding of organic materials [277, 278]. However, this postulation remained a matter of speculation. Currently, no theoretical model has been established and there is a lack of robust data and empirical evidence to support the above postulation [277, 278]. Other researchers believed that abrasion should be considered a mechanics problem linked to material fracture [249, 251, 252, 284-287]. Their models can explain the mechanism of abrasion fracture but cannot link the abrasion fracture to the concentration and size distribution of the emitted particulate matter. A mechanistic understanding framework urgently needs to be established to construct the linkage between abrasion fracture and particulate emission. Such a mechanistic model is expected to provide rational and quantitative guidance for mitigating the abrasion emissions of airborne particulate matter and marine microplastics. In this study, we developed a multi-scale theoretical framework to understand the organic material abrasion process and the generation of micro-particles, thus establishing a quantitative connection between organic material abrasion emission and material mechanical properties. Two main processes take place simultaneously for organic materials to be scratched off by hard objects. On the macro level, we believed that organic materials would form 60 macroscopic cracks on the scratched surface as the hard object cuts and scoops the organic material. This macrocrack will then propagate under cyclic loading and become the primary cause of material detachment. On the micro level, organic materials can be considered as cracked bodies containing micro-cracks that propagate under cyclic loads. These micro-cracks affect the material mechanical properties and lead to the formation of micro-particles during the material detachment. Both macroscopic and microscopic crack propagation result from fatigue behavior and are regulated by two energy release rates. On this basis, a theoretical framework was established, enabling the calculation of the concentration of the emitted particulate matter with varied sizes. Our theoretical framework was applied to five organic materials with varying mechanical properties, demonstrating a high degree of concordance with experimental data. This framework quantitatively relates the organic material wear emission to material mechanical properties. For the first time, the proposed model explains the mechanism for particulate matter emissions from organic material wear. This framework potentially provides guidance for reducing air particulate and oceanic microplastic generated from abrasion emissions. The plan of this paper is as follows: In Section 2, the experimental methods and results of the abrasion-induced particle matter emission of organic material are presented. In Section 3, we establish a theoretical framework to explain the fatigue fracture behavior of macroscopic cracks, the effect of microscopic cracks on material properties, and the energy release rate of microscopic cracks. Subsequently, we constructed a model that can calculate the concentration of emitted particles with varied sizes. In Section 4, we present the theoretically calculated 61 results of models and illustrate the comparison between the theoretical and experimental results. The conclusive remarks are given in Section 5. Figure 3.1. Overall impact of abrasion emission of tire particles. (a) Schematics to illustrate the abrasion emission of tire particles. (b) Airborne PM10 pollutant. (c) Microplastics in the ocean. 3.3 Experimental We selected polyurethane elastomers as the experimental organic materials for abrasion testing, because the fracture toughness of these materials could be easily tuned by varying the molar mass of the backbone molecule. Poly (tetrahydrofuran) (PolyTHF, average molar mass 250, 650, 1000, 2000 g/mol), isophorone diisocyanate (IPDI), Ethyl acetate, and dibutyltin dilaurate (DBTDL) were purchased from Sigma-Aldrich. The sample materials were prepared by preheating 0.025 mole Poly (tetrahydrofuran) ether glycol (PolyTHF) at 90 °C and bubbled with nitrogen for 1 h to remove water and oxygen. Five groups of samples (denoted by A to E) 62 were fabricated with varying molar mass of the backbone molecule to achieve different fracture toughness. Specifically, material A contained 0.025 mole of PolyTHF 250; material B contained 0.015 mole PolyTHF 250 and 0.01 mole PolyTHF 650; material C contained 0.015 mole PolyTHF 250 and 0.01 mole PolyTHF 1000; material D contained 0.015 mole PolyTHF 650 and 0.01 mole PolyTHF 1000; and material E contained 0.015 mole PolyTHF 250 and 0.01 mole PolyTHF 2000. Ethyl acetate (twice weight of PolyTHF) was mixed with the PolyTHF at room temperature under magnetic stirring for 1 h. Then 0.025 mole Isophorone diisocyanate (IPDI) was added into the mixture with magnetic stirring for another 1 h. To complete the synthesis, dibutyltin dilaurate (DBTDL, 2% weight of the mixture) was added to the mixture with magnetic stirring for 24 h. During the synthesis process, nitrogen was bubbled in the solution to prevent the reaction between the mixture and the oxygen. After the synthesis process, the obtained solution was poured into a rectangular mold of size 20 mm × 650 mm × 20 mm. The mold was then put into a vacuum chamber for 72 hours to evaporate the solvent. After the evaporation process, the elastomer materials can be obtained by separating them from the mold. 63 Figure 3.2. Experimental setup. (a) The proposed setup for abrasion emission experiments. A motor-controlled rotating wheel with a polymer sample firmly attached to the wheel is housed in a closed chamber. Sandpaper is forced to abrade the moving polymer sample to emit particles that are detected by a particle counter. (b) A zoom-in schematic to illustrate the contact between the sandpaper and the polymer sample. (c) A further zoom-in schematic to illustrate the abrasion fracture induced by a sandpaper grit. (d) A schematic to illustrate the abrasion-induced fracture process. The PM10 measurement was carried out by using a self-designed experimental setup (Fig. 3.2). A stepper motor that can control the rotation speed was purchased from Mophorn. The wheel with a diameter of 20 cm and a thickness of 2 cm was built by 3D printing and was 64 connected to the stepped motor. A clamp with a digital force sensor (LANDTEK FM-204) was purchased from Landtek, and a particle counter (TENMA, 72-10190) was purchased from TENMA. The sandpapers were purchased from 3M company. All the setups were covered by a box (size: 60 cm × 100 cm × 100 cm) made of acrylic plastics. The polymer samples were glued on the wheel surface with superglue. The sandpaper was cut into square shapes with a size of 15 mm × 15 mm and pressed against the material samples on the wheel by the clamps with the force meter. The particle counter was fixed at 15 cm away from the contact point of the sandpaper and the sample on the wheel. By adjusting the clamps, the contact force between the sample and the sandpaper wheel could be adjusted. During the test, particles with various sizes were emitted by abrasion of the sample material. The particle counter recorded the concentration of particles with diameters of 0.3 µm, 1 µm, 2.5 µm, 5 µm, and 10 µm, respectively. The concentration of PM10 was calculated as the summation of the above groups with particle diameter ≤ 10 µm. The speed of stepper motor in the current research was set as 5 rps. Different material samples, different sandpapers (P40, P60, P80, P120, P150, P180, P220) and different contact forces (0.5 N, 1 N, 2 N, 3 N, 4 N, 5 N, 6 N) were tested in current work. As an example, the measured size distributions of the abrasion-emitted particles from five polyurethan elastomer samples are shown in Fig. 3.3. Figure 3.3. Size distribution of the abrasion-emitted particles from five polyurethan elastomer samples: (a) material A contained 0.025 mole of PolyTHF 250, (b) material B contained 0.015 65 mole PolyTHF 250 and 0.01 mole PolyTHF 650, (c) material C contained 0.015 mole PolyTHF 250 and 0.01 mole PolyTHF 1000, (d) material D contained 0.015 mole PolyTHF 650 and 0.01 mole PolyTHF 1000, and (e) material E contained 0.015 mole PolyTHF 250 and 0.01 mole PolyTHF 2000. To measure the fatigue threshold of the materials, the single-notch method was adopted (Fig. 3.4). The notched and unnotched samples were prepared in dogbone shapes of width 5 mm, length 10 mm, and thickness 1mm (Figs. 3.4ab). The initial crack of length 1 mm on the notched sample was made by the sharp blade. The Dynamic mechanical analyzer (DMA850, TA instrument) was used to conduct cyclic tensile tests on the samples (Fig. 3.4c). For all the samples, the frequency was set as 5 Hz. In each experiment, cyclic tensile tests were conducted on the unnotched samples with a maximum applied stretch �78'@. The curves of nominal stress �% versus stretch �7 of the unnotched samples were obtained. The strain energy density under the �-th cycle was calculated as �C�78'@, �E = ∫ �% B)%#& 3 d�7 . Then the cyclic tensile tests with same �78'@ were conducted on the notched samples. The digital microscope (AM4815ZT, Dino-Lite; resolution, 20 mm/pixel) was used to record the crack length �(�) and crack growth rate d�/d�on notched samples over the �-th cycle. The applied energy release rate � in the notched sample with the maximum applied stretch of �78'@ was calculated as �C�78'@, �E = 2�7C�78'@E ∙ �(�) ∙ �(�78'@, �) , where �7 = 3/p�78'@. For each material, the curve of crack growth rate d�/d� versus the applied energy release rate � was acquired by repeating tests with different �78'@ . The material was considered to reach the fatigue threshold when the applied energy release rate � began to increase significantly (Fig. 3.4d). Note that the resolution of d�/d� is 0.002 µm/cycle, because the resolution of the digital camera is about 0.02 mm (20 µm/pixel). 66 Figure 4e illustrates the fatigue properties of 5 types of polyurethane samples: the crack propagation area rates d�⁄d� for cyclic loads in functions of the corresponding energy release rate �. The dashed lines indicate the fatigue toughness threshold �! . Qualitatively, with increasing molar mass of PolyTHF within the polyurethane sample, the toughness of the material is decreasing (Fig. 3.4e), and the corresponding emitted particle concentration is increasing (Fig. 3.3). A theoretical framework elaborated in section 3 will be used to quantitatively explain the emitted particle concentration and the size distribution. Figure 3.4. Measurement of the fatigue property of polymer samples. (ab) Schematics to illustrate the tests on (a) unnotched and (b) notched samples. (c) A schematic to illustrate the stress-stretch behavior of the organic sample under cyclic loadings. (d) A schematic to illustrate a representative relationship between the crack propagation area rate and the energy release rate. The deflection point indicates the fatigue toughness threshold �!. (e) Fatigue properties of 5 types of polyurethane samples: the crack propagation area rates ��⁄�� for cyclic loads 67 in functions of the corresponding energy release rate �. The dashed lines indicate the fatigue toughness threshold �!. 3.4 Theoretical framework 3.4.1. Problem statement In this section, a multiscale theoretical framework is developed to explain the mechanism of particulate matter emission. To capture the essential mechanics, it is claimed that the emission of particulate matter is a direct consequence of fracture propagation in both macroscopic and microscopic scales. During the abrasion process (Figs. 3.2cd), a groove is formed on the surface of material due to the abrasion and scratch. The generated groove is considered as a fracture surface, on which the strain energy of the elastic body is released. Meanwhile, some micro-cracks are accumulated in accordance with the macroscopic fracture propagation on the groove. the existence and propagation of microcracks are the fundamental reasons for the generation of microparticles during abrasion emission. The microscopically developed cracks considerably reduce the material stiffness, thus affecting the macroscopically propagated fracture as an interplay across both scales. To model the process, the total effective energy release rate �)*)'+ is ought to account for the macroscopic � and additional �'II in the microscopic scale. In the macroscopic scale, the fracture on the groove is modeled with a boundary value problem within the scope of linear elastic fracture mechanics. The cross section of groove is modeled as a rectangle of width � and depth �, and the fracture length is denoted by �! (Fig. 3.2d). The abrasion load consists of a contact force −�# in the vertical downward direction and frictional force �" in the horizontal direction. Given the geometry and load, a boundary value problem could be defined in terms of stress, as } �&� + 3 315 ��Θ = 0; � ⋅ � = 0; subjected to � ⋅ � = �, � ∈ Ω = {− I & , I & | × {− J & , J & | × [0, �!], (1) 68 where � is the Cauchy stress tensor, Θ = tr(�) is the volumetric stress, � is the outward normal vector of stress boundary, and � is the traction vector. The first two governing equations in Eq. (1) refer to Beltrami’s stress compatibility equation and stress equilibrium equation, respectively. After the elastic field is fully determined, a path-independent J-integral could be applied to calculate the energy release rate. 3.4.2 Boundary Value Problem of Macroscopic Fracture 3.4.2.1 Determination of Stress Components The schematic illustration in Fig. 3.2d simplifies the macroscopic model mentioned in the preceding section. A semi-infinite overlay is partially connected to a semi-infinite plane with a macroscopic fracture surface propagating on the interface. It is assumed that the overlay has a uniform cross section of width � and depth �. Additionally, as a quasistatic analysis, the length of the fracture surface is set as a constant �!. A set of horizontal and vertical boundary tractions �" and �# are applied upon the left tip of overlay at � = 0. By applying SaintVenant’s principle at the boundary � = 0, the following stress boundary conditions and moment boundary conditions are written in the integral form as ó �@(0) = ∫ �KK |KL?d� = −�"; �<(0) = ∫ �K@|KL?d� = �#; �K(0) = ∫ �K<|KL?d� = 0; (2) ô �@(0) = − ∫ ��KK|KL?d� = 0; �<(0) = ∫ ��KK|KL?d� = 0; �K(0) = ∫C��K@|KL? − ��K<|KL?Ed� = 0. (3) At arbitrary cross section � = �′, the moment resulted from the applied force � = (−�#, 0, �") on the stress boundary � = 0 and the moment resulted from the internal stress d�′ = C�K@, �K<, �KKEd� on the cross section � = �′ must be balanced, which is written as ∑� = �M × � + ∫ �" × �′d� KLK( = 0, (4) 69 where �M = (0,0, −�′) is the position vector from the plane � = �′ to the stress boundary � = 0, and �" = (�, �, 0) is the position vector from the centroid of section � = �′ to arbitrary point on � = �′. Similarly, the force balance is written as ∑� = � + ∫ �F d� KLK( = 0. (5) From the equilibrium equations in Eqs. (4) and (5), three following equations are obtained explicitly, as ô �"(�) = − ∫ �KKd� KLK = �"; �@(�) = − ∫ ��KKd� KLK = 0; �<(�) = ∫ ��KKd� KLK = 0 = ��#. (6) Based on the semi-inverse method, we tentatively presume that all in-plane stresses, including �@@ , �<< , and �@< vanish, and the remaining �K@ , �K< , and �KK are nonzero. Subsequently, the explicit form of normal stress �KK is hypothesized to take the form of �KK = �(�� + ��) + �. (7) By plugging the trial function of �KK in Eq. (7) into the equilibrium equations in Eq. (6) produces the following three identities ô �"(�) = −� ∫ (�� + ��)d� − KLK ∫ �d� KLK = −�C�@� + �<�E − �!� = �"; �@(�) = −� ∫ (�� + ��&)d� KLK − ∫ ��d� KLK = �C�@<� + �<�E + �<� = 0; �<(�) = � ∫ (��& + ��)d� KLK + ∫ ��d� KLK = ��#, (8) where �@ = ∫ �&d� = ��N/12, �< = ∫ �& d� = ��N/12, and �@< = ∫ ��d� are components of moment of inertia tensor; �! = �� is the cross sectional area. Since the moment of inertia tensor is real symmetric, the diagonalization makes �@< = 0 on a principal direction. Because the coordinate is established on the centroid of cross section, both first order moments �@ and �< are equal to zero. Therefore, the three unknown parameters are solved as � = M* O+ , � = 0, � = − M! (, . (9) Consequently, the normal stress component �KK is obtained as 70 �KK = M* O+ �� − M! (, . (10) By plugging the above Eq. (10) into stress equilibrium equation yields CP-& C@ + CP-+ C< + M* O+ � = 0. (11) In order to deal with the above identity, a stream function of stress vector is defined as follows ó �K@ = CM C< − M* &O+ �& �K< = − CM C@ . (12) So far, all stress components are expressed in terms of a stream function �(�, �). The following part will be focused on deriving the governing equation and boundary conditions of stream function [249]. 3.4.2.2. Governing Equations and Boundary Conditions of Stream Function The formulation mentioned above is stress-based. The governing equation takes BeltramiMichell stress compatibility equation, as �&� + 3 315 ��Θ = 0, (13) where Θ = tr(�) = �KK is a special case for volumetric stress, because �@@ = �<< = 0 are presumed in the semi-inverse method in last section. By plugging the stress components in Eq. (12) into the stress compatibility equation in Eq. (13) yields ó C C< (∇&�) − 5 315 M* O+ = 0; − C C@ (∇&�) = 0. (14) Consequently, the governing equation of stream function is readily obtained by integrating Eq. (14) as ∇&� = 5 315 M* O+ � − 2��, (15) 71 where the additional −2�� is an integral constant featuring the torsion effect in Prandtl’s stress function. If the shear center coincides the centroid for a highly symmetric cross section, this integral constant vanishes to zero. As a result, the physical meaning is that no warping effect occurs on the cross section during deformation. With the governing equation obtained in Eq. (15), the following effort will be focused on the determination of boundary conditions. Due to the stress-free condition on the lateral surface and Maxwell’s reciprocal theorem, the cross-sectional boundary must be a streamline, which is mathematically written as C�K@�@ + �K<�<E × d� = �. By plugging the stress components of Eq. (12) into the streamline, the streamline differential equation is written explicitly as ß �@ �< �K CM C< − M* &O+ �& − CM C@ 0 d� d� 0 ß = CM C@ d� + CM C< d� − M* &O+ �&d� = d� − M* &O+ �&d� = 0. (16) Therefore, the above streamline function indicates that the boundary condition is written as a tangential directional derivative of stream function QM Q7 = M* &O+ �& Q< Q7 , on � = �Ω ∩ {(�, �, �): ∀� ∈ (0, �!)}, (17) where �Ω is the boundary of the region Ω, and � is the boundary of cross section at arbitrary location 0 < � < �!. The governing equation and boundary condition are demonstrated in Eq. (15) and (17), and therefore the establishment of boundary value problem has been completed. 3.4.2.3. Series solution of a rectangular cross-section In order to solve the above boundary value problem for a rectangular cross section �, the governing equation and boundary condition are specified as follows. ∇&� = 5 315 M* O+ � on �, (18) which is subjected to the Neumann boundary conditions as 72 ó CM C< = M*I" RO+ , � = ±�/2; CM C< = 0, � = ±�/2. (19) First of all, the non-homogeneous boundary condition at � = ±�/2 is eliminated by introducing an auxiliary function Ψ such that � = Ψ + M*I" RO+ �. (20) And the boundary conditions in Eq. (19) is formulated with respect to the auxiliary function Ψ, as ó CS C< = 0, � = ± �/2; CS C@ = 0, � = ±�/2. (21) Now it turns out that Ψ is a non-homogeneous Poisson’s equation with homogeneous boundary conditions. The homogeneous Neumann boundary conditions indicate that Ψ is a constant value on the boundary, due to the fact that dΨ/d� = 0. Since the constant value in a potential function does not contribute to the stress, it is reasonable to prescribe Ψ = 0 on the boundary. Therefore, it is equivalent to applying Dirichlet boundary conditions Ψ = 0 as opposed to Neumann boundary conditions dΨ/d� = 0. Next, a particular solution is given as Ψ9 = 5M* T(315)O+ � ²�& − J" 0 ³, (22) which satisfies the boundary condition at � = ±�/2 but does not satisfy the boundary condition at � = ±�/2. Finally, the homogeneous solution of auxiliary function ΨA satisfies the following governing equation and boundary condition ∇&ΨA = 0 on �, (23) which is subjected to the Dirichlet boundary conditions as ¥ ΨA = 5M* T(315)O+ � ²�& − J" 0 ³ , � = ±�/2; ΨA = 0, � = ±�/2. (24) 73 The general solution could be obtained by separation of variables as ΨA = �(�)�(�), which is written as ΨA = ∑ �8 cosh ² &8U@ J ³ sin ² &8U< J ³ V 8L3 , (25) where the coefficient �8 is determined by the Dirichlet boundary condition at � = ±�/2 and the orthogonality of Fourier series as �8 = − & J ∫ 3 WXYZ(&8UI/J) 5M* T(315)O+ � ²�& − J" 0 ³ sin ² &8U< J ³ d� J/& 4J/& = − (43)%J. (8U). 3 WXYZ(&8UI/J) 5M* 0(315)O+ . (26) The general solution of stream function � is superposed as � = M*I" RO+ � + 5M* T(315)O+ � ²�& − J" 0 ³ − ∑ (43)%J. (8U). 5M* 0(315)O+ WXYZ(&8U@/J) WXYZ(&8UI/J) sin ² &8U< J ³ V 8L3 . (27) With the aid of Eq. (12), the stress components are determined as ⎩ ⎪ ⎪ ⎨ ⎪ ⎪ ⎧�!" = #! $%" ' &# ' − �$* + (#! )(+,()%" '3�$ − .# ' * − ∑ (/+)$.# (01)# (#! $(+,()%" 23456 #$%& ' 7 23456 #$%( ' 7 cos ' $018 . * ; 9 0:+ �!8 = − ∑ (/+)$.# (01)# (#! $(+,()%" 4;<56 #$%& ' 7 23456 #$%( ' 7 sin ' $018 . * 9 0:+ ; �!! = #! %" �� − #) =* . (28) It is observed that the stress components �KK and �K@ are two dominant factors, while the shear stress �K< in the � direction has minor contribution because the infinite series have absolute convergence. Therefore, the BVP can be approximated as a plane problem by using Filon average in the generalized plane stress problem, as ⎩ ⎪ ⎨ ⎪ ⎧�K@ ≈ �K@ = 3 J ∫ �K@ J/& 4J/& d� = TM* JI. ² I" 0 − �&³ ; �K< ≈ � K< = 3 J ∫ �K< J/& 4J/& d� = 0; �KK ≈ �KK = 3 J ∫ �KK J/& 4J/& d� = 3&M* JI. �� − M! JI . (29) The above stress components feature the presumption that �<< = 0 , which is equivalent to the plane stress condition. It can be also converted to plane strain condition by introducing �<< = −��KK , and thus the strain �<< = 0 will be satisfied. The difference in 74 plane stress and plane strain conditions is presented in the formulation of displacement field, as ó �@ = 3 / {� C\ CK − (1 + �) C] C@| + �@; �K = 3 / {� C\ C@ − (1 + �) C] CK | + �K, (30) where � = 1 for plane stress condition and � = 1 − �& for plane strain condition; �@, �< are two rigid body displacement functions; and the potential function �(�, �) is called Airy’s stress function which satisfies �@@ = C"] CK" ; �KK = C"] C@" ; �K@ = − C"] C@CK. (31) For a differentiable Airy’s stress function, there exists a corresponding harmonic function � that satisfies ∇&� = 0; C"\ C@CK = ∇&�. (32) Based on the above requirements, a trial solution of � is constructed as �(�, �) = M* ^I. ²− @/1K/ & + 3�&�&³ − M! ^I ��. (33) So far, the displacement and stress fields have been solved analytically. The next task is to calculate the energy release rate for macro-crack. As in the schematic illustration in Fig. 2, the potential energy change is due to the creation of the new fracture surface Ω7 = �:�!, where �! is the crack length of macro-crack, and �: = 2� + � is the perimeter edge of the cutter blade. Then the energy release rate � can be calculated by J integral, which involves the contribution from strain energy density and displacement gradient as � = 3 _0 ∮ ²��K − � ⋅ C� CK³ d�, a (34) where � is the closed boundary of the volume and d� = �d�. Then the energy release rate � can be split into two parts as 75 � = �3 + �& = J _0 C∮ ��K 7 d�E + J _0 ²∮� 7 ⋅ C� CK d�³, (35) where the strain energy density � is defined via Hooke’s law as � = c &/ �KK & + 315 / �K@ & . (36) The J integral defined in Eq. (35) is calculated as follows: ⎩ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎧�1 = − � �2 * + � 2� �33 4 + 1 + � � �35 4 3 d� 6⁄4 86⁄4 = 1 � 61 + 2� � 8 9− ��9 4 2��4 − 6�: 4(1 + �) 5��4 ? ; �4 = − � �2 * A�35 ��5 �� + �33 ��3 �� E d� 6⁄4 86⁄4 = 1 � 61 + 2� � 8 F ��9 4 ��4 − 12�: 4 6− � 8 − 1 + � 2 8 5��4 H; � = �1 + �4 = � �(�4�) 61 + 2� � 8 A 1 2 �9 4 + 3 10 �: 4E . (37) To obtain the overall strain energy, the strain energy density in Eq. (36) needs to be integrated as � = ∫ d� I⁄& 4I⁄& ∫ ² c &/ �KK & + 315 / �K@ & ³ d� e, ? = � ² M! "e, &/fJI + &M* "e, . /fJI. ³ + (1 + ν) TM* "e, g/fJI. (38) 3.4.3. Effective moduli of a cracked body The presence of microcracks within a solid continuum can result in a reduction of its effective modulus, consequently altering the energy release rate of the macro-crack under identical loading conditions. Simultaneously, the presence and propagation of micro-cracks in the body may generate additional energy release rates apart from those associated with the macro-crack. In this section, the self-consistent procedure will be described to get the effective moduli of the body with micro-cracks [288]. Results pertaining to both plane stress and plane strain are calculated and discussed. First let the plane strain condition be considered, and the constitutive law is �@ = 345" / ²�@ − 5 345 �<³ ; (39) �< = 345" / ²�< − 5 345 �@³. (40) 76 To calculate the bulk modulus of a material, we assume an uncracked, homogeneous, isotropic body in a state of uniform hydrostatic pressure � maintained by specified boundary conditions. Then the potential energy of its body can be expressed as � = −�&�/2�, where � = �⁄2(1 + �)(1 − 2�) is the bulk modulus of the material and � is the total volume. After the introduction of random oriented cracks, the effective bulk modulus �Æ of the cracked body and the potential energy change has the relation as [289] − 9"# &hi = − 9"# &h + Δ�. (41) By dimensional analysis, this energy loss must take the form of Δ� = − 9"'. / �(�), (42) where � is the characteristic linear crack dimension, �̅ is the effective Poisson’s ratio , �… is the effective Young’s modulus of the cracked body, and � is a non-dimensional shape factor depending on the crack shape and �. We assume that a single isolated crack has the same effect on the energy change in an infinite medium as in a cracked body and ignore the interaction between cracks. The energy change after the introduction of the micro-cracks is Δ� = − 9" /f �⟨�N�(�)⟩, (43) where � is the number of micro cracks per unit volume, and the angle bracket denotes an average operator with respect to the crack length and orientation [290]. Because the cracked body is still isotropic, and in plane stain condition, �… and �Æ have the relation as /f hi = 2(1 + �)(1 − 2�). (44) By substituting Eqs. (43-44) into Eq. (41), the effective bulk modulus can be yielded as hi h = 1 − >j'.,(5)k (315)(34&5) . (45) 77 By assuming that the no correlation exists between crack size and shape, Eq. (45) can be reduced to [288, 291] hi h = 1 − >j'.k⟨,(5)⟩ (315)(34&5) . (46) The process of calculation of effective Young’s modulus �… is quite similar by introduction of a uniaxial tension �7. The �… can be expressed using Eq. (39) and Eq. (40) in plain strain as − n345" o)) "# &/f = − p345"q)) "# &/ + Δ�. (47) Because the potential energy can be affected by the resolved stresses � and � which are normal and tangential to the plane of the crack, so the energy change should have the form of [292] Δ� = − '. / [�&�(�) + �&�(�)], (48) where �(�) and �(�) are non-dimensional shape factors that only depend on � . According to the coordinate transformation, the resolved stress can be expressed as � = �7 cos& � ; (49) � = �7 sin � cos �. (50) Substituting Eq. (49) and Eq. (50) into Eq. (48), the energy change can be expressed as Δ� = − '. / [�7 & cos0 � �(�) + �7 & sin& � cos& � �(�)]. (51) The effective �… can be derived by substituting Eq. (51) into Eq. (47), written as / / = 345" 345" {1 − &>j'.kjWXY/ r,(5)1Yst" r WXY" r$(5)k 345i" |. (52) Because the micro-cracks are randomly oriented with uniform distribution, the average of the following trigonometric functions can be defined and calculated as 78 ⟨cos0 �⟩ = ∮ WXY/ rQ7 ∮ 3Q7 = N R ; (53) ⟨sin& � cos& �⟩ = ∮ Yst" r WXY" rQ7 ∮ 3Q7 = 3 R . (54) With the aid of angle average on a unit circle that is explicitly given in Eq. (53) and Eq. (54), the expression of the effective �… can be to / / = 345" 345" Õ1 − >j'.k 0n345" o (3〈�(�)⟩ + 〈�(�)⟩)Œ. (55) Consider the introduction of single isolated crack with length of 2� in an infinite medium, for two dimensional cracks, the stress intensity factors �O and �OO [293] can be written as } �O = �√��; �OO = �√��. (56) The energy release rate has the relations with �O �OO as � = � ² h; " / + h;; " / ³, (57) where � = 1 for plane stress and � = 1 − �& for plane strain. By substituting Eq. (56) into Eq. (57), the � can be expressed as � = C1 − � & E ² P"1v" / ³ ��. (58) The relation between � and the energy change Δ� is defined as � = 3 J Q(wx) Q(&') , (59) where � is the thickness of the material. Then the energy change can be calculated by integrating Eq. (58) as Δ� = p345"q / (�& + �&)�&�. (60) For an isotropic body in a state of hydrostatic stress �, it is readily obtained that � = � and � = 0. Comparing Eq. (60) with Eq. (42), we can obtain �(�̅) as 79 �(�̅) = (1 − �̅ &)� ² J ' ³. (61) For an isotropic body in a state of a uniaxial tension �, comparing Eq. (60) with the Eq. (48), �(�̅) can be obtained as �(�̅) = (1 − �̅ &)� ² J ' ³. (62) Substituting Eq. (61) and Eq. (62) into Eq. (46), the effective bulk modulus �Æ can be calculated as hi h = 1 − >j'.,(5i)k (31i5)(34&5i) = 1 − >UJ〈'"〉(345i) 34&5i . (63) The same procedure can be applied to obtain the effective Young’s modulus �… by substituting Eqs. (61-62) into Eq. (55) as /f / = 345i" 345" (1 − ��〈�&〉). (64) Because the cracked body is still isotropic, �̅ can be obtained by combining Eq. (63), Eq. (64), and Eq. (44) as �̅= 1 − 345 345{ , (65) where � is the crack density which is defined as � = ��〈�&〉. The above is the derivation of the effective modulus of the cracked body under plane strain condition, and the results can be summarized as follows: ⎩ ⎪ ⎨ ⎪ ⎧ hi h = 1 − 34i5 34&5i �; /f / = 34i 5" 345" (1 − �); �̅= 1 − 345 345{ . (66) The similar derivation procedure can be applied to plane stress conditions, and thus critical results are provided without detailed derivation. In plane stress, the constitutive law is 80 ⎩ ⎪ ⎨ ⎪ ⎧�@@ = 3 / (�@ − ��<); �<< = 3 / (�< − ��@); �@< = 315 / �@<. (67) The relations between �… and �Æ under the plane stress condition is /f hi = 2(1 − �̅). (68) By introducing randomly oriented cracks, the effective bulk modulus �Æ of the cracked body and the potential energy change has the relations same with Eq. (41). The effective bulk modulus should have the following form as hi h = 1 − >j'.k⟨,(5i)⟩ 345i . (69) The effective Young’s modulus is expressed as /f / = 1 − 2� ⟨�N⟩{ N R 〈�(�̅ )〉 + 3 R 〈�(�̅)〉|. (70) The non-dimensional factors �(�̅ ) and �(�̅ ) can be calculated as ó �(�̅) = � ² J ' ³ ; �(�̅) = � ² J ' ³ . (71) By substituting Eq. (71) into Eq. (69) and Eq. (70), the effective modulus of cracked body under the plane stress condition can be calculated as h h = 1 − { 345 ; (72) /f / = 1 − �. (73) Combining Eq. (68), Eq. (72) and Eq. (73), �̅ can be calculated. And the results for plane stress can be summarized as ⎩ ⎨ ⎧ hi h = 1 − { 345i ; /f / = 1 − �; �̅= �(1 − �). (74) 81 3.4.4. Additional energy release rate model The previous abrasion model only accounts for the energy release rate associated with the macro-crack. Although this model can roughly explain the material detachment process, it fails to adequately elucidate the physical mechanisms underpinning PM10 generation and release. Building upon the abrasion model elaborated in section 3.4.2, the abraded region is treated as a cracked body with micro-cracks. The presence and propagation of micro-cracks within the body can contribute additional energy release rates beyond that due to the macro-crack. Due to the random nature of the micro-cracks within the abrasion body, the derivation for the additional energy release rate due to the micro-cracks is technically challenging. In order to roughly estimate the additional energy release rate, here we propose a method based on the definition of the energy release rate. From previous sections, the strain energy of uncracked body under the condition of plane strain has been calculated in Eq. (38). By substituting � with �… and � with �̅, the strain energy of the cracked body in plane strain can be expressed as � = (1 − �̅ &) ² M! "e, &/fJI + &M* "e, . /fJI. ³ + (1 + �̅) TM* "e, g/fJI. (75) We define the additional energy release rate as how much energy released when the microcracks propagate by a unit area. Since the crack process is force-controlled here, we can estimate the additional energy release rate as �'II = Cx CJ⟨'⟩ . (76) Note that there should be a minus sign when the crack process is a displacement controlled. With the aid of chain rule, the �'II can be expressed as |x |⟨'⟩ = Cx C{ |{ |⟨'⟩ . (77) 82 By substituting Eq. (66) into Eq. (75) and eliminating the �… and �̅ in Eq. (75) produces Cx C{ = (1 − �&) 3 / { M! "e, &JI + &M* "e, . JI. + TM* "e, gJI | 3 (34{)". (78) The additional energy release rate �'II for plain strain can be calculated by substituting ∂�/ ∂⟨�⟩ = 2��⟨�⟩ into the Eq. (76) as �'II = |x |J⟨'⟩ = (1 − �&) 3 / { M! "e, &JI + &M* "e, . JI. + TM* "e, gJI | &>U〈'〉 (34{)". (79) The same derivation can be applied to plain stress conditions. The strain energy of the cracked body in plain stress can be expressed as � = M! "e, &/fJI + &M* "e, . /fJI. + T(31i 5)M* "e, g/fJI . (80) By substituting Eq. (66) into Eq. (75) and eliminating the �… and �̅ in Eq. (75), �'II for plain stress condition can be calculated as �'II = |x |J⟨'⟩ = 3 / { M! "e, &JI + &M* "e, . JI. + TM* "e, gJI | &>U〈'〉 (34{)". (81) Notice that the results of �'II in plain strain differ from results in plain stress by a coefficient (1 − �&), and therefore we can merge the results into a general form as �'II = |x |J⟨'⟩ = c / { M! "e, &JI + &M* "e, . JI. + TM* "e, gJI | &>U〈'〉 (34{)" , (82) where � = 1 for the plane strain condition and � = (1 − �&) for plane stress condition. Then consider that the characteristic crack length of � )A micro crack can be expressed as �= = ⟨�⟩ + ∆�=, where ∆�= is the � )A difference to the average micro-crack length. By substituting 〈�∆�=〉 = 0 [294], we can get ⟨�&⟩ = ⟨〈�〉& + 2�∆� + ∆�&⟩ = 〈�〉& + 〈∆�&〉, (83) 83 where ⟨�⟩ is the average of the micro crack lengths and 〈∆�&〉 is the variance of the micro crack lengths. Then the expression of �'II can be reduced to �'II = |x |J⟨'⟩ = c / { M! "e, &JI + &M* "e, . JI. + TM* "e, gJI | &>U〈'〉 (34>UJ(〈'〉"1〈∆'"〉))". (84) The total energy release rate can be calculated by superposing energy release rate associated with the macro-crack and the additional energy release rate associated with microcracks, which is written as �)*)'+ = c /f(J"I)n31"< = o ² 3 & �" & + N 3? �# &³ + c / { M! "e, &JI + &M* "e, . JI. + TM* "e, gJI | &>U〈'〉 p34>UJ(〈'〉"1〈∆'"〉)q ", (85) where �⁄� = 1 − � for the plane stress condition and �⁄� = 345i" 345" (1 − �) for the plane strain condition. If we assume that the micro-crack length agrees with the particle size of the PM10 released, the ⟨�⟩ can be regarded as the average particle size of PM10 and 〈∆�&〉 can be regards as the variance of the PM10, which is the square of the standard deviation. Thus the Eq. (85) establish a relation between the fracture mechanics and the distribution characteristic of PM10. 3.4.5 Overall problem-solving process The developed model elucidates the process of PM10 emission, establishing a quantitative relationship between fracture mechanics and PM10 emission. In this section, with the knowledge of loading conditions, we employ the established mechanics model to calculate the particle size distribution of PM10. The overall problem-solving process is shown in Fig. 3.5. 84 Figure 3.5. The overall structure of the problem-solving framework. The first step is to assume that the particle size distribution of the PM10 released into air obeys Weibull distribution with parameters � and �, where � is the scale parameter and � is the shape parameter [295, 296]. We adopt Weibull distribution because the shape of the particle size distributions shown in Fig. 3.2 resembles that of Weibull distribution. The probability density function of the Weibull distribution is given as �(�, �, �) = } ~ B ( @ B )~43�4n & >o~ , � ≥ 0; 0, � < 0. (86) Using the Weibull distribution to describe the size distribution, the average and the variance of the particle size of PM10 can be calculated as [295, 296] 〈�〉 = �Γ ²1 + 3 ~ ³ ; (87) 〈Δ�&〉 = �& ×Γ ²1 + 3 ~ ³ − Γ ²1 + 3 ~ ³ & ÿ, (88) where Γ is the Gamma function. 85 The next step is to link the distribution parameters � and � to the fracture process. It is reasonable to assume that the total particle number per volume of the air �)*)'+ is linearly proportional to the particle number per volume of the solid material �9';)=!+: . Thus, �)*)'+ can be expressed as �)*)'+ = ��9';)=!+:, (89) where � is the dispersion rate from solid material to air. Within the solid material, we assume that the particle number per volume �9';)=!+: linearly scales with the crack number per unit volume � written as �9';)=!+: = �9�. (90) where �9 is a constant to indicate the topological relationship between the crack and the particle. To simplify the overall framework, we here only assume a 2D rectangular topology model to reveal the relationship between the crack particle density and the crack density (Fig. 3.6). As illustrated in Fig. 3.6, we can simply obtain �9 = 2. Figure 3.6. A 2D rectangular topology model to illustrate the relationship between the particle number and the crack number. The lateral denotes the crack. The laterals on the boundary only share half weight. From Eq. (89) and Eq. (90), �)*)'+ can be written as �)*)'+ = �9��. (91) 86 During the fatigue fracture process, Q! Q% �!)B , � ≥ 0; 0, � < 0. (96) Because the Weibull distribution is a continuous distribution, we need to discretize it to obtain a discrete distribution under each particle size �= . The discrete principle are as follows. Suppose we need to get the discrete distribution at points � = �= written as �I@ = �(� = �= ), the probability mass function of discrete diameter �= can be defined as �I@ = �(�= − 1/2(�= − �=43 )) − �(�= + 1/2(�=13 − �=)). (97) In our case, for example, the probability of 1 �m is defined as �(1.75) − �(0.75), and the probability of 2.5 �m is defined as �(3.75) − �(1.75). From the above definition, once the parameter � and � are determined, the �?.N , �?.g , �3 , �&.g , �&.g , �g , and �3? can be calculated. Thus, by substituting the �= into Eq. (95), d�/d� can be obtained. Then the next step is to obtain the total energy release rate �)*)'+ from d�/d�. Based on the fatigue property of the polymers as shown in Fig. 3.4e, the relationship between the fatigue crack growth rate d�/d� and the energy release rate �)*)'+ obeys a power law after the transition regime, which is written as lg ² Q! Q%³ = �lg�)*)'+ − �, (98) where � and � are material parameters that are determined by experiments. Based on Eq. (98), the predicted energy release rate �)*)'+ can be obtained. 88 Then we can try to obtain the predicted total particle number per volume �)*)'+ from predicted energy release rate �)*)'+. From Eq. (85), �)*)'+ is a function of following variables, which can be written as �)*)'+ = �(�", �#, �, �, �!, �,〈�〉,〈Δ�&〉), (99) where �", �#, �, �, �! can be experimentally determined. Upon determining the value of �, �, the average 〈�〉 and variance of the 〈∆�&〉 can be ascertained. According to Eq. (99), the micro-crack number per volume of the solid material � can be obtained. Based on the relation of � and �)*)'+ shown in Eq. (91), if the dispersion rate is properly selected, the concentration of particles in the air �)*)'+ can be obtained. Therefore, the distribution and the total number of the particles can be calculated (Fig. 3.5). 3.5 Results 3.5.1 Theoretical results for micromechanical model of cracked body This section presents the theoretical results for a micromechanical model of a cracked body, which is elaborated in section 3.3. The presence of microcracks in the body leads to a change in both the effective Young’s modulus and Poisson’s ratio. The theoretical results for both plane stress and plane strain conditions are provided for a cracked body with an original Poisson’s ratio of 0.1, 0.2, 0.3, 0.4, and 0.5. The ratio between the effective Young’s modulus and the original Young’s modulus, denoted as �…/�, decreases with increasing crack densitiesfor both plane stress and plane strain conditions (Fig. 3.7a). According to the equation Eq. (73), under the plane stress condition, �…/� is linearly proportional to the crack density and is not associated with the Poisson’s ratio. Thus, the relation between �⁄� and � is shown as a straight line in Fig. 3.7a. However, under plane strain conditions, �…/� are concave curves and the rate of decrease in effective 89 Young’s modulus changes with the original Poisson’s ratio. Similar trends apply to the effective Poisson’s ratio results. In the plane stress condition (Fig. 3.7b), the ratio between the effective Poisson’s ratio and the original Poisson’s ratio, denoted as �̅/�, scales linearly with the crack density and is independent of the original Poisson’s ratio. Conversely, in the plane strain condition, the rate of decrease in effective Poisson’s ratio changes with the original Poisson’s ratio. Figure 3.7. Mechanical properties of cracked body. (a) Effective young’s modulus of cracked body in a function of the crack density. (b) Effective Poisson’s ratio of the cracked body in a function of the crack density. 3.5.2. Effects of variables on energy release rates Next, we study the effect of variables including Young’s modulus �, scratch depth �, vertical force �# , and horizontal force �" on the total energy release rate �)*)'+ and additional energy release rate �'II. The parameters we employed is shown in Tables 3.1, 3.2. We first study the effect of Young’s modulus on the total and additional energy release rates (Fig. 3.8a). As Young’s modulus increases, both the total energy release rate and the additional energy release rate decrease, while the ratio between the additional energy release rate and the total 90 energy release rate �'II/�)*)'+ remains as ~22% regardless of the change of Young’s modulus. The decreasing trend is because that under the same abrasion loading, namely vertical force �#, and horizontal force �", the abrasion on the stiffer material is accordingly more difficult. Table 3.1. Employed parameters for the calculation for Figs. 3.8-3.9. The estimation basis is given for each parameter. Fracture length of macro-crack �!, the width of the fracture groove of the macro-crack �, and the depth of the fracture groove of the macro-crack � are estimated to be on the same scale of the grit size which is considered as the length scale associated with the surface roughness. For simplicity, we here estimate them to be equal to the mean grit size (100 µm). Note that the mean grit sizes are different for different types of sandpapers used in experiments for Fig. 3.13, and we thus estimate �!, �, and � as the respective grit sizes for different sandpapers in Fig. 3.13. The vertical traction on each grit �# is estimated by using �# = �Ç⁄�a , where �Ç is the vertical contact force applied on the sample which can be measured by force gauge, and �a is the number of sandpaper grits sustaining the vertical force. Horizontal traction on each grit �" is estimated by using �" = ��#, where � is the friction coefficient between the material and the sandpaper which is measured by experiments. The measured friction coefficients for various materials and sandpaper are shown in Table 3.2. Parameter Physical meaning Value Basis 〈�〉 (m) Average length of micro-cracks 5.235 × 10/> Experiment 〈∆�$〉 (m$) Average variance of the micro-cracks 8.497 × 10/+? Experiment � (MPa) Young’s modulus of material 5.80308 Experiment �@ (N) Horizontal traction on each grit 0.05 Calculation �A (N) Vertical traction on each grit 0.053 Calculation �B (m) Fracture length of macro-crack 10/' Estimated from grit size 91 � (m) The width of the fracture groove of the macro-crack 10/' Estimated from grit size � (m) The depth of the fracture groove of the macro-crack 10/' Estimated from grit size �CDCEF (�/?) Total particle number per unit volume in the air 7.297 × 10) Experiment � Dispersion rate from solid material to air 7.92447 × 10/G Calibrated Table 3.2. The mean diameter and standard deviation of the grit diameter of various types of sandpaper, and the measured kinetic friction coefficients between these sandpaper and various polyurethane samples. Sandpaper type (3M company) Mean diameter (μm) Standard deviation (μm) Kinetic friction coefficient (Material A) Kinetic friction coefficient (Material B) Kinetic friction coefficient (Material C) Kinetic friction coefficient (Material D) Kinetic friction coefficient (Material E) 120 120.22 10.19 0.9622 0.9712 0.9832 0.9934 1.0231 150 100.12 8.42 0.9254 0.9323 0.9473 0.9582 0.9622 180 79.92 7.41 0.8982 0.9083 0.9162 0.9283 0.9321 220 61.92 5.32 0.8610 0.8793 0.8723 0.8910 0.8982 Then, we examine the effect of the scratch depth � on the energy release rates (Fig. 3.8b). We find that the total energy release rate and the additional energy release rate follow similar patterns as the scratch depth � increases, initially exhibiting a sharp decline, followed by a deceleration in the rate of decrease. The decreasing trend reveals that the abrasion on the material becomes more difficult as the abrasion depth increases. In Figs. 3.8cd, we examine the effects of the horizontal and vertical forces on the energy release rates. It is found that both the total energy release rate and the additional energy release 92 rate increase as either horizontal or vertical force increases. However, there exists a disparity in their response to the two types of forces. Specifically, as the vertical force rises, there is a significant augmentation in the additional energy release rate (Fig. 3.8c), whereas an increase in the horizontal force does not result in a noteworthy increase in the additional energy release rate (Fig. 3.8d). It reveals that as the abrasion loading increases, the normal load plays a more important role in contributing to the generation of particulate matter during the abrasion process. Figure 3.8. Parameter study for the total energy release rate and the additional energy release rate. (a) the total energy release rate and the additional energy release rate in a function of young’s modulus. (b) the total energy release rate and the additional energy release rate in a function of depth of the macro crack. (c) the total energy release rate and the additional energy release rate in a function of frictional force in the horizontal direction. (d) the total energy 93 release rate and the additional energy release rate in a function contact force in the vertical downward direction. 3.5.3 On the critical point of �� = �� In this section, we study the conditions when the theoretically calculated total energy release rate reaches the critical energy release rate of the materials (Fig. 3.9). Four materials used in the experiments are selected, with the critical energy release rate (namely critical fatigue threshold shown in Fig. 3.4) measured as: material B 40.2 J/m&, material C 17.24 J/m&, material D 12.59 J/m&, and material E 9.60 J/m& . According to the Eq. (85), the total energy release rate �)*)'+ is a function of various parameters. When the material and sandpaper are fixed, some parameters can be determined, for example �, �, �, and �!. Therefore, under a given loading case, �)*)'+ is varied with varying parameters �# , �" , and 〈�〉. �)*)'+ is considered as a 3D field function in a configuration space with the three independent variables. The 3D density plots of the total energy release rate are demonstrated with �# on the X-axis, �" on the Y-axis, and 〈�〉 on the Z-axis. Different colors and transparency represent the value of the �)*)'+. The contour surfaces are also drawn when the total energy release rate �)*)'+ is equal to the critical energy release rate of the material. On the top side of this contour surface, �)*)'+ < �!;=)=!'+, and the material tends to have a small amount of particles emissions when conducting wear experiments. While on the bottom side of the contour surface, �)*)'+ > �!;=)=!'+, and the material tends to have great amount of particles emissions. By examining the contour surfaces of the four materials with different critical energy release rates, it becomes evident that to reach a higher critical energy release rate necessitates greater horizontal and vertical forces to be exerted on the material (Fig. 3.9). For example, for material B and E with the same fixed 〈�〉 as 2 × 104â m, the force applied on material B (�# ≈ 0.009 N, �" ≈ 0.009 N) will be much larger than the force applied on material E (�# ≈ 0.005 N, �" ≈ 0.005 N) to make the material reach the critical energy release rate. 94 Figure 3.9. Density plot of energy release rate � under various parameters ��, ��, and 〈�〉. Subplots (a) to (d) correspond to material B to material E. The iso-value surface represents critical energy release rate �!. 3.5.4 Calibration of dispersion rate � We next calibrate the dispersion rate � for the model framework. As shown in the problemsolving procedure in Fig. 3.5, we first fit the particle size probability distribution of the particulate matter emitted from Material C to a Weibull distribution to obtain two parameters � and � (Fig. 3.10a). From the obtained probability density of PM10 particles, we can estimate the crack propagation rate during the abrasion process using Eq. (95). Then, we refer to the fatigue property curve of Material C shown in Fig. 4e to find out the required the energy 95 release rate � corresponding to the estimated crack propagation rate. Next, considering Eq. (85), allowing � = �)*)'+ can lead to the calibration of the dispersion rate �, given a proper selection of �)*)'+ to make sure the calculated particle concentration to agree with the experimental results shown in Fig. 3.10b. In this problem, we obtain � = 7.92447 × 104å. In the following sections, we will use this dispersion rate � to calculate the density of the emitted PM10 in the air and then compared the calculated PM10 density with the corresponding experimental results. Figure 3.10. Calibration of the dispersion rate with experimental results of Material C. (a) probability size distribution of the emitted particles from Material C. (b) Particle concentration of the emitted particles from Material C. 3.5.5 Effect of material toughness on particulate emission Next, we use the calibrated dispersion rate � to calculate the concentrations of particulate matter emitted by materials with various material toughness. Similar to Section 3.4, following the procedure shown in Fig. 3.5, we first estimate the required energy release rate � from the fatigue fracture curve and then calculate the total energy release rate �)*)'+ from Eq. (85). Allowing � = �)*)'+ leads to the calculation of the PM10 concentration in the air (�)*)'+). 96 In this section, we use P150 sandpaper and normal force 3 N for compression force, and only vary the material toughness to study the effect of the material toughness on the particulate emission. The experimentally measured concentration distributions of PM10 particles emitted from five types of materials are shown in Fig. 3.3. We use the calibrated dispersion rate � to calculate the concentration distributions of PM10 particles emitted from Material B, D, and E are shown in Figs. 3.11a-c. Note that Material C has been used to calibrate � in Fig. 3.10. Material A is not selected to show here because the toughness of Material A is so high that the applied energy release rate is lower than the critical fatigue threshold. Ideally, Material A is not supposed to allow the crack propagation under the given loading condition, thus the measured PM10 concentration of Material A is also much lower than those of Materials B-E (Fig. 3.3). As shown in Figs. 3.11a-c, the calculated particle concentrations from the model agree with the experimentally measured particle concentrations, thus validating the proposed theoretical framework. 97 Figure 3.11. Effect of material toughness on the particulate emission. Particle concentrations of PM10 particles emitted from various materials: (a) Material B, (b) Material D and (c) Material E. P150 sandpaper and �# = 3 N are employed in experiments for each case. (d) PM10 concentration as a function of the fatigue threshold toughness �!. To summarize the effect of the material toughness on the particulate emission, we plot the PM10 concentration versus the fatigue threshold toughness �! (Fig. 3.11d). Under the same loading condition, the emitted PM10 concentration increases as the material toughness decreases and the increasing becomes more rapidly as the material toughness decreases. 3.5.6. Effect of normal force on particulate emission In this section, we study the effect of the normal compression forces on the particulate emission. We employ Material B and P150 sandpaper and vary the normal compression forces from 3 N to 6 N. Figs 3.12a-d show both model and experimental results for the particle concentrations 98 under normal compression forces of 3 N, 4 N, 5 N, and 6 N, respectively. The results calculated from the model agree well with the experimental results in each case. Such universal agreements demonstrate the validity of our theoretical framework again. We further summarize the PM10 concentration as a function of the normal compression force in Fig. 3.12e. The results reveal that the emitted PM10 concentration increases with the normal load roughly in a linear fashion. Such result further indicate that heavier vehicles may emit more PM10 particles if their tires maintain as the same and the emitted PM10 concentration may be proportional to the vehicle weight. 99 Figure 3.12. Effect of normal forces on the particulate emission. Particle concentrations of PM10 particles emitted from Material B under various compression forces: (a) 3 N, (b) 4 N, (c) 5 N, and (d) 6 N. P150 sandpaper is employed in experiments for each case. (e) PM10 concentration as a function of the normal compression force �#. 100 3.5.7 Effect of surface roughness on particulate emission Finally, we study the effect of the surface roughness on the particulate emission. We employ Material B and normal compression force 3 N, and vary the sandpaper types among P120, P150, P180, and P220. Fig. 3.13a-d shows both model and experimental results for the particle concentrations for sandpaper P120, P150, P180, and P220, respectively. Again, the model results agree well with the experimental results in each case. Scanning electron microscope images of the sandpaper surfaces reveal that the grit size of the sand decreases and the grit density increases from P120 to P220 (Fig. 3.13e), which is corresponding to an increase of the surface roughness. With increasing surface roughness, the emitted PM10 concentration indeed increases accordingly as shown in Fig. 3.13f. Our model framework can successfully calculate the PM10 concentration for each roughness case and the model result is consistent with the respective experimental result (Fig. 3.13f). 101 Figure 3.13. Effect of surface roughness on the particulate emissions. Particle concentrations of PM10 particles emitted from Material B with various sandpapers: (a) P120, (b) P150, (c) P180, and (d) P220. (e) Scanning electron microscope images and the corresponding grit diameter distribution of the surfaces of sandpapers P120, P150, P180, and P220. (f) PM10 concentration for different sandpaper types. 102 3.6 Conclusions This paper presents a theoretical framework for understanding the emission of particulate matter resulting from abrasion of organic materials. To elucidate the mechanisms theoretically, a multiscale model comprised of three sub-models is developed, including a macroscopic fracture model, a cracked body effective modulus model, and an additional energy release rate model. At the macroscopic level, the process of material wear and tear can be viewed as the crack propagation process of macroscopic cracks under cyclic loading. The macroscopic fracture model we developed effectively explains this process and quantitatively provides the energy release rate associated with the macro-crack. At the microscopic level, under cyclic loading, the material sustains damage due to the propagation of numerous randomly oriented microcracks. The generation and propagation of micro-cracks within the material have two effects. Firstly, it weakens the modulus of the original material, which is explained by the cracked body effective modulus model. Secondly, the propagation of microcracks results in an additional rate of energy release, providing a reasonable explanation for the generation of finer particles emission. Our model effectively combines the macro-crack and micro-cracks and considers the additional release rate that predecessors did not account for. To validate the theory, we conducted corresponding experiments and discovered that the model and experimental results exhibit high consistency. At the macroscopic scale, the fracture propagation results from the abrasion and scratch process, and it is described by a boundary value problem in terms of stress. The stress solution under contact loads �� and friction load �� is accurately solved in a form of infinite series via Beltrami’s stress compatibility equation. Since the stress components in the Y direction are negligible, the Filon average is performed on the three-dimensional (3D) elastic solution, and thus we convert this 3D BVP into a plane stress/strain problem. To get the complete solution 103 of the displacement field, two conjugate potential functions are constructed and determined by the Cauchy-Riemann equation. With the stress and displacement components established, the J integral is conducted to evaluate the strain energy release rate at the macroscopic scale. While at the microscopic scale, the damage inflicted on materials by cyclic loading can be modeled as the generation and propagation of 2D, randomly oriented microcracks within the material. We assume the energy loss generated by a single isolated microcrack in an infinite medium possesses the effective properties of a cracked body. The self-consistent procedure was subsequently described for obtaining the effective moduli of the body with micro-cracks under both plane stress and plane strain conditions. In accordance with the definition of energy release rate in fracture mechanics, we defined and calculated the additional energy release rate for microcracks. Upon comparing our results with experimental findings, we conclude that accounting for the additional energy release rate of microcracks can substantially improve the accuracy of the calculation results. The primary contribution of this research work lies in establishing a quantitative relationship between fracture mechanics and PM10 emissions. With appropriate assumptions, this framework enables the calculation of the concentration of abrasion-induced particulate emissions. This work not only provides a comprehensive understanding of the mechanism behind the formation of particulate emissions but also has the potential to contribute to the reduction of such pollutants in the future. Being the first-generation mechanics model for abrasion-induced particulate emission, this model makes certain assumptions to simplify the calculation process. First, the linear elasticity and small deformation conditions are presumed 104 throughout this work. Even though the polymer may endure a substantial deformation at the crack tip, the infinitesimal deformation theory with a linear constitutive model is helpful for researchers to understand the proposed cross-scale fracture mechanism, and it is at a minor sacrifice of accuracy. However, this generic concept and methodology can be extended to finite deformation theory with nonlinear constitutive models with the help of numerical methods. Second, the interaction of microcracks is not considered, which is reasonable for low microcrack density but may not be suitable for simulating the effect on material modulus at high microcrack density. Moreover, the microcracks are assumed to be randomly oriented 2D cracks that penetrate through the thickness direction. Future studies should carefully consider the 3D shape of the micro-cracks within the body. 105 Chapter 4 : Harnessing microorganisms to upcycle plastic waste to multifunctional engineered living materials 4.1 Objective Albeit those plastics have been intensively used in industry and our daily lives, they inevitably bring severe environmental pollution to ecosystem due to the lack of biodegradability. The current plastic recycling technology is still limited and in contrast to the progressively increasing demand on plastic products. Motivated by the environmental issue, we herein proposed a concept of harnessing microorganism to recycle plastics. The mechanically ground plastic powder could be reformed and reconstructed with the aid of bacterial adhesion. The fabricated plastic-bacteria composites had excellent mechanical properties in light of stiffness, strength, toughness, and energy absorption. To quantitatively understand the mechanical properties of plastic-bacteria composites, both static and dynamical tests were performed with various bacteria concentration and plastic particle diameter as parametric studies. Since the bonding between plastic and bacteria is dynamic, the bacterial attachment and detachment to plastic surface results in extraordinary living properties, including self-healing, selfstrengthening, etc. In addition, due to the bacteria mobility, the proposed composites exhibited power harvesting properties. The proposed scheme is expected to work out a possible solution to deal with plastic pollution. Moreover, the recycled plastic-bacteria composites have superior physical properties than their original constituent plastics. Therefore, the proposed upcycle scheme intends to serve as a possible approach to deal with plastic pollution and make contribution to future smart city and infrastructure. 4.2 Introduction Plastic have become an indispensable necessity in modern industry and our daily lives for its endless advantages. For instance, plastics are lightweight, low cost, mechanically durable, and 106 bioinert [297-300]. The first occurrence of plastics dated back to the early 20th century [301], and since then the usage and utilization of plastic products have been expanded rapidly. It was reported that the manufacturing of plastic products reached 348 million tons in recent decades [302]. Albeit the merits of plastics, they were balanced with overwhelm environmental issues. Currently, only 8.8% of solid plastic wastes were recycled in the United States, whereas the rest 91.2% were dumped into landfills [303]. The accumulating plastic wastes in landfill posed a global threat because the environmental pollution caused by plastics was considered as poorly reversible [304]. In addition, plastic pollution also does harm to human health. The presence of microplastics in seafood, processed food, and beverage was reported[305-308], which deleteriously affects human health by contaminating the food chain[309]. The increasingly severe plastic debris issue has attracted extensive attention of researchers. To prescribe the excessive exploit of plastics, a series of policies were enacted in different countries [310-312]. However, the policy could only mitigate the plastic pollution, but the current technology of recycling plastic wastes remained greatly immature [313, 314]. The recycle process could be classified into primary recycling, secondary recycling, chemical recycling, and energy recovery [315]. The primary and secondary recycling are mainly considered as mechanical recycling. The mechanical recycling approach requires that the plastic wastes to be clean or semi-clean, and it can be only performed on single-polymer plastics[316]. Compared with mechanical recycling, chemical recycling is less commonly used, because it requires considerable energy input [317]. Energy recovery mainly refers to as incarnation, during which a portion of energy can be recovered in the form of combustion heat [315]. Nevertheless, the critical drawback of incarnation is the hazardous gas produced during the combustion. The difficulties associated with the above four approaches still hinder the large-scale plastic recycling industry. 107 With the aid of biotechnology, it provides a plausible solution regarding the difficulty in conventional plastic recycling approaches. Bacterial adhesion, a phenomenon that usually used to describe the interaction between bacteria and host cells, was discovered and studied in oral/dental science and orthopedics [318-320]. To better understand the bacterial adhesion mechanism, a previous work established a thermodynamic framework to account for the bacterium-substratum interfacial tension [321]. And it was also discovered that the bacterial adhesion strength is relevant to the surface hardness to which the bacteria is adhered [322, 323]. Moreover, it was claimed in recent study that the fungal-bacterial adhesion could be utilized to reform agricultural byproduct into engineering living composites [324]. In addition, the bacteria and fungal also could be used to enable natural bulk material with multi-physical properties. Previous study demonstrated that the piezoelectricity of natural wood could be greatly enhanced by fungal [325]. Moreover, researchers also discovered that bacteria could be used to produce biopolymer [326, 327]. Inspired by the microorganism-assisted material synthesis and modification, we herein proposed a novel plastic recycling scheme by harnessing the advantage of bacterial adhesion. Firstly, the plastic wastes were mechanically ground to microplastics in the form of particle and powder. Subsequently, the microplastics were mixed with biofilm produced by Sporosarcina Pasteurii (S.P.) to enable bacterial adhesion among plastic particles. The fabricated plastic-bacteria composites exhibit extraordinary mechanical properties. Since the bacterial adhesion acted as a covalent network bridging plastic islands, the property of selfhealing could be realized as a consequence of alternating kinetic process of chain attachment and detachment [328, 329]. Besides, the bacteria could also assist to strengthen the plasticbacteria composites by growing calcium carbonate in the crevice [330, 331]. The most significant feature of the plastic-bacteria composites is that they are capable of harvesting electricity. In electrochemistry, utilizing microbiology as a primary battery refers to as 108 microbial fuel cells (MFCs) [332, 333]. The above attributes are due to the metabolism of bacteria, and thus we claim the proposed plastic-bacteria composites as engineered living materials [334, 335]. In the convention plastic recycling approach, plastics can only be recycled with degraded physical properties or recovered to heat that is thermodynamically irreversible [315]. The conventional recycling approaches only downcycle the plastics ascribing to the less available value that can be exploited [336, 337]. In contrast, our proposed approach provides a feasible upcycle scheme that enables the plastic-bacteria composites with living properties that are superior to original material properties, including self-healing, self-strengthening, and selfpowering. This work intends to provide not only a promising solution to deal with plastic debris, but also provide a possible blueprint of future infrastructure built with the proposed engineered living materials. With the prevailing concepts of smart city and smart building [338], our proposed work will contribute to a plethora of potential applications for future urban development. 4.3 Results and discussions 4.3.1 Manufacturing concept A variety of plastic wastes were recycled and manufactured into arbitrary shaped structures by virtue of a bio-friendly process (Fig. 4.1ab). First, the plastic wastes were mechanically ground to powder-like particles whose diameter ranges from 125 µm to 1000 µm (Fig. 4.1ac, Fig. 4.S1). Afterward, the ground powder was mixed with biofilm that contains a cluster of bacteria (Fig. 4.1b). The bacterial adhesion between abiotic plastic surfaces and biofilm is the crux to upcycle plastic waste and assemble engineering living materials [339], as demonstrated in Fig. 4.1d. Due to the fluid environment of biofilm, the mixture of plastic particles and biofilm exhibits extraordinary processibility and is thus readily molded into arbitrary geometry and 109 shapes. After the moisture in biofilm evaporated, the viscous mixture was gradually consolidated to engineering living material with considerable mechanical strength and durability. The bonding mechanism is valid for a wide range of plastic types. Four example structures were herein presented in Fig. 4.1e, each of which was made of a different plastic type, including a polyethylene (PE) flowerpot, a polystyrene (PS) egg tray, a polypropene (PP) chair, and a polyethylene terephthalate (PET) house. These structures were fabricated by molding. Unlike conventional construction materials including concrete and cement, our proposed engineered living material had negligible shrinkage as moisture evaporated, which was accredited to the presence of plastic particle skeleton. Additionally, the evaporation only took a few hours, and afterward the material was capable of withstanding mechanical load. The merits of excellent processibility and rapid fabrication guarantee great potential applications in civil engineering, manufacturing, and infrastructure. 110 Figure 4.1. The manufacturing concept of upcycling waste plastics into engineering living materials. a, The schematic displaying the waste plastics commonly used in daily life. b, The schematic displaying the plastic particles formed by the grinding of the waste plastics. c, A zoom-in schematic displaying the waste plastic particles and the gap between them. d, The schematic displaying the bacteria Sporosarcina Pasteurii. e, The structure built by engineering living material consisting of plastic particles and bacteria. f, A zoom in schematic displaying the bacterial bonding between the waste plastic particles. g, The potential applications and their examples built by the engineering living material. 111 4.3.2 Living bacterial binding enables exceptional mechanical properties As mentioned in the preceding section, the bonding mechanism is the consequence of microbiological adhesion between bacteria and plastic powder. The biofilm could provide adequate nutrients for bacteria reproduction, and thus enabling the assembly of bacteria-plastic composites with biological living properties, as illustrated in Fig. 4.2a. The fabricated composites were capable of withstanding massive loads (Fig. 4.2b and could serve as functional load-bearing materials. Although it was claimed that the bacteria adhesion in biofilm triggered the bonding mechanism, the effect of other ingredients in biofilm that may also contribute to bonding mechanism should be eliminated, including growth medium and agar gel (the detailed preparation method for biofilm could be found in Section 4.6 and Fig. 4.S2). Therefore, a control experiment was designed and performed as follows. In experimental group, the PET plastic powder was mixed with biofilm. In contrast, the PET plastic powder was mixed with only aqueous growth medium in control group I and with only agar gel in control group II. Subsequently, a three-point flexural test was conducted for all three groups. It turned out that only the experimental group exhibited the highest stiffness, flexural strength, and toughness, while the two control groups possessed negligible mechanical robustness, as indicated in Fig. 4.2cd. The reason that led to the difference between experimental group and control group was best attributed to the fact that both growth medium and agar gel merely formed hydrogen bonds among plastic particles, whereas bacteria could form covalent bonds that far exceeded hydrogen bonds in strength. As a result, it has been testified that bacteria are the major contribution that accounted for the assembly of engineered living materials, and thus the effect of growth medium and agar gel in biofilm are excluded. With regards to the effect of bacterial concentration on mechanical properties, threepoint flexural tests were conducted for six groups of samples with different bacterial 112 concentration ranging from 10% to 100%. It is worth mentioning that the nomenclature bacterial concentration is defined as the mass ratio between biofilm and plastic powder, which is mathematically written as � = 8C#,D0E@# 8?A#)D@,_?GH<0E . (1) It is demonstrated in Fig. 4.2ef that the stiffness, flexural strength, and toughness tend to firstly increase and then decrease as the bacterial concentration rises from 10% to 100%. This parametric study reveals that an optimal material design can be reached with bacterial concentration between 50% to 70%. For instance, the bacterial concentration 70% group has an average Young’s modulus of 137.16 MPa, flexural strength of 4.09 MPa, and toughness of 41.86 kJ/m3 . The effect of bacterial concentration is perceived in two aspects: (1) at a very low bacterial concentration, the lack of bacterial bonding results in poor mechanical properties; (2) at a very high bacterial concentration, in spite of adequate bacterial adhesion, the excessive biofilm produces superfluous moisture, which inevitably leaves air voids and causes stress concentration after evaporation. Apart from bacterial concentration, the size of plastic particles also plays a critical role in mechanical properties. In light of the size effect, three-point flexural tests were conducted for four groups of samples with different average PET plastic particle diameter ranging from 188 µm to 855 µm (the detailed grinding and sieve filtration process were documented in Section 4.6 and Fig. 4.S1). The parametric study demonstrates that samples with smaller diameter are prone to exhibit better mechanical properties, as illustrated in Fig. 4.2gh. The negative correlation between particle size and mechanical properties demonstrated in Fig. 4.2h results from a dimensional analysis that large size particles have low specific surface area. In addition to the static tests performed above, dynamical tests were performed as well. Firstly, PET-bacterial samples were labeled as experimental group, and PET plastic samples were labeled as control group. Both groups were tested on dynamical mechanical analysis 113 (DMA) by using frequency sweep mode. It is observed in Fig. 4.2i that experimental group exhibits much higher dissipation factor tan � with frequency ranging from 0.1 Hz to 10 Hz, indicating that the damping effect could be greatly magnified with the aid of bacterial bonding. A more intuitive drop test was conducted by dropping a metal weight from a height of 304.8 mm (equivalent to 1 ft) onto a PET plastic substrate and PET-bacterial substrate, respectively. In Fig. 4.2j, it was discovered that the metal weight bounced back to 184.1 mm on the PET plastic substrate, and it bounced back to only 18.8 mm on the PET-bacterial substrate. This drop test had demonstrated that this engineering living material could dissipate much higher impact energy than conventional plastics did. Analogous to the size effect in static test, size effect also exists in dynamical test. Frequency tests were performed for four groups with different average PET plastic particle diameter ranging from 188 mm to 855 mm. A negative correlation between particle size and damping performance was also observed. As demonstrated in Fig. 4.2l, our proposed plastic-bacterial composites exhibit the highest damping effects among the existing engineering[340]. The dissipation factor tan � of this work could reach 0.15, exceeding that of polymer foam by far. Additionally, the fabricated plastic-bacterial composites have lower density than their constituent plastics. The density of constituent plastics, including PP, PE, PS, and PET, ranges from 0.90 g/cm3 to 1.39 g/cm3 before recycled [341-343]. After upcycled by bacteria, the density of plastic-bacterial composite decreases to 0.67 g/cm3 to 1.01 g/cm3 due to the porous microstructure. 114 Figure 4.2. Mechanical properties of the engineering living material consisting of plastic powder and the biofilm. a, The schematic displaying the engineering living material consisting of plastic powder and the biofilm. b, The heavy object bearing test for the living material. c, The stress strain curve of the living material comparing with materials in the control groups. d, The Young’s modulus, flexural strength, and toughness of the living materials comparing with the materials in the control groups. e, The stress strain curve of the living material made of different bacteria concentrations. f, The Young’s modulus, flexural strength, and toughness of the living materials made of different bacteria concentrations. g, The stress strain curve of the living material made of plastic powders of different diameters. h, The Young’s modulus, flexural strength, and toughness of the living materials made of plastic powders of different diameters. i, Dynamical mechanical analysis (DMA) by using frequency 115 sweep mode for living material comparing with PET. j, Metal ball dropping tests indicating superior damping behavior than PET material. k, The Loss modulus of the living material made of plastic particles of different diameters. l, The Ashby chart indicating superior mechanical properties of the living material. 4.3.3. Living bacteria enable solids capable of giant energy generation The living bacteria enable solids can be harnessed as microbial fuel cells (MFCs) to generate electricity while still maintaining the high stiffness of the structures (Fig. 4.3). The mechanism relies on the metabolism of the bridging bacteria, which oxidizes the organic compounds in the nutrient and generates and transfers the electrons to the embedded anode electrode (Fig. 4.3a) [344]. The bacteria-generated electrons flow through an external electrical circuit that includes a capacitor and LED bulb to the cathode electrode. At the cathode electrode, the oxygen serves as the terminal electron acceptor that reacts with protons and electrons to produce water and form a continuous power-generation system. To evaluate the power generation capacity of the plastic-bacteria composite, we first characterized the power generated from the composite with an average particle diameter of 188 μm on various days. The composite started generating power on day 1; after 4 days, the LED bulb began blinking, indicating the power generated from the composite was ≈ 25 μW (Fig. 4.3b and 4.S10). The power generation keeps increasing after 4 days until it reaches a plateau power of 50 μW at day 12 and remains for at least another week. Note that the plastic-bacteria composites MFCs system does not require any additional nutrients or energy supply during the power generation process. We further characterize the plateau power generated from the plastic-bacteria composite with various particle sizes. The experimental results show that the smaller average diameter of the plastic particles gives a higher plateau power generation (Fig. 4.3c); this is because a smaller particle size within the composite corresponds to a larger relative specific surface area to facilitate power generation and transportation from bacteria. 116 To harness the plastic-bacteria composite as a potential self-powered building material that requires excellent mechanical strength and power generation capacity (Fig. 4.3a), we remove the excess water in the system by gradually drying the composite at a 30℃ incubator. We employ indentation tests to measure the stiffness of the composite at various drying states (Fig. 4.S12). During the drying process, the plateau power generated from the composite gradually decreases (Fig. 4.3d); this is due to the lack of water in the system limiting the mobility and electron transportation of bacteria. The indentation test shows that the stiffness of the composite gradually increases and reaches 0.5 MPa while the composite still maintains an average power generation of 25 μW. To compare the presented plastic-bacteria composite has excellent mechanical properties and power generation capacity, we construct an Ashby diagram in Fig. 4.3e that shows the relationship between power density and stiffness. The plastic-bacteria composites feature a power density of 6-10 mW/mm2 , which are comparable to the existing MFC system, and exceptional stiffness of up to 0.5 MPa. The plastic-bacteria composites can be harnessed to construct building components, such as walls and bases, to generate power for the structures. We build a self-powered prototype structure by embedding the anode electrode in the base and placing a cathode electrode on the top of the structure (Fig. 4.3f). The electricity generated from the walls and base is transported through the embedded anode electrode to the roof of the structure to power a LED bulb. When the cathode electrode is removed and not in contact with the wall, the circuit is open and causes the LED bulb stops blinking. 117 Figure 4.3. Energy generation property of the living material consisting of plastic powder and the bacteria. a, An example of self-powering building. b, The schematic displaying the energy generation of the biological fuel cell. c, The schematic displaying the transportation of the electron and hydrogen ions around the anode within the living material. d, The schematic displaying the metabolic behavior of the bacteria around the anode within the material. e, The generated power by the living material in a function of time. f, The plateau power generated by the living materials made of plastics powders with different diameters. g, A tradeoff between the plateau power and the average diameters. h, An Ashby chart displaying a superior power generation behavior of the living material in functions of the stiffness. 118 4.3.4 Living bacteria enable self-healing and re-processing Because bacterial adhesion is considered as dynamic bond, bacteria may detach from plastic surface when loaded by a large force and may attach to the plastic surface in turn when unloaded, thus enabling the plastic-bacterial composites a plethora of unique living properties. The self-healing mechanism is schematically illustrated in Fig. 4.4a. The propagated fracture surface was due to the detachment between bacteria and plastics. Subsequently, the broken sample was put back together and immersed with growth medium. The growth medium provided bacteria with nutrients, and thus the activated bacteria could attach to the plastic surface again. This healing process could take several hours until the fractured samples were fully cured. The stress-strain curves of samples with different healing time are demonstrated in Fig. 4.4b. In order to evaluate the healing efficiency, a parameter called strength ratio � is defined as the ratio between healed flexural strength and original flexural strength, as � = PI0#A0< PGE@$@J#A × 100%. (2) The strength ratio as a function of healing time is plotted in Fig. 4.4c. It demonstrates that the self-healing is accomplished within 6 hours with the strength healing ratio dramatically increasing to 77.32%. As the healing time continues to 24 hours, the strength healing ratio gradually increases and stabilizes at 85.46%. The kinetics of bacterial adhesion attachment and detachment also accounts for the reprocessing properties. As illustrated in Fig. 4.4d, a PET bottle was used to fabricate PETbacterial structure by grinding the plastics into powder and mixing with biofilms. After the structure finished its service life span and turned malfunctional, it could be recycled and reprocessed to functional structures again for other purposes. The reprocessing procedure is identical to the fabrication of PET-bacterial composites, involving mechanical grinding process and bacterial attachment. With the aid of reprocessing property, the fabricated structures could be recycled and rebuilt for multiple cycles. During the reprocessing cycle, the mechanical 119 performance was enhanced due to the continuous inflow and reproduction of bacteria colony. The stress-strain curves of samples with different reprocessing cycles are plotted in Fig. 4.4e. It indicates that the flexural strength and ductility increase after each reprocessing cycle. In Fig. 4.4f, the Young’s modulus, flexural strength, and toughness are plotted as a function of reprocessing cycle. It is observed that the flexural strength increases until it reaches a plateau at the fifth cycle. Because the ductility keeps increasing, the Young’s modulus firstly increases during the first three reprocessing cycles but then decreases starting from the fourth reprocessing cycle. And the toughness dramatically increases as a result of enhancement in both strength and ductility. 120 Figure 4.4. The schematic displaying the self-healing and re-processing process of the living material. a, The schematic displaying the self-healing process of the living material consisting of waste plastic powder and bacteria. b, The stress strain curves of the original material and the healed materials. c, The healing strength ratio of the living material in functions of time. d, The creation and the reprocessing process of the living material. e, The stress strain curves of the living material after different reprocessing cycles. f, The Young’s modulus, flexural strength, and toughness of the living materials after different reprocessing process. g, The schematic displaying the creation of the living material made of three different plastics. h, The schematic displaying the reprocessing process and the self-healing process of 121 the living material made of three different plastics. i, The Young’s modulus, flexural strength, and toughness of the living materials made of three different plastic powders after different reprocessing process. 4.4 Discussions This work proposed a microbiological methodology to upcycle and rebuild plastic wastes into functional load-bearing structures. The intrinsic philosophy of this methodology is to exploit the bacterial adhesion on plastic surface, and this mechanism could initiate the assembly of plastic-bacterial composites (Fig. 4.1abcd). This upcycle scheme is generic, because the bacterial adhesion mechanism applies to a variety of plastics, including PE, PS, PP, and PET (Fig. 4.1e). The material fabrication is a spontaneous microbiological process, and thus it is environment-friendly and does not produce any hazardous chemicals. During the fabrication process of plastic-bacterial composites, they exhibited viscous and rheologic properties, and thus they were suitable for molding into arbitrary and complex shapes (Fig. 4.1e). After molding and air dry, the prepared composites exhibited excellent mechanical performance as an engineered material. After the bonding mechanism was elucidated and validated via a control experiment (Fig. 4.2cd), we performed parametric studies on the static mechanical behavior and dynamic dissipation behavior, both of which were determined by two factors, including bacterial concentration and plastic particle diameter. It was discovered that an intermediate bacterial concentration of 70% could achieve best stiffness, strength, and toughness (Fig. 4.2ef). An intermediate concentration could provide adequate bacterial adhesion and meanwhile mitigate the porous microscopic morphology induced by moisture evaporation. In terms of the plastic particle size effect, a smaller diameter resulted in better static and dynamic performance (Fig. 4.2ghk), because a smaller diameter would lead to a larger specific surface area for more bacteria to adhere. Having understood the effect of two factors, our designed plastic-bacterial 122 composites were capable of carrying static and dynamic loads (Fig. 4.2aj). In addition, our proposed material demonstrated excellent damping effect that far exceeded the existing plastics (Fig. 4.2jl). Apart from the excellent mechanical properties, the most outstanding merit that distinguishes our proposed upcycle scheme from other plastic recycle technology is that the plastic-bacterial composites are enabled with living properties, including self-powering, selfhealing, and re-processing. The self-powering function is attributed to the mobility of bacteria. The by-product of bacterial metabolism contains hydrogen ion and electron. As bacteria move in the aqueous nutrients, they carry electron and thus form a microbial fuel cell from which a continuous electricity source could be harvested (Fig. 4.3a). The recycled plastic with bacteria could produce a power output up to ~50 �W (Fig. 4.3b). Additionally, our proposed material exhibited stiffness of 0.5 MPa while generating electricity to light up an LED light (Fig. 4.3c). Compared with the existing microbial fuel cells that are all designed with fluid electrolyte environment, only our proposed plastic-bacterial composites can serve as energy harvesting unit and structural material simultaneously (Fig. 4.3d). Other than that, the self-healing and reprocessing properties were regarded as the reformation of bacterial attachment (Fig. 4.4ad). The self-healing could be finished within 24 hours, and the strength healing ratio approached to ~80% (Fig. 4.4c). What we have presented above is not only limited to recycling plastic wastes, but we propose a concept of harnessing microbiological method to turn plastic wastes into engineered living material with versatile future applications. The above concept could also be scaled up to the next-generation infrastructure. By following the trend of sustainable development and renewable energy, our proposed material could provide a possible solution regarding future energy crisis and plastic pollution. 123 4.5 Material and Methods 4.5.1 Materials. The bacteria seed Sporosarcina Pasteurii (ATCC 11859, non-infectious) was purchased from ATCC. The chemicals used to synthesize growth medium and precipitation medium were purchased in the following vendors. Tryptone, ammonium sulfate, ammonium chloride, sodium bicarbonate, hydrochloric acid, and calcium chloride were purchased from SigmaAldrich. Urea, tricine, yeast extract, agar, and L-glutamic acid were purchased from Alfa Aesar. Difco nutrient broth was purchased from Fisher Scientific. The sodium hydroxide was purchased from EMD Millipore cooperation. All chemicals were used without further purification. 4.5.2 Preparation of plastic powder in various diameter. PET sheets of thickness 1mm (CALPALMY, Amazon) were cut into 1 mm x 1 mm square pieces and ground with a coffee grinder (Hamilton, Amazon) for various time duration. The ground PET powder was placed on 4 sieves (U.S. Standard No. 25 (710 �m), No. 35 (500 �m), No. 60 (250 �m), No. 120 (125 �m), Cole-Parmer USA) and shaken gently. The remaining PET powder on each sieve has average diameters of 855 �m, 605 �m, 375 �m, and 188 �m, respectively. The schematic illustration of the grinding process and sieve filtration is demonstrated in Fig. 4.S1. 4.5.3 Cultivation of S.P. bacteria (ATCC 11859) on agar gel. The growth medium was prepared by adding the following chemicals to 500 mL deionized (DI) water: 10 g tryptone, 5 g yeast extract, 4.5 g tricine, 5 g ammonium sulfate, 2 g L-glutamic acid, and 10 g urea. Then, the pH value of solution was adjusted to 8.6 ± 0.1 with 1 M Sodium hydroxide solution. Finally, the growth medium was filter-sterilized through a filter of 0.2 �m pore size. 124 The agar solution was prepared by adding 15 g agar powder to 500 mL DI water. Then, the solution was autoclaved at 121 °C for 15 minutes. After the temperature of agar solution decreased to 70 °C, the 500 mL agar solution was mixed with the as-prepared 500 mL growth medium. Thereafter, the mixture was quickly distributed to several Petri dishes, each of which contained 40 to 60 mL mixture. Shortly afterward, the liquid mixture solidified into solid agar gel as the temperature decreased to room temperature. The bacteria seed was transferred from the vial to agar gel for reproduction. Initially, the inoculated area appeared as several dim lines. After incubation at 30 °C for 48 hours, the incubated area turned to several yellow-colored lines. The yellow-colored substance produced by bacteria was referred to as biofilm. The biofilm was collected by a spatula for later use. The bacteria reproduction procedure is illustrated in Fig. 4.S2. All glassware and containers used in the above procedure were autoclaved at 121 °C for 15 minutes before use in case of contamination. All as-prepared solutions were store in a refrigerator at 4 °C for no more than 14 days. The bacteria seed in the vial was kept in a freezer at -80 °C. 4.5.4 Preparation of samples. The ground PET powder was mixed with biofilm to fabricate PET-bacteria composites. Due to the moisture in biofilm, the composites exhibited viscousness and fluidity. Afterward, the composites were molded into arbitrary shapes. Then, samples were air-dried at room temperature for 48 hours. After the moisture evaporated, samples were solidified with stiffness and strength and were ready for mechanical tests. In order to elucidate the bonding mechanism between plastics and biofilm, a static controlled experiment was designed as follows. PET powder and biofilm were mixed to fabricate samples in the experimental group, while PET powder was mixed with aqueous growth medium in control group 1 and were mixed with ground agar gel in control group 2. In 125 the experimental group, the mass ratio is defined as the mass of biofilm �^=*,=+8 over mass of PET powder �_/". Similarly, the mass ratio in control group 1 is defined as the mass of growth medium �$;*è)A_8:I=ë8 over mass of PET powder �_/" , and the mass ratio in control group 2 is defined as the mass of agar gel �'$';_$:+ over mass of PET powder �_/". To eliminate the effect of mass ratio, the mass ratios in all groups are stipulated to be identical to 50%, as 8C@GK@A% 8LM! = 8$EGHDI_%0<@N% 8LM! = 8#$#E_$0A 8LM! = 50%. (S1) The static mechanical properties of control groups 1 and 2 were illustrated in Fig. 4.S3. The static mechanical properties of experimental group were illustrated in Fig. 4.S4c. The detailed characterization of static mechanical properties was discussed in the next section. 4.5.5 Characterization of the static mechanical properties. Three-point flexural test was performed on an Instron mechanical tester (Instron, model 5942) for static mechanical properties. Samples were molded to rectangle parallelepiped of length 15 mm, width � = 3 mm, and height ℎ = 3.175 mm (corresponding to 1/8 inch). Samples were simply supported with a span of � = 10 mm, and a concentrated load was applied at the midspan. The loading rate was 1 mm/min. The original data collected by Instron was force � vs displacement �, and they could be converted to flexural stress �, vs flexural strain �, via the following quantities. �, = NMe &^A" ; (S2) �, = TíA e" . (S3) After the stress-strain curve was obtained, the Young’s modulus � could be calculated via a linear regression between stress and strain data. The flexural strength �7 was defined as the ultimate flexural stress before rapture. The toughness � as defined as the area under stressstrain curve, which is mathematically written as 126 � = ∫ �,d�, P) ? . (S4) The static mechanical properties of PET-bacteria composites with the same average PET diameter 188 �m and different mass ratios are illustrated in Fig. 4.S4 and 4.S5. The static mechanical properties of PET-bacteria composites with the same mass ratio 50% and different average PET diameters are illustrated in Fig. 4.S6. 4.5.6 Characterization of the dynamical mechanical properties. Frequency sweep test was performed on a Dynamical Mechanical Analysis (DMA, model XXXX) for dynamical mechanical properties. The frequency sweep ranged from 0.1 Hz to 100 Hz. Cylinder samples of radius of 10 mm and depth 3.175 mm (corresponding to 1/8 in) were used in the dynamical compression test. At a given frequency, the sample was preload with 5 N as a uniaxial compression, and a periodic displacement was imposed on the sample with a magnitude of 3 �m. Based on the stress-strain relation, DMA automatically calculated the storage modulus �′, loss modulus �′′, and phase shift tan �. The following quantity could be established. tan � = /FF /F . (S5) The dynamical mechanical properties of PET-bacteria composites with the same average PET diameter 188 �m and different mass ratios are illustrated in Fig. 4.S7. The dynamical mechanical properties of PET-bacteria composites with the same mass ratio 50% and different average PET diameters are illustrated in Fig. 4.S8. 4.5.7 Preparation of the PET microbial fuel cells. 200 g of different diameters PET particles were mixed with 200 g of the nutrient solution and 0.2 g of the bacteria. 40 g of the mixed damped PET particles were filled into the microbial fuel cell vessel. The anode electrode connected with a green wire was placed on the damped PET particles located at 1 cm from the bottom of the vessel. 160 g of damped PET particles 127 were filled in the vessel and buried the anode electrode in the PET particles. The cathode electrode connected with an orange wire was placed on top of the PET particles, which has one side exposed to the air. In each step, the PET particles and electrodes were gently pressed to avoid air bubbles. The vessel was then covered with a plastic lid and let the wires pass through the small holes on the lid. The green wire from the anode electrode was connected to the negative port of the hacker board and the orange wire from the cathode electrode was connected to the positive port of the hacker port. A red LED was plugged into the port 5 and 6 of the hacker board and a capacitor were plugged into the port 1 and 2 of the hacker board. A camera was used to monitor the LED blinking period. All the devices used for microbial fuel cells experiments were purchased from the Home Science Tools. 4.5.8 Characterization of the PET microbial fuel cells power output. To measure the PET microbial fuel cell power output, the LED and capacitor were removed from the hacker board, and then the orange wire was plugged into port 3. A 2.2 kΩ resistor was plugged into port 5 and 6 (Fig. 4.S11). The output voltage was then measured using a multimeter after the resistor was plugged in for 15 minutes. The power output was calculated by the Ohm’s law, which is written as � = #" . , (S5) where � is the power, � is the measured voltage, and � is the resistance. 4.5.9 Characterization of the PET microbial fuel cells stiffness. To measure the PET microbial fuel cell stiffness, the cathode electrode was first removed from the vessel. A round-flat end cylinder indenter of radius 1.6 mm was loaded on the Instron mechanical tester (Instron, model 5942) and indented different location of the PET samples. The Young’s modulus of each location was calculated as � = Mp345"q &.í , (S6) 128 where � is the applied force, � = 0.5 is the Poisson’s ratio of the material, � is the radius of the indenter, and � is the indentation depth. 4.5.10 Characterization of the healing process. All samples were fabricated with average PET diameter 188 �m and mass ratio 50%. The static mechanical properties were obtained by three-point flexural tests on Instron. After the test, all samples were broken into two pieces from midspan. The self-healing process was triggered by dropping 3 droplets of growth medium on fracture surface. The excessive growth medium on the sample was wiped away. After the samples were healed, the static mechanical properties were test again. The healing strength ratio � was defined as the ratio between healed flexural strength and original flexural strength, which was written as � = PK_I0#A0< PK_GE@$@J#A . (S7) The healing time varied from 1 hour to 48 hours, and the corresponding mechanical properties were illustrated from Fig. 4.S13 to Fig. 4.S20. 4.5.11 Characterization of the reprocessing (method 1: adding biofilm). All samples were fabricated with average PET diameter 188 �m and mass ratio 50%. The samples made from PET powder and biofilm were referred to as cycle 0. Firstly, the order 0 samples were ground to powder. Afterward, the ground powder was mixed with biofilm with mass ratio 50%. The mixture was molded to samples, which were called cycle 1 samples. By repeating the process, cycle 1, 2, 3, and 4 samples were fabricated and tested. In each cycle, the mass ratio was constantly 50%, and the mechanical properties were illustrated from Fig. 4.S21 to Fig 4.S25. 4.5.12 Characterization of the reprocessing (method 2: adding growth medium). All samples were fabricated with average PET diameter 188 �m and mass ratio 50%. The samples made from PET powder and biofilm were referred to as cycle 0. Firstly, the order 0 129 samples were ground to powder. Afterward, the ground powder was mixed with growth medium with mass ratio 50%. The mixture was molded to samples, which were called cycle 1 samples. By repeating the process, cycle 1, 2, 3 samples were fabricated and tested. In each cycle, the mass ratio was constantly 50%. The schematic illustration of reprocessing procedure was demonstrated in Fig 4.S26a, and the mechanical properties in each reprocessing cycle were illustrated in Fig 4.S26b and Fig. 4.S27. 4.5.13 Characterization of the self-strengthening. The precipitation medium was prepared by adding the following chemicals to 500 mL deionized (DI) water: 1.5 g Difco nutrient broth, 10 g urea, 4 g ammonium chloride, and 1.1 g sodium bicarbonate. Then, the pH value of solution was adjusted to 6.0 ± 0.1 with 1 M hydrochloric acid solution. Finally, the precipitation medium was obtained through an autoclave at 121 °C for 45 minutes. In order to strengthen the PET-bacteria composites, samples were immersed in the precipitation medium with calcium chloride of concentration 28 g/L. After the samples were immersed in the precipitation medium with calcium chloride for 48 hours, samples were airdried at room temperature for 48 hours. 130 4.6 Supplementary Information Figure 4.S1. Grinding process and characterization of plastic powder. (a) Plastic wastes were cut, ground, and sieve-filtered into four different diameter groups. (b) Photos (the first row) and SEM images (the second row) of the ground powder with four different diameters. Plastic wastes were roughly cut into granules of diameter around 1000 �m. And the granules were subsequently ground into powder by a coffee grinder. A set of standard sieves were used to filter the powder with four different diameters. The diameter difference was clearly observed in SEM images in Figure. 4.S1b. 131 Figure 4.S2. Bacteria cultivation process. The bacteria seed from commercial ATCC product was inoculated from vial to an agar gel culture medium. After incubation for 48 hours, the bacteria were clustered, and they produced biofilm on the yellow-colored lines. The viscous biofilm could be collected by a spatula. 132 Figure 4.S3. Stress-strain curves, Young’s modulus, flexural strength, and toughness of control groups. (a) Control group 1. (b) Control group 2. 133 Figure 4.S4. Stress-strain curves, Young’s modulus, flexural strength, and toughness of PET-bacteria composites with different mass ratios. (a) Mass ratio 10%. (b) Mass ratio 30%. (c) Mass ratio 50%. 134 Figure 4.S5. Stress-strain curves, Young’s modulus, flexural strength, and toughness of PET-bacteria composites with different mass ratios. (a) Mass ratio 70%. (b) Mass ratio 85%. (c) Mass ratio 100%. 135 Figure 4.S6. Stress-strain curves, Young’s modulus, flexural strength, and toughness of PET-bacteria composites with different average PET diameters. (a) 375 �m. (b) 605 �m. (c) 855 �m. The average diameter 188 �m case was already included in Figure. 4.S4c. 136 Figure 4.S7. Frequency response of storage modulus �′, loss modulus �′′, and phase shift �� with different mass ratios. (a) Mass ratio 10%. (b) Mass ratio 30%. (c) Mass ratio 50%. (d) Mass ratio 70%. (e) Mass ratio 85%. (f) Mass ratio 100%. 137 Figure 4.S8. Frequency response of storage modulus �′, loss modulus �′′, and phase shift �� with different average PET diameters. (a) 188 �m. (b) 375 �m. (c) 605 �m. (d) 855 �m. 138 Figure 4.S9. Drop test on a cushion substrate. Experimental setups on both sides are identical. A metal ball of radius 9.48 mm was dropped from a height of 304.8 mm (corresponding to 1 ft). The horizontal motion of metal ball was constrained by a plastic tube of inner radius 15 mm. The metal ball hit the cushion substrate on the bottom and bounced back in the tube. Compared with the traditional PET plastic demonstrated on the left side, the proposed PETbacterial composites could absorb tremendous impact such that the metal ball barely bounced back. 139 Figure 4.S10. Schematic illustration of PET microbial fuel cell. 140 Figure 4.S11. Hacker board setup to measure power. 141 Figure 4.S12. Schematic illustration of indentation test on a PET microbial fuel cell. 142 Figure 4.S13. Mechanical test data of self-healing for 1 hour. (a) Photos of an original sample, fractured sample, and healed sample. (b) Stress-strain curves of original samples. (c) Stress-strain curves of healed samples. (d) Healing strength ratio. 143 Figure 4.S14. Mechanical test data of self-healing for 2 hours. (a) Photos of an original sample, fractured sample, and healed sample. (b) Stress-strain curves of original samples. (c) Stress-strain curves of healed samples. (d) Healing strength ratio. 144 Figure 4.S15. Mechanical test data of self-healing for 4 hours. (a) Photos of an original sample, fractured sample, and healed sample. (b) Stress-strain curves of original samples. (c) Stress-strain curves of healed samples. (d) Healing strength ratio. 145 Figure 4.S16. Mechanical test data of self-healing for 6 hours. (a) Photos of an original sample, fractured sample, and healed sample. (b) Stress-strain curves of original samples. (c) Stress-strain curves of healed samples. (d) Healing strength ratio. 146 Figure 4.S17. Mechanical test data of self-healing for 12 hours. (a) Photos of an original sample, fractured sample, and healed sample. (b) Stress-strain curves of original samples. (c) Stress-strain curves of healed samples. (d) Healing strength ratio. 147 Figure 4.S18. Mechanical test data of self-healing for 18 hours. (a) Photos of an original sample, fractured sample, and healed sample. (b) Stress-strain curves of original samples. (c) Stress-strain curves of healed samples. (d) Healing strength ratio. 148 Figure 4.S19. Mechanical test data of self-healing for 24 hours. (a) Photos of an original sample, fractured sample, and healed sample. (b) Stress-strain curves of original samples. (c) Stress-strain curves of healed samples. (d) Healing strength ratio. 149 Figure 4.S20. Mechanical test data of self-healing for 48 hours. (a) Photos of an original sample, fractured sample, and healed sample. (b) Stress-strain curves of original samples. (c) Stress-strain curves of healed samples. (d) Healing strength ratio. 150 Figure 4.S21. Mechanical test data of reprocessing, cycle 0, method 1. (a) Photos of reprocessing cycle 0 procedure. (b) Stress-strain curves, Young’s modulus, flexural strength, and toughness of reprocessing cycle 0 samples. 151 Figure 4.S22. Mechanical test data of reprocessing, cycle 1, method 1. (a) Photos of reprocessing cycle 1 procedure. (b) Stress-strain curves, Young’s modulus, flexural strength, and toughness of reprocessing cycle 1 samples. 152 Figure 4.S23. Mechanical test data of reprocessing, cycle 2, method 1. (a) Photos of reprocessing cycle 2 procedure. (b) Stress-strain curves, Young’s modulus, flexural strength, and toughness of reprocessing cycle 2 samples. 153 Figure 4.S24. Mechanical test data of reprocessing, cycle 3, method 1. (a) Photos of reprocessing cycle 3 procedure. (b) Stress-strain curves, Young’s modulus, flexural strength, and toughness of reprocessing cycle 3 samples. 154 Figure 4.S25. Mechanical test data of reprocessing, cycle 4, method 1. (a) Photos of reprocessing cycle 4 procedure. (b) Stress-strain curves, Young’s modulus, flexural strength, and toughness of reprocessing cycle 4 samples. 155 Figure 4.S26. Reprocessing samples by method 2. (a) Reprocessing procedure. (b) Young’s modulus, flexural strength, and toughness in each reprocessing cycle. 156 Figure 4.S27. Stress-strain curves, Young’s modulus, flexural strength, and toughness of reprocessed samples in different cycles, method 2. (a) Cycle 1. (b) Cycle 2. (c) Cycle 3. 157 Figure 4.S28. Self-strengthening of samples. (a) Procedure and mechanism of selfstrengthening. (b) Stress-strain curves, Young’s modulus, flexural strength, and toughness of samples in control group and experimental group. In the experimental group, the samples were soaked in precipitation medium, from which the carbonate ions were provided. Calcium chloride was added in precipitation medium, and the obtained calcium carbonate could strengthen the original PET-bacteria composites. In control group, samples were soaked in precipitation medium, but no calcium chloride was added. 158 Chapter 5 : Upcycling Laundry Fibers into Multifunctional Engineered Living Materials 5.1 Objective Laundry microfibers represent the predominant source of marine microplastic pollution (35%). Following their physical capture from wastewater treatment systems, large quantities of these microfibers are casually discarded, posing ongoing environmental hazards. Despite the proposition of various chemical degradation methods, their high costs and inefficiencies render them inadequate for the large-scale recycling of microfibers. Here, we leverage living microorganisms to transform laundry fibers into a class of engineered living materials. Biofilms produced by bacteria facilitate the interconnection and immobilization of microfibers, yielding composite materials with excellent mechanical properties and superior fracture toughness. This recycling approach not only effectively addresses the issue of microfiber recovery but is also characterized by its simplicity and cost-effectiveness. It circumvents the need to disrupt robust chemical bonds and obviates any supplementary energy input. Moreover, the composite living materials exhibit numerous advantages such as 3D printability, self-healing, and self-powering capabilities. The outstanding performance of these materials heralds promising prospects for future applications, including skins for biomimetic robots, flexible living armor, and components of future self-powered infrastructures. 5.2 Introduction Microplastic pollution constitutes one of the preeminent modalities of environmental contamination in contemporary contexts, with its severity escalating progressively[345-352]. Microplastics, defined as diminutive plastic particulates with dimensions not exceeding 5 millimeters, can sometimes be smaller than half the diameter of an erythrocyte[349]. These microplastics originate from a multitude of sources, including the fragmentation of larger plastic debris such as bottles, automotive tires, plastic microbeads, and synthetic fibers[353]. 159 Among these production pathways, the microplastic synthetic fibers shed from clothing, bedding, and other textile products during the washing process account for 35% of the microplastic pollution in marine ecosystems, thus constituting the largest known source of marine microplastic contamination [57, 354]. During the laundering process, these microplastic fibers are detached from synthetic plastic products made of nylon, wool, and polyester and carried away by mechanical abrasion and turbulent water flows into the wastewater systems [355]. Studies have indicated that a typical 5 kg wash load of polyester fabrics could release an impressive number of microplastic fibers, in the range of 6,000,000–17,700,000, with approximately 2.2 million metric tons of microplastic fibers entering the marine environment annually through this mechanism [356]. Due to their minute size, these microplastic fibers within freshwater bodies and marine ecosystems are easily ingested or inhaled by planktonic organisms [357] and then ascend the food chain [358], culminating in the seafood consumed by humans. A study in California found that a quarter of sampled finfish contained microplastic fibers [359]. In Germany, chemists detected microplastic fibers in all 24 sampled beer varieties [360]. Microplastic fibers are predominantly composed of polyester, which in turn consists of an ester, a dihydric alcohol , and a terephthalic acid. This means they are inorganic and non-biodegradable. Upon ingestion, they can infiltrate the cardiovascular and pulmonary systems of human beings, posing significant health risks and substantially reducing the life expectancy of individuals [234-237]. Microplastic fibers pose significant hazards, yet the industry currently lacks effective methods for their recycling. Although most of the microplastic fibers within the wastewater could be separated by physical trapping techniques, which include coagulation [361, 362], membrane-based filtration [363, 364], and adsorbent absorption [365], the separated microplastic fibers are mainly retained in the sludge, which is mostly directly landfilled or further processed as farmland fertilizer, without actual degradation [366]. Unfortunately, most 160 microplastic fibers within the sludge will re-enter the environment through soil erosion or surface runoff shortly afterward. Taking North America for example, 4.4 × 100 to 3.0 × 10g tons of microplastic fibers return to environment in this way [147]. Some research efforts are exploring chemical degradation approaches to degrade microplastic fibers, primarily through the use of catalysts and light to generate reactive oxygen species (ROSs) [367-371]. However, these methods are feasible only for certain specific types of microplastic fibers and are characterized by low efficiency and high costs, making them unsuitable for large-scale industrial application [372]. Therefore, there is an urgent need in the industry for a method capable of recycling and upcycling laundry microfibers. To address the aforementioned issue, we harness living microorganisms to upcycle laundry microfibers into a class of engineered living materials. The biofilm produced by Sporosarcina Pasteurii (S.P.) can serve as a covalent network bridging microfibers, resulting in an engineered composite material with superior strength and exceptional fracture toughness. Additionally, bacteria function not merely as conventional adhesives; as living organisms, bacteria possess the capability for hierarchical organization of building blocks across multiple length scales, exhibit optimal transport properties, and have decision-making capabilities[373]. Thus, the synthesized living materials possess superior self-healing properties in response to environmental pressures. When the material is damaged, bacteria will autonomously organize and transport themselves to the site of injury, where they proliferate and facilitate the repair of the material, like regenerative processes observed in biological materials such as animal bones and plant stems. Simultaneously, the bacteria’s ability to metabolize organic matter in the environment allows these biomaterials to function as biofuel cells with self-powering characteristics [374-379]. Furthermore, these materials can be fabricated using both molding and 3D printing techniques. If produced through 3D printing, the orientation of the microfibers within the material can be precisely controlled and aligned by the shear stress transmitted by 161 the conical tube wall, significantly enhancing tensile strength of the material in predetermined directions. This innovative upcycling approach utilizes microorganisms to convert microfibers into engineering living materials without necessitating energy input or pressure to break the chemical bonds of microfibers, fundamentally overturning chemical degression methods characterized by high costs and low efficiency. Due to the microscopic size of bacteria, the biofilms they produce can adhere to microfibers of similarly small dimensions, rendering it difficult for these microfibers to escape from the composite material and subsequently re-enter the environment. Additionally, the produced living material exhibits superior mechanical properties, including high tensile strength, superior fracture toughness, and inherent selfhealing properties. These qualities render the material apt for applications such as flexible living armor or robotic living skins. Owing to its inherent self-powering characteristics, this material also holds promise for use in construction, thereby presenting a potential paradigm shift in the development of future self-powered infrastructure. 5.3 Paradigm concept During the laundering or drying process, the filters of washing machines and dryers accumulate a considerable volume of microfibers, which are typically discarded in landfills or released into wastewater systems without adequate recycling measures. This practice results in the persistent reintroduction of these microfibers into the environmental loop. We collected these microfibers from the washing machine filter screen (Fig. 5.1b), followed with elementary cleaning and drying processes (see Materials and Methods section: Preparation of microfibers), and then combined them with a microorganism termed Sporosarcina Pasteurii (S.P.) (Fig. 5.1d) (see Materials and Methods section: Cultivation of S.P. bacteria (ATCC 11859) on agar gel). Sporosarcina Pasteurii (S.P.) is adept at producing a biofilm that functions as a covalent 162 network, thereby bridging microfibers to fabricate an engineered composite material. Distinct from traditional adhesives, the bacterium displays optimal transport properties inherent to scale-free fractal networks and exhibits decision-making capabilities. These properties enable it to autonomously identify and bind to the more substantial gaps among microfibers, effectively bonding larger microfibers while simultaneously entrapping smaller ones within the network, thus mitigating the risk of minuscule microfibers escaping and re-entering the environmental cycle. Moreover, the matrix formed by the microfibers creates a propitious environment for the proliferation of this biological species. Prior to complete desiccation, the morphology of this hybrid material is mutable, thereby facilitating its fabrication via molding methods. However, to fabricate components with complex geometries that align with the functional demands of specific engineering applications, we have further investigated the potential of shaping this living material through 3D printing techniques (Fig. 5.1f). By adjusting the ratio of bacteria to microfiber components, the rheological properties of the composite material can be modified, thereby enabling its use as a feedstock ink for 3D direct writing. Structures with complex, curved network geometries, which are challenging to produce through conventional methods, can be readily fabricated using 3D direct writing techniques (Fig. 5.1h). Concurrently, during the 3D printing process, when the mixed material is introduced into a desktop extrusion-based printer, the orientation of the microfibers within the material is initially random. However, during the subsequent extrusion process, the orientation of the microfibers within the composite material gradually becomes aligned due to the shear stress imparted by the conical tube walls, ultimately orienting in the direction of the nozzle (Fig. 5.1f). This precise control for the alignment of the microfibers’ orientation within the composite material can endow it with the potential to create materials with editable anisotropy or functionally graded properties. 163 Also, high crack resistance of the living material is attributed to the precise control over the orientation of microfibers, which forces cracks to follow longer and more tortuous paths when they encounter microfibers with greater strength, thereby encountering increased resistance to propagation (Fig. 5.1i). Additionally, the growth and reproductive activity of the bacteria confer remarkable living and self-healing properties on our 3D-printed objects (Fig. 5.1i). When the composite material is severed, bacterial regrowth repairs the fracture, restoring the material to its pre-fracture tensile strength. A potential application of this concept is the creation of protective living skins for robots, which require not only mechanical strength and substantial crack resistance but also sufficient vitality to allow for the self-regeneration of damaged areas. Furthermore, the bacteria’s capability to metabolize organic matter in the environment enables these biomaterials to function as biofuel cells with self-powering characteristics (Fig. 5.1i). By connecting electrical appliances to both ends of the structure, the anoxic side becomes the anode of the microbial fuel cell, while the opposite side acts as the cathode, continuously supplying electrical energy to the connected devices. This feature imbues this material with unique potential as a future self-sustaining green building material. 164 Figure 5.1. The sources of microfibers, the microfiber living composite materials made by 3D printing and their superior properties. a, The sources of microfibers pollution in the ocean and the effects of microfibers on the marine organism. b, The microfibers collected from the washing machine filter screen which shown in the left corner. c, A SEM image of microfibers. d, The bacteria Sporosarcina Pasteurii which can produce adhesive biofilm. e, A 165 SEM image of bacteria Sporosarcina Pasteurii. f, The schematic displaying the 3D printing process of the microfibers-bacterial living composite material. Within the chamber of the 3D printer, the microfibers are randomly orientated and non-aligned at first. Along with the 3D printing process, the orientation of the microfibers gradually becomes aligned due to the shear stress imparted by the conical tube walls. g, A SEM image of 3D printed structure in which the microfibers are well aligned. h, An image of 3D printed structure which has three dimensional curvature. i, Superior properties of the living composite materials: high crack resistance (left), self-healing property (middle), and self-powering property (right). 5.4 Mechanical properties To evaluate the effects of biofilms produced by bacteria on the adhesive interactions of microfibers and their influence on the mechanical properties of composite materials, we designed three groups of material for experimentation (Fig. 5.2a) (see Materials and Methods section: Preparation of samplesfor mechanical testing). The Group Ι used materials composed of microfibers and agar gel, which were fabricated using 3D direct writing techniques. It was observed that the microfibers in the composite material were neatly aligned with a consistent orientation, and their surfaces were exceptionally clean without any bacterial adhesion. The materials in Group ΙΙ consisted of microfibers and bacteria but were formed using a molding process instead of 3D printing, resulting in a disorganized and intertwined arrangement of microfibers with substantial bacterial adhesion. The materials in the Group ΙΙΙ also consisted of microfibers and bacteria, but were fabricated using 3D direct writing, which led to the reestablishment of orderly oriented microfibers with bacterial colonization. This demonstrates the efficacy of the 3D printing process in controlling and organizing the orientation of microfibers within composite materials. Subsequently we conducted tensile strength tests (see Materials and Methods section: Characterization of the static mechanical properties) on the three groups of materials and fitted 166 their elastic regions to derive their Young’s modulus (Fig. 5.2bc). Upon comparison, it was noted that the materials in both the Group ΙΙΙ and Group Ι exhibited neatly aligned microfiber configurations. However, the strength of the Group ΙΙΙ’s material was over ten times higher than that of Group Ι, indicating that the bacterial biofilm’s contribution to the covalent connections among microfibers is a primary determinant of the composite material’s strength. Further comparisons between the Group ΙΙΙ material and Group ΙΙ material highlighted that the orientation of microfibers also significantly contributes to the material’s tensile strength in specific directions. Regarding the Young’s modulus, it was observed that the material in Group ΙΙΙ exhibited a superior resistance to deformation compared to those of both Group Ι and Group ΙΙ (Fig. 5.2c). We further explored the impact of the ratio between bacteria and microfibers on the material properties (Fig. 5.2de). To keep the mass fraction of microfibers constant, we introduced a third component, agar gel. By maintaining the mass fraction of microfibers at 1/21, we incrementally increased the mass fraction of bacteria in the composite, shifting the mass ratio from 1:0:20 to 1:20:0 of microfibers, bacteria, and agar gel, respectively. Following tensile testing, as expected, with the increase in bacterial content, both the tensile strength and Young’s modulus of the material significantly improved (Fig. 5.2de). This further substantiates the role of bacterial biofilm adhesion in enhancing the strength and stiffness of microfiber materials. Additionally, we investigated the tensile strength and Young’s modulus of materials fabricated using different 3D printer nozzle sizes (Fig. 5.2fg). Materials produced with smaller nozzle diameters showed a marked increase in tensile strength along the direction of the microfibers. This improvement can be attributed to smaller nozzles facilitating a greater degree of alignment of the microfibers in a uniform direction. Conversely, slightly larger nozzle diameters provide less shear stress during material extrusion, resulting in a lower degree of orderly microfiber alignment within the material (Fig. 5.2fg). This also confirms that the degree 167 of orderly arrangement of microfibers within the material indeed enhances its mechanical properties. Upon comparing this material with other engineering materials, we ascertained that it possesses exceptional specific strength and specific Young’s modulus, characteristics akin to those of commonly used engineering plastics material (Fig. 5.2h). The specific strength of this material surpasses nearly all natural materials, including cork, and exceeds over half of the elastomers and non-technical ceramics, such as concrete and cement. The specific Young’s modulus also surpasses the majority of elastomer material. Additionally, this material demonstrated extraordinary resistance to fracture. Its fracture toughness � is measured at 4805 N ∙ mm/mm& by a standardized testing method known as the pure shear test (see Material and Methods section: Characterization of the fracture toughness). Its specific fracture toughness exceeds that of the majority of currently utilized engineering materials, and only certain metal alloys, composites, and elastomer materials exhibit superior fracture resistance compared to this material (Fig. 5.2i). 168 Figure 5.2. The mechanical property of the microfiber living composite materials. a, Three groups of material for experiments. Group 1 is composed of microfibers and agar gel and made by 3D direct writing. Group 2 is consisted of microfibers and bacteria formed using the molding method instead of 3D printing. Group 3 is consisted of microfibers and bacteria, made by 3D direct writing. b, The stress strain curve of the materials of three groups. c, the strength and the Young’s modulus of the materials of three groups. d, The stress strain curve of the materials made by 3D printing with different Fiber/bacteria/agar weight ratio. e, The strength and the Young’s modulus of the materials made by 3D printing with different Fiber/bacteria/agar weight ratio. f, The stress strain curve of the materials made by 3D printing with different 169 nozzle diameters. g, The strength and the Young’s modulus of the materials made by 3D printing with different nozzle diameters. h, The ashby chart displaying specific Young’s modulus and specific strength. i, The ashby chart displaying fracture toughness (critical energy release rate �). 5.5 Crack-healing property Biological living materials, such as animal bones and plant stems, possess the capacity for selfhealing, regeneration, adaption, and making decisions under environmental pressures. When bacteria are embedded into a microfiber matrix to form composite materials, these composites exhibit self-healing and regenerative abilities similar to those of biological materials. Bacteria function not merely as conventional adhesives; as living organisms, bacteria possess the capability for hierarchical organization of building blocks across multiple length scales, exhibit optimal transport properties of scale-free fractal networks, and have decision-making capabilities that emerge from the decentralized cooperative action of information-processing cells. When the composite material experiences external damage, the bacterial properties facilitate effective self-organization, transport, and proliferation at the necessary locations. The bacteria can attach to and eventually rejoin the damaged microfibers, creating strong new tissue at the site of injury. Fig. 5.3abc delineate the four approximate stages of self-healing in composite materials. The initial stage displays the original material, with Scanning Electron Microscope (SEM) images depicting the surface morphology characterized by bacterial biofilm and interlinked microfibers. The second stage illustrates the material’s rupture after tensile strength testing, where the original material fractures into two segments, revealing transversely aligned microfibers at the fracture location. During the third stage, the two ends of the material are reconnected after immersion in a healing liquid composed of bacteria and nutrient solution. Following a 1 h healing process, bacteria progressively reaccumulate at the fracture location, 170 initiating the reconnection of the microfibers, thereby rendering the crack initially imperceptible. In the final stage, after a 24 h healing process, the continuous transport, proliferation, and re-adhesion of bacteria to the microfibers at the fracture location result in a complete repair of the material, elimination any visible traces of the initial fracture. We have also investigated the impact of varying bacterial percentages in the healing liquid on the self-healing efficacy of composite materials (Fig. 5.3de) (see Materials and Methods section: Characterization of the healing process). Four different healing liquids were prepared, containing bacterial concentrations of 1%, 10%, 50%, and 100%, respectively. Even with a healing liquid containing just 1% bacterial content, after a 24 h healing process, the cracked material was able to regain 20% of the original material’s strength. For the healing liquid with 10% bacterial content, the repaired material achieved 60.9% of the original strength, and the tensile strain reached 80.7% of the original material. With the 50% bacterial healing liquid, the material was restored to 89.9% of its original strength, with the post-healing tensile strain reaching 96.1%. For the healing liquid with 100% bacterial content, the material essentially regained its initial strength and ductility, with 91.8% healing strength and 98.4% healing strain (Fig. 5.3e). These findings demonstrate the significant role of bacterial selforganization, proliferation, transportation, and biofilm formation in adhering to microfibers, endowing the composite material with robust self-healing capabilities within a short timeframe. 171 Figure 5.3. The self-healing property of the microfiber living composite materials. a, The schematic displaying the self-healing process of the living material. The overall process can be divided into 4 stages: the original material, fractured material, self-healing material and the healed material. b, The photo displaying the surface appearance of the material at 4 stages. c, The SEM image displaying the surface appearance of the material at 4 stages. The crack gradually disappears along with the self-healing process. d, The stress strain curve of the material healed with healing liquid of different ratio of bacterial concentration from 1%, 10%, 50% to 100%. e, The healing strength ratio of the material healed with healing liquid of different ratio of bacterial concentration from 1%, 10%, 50% to 100%. 172 5.6 Energy generation property Bacterial metabolic activities facilitate the simultaneous assimilation of oxygen from the environment and the decomposition of organic matter to produce carbon dioxide and water (Fig. 5.4a). These metabolic activities can be considered a form of microbial-catalyzed redox reactions. Biofuel cells are specifically designed to separate and conduct oxidation and reduction reactions at the anode and cathode, respectively (Fig. 5.4c). Consequently, the engineered living materials involving bacterial participation exhibit potential for biofuel cell applications. Due to the differential oxygen concentration often observed between the base and the apex of structures fabricated from this composite material, the base typically becomes the anode in an oxygen-deficient environment. At this location, oxidation reactions transpire, whereby organic substances are oxidized, resulting in the formation of carbon dioxide, hydrogen ions, and electrons. Taking glucose as an example, the oxidation reaction formula at the anode can be represented as: CTH3&OT + 6H&O → 6CO& + 24H1 + 24e4. Conversely, at the oxygen-rich top, reduction reactions occur, involving the uptake of electrons and protons from the base, reacting with oxygen to form water, with the corresponding reaction equation noted as 24H1 + 6O& + 24e4 → 12H&O. The electrons generated at the anode base travel through external conductors to the top, producing an electrical current (Fig. 5.4c). Due to the influence of the microstructural organization within the composite material on the transfer of electrons and protons, substantial effects on the power generation of biofuel cells can be observed. We have designed and fabricated three types of composite materials with different microfiber arrangements and studied the variations in their electrical energy generation capabilities (Fig. 5.4b) (see Materials and Methods section: Preparation of the 173 microfiber-microbial fuel cells). In Material 1, the microfibers are aligned vertically, assuming that the top and bottom of the structure correspond to the cathode and anode of the biofuel cell, respectively. Our hypothesis is that this vertical arrangement facilitates the shortest and most direct pathways for the internal transfer of electrons and hydrogen atoms. Material 2 features a random arrangement of microfibers, where protons and electrons must independently navigate to find suitable paths for transfer. Conversely, in Material 3, the microfibers are arranged horizontally, which may significantly increase the path length for the vertical transfer of protons and electrons, thereby potentially reducing the power generated by the cell (Fig. 5.4d). To enhance the observation and measurement of the energy produced by biofuel cells, we have extended wires from both the top and bottom of the composite material and connected them to a hacker board. Also connected to the hacker board are an electrical capacitor and an LED (Fig. 5.4c). Initially, the energy generated by the biofuel cell is insufficient to induce LED blinking. As the bacteria continue to proliferate and metabolize, the energy output of the biofuel cell gradually increases, eventually reaching a level sufficient to trigger the LED’s blinking (Fig. 5.4f). The time it takes for the LED to start blinking serves as an indicator of the rate at which the biofuel cell accumulates electrical energy. Additionally, every half hour, we replace the LED with a resistor, and by measuring the voltage across the resistor and applying Ohm’s Law, we calculate the energy produced by the materials (Fig. 5.4e) (see Materials and Methods section: Characterization of the microfiber-microbial fuel cells power output). As hypothesized, the material with vertically aligned microfibers stimulated the LED to blink fastest and achieved the highest maximum power output. This was followed by the material with random fiber orientation. In contrast, the material with horizontally aligned microfibers took longer to accumulate sufficient energy to make the LED blink, and it also achieved a significantly lower maximum power output (Fig. 5.4g). 174 To further verify the effect of the alignment and uniformity of microfibers on the material’s ability to generate electricity, we fabricated four groups of materials with vertically aligned microfibers using 3D printers with different nozzle diameters: 2mm, 3mm, 5mm, and 6mm. Although all four groups of materials featured vertically aligned microfibers, the alignment uniformity varied due to the different nozzle diameters used in the fabrication process. Materials produced with smaller nozzle diameters had more orderly aligned microfibers, whereas those produced with larger nozzle diameters exhibited a more random alignment of microfibers. Through power generation measurements, we discovered that materials fabricated with smaller nozzle diameters exhibited greater maximum power generation and could accumulate enough energy to light an LED in the shortest time. This indicates that materials with more orderly longitudinal alignment of microfibers possess superior self-powering performance (Fig. 5.4h). 175 Figure 5.4. The self-powering property of the microfiber living composite materials. a, the schematic displaying the metabolic activity of the bacteria around the anode. b, The schematic displaying materials of the vertically aligned microfibers (left), randomly aligned microfibers (middle) and the horizontally aligned microfibers (right). c, The schematic displaying the biological fuel cell made by the microfibers living material. On the bottom of the fuel cell is the anode where the nutrient is degraded into hydrogen ions, carbon dioxide and electrons with the help of bacteria. On the top of the fuel cell is the cathode where the hydrogen ions, carbon dioxide and electrons react to form water. Electrons flow from anode along the conductor, passing through the electrical device, and subsequently reach the cathode. d, The schematic displaying the transport of electros and hydrogen ions within the materials of the vertically aligned microfibers (left), randomly aligned microfibers (middle) and the horizontally aligned microfibers (right). e, The power generation in a function of time within the material of vertically aligned, randomly aligned and the horizontally aligned microfibers. f, LED blinking and no blinking. g, The max power generation, and time to blink with vertical, random, and horizontal alignment. h, The max power generation, and time to blink with different nozzle diameters. 5.7 Potential applications The adhesive properties of biofilms produced by bacteria enable the formation of a covalent network among previously independent microfibers, thereby endowing the resultant composite material with enhanced mechanical strength and superior fracture toughness. Utilizing this material, we fabricated a circular specimen with a thickness of 1 mm and a diameter of 5 cm, incorporating a 2.5 cm long crack at its diameter. Subsequently, we subjected it to Mode I and Mode III fracture tests. Remarkably, the cracked circular specimen fabricated from this composite material could easily sustain the weight of a full 600 ml bottle of water without further propagation of the crack (Fig. 5.5a, 5.S7). Additionally, we conducted impact resistance 176 tests using a toy gun (Fig. 5.5c) (see Material and Methods section: Characterization of the impact resistance property); unlike the control materials, which shattered upon impact, this 1 mm thick material withstood the impact of toy gun pellets unscathed. In similar impactresistance tests, this material also showed excellent impact-resistant property (Fig. 5.S8). The exceptional mechanical strength, superior fracture toughness, and impact resistance, coupled with the material’s flexibility and 3D printability, render it highly promising for crafting highly complex shapes or 3D curvature-based flexible living armor (Fig. 5.5b) and active robotic skins. Hypothetically, if used in robotic applications, the self-organizing, self-transporting, and selfhealing capabilities of the bacteria within this material could enable the robotic skin to mimic multiple functionalities of biological animal skin. Upon damage, this robotic skin would also manifest self-healing properties similar to those of biological animal skin. Besides, this material demonstrates excellent chemical resistance properties (Fig. 5.S9). In addition to applications in flexible armor and robotic skins, the bacteria in this material can grow across air gaps between separate objects positioned adjacent to one another (Fig. 5.5defg). The bacteria autonomously decide and select larger spaces to migrate, proliferate, and adhere. This capability facilitates the creation of complex-shaped living structures by simply joining individually manufactured parts. Utilizing the 3D printing method, we fabricated three planar components from this material and applied bacteria at their junctions to form a 3D complex structure (Fig. 5.5de). Fig. 5.5f indicates the growth of bacteria at the contact interface causing the components to adhere together. After a 24 h connection process, the entire structure exhibited great mechanical strength (Fig. 5.5g). This characteristic provides a natural advantage for the material in the fabrication of complex structures and buildings. Additionally, given the self-powering properties of this material, it can be used to create various self-powered infrastructures. We have constructed a demo streetlamp using this material, where an anode and a cathode are embedded at the bottom and top of the lamp base, 177 respectively, with two wires leading out to a hacker board equipped with an LED and a capacitor (Fig. 5.5hi). Fig. 5.5i shows some streetlamp components which embed the electrode (Anode) and electrode (Cathode). When installed, the streetlamp does not generate enough electricity to cause the LED to blink (Fig. 5.5j). As the bacteria in the base of the streetlamp proliferate and begin to decompose organic materials in the environment, the structure progressively generates electrical energy. After one hour, the electricity produced by the base of the lamp is sufficient to illuminate the LED (Fig. 5.5k). 178 Figure 5.5. The potential applications of the microfiber living composite materials. a, The heavy object suspension on cracked material. b, A potential application as flexible armor. c, bullet penetration test for acrylic, PS, and microfiber living material. d,e, Joining of 3D-printed parts (d) into a single object (e) through the growth of bacteria between surfaces in physical contact. f, The schematic displaying the growth of bacteria at the contact interface causing the components to adhere together. g, The heavy object bearing test on joined structure. h, The schematic displaying the self-powering streetlight made by the microfiber living material. the anode is embedded at the base of the streetlight pole and the cathode is embedded at the top of the pole. i, The components of the self-powering streetlight made by microfiber living material. j,k, LED not blink before (j) and LED blink after 1 h (k). 5.8 Conclusion Laundry microfibers constitute the most significant contributor (35%) to marine microplastic pollution. After being physically captured by wastewater treatment systems, large quantities of microfibers are frequently discarded, presenting persistent environmental risks. We innovatively propose a method to harness living microorganisms to upcycle laundry fibers into a novel class of engineered living materials. This approach enables laundry microfibers separated from wastewater to be interconnected by biofilms produced by bacteria, yielding composite materials that possess excellent mechanical properties and superior fracture toughness. This recycling method does not require the disruption of the microfibers’ robust chemical bonds, obviating the need for any additional energy input, fundamentally overturning chemical degradation approaches to achieve efficient and low-cost recycling of microfibers. Furthermore, biogenic materials derived from microfibers also present numerous advantages such as capability for 3D printing, self-healing properties, and self-powering functionalities. These attributes render such materials promising candidates for various future applications, including as skins for bio-robots, flexible living armor, and components of future self-powered 179 infrastructures. This strategy of incorporating bacteria into environmental pollutants to immobilize and convert them into advanced engineered living materials offers a novel perspective in the field of environmental protection and inspires the management of other environmental pollutants. 5.9 Materials and Methods 5.9.1 Materials. The bacteria seed Sporosarcina Pasteurii (ATCC 11859, non-infectious) was purchased from the American Type Culture Collection (ATCC). The reagents required for the formulation of the growth were procured from various suppliers. Specifically, Sigma-Aldrich supplied sodium bicarbonate, hydrochloric acid, tryptone, ammonium sulfate, ammonium chloride, and calcium chloride. Alfa Aesar provided urea, tricine, yeast extract, agar, and L-glutamic acid. Difco nutrient broth was obtained from Fisher Scientific, while sodium hydroxide was sourced from EMD Millipore Corporation. All chemicals were used without further purification. 5.9.2 Preparation of microfibers. All microfibers used in the study were collected from garments after being laundered in a washing machine and subsequently dried in a tumble dryer (Fig. 5.S1). The washing machine employed for this process was a 2.4 cu. ft. High-Efficiency Stackable White Front Loading Washing Machine with Steam functionality. The dryer used was an LG DLE3400W 7.4 Cu. Ft. Front Load Electric Dryer with Sensor Dry in White. The garments subjected to this experiment were initially washed in the washing machine for one hour followed by a drying session of one hour in the dryer. Microfibers were then collected from the lint filter of the dryer. These microfibers were further washed in distilled water and centrifuged multiple times to remove impurities such as paper scraps and broken hair strands. Finally, the fibers were airdried naturally for 48 hours before use. 180 5.9.3 Cultivation of S.P. bacteria (ATCC 11859) on agar gel. Fig. 5.S2 displays the cultivation process of S.P. bacteria. The preparation of the growth medium was achieved by dissolving the following chemicals into 1 L of deionized water: 20 g of tryptone, 10 g of yeast extract, 9 g of tricine, 10 g of ammonium sulfate, 4 g of glutamic acid, and 20 g of urea. The pH of the solution was then adjusted to 8.6 ± 0.1 using a 1 M solution of sodium hydroxide. To eradicate bacteria from the growth medium, it was filtersterilized using a filter with a 0.2 μm pore size. The agar solution was prepared by adding 40 g of agar powder to 1 L of deionized water. This solution was subsequently autoclaved at 121°C for 20 minutes. After sterilization, the 1 L of sterilized agar solution was mixed and homogenized with 1 L of the prepared growth medium. The mixture was then quickly distributed into several Petri dishes, each containing approximately 50 mL of the mixture. As the temperature of the mixture returned to room temperature, the liquid solidified into a solid agar gel. Bacterial inoculation was performed by transferring the bacterial seed from the vial to the surface of the agar gel in the Petri dishes using an inoculating loop. During inoculation, the bacteria were spread in streaks across the surface of the agar with sufficient spacing to prevent overlap. The Petri dishes were then covered, wrapped in aluminum foil, and inverted to allow nutrients to continuously gravitate towards the surface of the agar gel to nourish the bacteria. The inoculated Petri dishes were placed in a thermostatically controlled incubator set at 30°C and incubated for 48 hours, resulting in visible yellow bacterial colonies along the streaks. The yellow substance produced by the bacteria, referred to as biofilm, was subsequently collected using a spatula for further analysis. The bacterial culturing process is depicted in Fig. 5.S2. All glassware and containers to be used were sterilized in an autoclave at 121°C for 20 minutes prior to usage to prevent contamination by extraneous microorganisms. 181 5.9.4 Preparation of samples for mechanical testing. This study initially designed controlled experiments to investigate the adhesive properties of bacterial biofilm within composite materials. In control group 1, the materials consisted of microfibers and agar gel. The agar gel was fabricated by dissolving 4 g of agar powder in 100 ml of deionized water, followed by autoclaving at 121°C for 15 minutes. After cooling to 50 °C the gel was mixed with microfibers and stirred until homogeneous. The resultant gel-like mixture was then fed into a desktop extrusion-based 3D printer, where it was extruded at a nozzle temperature of 20 °C. The extruded sample was left to air-dry at room temperature for 48 hours to evaporate the moisture. In control group 2, the materials were composed of bacterial biofilm and microfibers. Microfibers and biofilm were mixed in a specific weight ratio and then placed into molds measuring 3mm x 5mm x 15mm. The sample can be obtained by air-drying the mixture at room temperature for 48 hours. In the experimental group, the samples consisted of microfibers and bacterial biofilm. A predetermined weight ratio of bacteria and biofilm was mixed thoroughly before being placed in the 3D printer, whose nozzle size is adjustable. The sample was extruded at a nozzle temperature of 40 °C and then left to air-dry at room temperature for 48 hours. After the moisture evaporated, samples were solidified with stiffness and strength and were ready for mechanical tests. 5.9.5 Preparation of bacterial sample for SEM imaging. A bacterial suspension of 100 mL is centrifuged for 10 minutes, and the resulting supernatant is discarded while retaining the sediment for subsequent processing. Following sedimentation, the material is fixed in 2.5% glutaraldehyde for 4 hours. Subsequently, it undergoes three 15- 20 minute washes with 0.2 M buffer solution (pH 8.6) to remove excess fixative. Dehydration is then carried out through sequential immersion in ethanol solutions of increasing 182 concentrations: 30%, 50%, 70%, 85%, and 95%, each for 15-20 minutes. This is followed by two immersions in 100% ethanol for the same duration. To ensure thorough dehydration, the sample is further treated with isoamyl acetate for over 3 hours to displace any remaining ethanol, promoting the plumpness of bacteria and preventing deformation or shrinkage. The optical microscope and SEM images of the bacteria are shown in Fig. 5.S3. 5.9.6 Characterization of the static mechanical properties. The uniaxial tensile test was performed on an Instron mechanical tester (Instron, model 5942) for static mechanical properties. Prior to testing, the dimensions of the material under examination must be precisely measured to determine its cross-sectional area. Subsequently, the sample is secured on an Instron mechanical tester, with the length of the sample between the fixtures set at 5 mm. The loading rate of the Instron is maintained at 10 mm/min. The initial data collected by the Instron, which records force F versus displacement δ, can be transformed into tensile stress versus tensile strain. 5.9.7 Characterization of the fracture toughness. To accurately determine the toughness of the material, defined as tearing or fracture energy, we employed a standardized testing protocol known as the pure shear test (Fig. 5.S4a). This method involved two sets of samples, each sharing identical geometrical dimensions: one set remained uncut, and the other was precut with a deliberate crack. The uncut samples consisted of rectangular strips, each measuring 10 mm by 50 mm. These strips were securely fastened in the grips of a tensile tester, where a constant mechanical load was applied at a rate of 30 mm/min. The initial vertical dimension, labeled as �, extended to �� upon the application of the load, with � denoting the applied stretch. This stretch constrained the horizontal deformation due to the rigidity of the grips, and we documented the resultant stress-stretch relationship. 183 Conversely, the cut samples were prepared identically in dimensions but featured a 20- mm crack at one edge, introduced using a razor blade. These samples underwent the same loading process to observe the initiation and subsequent propagation of the crack. The critical point of stretch, �Ç, was identified as the moment when crack propagation commenced. Then the energy per volume of the uncut samples �(�) can be calculated by integrating the area beneath the stress-stretch curve obtained from the uncut samples (Fig. 5.S4b). The theoretical framework for the pure shear test defines the energy release rate, as � = ��(�) , where � is the initial separation between the grips. Finally, toughness is conceptualized as the critical energy release rate, which corresponds to the juncture at which the crack in the precut samples begins to propagate. This is calculated by substituting �Ç into the formula �(�Ç). Each measurement was conducted three times to ensure consistency, noting a deviation within 10%. 5.9.8 Characterization of the healing process. To verify the self-healing capabilities of the material, testing material with a bacteria-to-fiber weight ratio of 20:1 was prepared. Additionally, solutions with bacteria and growth media to promote self-healing in the material were formulated. These solutions contained varying weight ratios of bacteria and growth media: 1%, 10%, 50%, and 100%. Initially, uniaxial tensile tests on the original material using an Instron machine were conducted, and the stress-strain curve and maximum tensile stress were recorded. After the testing, the original material was broken into two pieces. Subsequently, the fractured ends were dipped into the prepared healing solutions and were reconnected with each other. After 24 hours of self-healing process, the uniaxial tensile tests on the healed material were conducted again using the Instron machine and the stress-strain curves and maximum tensile stress of the healed material were recorded. The healing strength ratio � was defined as the ratio between healed tensile strength and original tensile strength, which was written as 184 � = PK_I0#A0< PK_GE@$@J#A . (S1) 5.9.9 Preparation of the microfiber-microbial fuel cells. As shown in Fig. 5.S5a, we made three kinds of microbial fuel cells made of randomly aligned microfibers, horizontally aligned microfibers, and vertically aligned microfibers. Microbial fuel cells have a three-layer structure in a container. Anode electrodes are placed at the bottom of the container. Microfiber living material is placed above the anode electrode, and a cathode electrode is placed above the anode electrode. First, we will place anode electrodes on the bottom. When making randomly aligned microfibers (Fig. 5.S5a left), we will mix the microfibers and bacteria according to a certain proportion of weight and stir well. Fill the microbial fuel cell directly into the container and place it on the anode electrode in close contact. A cathode electrode is then placed above the microfiber living material. When horizontally aligned microfibers are made (Fig. 5.S5a middle), the microfibers and bacteria are mixed according to a certain weight ratio, thoroughly stirred and filled into the 3D printer, and printed layer by layer from left to right. So that the microfibers are arranged parallel to the horizontal plane. When making vertically aligned microfiber material (Fig. 5.S5a right), fill the mixture of microfibers and bacteria into the 3D printer, place the fuel cell container horizontally so that the mouth of the container faces right, and print layer by layer along the wall of the cup from inside to outside. So that the microfibers are arranged parallel to the vertical plane. 5.9.10 Characterization of the microfiber-microbial fuel cells power output. To accurately assess the power generation capabilities of the microfiber-microbial fuel cell, the experimental setup involved modifying the configuration of the hacker board. Initially, both the LED and capacitor were detached from the board. Subsequently, the Cathode wire was connected to port 3 and the Anode wire was connected to port “- “. A 1 kΩ resistor was carefully inserted across ports 5 and 6. Following this setup, a multimeter was connected to the 185 two sides of the resistor and the output voltage can be precisely measured with a multimeter after allowing the system to stabilize for a period of 10 minutes. The power output of the system was calculated using Ohm’s Law, expressed mathematically as � = �&/� , where � represents the power output in watts, � denotes the voltage measured across the resistor in volts, and � symbolizes the resistance, in this case, 1 kΩ. The schematic is shown in Fig. 5.S6b. 5.9.11 Characterization of the impact resistance property To evaluate the material’s impact resistance property, two different types of impact tests were designed. In test 1, test materials with a thickness of 1mm and a diameter of 5 cm were fabricated. Additionally, a toy gun was purchased from Amazon, which fires PP (polypropylene) bullets weighing 3 g, capable of reaching an initial velocity of 20 m/s. To ensure controlled bullet trajectory and precise impact on the test area, two acrylic tubes of varying lengths with an outer diameter of 3 cm and an inner diameter of 2.9 cm were constructed, measuring 2 cm and 15 cm in length, respectively. The shorter tube was tightly placed against the support structure, while the longer tube pressed the material firmly against the shorter tube. This arrangement ensured that the bullets would accurately hit and potentially penetrate the test area. In test 2, test materials with a thickness of 1mm and a diameter of 5 cm was prepared. Additionally, a screw was purchased from Amazon, which is 3.5 cm in length, weighs 18 g, and has a conical head. To ensure the controlled descent of the screw and its precise impact on the test area of the material, two sections of acrylic tubing with an outer diameter of 3 cm and an inner diameter of 2.9 cm were constructed, measuring 2 cm and 160 cm in length, respectively. The shorter tube was firmly positioned against the basement, while the longer tube pressed the material against the shorter tube. Subsequently, the screw was dropped from a height of 1.6 meters above the test material, allowing it to free-fall and strike the 186 predetermined area of the experimental material. The schematic and test results are shown in the Fig. 5.S8. 5.9.12 Characterization of the chemical resistance To assess the chemical resistance of the material, samples measuring 15mm x 15mm were fabricated using a 3D printer with a 2mm nozzle diameter. These samples had a bacteria-tofiber weight ratio of 1:20. After fabrication, the samples were placed in glass petri dishes, and approximately 50ml of various chemical reagents were added to each dish. The chemical reagents included organic solvents such as ethyl alcohol, chloroform, and ethyl acetate, as well as solutions of different concentrations of hydrochloric acid and sodium hydroxide, including 18M sodium hydroxide, 37% hydrochloric acid, pH14 sodium hydroxide, and pH0 hydrochloric acid. Chemical resistance tests commenced with photographic documentation after 30 minutes and again after 24 hours. The chemical resistance performance within different chemicals were illustrated in Fig. 5.S9. 5.10 Supplementary Information Figure 5.S1. Laundry microfibers collected from the washing machine filter screen. 187 Figure 5.S2. Bacteria cultivation process. The bacteria seed from commercial ATCC product was inoculated from vial to an agar gel culture medium. After incubation for 48 hours, the bacteria were clustered, and they produced biofilm on the yellow-colored lines. The viscous biofilm could be collected by a spatula. 188 Figure 5.S3. Optical microscope image (a) and SEM image (b) of the bacterial Sporosarcina Pasteurii. 189 Figure 5.S4. Characterization of the fracture toughness (critical energy release rate �) using pure shear test. a, Pure shear test process. b, Nominal stress stretch curve of unnotched sample. 190 Figure 5.S5. Preparation of the microfiber-microbial fuel cells. a, The schematic displaying the three microbial fuel cells made of random aligned microfibers (left), horizontal aligned microfibers (middle) and vertical aligned microfibers (right). b, The schematic of microfibermicrobial fuel cells. 191 Figure 5.S6. Hacker board setup for LED blinking (a) and power generation measurement (b). 192 Figure 5.S7. The simulation results of the heavy object suspension test using finite element method. a, The mises stress of the material. b, The displacement of the material. 193 Figure 5.S8. Screw spikes drop test on the material. A screw spike weight 18 g was dropped from a height of 1.6m. The horizontal motion of screw spike was constrained by a plastic tube of inner radius 15 mm. The screw spike hit the material on the bottom and make the traditional PS and Acrylic break into pieces. Compared with the traditional PS and Acrylic, the microfiber living material could absorb tremendous impact without any defect. 194 Figure 5.S9. Characterization of the chemical resistance of the microfiber living material. 195 Chapter 6 : Research summery and outlook This report delineates findings from four distinct research projects, focusing primarily on two critical areas. First, from the perspective of fatigue fracture mechanics, we elucidate the mechanisms underlying the generation of particles due to organic material abrasion emissions. Based on these mechanistic insights, we propose a series of effective interventions aimed at mitigating the production of abrasion emissions. Second, we address the challenge of plastic and microplastic waste, which are notoriously difficult to recover. We propose a novel approach leveraging biotechnological advancements to upcycle these wastes into engineered living materials. These engineered living materials not only exhibit superior mechanical properties but also possess self-healing and self-powering capabilities. In Chapter 2, this study reveals the mechanistic link between the abrasion emissions of organic materials and their mechanical properties. Using a mechanics model, we propose that abrasion emissions result from a fatigue fracture process under cyclic loading. Emissions increase significantly when the abrasion-induced effective energy release rate surpasses the material’s fatigue toughness threshold. Above this threshold, the particulate matter’s surface area density correlates with the crack propagation area rate. These findings were experimentally validated with synthetic polyurethane polymers and various real-life sources, such as automobile tires, bicycle brake pads, and footwear soles. Our results challenge the thermal-burning hypothesis and provide guidance for reducing abrasion emissions of airborne particulate matter and marine microplastics. In Chapter 3, this study initials a multi-scale fatigue fracture model with the introduction of the notion of the micro-crack Additional Energy Release Rate to understand the mechanism of the abrasion emission of the organic materials. On the macro level, the original materials would form a macroscopic crack on the scratched surface as the hard object cuts and scoops the original material. The macrocrack are the primary cause of material 196 detachment. Meanwhile, the original material will generate numerous microcracks within the matrix. These microcracks will also continue to propagate under cyclic loads and become the reason for the material to split into smaller particles after detaching from the matrix. Employing the introduced multi-scale fatigue fracture framework, a predictive model was conceived that accurately projects the size distribution and number of abrasion particles. Experimental validation shows high consistency with the model’s predictions. This research establishes a quantitative link between fracture mechanics and PM10 emissions, providing insights into particulate formation and potential pollution reduction. Despite simplifying assumptions, the model lays the groundwork for future studies to extend its applicability. In Chapter 4, this study introduces a microbiological method to upcycle plastic wastes into functional load-bearing structures. Utilizing bacterial adhesion on plastics (e.g., PE, PS, PP, PET), the process is spontaneous, eco-friendly, and forms composites with excellent mechanical properties. Optimal bacterial concentration (70%) and smaller plastic particle size enhance stiffness, strength, and toughness. The composites also exhibit superior damping and unique living properties, such as self-powering (up to ~50 μW), self-healing (~80% strength recovery in 24 hours), and re-processing capabilities. This method not only recycles plastics but also offers sustainable solutions for future infrastructure, energy, and pollution challenges. In Chapter 5, this study proposes an innovative method to upcycle these fibers using living microorganisms. By utilizing bacterial biofilms, laundry microfibers can be transformed into composite materials with excellent mechanical properties and superior fracture toughness. 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Abstract (if available)

Abstract Microplastic pollution constitutes a substantially detrimental type of environmental contamination and poses threats to human health. Among the sources of airborne and marine microplastics, evidence indicates that non-exhaust emissions resulting from tire abrasion and other organic materials have emerged as a notable contributor. However, the mechanistic comprehension of abrasion emissions has thus far remained obscure. We hypothesize the abrasion emission as a fatigue fracture process and discover that abrasion emissions are quantitatively relevant to the crack propagation area rate of the fatigue fracture. Furthermore, we developed a multi-scale scratching model using the principles of linear elastic fracture mechanics. Macroscopically, material wear and tear can be viewed as a process of macro-crack propagation associated with the fatigue fracture. Microscopically, we consider the effect of microcracks propagating under cyclic loading on the material's modulus and energy release rate during fatigue fracture. This framework enables a more accurate calculation of the energy release rate, effectively simulates material abrasion and fragmentation, and elucidates the emission mechanism of finer particles. Furthermore, addressing the issue of microplastic recycling, we propose a solution to upcycle microplastic fibers into engineering living materials harnessing microorganisms. The microplastics recovered from washing machines and dryers can be restructured and rebuilt using bacterial adhesion and then shaped into complex components with 3D printing technology. This material not only exhibits excellent mechanical properties but also possesses outstanding self-healing and self-powering capabilities. These attributes make the material potentially applicable in flexible armor, robotic skin, and self-powering infrastructure.

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Asset Metadata

Creator Li, Ketian (author)

Core Title Mechanics of abrasion emissions of particulate matter and microplastics

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Civil Engineering

Degree Conferral Date 2024-08

Publication Date 07/16/2024

Defense Date 07/03/2024

Publisher Los Angeles, California (original), University of Southern California (original), University of Southern California. Libraries (digital)

Tag abrasion emission,engineering living material,fatigue fracture,marine microplastics,particulate matter

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Wang, Qiming (committee chair), Sioutas, Constantinos (committee member), Zhao, Hangbo (committee member)

Creator Email ketian_li@outlook.com,ketianli@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113997P1K

Unique identifier UC113997P1K

Identifier etd-LiKetian-13206.pdf (filename)

Legacy Identifier etd-LiKetian-13206

Document Type Dissertation

Format theses (aat)

Rights Li, Ketian

Internet Media Type application/pdf

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Source 20240716-usctheses-batch-1183 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu