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Vibrational spectroscopy and molecular orientation at interfaces: correlating the structure of a material's surface to its properties

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Vibrational spectroscopy and molecular orientation at interfaces: correlating the structure of a material's surface to its properties

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VIBRATIONAL SPECTROSCOPY AND MOLECULAR ORIENTATION AT INTERFACES:
CORRELATING THE STRUCTURE OF A MATERIAL’S SURFACE TO ITS’ PROPERTIES

by

Ariel E. Vaughn

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(CHEMISTRY)

August 2021

Copyright 2021 Ariel E. Vaughn
ii

for my family, Nana and Papa,
and all the kids who don’t know that they can be scientists too

iii

Acknowledgements

I would like to present the formal land acknowledgment from the University of Southern
California (USC) for the Tongva people.
We, the Indian people, the traditional caretakers of this landscape are the direct descendants of the first people who
formed our land, our worlds during creation time.
We have always been here.
Our ancestors, prepared and became the landscapes and worlds for the coming of humans with order, knowledge
and gifts embedded in the landscape.
Our ancestors, imbued the responsibility and obligation to our original instructions, guided by protocol and
etiquette to be part of, take care of and ensure the welfare of the extended family and community defined in its most
inclusive expression, the NATURE and to pass those teachings and responsibilities onto our children, grandchildren
and many generations to come. And to all those that live here.
By, Julia Bogany, Gabrieleno Tongva San Gabriel Band of Mission Indians
I would like to express my personal gratitude for the stewardship of the land that the
Tongva people have provided. As a Miwok, I cannot express how important these land
acknowledgements are. That being said, I recognize that formally acknowledging that this land
was stolen from the Tongva people is only the beginning. I believe the government and universities
should do more than simply acknowledge the people, such as return land or offer scholarships
specific for the native communities. If anyone were to question this stance, I ask you to think of
the 354 children found in unmarked graves in residential schools in Canada between May – June
2021, at the time of writing this. As they continue to look, more children will be found. I know that
my acknowledgement here is not enough. It is my sincerest hope that during my tenure at USC
iv

that I have helped give back to the community through the outreach opportunities I have founded,
developed, helped organize, and participated in over the five years I have been here. All of this
work would not have been possible without the support of organizations such as SACNAS and
ITEC. Thank you to the organizations that provided this support and thank you to the Tongva
people. I see you. I hear you. I am listening.
First and foremost, I need to thank my advisors Alexander Benderskii and Brent Melot.
Alex, I will always appreciate how you would drop everything to help me in lab or in your office.
I will never forget dragging you into lab at 7:30 pm on a Friday night to show you my new SPS
spectra because I knew you would be just as excited as I was. Your guidance, compassion, and
mentorship have helped me grow as a student, a chemist, and a person. You made graduate school
a much more welcoming place. I also need to thank you for encouraging me to apply to these
visiting professor positions. You believed in me when I didn’t, and I never would have gotten this
job without your support. Brent, one of the first things you did was bring me into the SACNAS
community. You introduced me to the network that I consider my family and for that, I will be
forever grateful. You pushed me to be the best scientist I could be, and I want you to know that I
appreciated it and I appreciate you. Thank you for exposing me to grant writing. That experience
was invaluable for the next phase of my career. Thank you also for sending me to England for
beamtime. It was an experience I will never forget.
I would like to thank every member, past and present, of the Benderskii group. You all
provided guidance and learning opportunities. Thank you to Angelo Montenegro, for not only
being an incredible coworker, resource, and teacher, but also for being a wonderful friend. I think
I have eaten more meals with you than anyone else at USC. Chayan Dutta, I shadowed you during
my immersion and my first year. Thank you for being such a kind, generous man. I will be forever
v

grateful for you and Angelo because you two taught me how to run and troubleshoot the laser.
Thank you to Muhammet Mammetkuliyev, Dhritiman Bhattacharyya, and Mythreyi Rayaluru.
You all have been wonderful coworkers. Thank you Kyowon Koo for being a wonderful student.
I have enjoyed co-mentoring you in our lab.
I would also like to thank all members of the Melot group. Taylor Hodgkins, you were my
best friend in the department. Since you graduated, I have missed you dearly. Graduate school
hasn’t been nearly as fun since you left. Thank you, for all the late-night wine nights and help with
VESTA structures. I will always cherish you and I am honored to call you a friend. Erica Howard
and Jingyi Ran, thank you for all of your work on the clay project with me. Bethany Seckman,
thank you for being my coffee buddy. I have also missed you dearly since your graduation. Sabrina
Falcon, thank you for working with me. Having you as an undergraduate researcher has been a
wonderful experience. Thank you for being such a hard worker. I am grateful to call you my friend.
Thank you to Nicholas Bashian, JoAnna Milam-Guerrero, Eric McClure, Gemma Goh, Megan
Cassingham, Jessie Andrews, and Michael Brady for being amazing coworkers.
I would like to thank Jahan Dawlaty for all of his guidance and mentorship. You were not
my PI, you were never obligated to help me, you always did so out of the kindness of your heart.
You went through every essay I wrote, line-by-line, with me for all five fellowships I applied for.
I appreciate you more than you know. Thank you to Jahan Dawlaty and his group and Steve
Bradforth and his group. You all provided wonderful feedback in our collaborative group
meetings. Thank you for helping make me a better scientist.
Jessica Parr, I cannot begin to express the gratitude I have for you. In this last year, you
have done so much for me. You mentored me. You provided me with the opportunity to co-teach
vi

with you in the Chem 105B class. You embraced my interest in chemical education research and
worked with me on my first project. I have completely fallen in love with this work, and I am so
grateful you gave me a chance to try this out. Thank you for being my letter writer and my mentor.
I will forever appreciate all you have done for me.
Dr. Erickson, thank you for believing in my teaching abilities and sending students in need
of tutoring my way. I loved all of the students you sent me and appreciate the opportunity more
than you know. Thank you for being my letter writer and constantly encouraging me.
Thank you to Michele Dea, Magnolia Benitez, and Judy May Fong. All of your hard work
keeps the department running. I am grateful for all the help you have offered throughout the years.
Thank you to my committee members, from screening, quals, and my defense. Sri Narayan, Barry
Thompson, Jahan Dawlaty, Shaama Sharada, and Moh El-Naggar, thank you all for your feedback,
questions, and criticism. You all have helped contribute to my learning as a scientist.
I would like to thank the NSF for providing me with the NSF GRFP and all people who
volunteer as reviewers for NSF fellowships. The funding I received through them has been
invaluable. I have been able to attend six conferences over the three years with their funding. I
even posted my reviewer comments on the wall by my desk. Many times, reading the comments
has helped me battle my imposter syndrome. Additionally, I must thank the GEM Consortium and
Los Alamos National Laboratories for the GEM Fellowship. I would like to give a special thanks
to Dmitry Yarotski for serving as my advisor there. Working at Los Alamos was an incredible
opportunity and helped improve my confidence as a scientist.
I have so many people to thank through the two student organizations I spent the most time
involved with during my tenure at USC. SACNAS, you were my family. I felt at home in graduate
vii

school first through finding you. Geo Rangel, thank you for restarting this student organization
with me. I was so grateful for all of your guidance. Laura Mugica, thank you for taking over this
student organization. You are a wonderful friend, scientist, and mentor. I know you will continue
the mission driven focus that Geo and I established. I could write pages and pages to explain what
SACNAS has meant to me, but instead I will thank everyone involved. Thank you for volunteering
your time, energy, money, and resources to help make some of my crazy ideas happen. I can’t
believe we started a summer program and a community college scholarship! Working with you all
has been one of the most emotionally rewarding parts of graduate school. Thank you to the rest of
my SACNAS familia at USC: Carlos Navarro, Melody Aleman, Gwen Noda, Damir Popov, and
Marko Chavez.
I also must thank the Women in Chemistry (WiC) organization at USC. You all have been
a safe space on campus for me to bring my whole self to. While there are many people I could
thank throughout the years, I would like to give a special thanks to Renata Rezende Miranda for
helping run WiC and for being a great friend. I wish we had found each other earlier in graduate
school. I also have to thank Megan Fieser, our faculty advisor. You have been such a wonderful
addition to our department. Thank you for all of your hard work and for being a mentor to all of
us. I appreciate you more than you know. I have confided in you more than any professor in the
department, and you have helped guide me through some of the more difficult conversations I have
had to have while here. Thank you for everything.
I have to thank a few of the friends I have made at USC that haven’t been mentioned yet.
Vicente Galvan, I am so grateful to have co-taught with you in the Chem 105A labs. The
adventures we went on with Taylor in the first year or two of graduate school are some of my
fondest memories. Narcy Ukwitegetse, you are such an amazing man. Thank you for all the late-
viii

night wine nights, dinners, and just overall support. It is an honor to call you my friend. Konstantin
Mallon, thank you for being my coffee buddy and wine buddy throughout the years. I have
appreciated all of your support. To all of the members of the MET group, thank you for your
acceptance. I felt like an honorary member and appreciated all of your support, guidance, and help
over the years. I would like to thank the cast of Hamilton. The Hamilton soundtrack was not only
my pump-up music, but also my writing music. I listened to My Shot before every interview and
major meeting. Thank you for your beautiful performance.
I must thank the support of friends and colleagues from before my time at USC. Dr.
William Casey, thank you for giving me my first research opportunity. I loved working for you
and learned so much in my years with you. You really sparked my love for research, water, and
geochemistry. Brent Pautler, I am so grateful for you and Christine. You two were my support
network, my mentors, and my friends. Thank you for teaching me early on the importance of not
taking research too seriously. It helped me not attach my ability as a scientist to whether or not the
science was working. This was truly invaluable. Dr. Joseph Ryan, thank you for being my first
college chemistry professor at Columbia College. You ignited my love for chemistry and for active
learning. You shaped my career path more than either of us could have anticipated. Thank you.
I also must thank those who came before college. I would like to thank all of my teachers
throughout elementary and high school. I think you all saw the potential in me before I did. I have
two teachers that I must thank personally. Mr. Miller, thank you for inspiring my love for teaching
at an early age. Looking back, I fell in love with science on your annual camping trips, I just didn’t
know it. Mr. Aitken, thank you for believing that I could be a scientist. And more than your belief,
thank you for verbalizing it. I didn’t know I could be a scientist before that moment, and I can
honestly say I probably wouldn’t be one now without your guidance.
ix

I need to thank my best friend, Ashwin Gore. You met me during this process and weren’t
scared away by me being a Ph.D. candidate. Your guidance has helped me grow as a person, a
scientist, a mentor, and a teacher. You have been one of my biggest advocates. Your love and
support mean the world to me. Thank you for making me a better person. I will forever be grateful
for all you have done for me during this time.
Lastly, I have to thank my family. Without your love and support, I would not be the
woman I am today. I know the last five years haven’t been the easiest on the Vaughn family, but
you have all constantly banded together. I wish I could have been more present during some of
those times, but I am grateful that you all supported my continuation of my studies. I dedicated
this dissertation to my grandparents, who saw me begin the journey, but didn’t get to see me finish
it. I hope I made you proud. Aunt Denise and Uncle Bob, thank you for your help and support
through college. I don’t know how I would’ve done this without you. Morgan, my little sister, you
inspire me every day. I am so proud of you and all your hard work. I may have gotten my Ph.D.,
but you started your own business. You constantly impress me. I love you. Dad, thank you for
being my phone call when things went wrong in the lab. Thank you for the hours of FaceTime in
lab to try to help me fix broken equipment. I always call you when I feel like I have no one else to
ask for help and you are always there. I believe I got my work ethic from you. Thank you. I love
you. Mom, thank you for bringing me into this world. I truly believe I got my strength from you.
You taught me the importance of endurance and balance. I know I have you to thank (blame) for
my superwoman complex. You taught me the time management skills I have used to complete all
of the activities I took on while here. Thank you. I love you.

x

Table of Contents

Dedication ....................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Tables ............................................................................................................................... xiv
List of Figures ............................................................................................................................... xv
Abstract ...................................................................................................................................... xxiv
Chapter 1: The Importance of Interfaces in Chemistry .................................................................. 1
1.1: Introduction .............................................................................................................. 1
1.2: Examples of applications of interfaces .................................................................... 2
1.3: Techniques to study interfaces ................................................................................. 3
1.3.1: Linear techniques .......................................................................................... 3
1.3.2: Nonlinear techniques .................................................................................... 3
1.4: Dissertation outline .................................................................................................. 6
Chapter 2: Vibrational Sum Frequency Generation Spectroscopy ............................................... 10
2.1: Introduction ............................................................................................................ 10
2.2: Applications of Vibrational Sum Frequency Generation (VSFG) spectroscopy ... 11
2.3: Experimental setup ................................................................................................. 13
2.4: Orientational analysis ............................................................................................. 16
2.5: Conclusion .............................................................................................................. 20
Chapter 3: Application of Total Internal Reflection to Orientational Studies of Vibrational Sum
Frequency Generation Spectroscopy ............................................................................................ 21
3.1: Introduction ............................................................................................................ 21
3.2: Parameters influencing the signal count ................................................................ 22
3.3: Application to nickel phyllosilicate clays .............................................................. 23
3.4: Results and discussion ............................................................................................ 25
3.5: Conclusion .............................................................................................................. 26
3.6: Acknowledgements ................................................................................................ 27
xi

Chapter 4: Orientational Analysis and Vibrational Sum Frequency Generation Spectroscopy of
Nickel Phyllosilicate Clays ........................................................................................................... 28
4.1: Introduction ............................................................................................................ 28
4.2: Structural characterization of powders and thin films of nickel phyllosilicate ...... 29
4.2.1: Sample preparation ..................................................................................... 29
4.2.2: Experimental determination of the number of layers from Transmission
Electron Microscopy (TEM) ................................................................................. 30
4.2.3: Thin film preparation .................................................................................. 32
4.3: Experimental development of high-resolution spectroscopy ................................. 33
4.4: Vibrational spectroscopy characterization of hydroxyls of nickel phyllosilicate .. 35
4.4.1: FTIR ............................................................................................................ 35
4.4.2: Vibrational Sum Frequency Generation (VSFG) spectroscopy ................. 36
4.5: Orientational analysis of nickel phyllosilicate ....................................................... 39
4.8: Conclusion .............................................................................................................. 42
4.9: Acknowledgements ................................................................................................ 43
Chapter 5: Characterization and Vibrational Sum Frequency Generation Spectroscopy of the
Thermally Treated Nickel Phyllosilicate Analog ......................................................................... 44
5.1: Introduction ............................................................................................................ 44
5.2: Characterization of activated nickel phyllosilicate ................................................ 47
5.2.1: Synthesis of activated nickel phyllosilicate ................................................ 47
5.2.2: Structural differences as determined by X-ray diffraction, Pair Distribution
Function (PDF), X-ray photoelectron spectroscopy, and Transmission Electron
Microscopy (TEM) ............................................................................................... 47
5.2.3: Differences in thin film as demonstrated by Atomic Force Microscopy
(AFM) ................................................................................................................... 49
5.3: Vibrational spectroscopy comparison of the pristine and activated films ............. 50
5.3.1: FTIR ............................................................................................................ 50
5.3.2: Vibrational Sum Frequency Generation (VSFG) spectroscopy ................. 52
5.4: Conclusion .............................................................................................................. 54
5.5: Acknowledgements ................................................................................................ 55
Chapter 6: Inelastic Neutron Scattering of Pristine and Activated Nickel Phyllosilicate Clays .. 56
6.1: Introduction to Inelastic Neutron Scattering (INS) ................................................ 56
6.2: Nickel phyllosilicate catalytic activity ................................................................... 58
6.3: Experimental design for Inelastic Neutron Scattering (INS) experiments ............. 60
xii

6.4: Computational results ............................................................................................. 63
6.4.1: Description of the computational methods used ......................................... 63
6.4.2: Comparison of the pristine and activated calculations ............................... 63
6.4.3: Comparison of the activated, CO2 dosed, and H2 dosed calculations......... 67
6.5: Results and discussion ............................................................................................ 73
6.5.1: Comparison of pristine and activated samples to computational models ... 73
6.5.2: Comparison of pristine and activated samples to one another .................... 77
6.5.3: Comparison of the activated sample to the CO2 dosed, H2 dosed, and post-
reaction activated samples .................................................................................... 80
6.6: Conclusion .............................................................................................................. 82
6.7: Appendix ................................................................................................................ 83
6.8: Acknowledgments .................................................................................................. 85
Chapter 7: Orientation of Water at a Charged Interface and Expansion to Gibbs Free Energy
Potentials ....................................................................................................................................... 87
7.1: Introduction ............................................................................................................ 87
7.2: Previous results ...................................................................................................... 89
7.3: Experimental setup ................................................................................................. 91
7.3.1: Preparation of graphene electrode .............................................................. 91
7.3.2: Electrochemical cell assembly .................................................................... 91
7.3.3: Raman spectroscopy ................................................................................... 92
7.3.4: Vibrational Sum Frequency Generation (VSFG) spectroscopy ................. 94
7.4: Vibrational Sum Frequency Generation (VSFG) spectroscopy at the graphene/D2O
interface ......................................................................................................................... 96
7.4.1: Vibrational Sum Frequency Generation (VSFG) spectra of D2O at the
graphene interface ................................................................................................. 96
7.4.2: Selection of orientational analysis parameters through selection of
distribution function and distribution width ......................................................... 97
7.4.3: Selection of Gaussian distribution function orientational analysis parameters
through calculation of Gibbs free energy potential curves ................................. 102
7.4.4: Combination of the two methods to determine orientational analysis
parameters ........................................................................................................... 104
7.4.5: Calculated orientation for D2O at the graphene interface as a function of
charge density ..................................................................................................... 106
7.6: Conclusion ............................................................................................................ 107
7.7: Acknowledgements .............................................................................................. 108
xiii

Chapter 8: Science Education in the Real World........................................................................ 109
8.1: Introduction to chemical education research ........................................................ 109
8.2: Justification and parameters for our flipped classroom model ............................ 111
8.3: Theoretical framework ......................................................................................... 112
8.4: Results and discussion .......................................................................................... 113
8.4.1: Type of example provided ........................................................................ 113
8.4.2: Use of scientific language in the provided response ................................. 116
8.4.3: Additional explanation provided .............................................................. 119
8.5: Analysis still being processed .............................................................................. 121
8.5.1: Evaluation ................................................................................................. 121
8.5.2: Comparison to topics discussed in class ................................................... 122
8.5.3: Demographics and student backgrounds .................................................. 124
8.6: Conclusions and future work ............................................................................... 124
8.7: Acknowledgements .............................................................................................. 125
References ................................................................................................................................... 126

xiv

List of Tables

Table 4.1. Example outer and inner diameters and layers determined from TEM. Table reproduced
from the supplemental information of Vaughn et al.
45
................................................................. 31
Table 4.2. Experimental fitting parameters from IgorPro
79
fits. Table reproduced from the
supplemental information of Vaughn et al.
45
................................................................................ 38
Table 6.1. Description of the hydroxyl identified by INS computation and corresponding
frequency for the pristine and activated calculations. ................................................................... 67
Table 6.2. Description of the hydroxyl identified by INS computation and corresponding
frequency for the activated and CO2 dosed activated calculations. .............................................. 69
Table 6.3. Description of the hydroxyl identified by INS computation and corresponding
frequency for the activated and H2 dosed activated calculations. ................................................. 72
Table 8.1. Type of example from student responses categorizing the type of chemistry students
identified. The number of responses for each example and the corresponding percentages are
provided. ..................................................................................................................................... 115
Table 8.2. Responses categorizing the type of language used in student responses and
corresponding percentages. Categories include clearly scientific language, some scientific
language, and no scientific language. ......................................................................................... 117
Table 8.3. Table showing the number of responses and corresponding percentages for the number
of students who provided an explanation of why their example was chemistry, referenced
researching the explanation, or provided no explanation of why the example was chemistry. .. 120

xv

List of Figures

Figure 1.1. Cartoon representation of Sum Harmonic Generation (SHG) and Sum Frequency
Generation (SFG) spectroscopies. 1.1a: SHG energy level diagram where ω1 interacts with the
sample twice to induce a virtual state (dashed line), generating SHG signal with ωSHG = 2ω1. 1.1b:
SFG energy level diagram where ω IR interacts with the sample inciting a vibration. Then ωVIS
interacts with the sample to upconvert to induce a virtual state (dashed line). The resulting SFG
signal is generated with ωSFG = ω IR + ωVIS. .................................................................................... 5
Figure 2.1. Cartoon representations of Vibrational Sum Frequency Generation (VSFG)
Spectroscopy. 2.1a: Cartoon of interface with E(ω IR) representing the IR pulse, E(ωVIS)
representing the visible pulse, and ESFG representing the generated VSFG signal. The signal comes
from the chromophores on the surface in yellow. 2.1b: Sum Frequency Generation (SFG) energy
level diagram where ω IR interacts with the sample inciting a vibration. Then ωVIS interacts with the
sample to upconvert to induce a virtual state (dashed line). The resulting SFG signal is generated
with ωSFG = ω IR + ωVIS. ................................................................................................................. 11
Figure 2.2. Schematic of the Vibrational Sum Frequency Generation (VSFG) spectroscopy laser
setup. Reproduced from Dutta.
81
.................................................................................................. 14
Figure 2.3. Visible profile showing gaussian profile with 3.6 cm
-1
full-width-half-maximum. .. 15
Figure 2.4. Cartoon representation of experimental geometries used in the Benderskii lab. Sample
in teal, calcium fluoride window and prism in white, and red arrows represent experimental beams.
Geometry: 2.4a: Straight reflection. 2.4b: Inverted. 2.4c: Prism. ................................................ 16
Figure 3.1. Proposed crystal structure of Ni3Si2O5(OH)4 with multiple layers with the following
color scheme: nickel teal, silicon grey, oxygen orange, and hydrogen black. Grey dashed lines
represent the weak hydrogen-bonds holding the layers together. Circles show the three different
types of hydroxyls. Hydroxyls 1 and 3 are the “outward” hydroxyls (1: free-hydroxyl and 3:
weakly-hydrogen-bonded hydroxyl). Hydroxyl 2 is the “inward” hydroxyl. Structure modeled
using VESTA program.
88
Figure reproduced from Vaughn et al.
45
............................................. 24
Figure 3.2. TEM of Ni3Si2O5(OH)4 showing multiwalled nanoscroll morphology of powder. Left
panel inset: Cartoon representation showing that the outer layer of the scroll is the nickel sheet
(teal) and the inner layer is the silicate sheet (grey) of the phyllosilicate layer. Figure modified
from Vaughn et al.
45
...................................................................................................................... 24
Figure 3.3. 3.3a: VSFG spectra comparing straight reflection, inverted, and prism geometries.
Inset shows straight reflection and inverted geometry. 3.3b-d: Cartoon representation of
experimental geometries. Nickel phyllosilicate in teal, calcium fluoride window and prism in
white, and red arrows represent experimental beams. Geometry: 3.3b: Straight reflection. 3.3c:
Inverted. 3.3d: Prism. ................................................................................................................... 26
xvi

Figure 4.1. Proposed crystal structure of Ni3Si2O5(OH)4 with multiple layers with the following
color scheme: nickel teal, silicon grey, oxygen orange, and hydrogen black. Grey dashed lines
represent the weak hydrogen-bonds holding the layers together. Circles show the three different
types of hydroxyls. Hydroxyls 1 and 3 are the “outward” hydroxyls (1: free-hydroxyl and 3:
weakly-hydrogen-bonded hydroxyl). Hydroxyl 2 is the “inward” hydroxyl. Structure modeled
using VESTA program.
88
Figure reproduced from Vaughn et al.
45
............................................. 30
Figure 4.2. 4.2a: TEM of Ni3Si2O5(OH)4 showing multiwalled nanoscroll morphology of powder.
Left panel inset: Cartoon representation showing that the outer layer of the scroll is the nickel sheet
(teal) and the inner layer is the silicate sheet (grey) of the phyllosilicate layer. 4.2b: Atomic Force
Microscopy topography image of Ni3Si2O5(OH)4 80 nm thin films used for vibrational
spectroscopy analysis covering a 10 x 10 μm. Color scale in units of nm. Figure reproduced from
Vaughn et al.
45
.............................................................................................................................. 31
Figure 4.3. Diagram of the 4f-stretcher. From left to right, the beam hits a diffraction grating and
expands. The lens collimates the beam. The collimated beam moves to the mechanical slit which
was optimized in both directions as indicated by the arrows. The slit selects a narrow portion of
the beam. The beam then expands until the collimating lens. The collimated beam reaches a
diffraction grating and then continues through the path. Each optic is a focal distance f from the
others, hence the name 4f. ............................................................................................................. 34
Figure 4.4. Visible profile showing gaussian profile with 3.6 cm
-1
full-width-half-maximum
defining the resolution of the Vibrational Sum Frequency Generation spectra. Figure reproduced
from the supplemental information of Vaughn et al.
45
................................................................. 35
Figure 4.5. FTIR of the hydroxyl stretch region of the 80 nm thin film of Ni3Si2O5(OH)4. Figure
reproduced from Vaughn et al.
45
................................................................................................... 36
Figure 4.6. Vibrational Sum Frequency Generation (VSFG) spectra of the hydroxyl stretch region
of Ni3Si2O5(OH)4 in a given polarization combination. Top portion of graph shows raw data in
black hollow circles, overall fit in colored solid line, and each individual Lorentzian contributing
to signal in thin black lines. Bottom portion shows residual of the fit in units of percent. 4.6a:
VSFG spectrum of the hydroxyl stretch region of Ni3Si2O5(OH)4 in PPP polarization. Fit presented
in blue. 4.6b: VSFG spectrum of the hydroxyl stretch region of Ni3Si2O5(OH)4 in SSP polarization.
Fit presented in red. 4.6c: VSFG spectrum of the hydroxyl stretch region of Ni3Si2O5(OH)4 in SPS
polarization. Fit presented in green. Figure reproduced from Vaughn et al.
45
............................. 37
xvii

Figure 4.7. Calculated orientation curves mapped onto orientation angle (𝜃 ) are shown in blue.
Experimentally determined ratios are shown in red. Dashed red shows orientation for given
experimental ratios. 4.7a: SSP/PPP ratio corresponds to 140–164 of uncompensated scroll. 4.7b:
SPS/SSP ratio corresponding to 146–152 of uncompensated scroll. 4.7c: SPS/PPP ratio resulting
in 146–151 of uncompensated scroll. 4.7d: Cartoon representation of scrolling. Teal shows inner
compensated portion of scroll. Purple shows uncompensated portion of scroll contributing to
VSFG signal. 𝜃 ∘ is the orientation angle depicted on the x-axis of the orientation curves. 4.7e:
Step-distribution function considered for orientational analysis where 𝜃 ∘ is the cut-off angle for
the step function. Figure reproduced from Vaughn et al.
45
........................................................... 40
Figure 5.1. Proposed crystal structure of Ni3Si2O5(OH)4 with multiple layers with the following
color scheme: nickel teal, silicon grey, oxygen orange, and hydrogen black. Grey dashed lines
represent the weak hydrogen-bonds holding the layers together. Circles show the three different
types of hydroxyls. Hydroxyls 1 and 3 are the “outward” hydroxyls (1: free-hydroxyl and 3:
weakly-hydrogen-bonded hydroxyl). Hydroxyl 2 is the “inward” hydroxyl. Structure modeled
using VESTA program.
88
Figure reproduced from Vaughn et al.
45
............................................. 46
Figure 5.2. Proposed activation mechanism for nickel phyllosilicate. Left: Dehydroxylation
where water is off-gassed. A frustrated Lewis acid-base pair is made where the unsaturated metal
is the acid and the oxygen is the base. Right: Deprotonation where hydrogen gas is evolved, and
two oxygens (basic sites) remain. To maintain charge, this corresponds to an oxidation state change
in the nickel from Ni
2+
to Ni
3+
. ..................................................................................................... 46
Figure 5.3. Laboratory powder X-ray diffraction of pristine nickel phyllosilicate and activated
nickel phyllosilicate. Miller indices are based on the reported monoclinic structure (Cc). The two
peaks that reduce in intensity upon activation are the [002] and [004].
94
..................................... 48
Figure 5.4. 5.4a-d: Least-squares fits to the experimental Pair Distribution Function (PDF). 5.4a:
synchrotron X-ray PDF and 5.4b: spallation neutron PDF for pristine nickel phyllosilicate. 5.4c:
synchrotron X-ray PDF and 5.4d: spallation neutron PDF for activated nickel phyllosilicate. 5.4e:
Oxygen 1S core level X-ray photoelectron spectroscopy of pristine (light blue) and activated (dark
blue) nickel phyllosilicate. 5.4f: Fits for the pristine nickel phyllosilicate. 5.4g: Fits for the
activated nickel phyllosilicate. Shifts between 5.4f and 5.4g correspond to a decrease in hydroxyl
environments. Figure modified from Howard.
94
........................................................................... 48
Figure 5.5. TEM of the 5.5a: pristine nickel phyllosilicate and 5.5b: activated nickel
phyllosilicate. Scrolls maintain shape after thermal treatment. .................................................... 49
xviii

Figure 5.6. Atomic Force Microscopy (AFM) of the pristine (5.6a-b) and activated (5.6c-d) thin
films used in this study. Color scale is in units of nm. Topography vs distance height profiles
(5.6b,d) are in nm on the y-axis and microns on the x-axis. Yellow line represents the position of
the height profile scan. 5.6a: AFM topography image of pristine 80 nm thin films used for
vibrational spectroscopy analysis covering a 10 x 10 μm. 5.6b: Height profile scale of the pristine
thin films showing variations in the height across the scanning area. 5.6c: AFM topography image
of activated 40 nm thin films used for vibrational spectroscopy analysis covering a 10 x 10 μm.
5.6d: Height profile scale of the activated thin films showing variations in the height across the
scanning area. ................................................................................................................................ 50
Figure 5.7. FTIR of the hydroxyl region in units of absorbance for the thin films. 5.7a: Pristine
thin film. Data collected over 3 hours. 5.7b: Activated thin film. Data collected over 10 hours.
Oscillations are interference from water in the air........................................................................ 51
Figure 5.8. Comparison of pristine and activated VSFG signals. Raw data is represented in solid
lines to make the spectral differences clearer. 5.8a: PPP polarization comparison of pristine and
activated samples. Activated samples show over an order of magnitude decrease in signal intensity.
There also appears to be a slight frequency shift upon activation. 5.8b: SSP polarization
comparison of pristine and activated samples. Activated samples show an one order magnitude
decrease in intensity. There appears to be a slight broadening on the high frequency side upon
activation. 5.8c: SPS polarization comparison of pristine and activated samples. The activated
samples show a decrease in signal by a factor of 2. Additionally, the lower frequency peak has
decreased in intensity relative to the higher frequency feature. .................................................... 53
Figure 5.9. 5.9a: PPP, SSP, and SPS polarization VSFG signal intensity for the pristine sample.
5.9b: PPP, SSP, and SPS polarization VSFG signal intensity for the activated sample. Comparison
shows that the relative ratios of SSP/PPP, SPS/PPP, and SPS/SSP are significantly different upon
activation. ...................................................................................................................................... 54
Figure 6.1. 6.1a: Proposed crystal structure of Ni3Si2O5(OH)4 with multiple layers with the
following color scheme: nickel teal, silicon grey, oxygen orange, and hydrogen black. Grey dashed
lines represent the weak hydrogen-bonds holding the layers together. Circles show the three
different types of hydroxyls. Hydroxyls 1 and 3 are the “outward” hydroxyls (1: free-hydroxyl and
3: weakly-hydrogen-bonded hydroxyl). Hydroxyl 2 is the “inward” hydroxyl. Structure modeled
using VESTA program.
88
6.1b: TEM of Ni3Si2O5(OH)4 showing multiwalled nanoscroll
morphology of powder. Left panel inset: Cartoon representation showing that the outer layer of
the scroll is the nickel sheet (teal) and the inner layer is the silicate sheet (grey) of the phyllosilicate
layer. Figure modified from Vaughn et al.
45
................................................................................. 59
Figure 6.2. Proposed activation mechanism for nickel phyllosilicate. Left: Dehydroxylation
where water is off-gassed. A frustrated Lewis acid-base pair is made where the unsaturated metal
is the acid and the oxygen is the base. Right: Deprotonation where hydrogen gas is evolved and
two oxygens (basic sites) remain. To maintain charge, this corresponds to an oxidation state change
in the nickel from Ni
2+
to Ni
3+
. ..................................................................................................... 59
xix

Figure 6.3. Flow chart for experimental process for INS data collection. Nickel phyllosilicate
sample was split into two batches: A and B, each ending with activated nickel phyllosilicate
exposed to gases. INS data was collected in numerical order. ..................................................... 61
Figure 6.4. Calculated INS spectra using D3 functional for the pristine (blue) and activated (black)
samples. Data on same scale to show relative loss of intensity upon activation. ......................... 64
Figure 6.5. Calculated INS spectra using D3 functional for the pristine (blue) and activated (black)
samples. Frequency regions blocked to represent those covered experimentally. Left: 100–900 cm
-
1
. Center: 600–1800 cm
-1
. Right: 1800–4000 cm
-1
. .................................................................... 64
Figure 6.6. Calculated INS spectra using D3 functional for the pristine (blue) and activated (black)
samples. Frequency regions covering the hydroxyl stretches. ...................................................... 66
Figure 6.7. Calculated INS spectra using D3 functional for the activated (black) and activated with
adsorbed CO2 (purple) samples. Frequency regions blocked to represent those covered
experimentally. Left: 100–900 cm
-1
. Center: 600–1800 cm
-1
. Right: 1800–4000 cm
-1
. ............ 68
Figure 6.8. Computed structure for activated sample with CO2 adsorbed. CO2 on defect site circled
in red. Figure created in ChemCraft.
170
........................................................................................ 69
Figure 6.9. Calculated INS spectra using D3 functional for the activated (black) and activated with
adsorbed H2 (light blue) samples. Frequency regions blocked to represent those covered
experimentally. Left: 100–900 cm
-1
. Center: 600–1800 cm
-1
. Right: 1800–4000 cm
-1
. ............ 71
Figure 6.10. Computed structure for activated sample with H2 adsorbed. H2 on defect site circled
in red. Figure created in ChemCraft.
170
........................................................................................ 71
Figure 6.11. Still frames from ChemCraft
170
showing 3317 cm
-1
vibrational motion of the
adsorbed H2 on the activated sample defect site. Each number corresponds to the individual frame.
Red circle draws attention to the adsorbed H2 motion. ................................................................. 73
Figure 6.12. Comparison of experimental INS spectra and the computational spectra using D3
functional for the pristine (blue) comparing two models. Pristine sample was collected in the
aluminum can. Left: Data collected at 120 meV, corresponding to increased resolution from 100–
900 cm
-1
. Center: Data collected at 250 meV, corresponding to increased resolution from 600–
1800 cm
-1
. Right: Data collected at 650 meV, corresponding to increased resolution from 1800–
4000 cm
-1
. 6.12a: Pristine (blue) experimental results compared to the computational model
including water between the layers (green). 6.12b: Pristine (blue) experimental results compared
to the computational model excluding water between the layers (red). ....................................... 74
xx

Figure 6.13. Comparison of experimental INS spectra and the computational spectra using D3
functional for the activated (black) comparing two models. Activated sample was collected in the
aluminum flow can. Left: Data collected at 120 meV, corresponding to increased resolution from
100–900 cm
-1
. Center: Data collected at 250 meV, corresponding to increased resolution from
600–1800 cm
-1
. Right: Data collected at 650 meV, corresponding to increased resolution from
1800–4000 cm
-1
. 6.13a: Activated (black) experimental results compared to the computational
model including water between the layers (green). 6.13b: Activated (black) experimental results
compared to the computational model excluding water between the layers (red). ....................... 75
Figure 6.14. Comparison of the experimental INS spectra for the pristine (blue) and activated
(black) samples. Pristine sample was collected in the aluminum can. Activated sample was
collected in the aluminum flow can. Left: Data collected at 120 meV, corresponding to increased
resolution from 100–900 cm
-1
. Center: Data collected at 250 meV, corresponding to increased
resolution from 600–1800 cm
-1
. Right: Data collected at 650 meV, corresponding to increased
resolution from 1800–4000 cm
-1
. .................................................................................................. 78
Figure 6.15. Experimental INS spectra for the activated (black) and background subtracted
activated (grey) samples. Samples were collected in the aluminum flow can. Demonstrates that the
low frequency contributions may come from the can. Data collected at 250 meV. This is not ideal
for resolution, but was the only sample in which we had a background run. ............................... 78
Figure 6.16. Experimental INS spectra using 650 meV for the pristine (blue) and activated (black)
samples. Frequency region covering the hydroxyl stretches. ....................................................... 79
Figure 6.17. Comparison of the experimental INS spectra for the activated (black) and activated
dosed with CO2 (purple) samples. Both samples were collected in the aluminum flow can. Left:
Data collected at 120 meV, corresponding to increased resolution from 100–900 cm
-1
. Center:
Data collected at 250 meV, corresponding to increased resolution from 600–1800 cm
-1
. Right:
Data collected at 650 meV, corresponding to increased resolution from 1800–4000 cm
-1
. ......... 81
Figure 6.18. Comparison of the experimental INS spectra for the activated (black) and activated
dosed with H2 (light blue) samples. Activated sample was collected in the aluminum flow can. H2
dosed sample was collected in the aluminum can. Left: Data collected at 120 meV, corresponding
to increased resolution from 100–900 cm
-1
. Center: Data collected at 250 meV, corresponding to
increased resolution from 600–1800 cm
-1
. Right: Data collected at 650 meV, corresponding to
increased resolution from 1800–4000 cm
-1
................................................................................... 81
Figure 6.19. Comparison of the experimental INS spectra for the activated (black) and activated
post-reaction (pink) samples. Activated sample was collected in the aluminum flow can. Post-
reaction sample was collected in the aluminum can. Left: Data collected at 120 meV,
corresponding to increased resolution from 100–900 cm
-1
. Center: Data collected at 250 meV,
corresponding to increased resolution from 600–1800 cm
-1
. Right: Data collected at 650 meV,
corresponding to increased resolution from 1800–4000 cm
-1
....................................................... 82
xxi

Figure 6.20. Still frames from ChemCraft
170
showing the 3726 cm
-1
vibrational motion of
Hydroxyl #2 on the pristine sample. Each number corresponds to the individual frame. Red circle
draws attention to the hydroxyl motion. ....................................................................................... 83
Figure 6.21. Still frames from ChemCraft
170
showing the 3647 cm
-1
in-phase vibrational motion
of Hydroxyl #3 on the pristine sample. Each number corresponds to the individual frame. Red
circle draws attention to the hydroxyl motion. ............................................................................. 84
Figure 6.22. Still frames from ChemCraft
170
showing the 3631 cm
-1
out-of-phase vibrational
motion of Hydroxyl #3 on the pristine sample. Each number corresponds to the individual frame.
Red circle draws attention to the hydroxyl motion. ...................................................................... 84
Figure 6.23. Still frames from ChemCraft
170
showing the 3717 cm
-1
vibrational motion of
Hydroxyl #2 on the activated sample. Each number corresponds to the individual frame. Red circle
draws attention to the hydroxyl motion. ....................................................................................... 84
Figure 6.24. Still frames from ChemCraft
170
showing the 3625 cm
-1
in-phase vibrational motion
of Hydroxyl #3 on the activated sample. Each number corresponds to the individual frame. Red
circle draws attention to the hydroxyl motion. ............................................................................. 85
Figure 6.25. Still frames from ChemCraft
170
showing the 3614 cm
-1
out-of-phase vibrational
motion of Hydroxyl #3 on the activated sample. Each number corresponds to the individual frame.
Red circle draws attention to the hydroxyl motion. ...................................................................... 85
Figure 7.1. Cartoon representing the free-OD orientation at the graphene interface. Figure
modified from Montenegro et. al.
46
.............................................................................................. 90
Figure 7.2. Electrochemical flow cell used in VSFG spectroscopy experiments. 7.2a: The calcium
fluoride prism, index-matching fluid, calcium fluoride window, and graphene electrode are nearly
transparent to the IR, visible, and SFG beams. The cell is shown in the three-terminal
configuration; graphene, the Ag/AgCl reference electrode, and the glassy carbon counter electrode
were connected to a potentiostat. In the two-terminal configuration, the Ag/AgCl reference
electrode is absent; graphene and glassy carbon electrodes were connected to a two-terminal
voltage source. 7.2b: Detailed view of the graphene electrode (labels in grey); it is inverted (so
that graphene faces D2O) when placed in the electrochemical flow cell. Figure from Montenegro
et al.
46
............................................................................................................................................ 92
Figure 7.3. Electron doping of graphene using the G-band shifts from the Raman mode.
Demonstrates the relationship between the applied voltage and the electron density at the surface.
Figure from Montenegro et. al.
46
.................................................................................................. 94
Figure 7.4. Electric-Field dependent VSFG spectra of the graphene/D2O interface collected in the
7.4a: SSP configuration and 7.4b: PPP configuration. The amplitudes generally increase linearly
with the applied electric-field magnitude. Shape corresponds to raw data and line corresponds to
mathematical fit. Figure modified from Montenegro et. al.
46
....................................................... 96
xxii

Figure 7.5. 7.5a: Experimental PPP (black) and SSP (red) amplitudes, and their ratio (blue), for a
7.5b: decaying exponential distribution of orientation angles. The experimental amplitude ratio of
PPP to SSP, ranges from about 1.0 to 1.4, points that do not exist on the calculated blue curve in
7.5b. Thus, the distribution of free-OD angles at the graphene/water interface is not well described
by a decaying exponential. Figure modified from Montenegro et al.
46
........................................ 99
Figure 7.6. Gaussian distribution of orientation angles with a width of σ = 15˚. Calculation
corresponds more closely to the experimental spectra in Figure 7.5a compared to the model in
Figure 7.5b. Figure modified from Montenegro et al.
46
............................................................... 99
Figure 7.7. Orientational distribution width and average orientation angle. The calculated average
orientation angle depends on the orientational distribution width. Narrower widths correspond to
a more rigid free-OD ensemble with respect to reorientation. Figure from Montenegro et al.
46
101
Figure 7.8. Top: Cartoon representing the orientation angle at the surface of graphene. 7.8a:
Calculated free energy potential curves, where the observed shift to steeper angles (with respect to
the surface normal) is modeled solely through reorientation of water in the applied field. This
reorientation mechanism accounts for a shift in orientation angle from about 45˚ to 30˚. 7.8b: The
Gaussian orientational distribution (𝜎 = 15˚) shifts with the applied field. Figure modified from
Montenegro et al.
46
...................................................................................................................... 103
Figure 7.9. Determination of the orientational distribution width. 7.9a-f: Orientation of D2O as a
function of charge density on graphene for each distribution width. Widths corresponding to 7.9a:
5˚, 7.9b: 10˚, 7.9c: 15˚, 7.9d: 20˚, 7.9e: 25˚, and 7.9f: 30˚. The orientational distribution width
that yields orientation angles that are consistent between the orienting potential and H-F Wang
methods. 7.9g: The sum of the residuals squared is calculated on the right; the shown polynomial
fit suggests that the optimal distribution width is 𝜎 = 16˚. This is closest to our 𝜎 = 15˚ model.
Figure modified from Montenegro et al.
46
.................................................................................. 105
Figure 7.10. Top: Cartoon representing the orientation angle at the surface of graphene. Bottom:
Experimental electric-field dependent average orientation angle of free-OD for a Gaussian
distribution width of 𝜎 = 15˚. ...................................................................................................... 107
Figure 8.1. The number of different example types provided and the corresponding number of
students who gave those responses. ............................................................................................ 116
Figure 8.2. Graph showing the number of students for a given week whose student responses fell
into the following description categories: clearly scientific language (clearly), some scientific
language (some), and no scientific language (none). .................................................................. 118
Figure 8.3. Word cloud from NVivo Pro
302
for the data corresponding to all sixteen weeks
analyzed in this chapter. The frequency of the word is directly associated with the size in the figure.
..................................................................................................................................................... 119
xxiii

Figure 8.4. Graph showing the number of students for a given week whose student responses fell
into the following categories: explanation provided (explanation), researched the explanation
(research), and none. ................................................................................................................... 121
Figure 8.5. Associated word clouds for Weeks 10 and 12, demonstrating that specific language
tied to the topic in class is referenced by students in the assignment. ........................................ 123

xxiv

Abstract
Interfaces are the boundary in which two surfaces meet i.e., a gas and a solid or a solid and
a liquid. Surfaces are where the vast majority of fundamental chemistry happens. This makes
interfaces of particular interest for chemical applications from adsorption to reactivity. Therefore,
understanding how the structure of a material’s surface correlates to its’ properties is key to fields
like materials design. Even for molecules like water, the dynamics at the interface are still a topic
of active research.
A variety of linear and nonlinear techniques aimed to determine what is happening at the
interface have been developed over the last few decades. Vibrational Sum Frequency Generation
(VSFG) spectroscopy is one inherently surface-specific nonlinear optical technique that looks at
the IR and Raman active vibrational modes on the surface of a material. The application of
polarization-selected VSFG spectroscopy allows the molecular orientation of the vibrating moiety
to be determined. Looking at molecular orientation provides a deeper level of understanding of the
dynamics at the interface.
By developing and applying high-resolution VSFG spectroscopy to clay catalysts, we
looked at the role of the hydroxyl structure in the adsorption properties and catalytic activity of
these systems. This was done for a nickel phyllosilicate catalyst and its’ thermally treated analog.
Complimentary techniques such as Inelastic Neutron Scattering were applied to help understand
the role of the hydroxyls in the catalytic properties of the material.
We also looked at the structure of water at the interface of a charged graphene electrode.
Specifically, we studied the orientation of water as a function of charge density on the graphene.
xxv

This broadens our understanding of water’s structure for applications ranging from atmospheric
sciences to biological processes. In this dissertation, we look at how the structure of a material at
the interface relates to its’ properties. From catalysis to water, this expands our fundamental
knowledge of the dynamics at the surfaces of materials.
Understanding how students learn is crucial in influencing their perceptions of chemistry
in the world around them. Students struggle with understanding the real-world applications of the
chemistry they learn in the introductory general chemistry courses. Many innovative teaching
techniques have been applied to change the way students view chemistry in their daily lives, but
these oftentimes require drastic changes to lesson plans and significant overhauls to the course
content. It is my goal to modify these perceptions with smaller scale assignments, requiring less
modifications on the professor’s lesson plans and leaving the learning in the hands of the students.
In the last chapter of this dissertation, we look at answering the question Does asking students to
identify chemistry in the world around them change their perceptions of the role of chemistry in
their daily lives? By quantifying the responses to the question “How have you observed chemistry
outside of class this week?”, we can determine if asking this question weekly alters students’
views. Modifying the way students see chemistry will allow them to reach a deeper understanding
of the world around us and chemistry’s role in that world.

1

Chapter 1

The Importance of Interfaces in Chemistry

1.1: Introduction
A surface can be broadly defined as a boundary between two phases i.e., where a solid
material meets the air. The technical definition is more complicated. IUPAC has three sub-
definitions under the definition of surface: surface (general), physical surface, and experimental
surface. The surface (general) is the outer layer of a material of undefined depth. The physical
surface is defined as the outermost atomic layer whereas the experimental surface is the material
to the depth at which there is significant interaction.
1
With a warning from IUPAC emphasizing
the importance of clarifying how you define a surface, is it any wonder that Wolfgang Pauli once
said: “God created the bulk; the devil invented surfaces”? What is defined as a surface is
complicated and surfaces themselves tend to be more complex than the bulk material.
More interestingly, surfaces tend to be where the fundamental chemistry happens. This is
in part due to the different intermolecular forces occurring at surfaces compared to the bulk.
2

Surface tension is one consequence of this effect. The boundary in which two surfaces meet is
referred to as an interface. Interfaces are where most chemical processes occur, including
adsorption. This makes interfaces of particular interest for chemical applications.
2

1.2: Examples of applications of interfaces
One incredibly important and unique interface is that of water.
3
Water is the most abundant
liquid on Earth. Without water, life as we know it would cease to exist. Despite the grave
importance of water to our livelihoods, water is not well understood, particularly at the interface.
Within the bulk, water molecules are arranged in a hydrogen-bonded network. At the surface, this
network is terminated resulting in a markedly different structure compared to the bulk. The water
molecules are forced to rearrange to minimize the surface free energy at the interface.
4
This
rearrangement results in the presence of non-hydrogen-bonded species referred to as ‘free-OH’
bonds at the surface which have a wide range of effects.
5, 6
The presence of the free-OH impacts
the concentration of salts
7-9
and organic molecules
10, 11
at the surface as well as influences the
acidity of the surface.
10
These have potential broader impacts on the compositions of the ocean,
12

the atmosphere,
10
and the Earth’s subsurface.
13-15
The specific impacts of the free-OH on the
properties of water is still under investigation. In Chapter 7, we look at water at a charged interface.
Understanding interfaces is also crucial to the field of materials design. Reactions occur at
the interface; therefore, the structure of the surface of a material is critical for determining its
properties. In fact, surface science has been driven largely by the field of heterogeneous
catalysis.
16-20
Initially, the field was limited to techniques which required ultrahigh vacuum
technology. But in more recent years, a variety of surface-sensitive techniques that can be applied
under atmospheric pressure have been developed.
16

3

1.3: Techniques to study interfaces
1.3.1: Linear techniques
There are a large number of linear techniques commonly used for studying surfaces. X-ray
scattering,
21
High Resolution Electron Energy Loss Spectroscopy (HREELS),
22
spectroscopic
ellipsometry,
23
scanning electron microscopy (SEM),
24
and tunneling electron microscopy
(TEM)
25
are all commonly used surface specific techniques that rely on X-rays or electrons.
Unfortunately, all of these techniques require the sample be under ultrahigh vacuum (UHV)
conditions. This significantly limits the applicability of these techniques to a variety of samples.
Additionally, optical spectroscopy techniques that are traditionally used for bulk measurements
can be modified to be surface specific. For Raman spectroscopy, one example of this modification
is Surface Enhanced Raman Spectroscopy (SERS)
26
and for FTIR spectroscopy, an example is
Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS).
27
These modifications
have been crucial in expanding our understanding of surfaces but are limited by the potential for
bulk contributions.
1.3.2: Nonlinear techniques
An alternative to optical linear techniques is to use nonlinear spectroscopies. When the
optical field is intense, the induced polarization within a material can be written as a power series
in the field strength Ej:
28, 29

𝑃 = 𝜒 𝑗𝑘
( 1)
𝐸 𝑗 ( 𝜔 𝑗 )+ 𝜒 𝑖𝑗𝑘 ( 2)
𝐸 𝑗 ( 𝜔 𝑗 ) 𝐸 𝑘 ( 𝜔 𝑘 )+ 𝜒 𝑖𝑗𝑘𝑙 ( 3)
𝐸 𝑗 ( 𝜔 𝑗 ) 𝐸 𝑘 ( 𝜔 𝑘 ) 𝐸 𝑙 ( 𝜔 𝑙 )+ ⋯
𝑃 = 𝑃 ( 1)
+ 𝑃 ( 2)
+ 𝑃 ( 3)
+ ⋯
4

The term P
(1)
is the first order response and is linear with the electric field. This covers our
linear spectroscopy techniques with χ
(1)
being the linear susceptibility. Nonlinear optical processes
are considered any processes of P
(n)
with n≥2. Overall, nonlinear spectroscopies provide more
information about complex molecular processes not achievable by linear techniques.
28, 29

Second-order nonlinear spectroscopies (P
(2)
) are inherently surface specific as they are
forbidden in centrosymmetric media by the electric-dipole approximation.
28
By definition,
interfaces have no center of inversion, making them noncentrosymmetric; therefore, second-order
nonlinear spectroscopies are only sensitive to surface species. This makes second order
spectroscopies such as Sum Harmonic Generation (SHG) and Sum Frequency Generation (SFG)
ideal probes for the surfaces of centrosymmetric mediums.
28, 30
This can be understood
mathematically as follows:
𝑃 ( 2)
= 𝜒 𝑖𝑗𝑘 ( 2)
𝐸 𝑗 ( 𝜔 𝑗 ) 𝐸 𝑘 ( 𝜔 𝑘 )
This is the second-order polarization as a function of the electric field and the second-order
susceptibility χ
(2)
. χ
(2)
is a third rank tensor consisting of 27 elements. By applying the inversion
operator, we change the sign of the electric fields and the second order polarization. This results
in the following equation:
−𝑃 ( 2)
= 𝜒 𝑖𝑗𝑘 ( 2)
(−𝐸 𝑗 ( 𝜔 𝑗 ) )( −𝐸 𝑘 ( 𝜔 𝑘 ) )
−𝑃 ( 2)
= 𝜒 𝑖𝑗𝑘 ( 2)
𝐸 𝑗 ( 𝜔 𝑗 ) 𝐸 𝑘 ( 𝜔 𝑘 )
These conditions can only be true when χ
(2)
= 0; therefore, centrosymmetric environments
do not contribute to the second-order response. At an interface, there is no inversion symmetry.
5

This results in interfaces producing a second-order response. This is mathematically why even-
ordered responses are surface-specific techniques.
Shen and colleagues developed the theoretical framework for SHG and SFG and
demonstrated their experimental feasibility as surface specific techniques.
31-36
For the last three
decades, these two techniques have demonstrated their power as spectroscopic probes for the
analysis of molecular interfaces.
30, 37-44
In both SHG and SFG, two input optical beams interact
with a surface or interface to generate a third pulse that is the sum of the two. For SHG, the two
pulses are the same frequency whereas in SFG the two pulses are different frequencies. These two
cases are compared in Figure 1.1.

Figure 1.1. Cartoon representation of Sum Harmonic Generation (SHG) and Sum Frequency Generation (SFG)
spectroscopies. 1.1a: SHG energy level diagram where ω 1 interacts with the sample twice to induce a virtual state
(dashed line), generating SHG signal with ω SHG = 2ω 1. 1.1b: SFG energy level diagram where ω IR interacts with the
sample inciting a vibration. Then ω VIS interacts with the sample to upconvert to induce a virtual state (dashed line).
The resulting SFG signal is generated with ω SFG = ω IR + ω VIS.

In the following chapters of this dissertation, we will discuss the case of Vibrational Sum
Frequency Generation (VSFG) spectroscopy in which a broad-band IR pulse excites a vibration in
6

the material and a narrow-band visible pulse creates an upconversion to a virtual state, resulting in
the generation of a sum frequency pulse (Figure1.1b). This provides a clear demonstration of the
selection rules for the technique. As the IR pulse must excite a vibration, this means that a
transition dipole moment must be present for the excitation to occur. The upconversion from the
visible pulse is the equivalent to the anti-Stokes Raman process; therefore, the polarizability of the
molecule must change during the excitation. This results in VSFG processes requiring the
vibrational mode be both IR and Raman active.
1.4: Dissertation outline
The aim of this dissertation is to understand the molecular orientation and structure of
materials at interfaces. We defined an interface and discussed the roles of interfaces in a variety of
applications. We then summarized the linear techniques for interfaces and introduced nonlinear
spectroscopy. The following is an overview of the rest of the dissertation.
In Chapter 2, we will explore the uses of VSFG spectroscopy. We will discuss applications
of the technique as well as the experimental setup. The mathematics behind orientational analysis
will be reviewed. The information within this chapter will be applied to Chapters 3, 4, 5, and 7 in
the dissertation.
In Chapter 3, we will discuss the importance of improving the signal-to-noise ratio in
VSFG spectroscopy experiments. To increase the signal count in our experiments, we used a
calcium fluoride prism to approach total internal reflection conditions. The results from three
experimental configurations are explored within this chapter. This technique was crucial for the
analysis of the results in Chapters 4, 5, and 7.
7

In Chapter 4, the VSFG spectra for nickel phyllosilicate clay [Ni3Si2O5(OH)4] at the nickel
phyllosilicate/air interface is reported for the first time.
45
Through use of the prism discussed in
Chapter 3, we were able to identify three unique in-phase hydroxyl groups in the sample whereas
FTIR can only identify one. Beyond identification of the species in the system, orientational
analysis of the outermost surface in-phase hydroxyl stretch was completed. This resulted in the
determination of the amount of uncompensated nanoscroll––a specific particle morphology. For
example, if the nanoscroll completed 2.25 rotations, this would correspond to an orientation of
90˚. This is the first time VSFG spectroscopy has been applied to a nanoscroll material and the
first time the total internal reflection technique has been used for orientational analysis.
In Chapter 5, we look at the thermally treated analog of the nickel phyllosilicate clay
discussed in Chapter 4. Upon thermal treatment, the nickel phyllosilicate undergoes activation in
which water and hydrogen evolves, resulting in a material with different catalytic activity. The
bulk activated material has been characterized in comparison with the nickel phyllosilicate using
powder X-ray diffraction, pair distribution function, and FTIR. VSFG spectroscopy was applied
to this system. While additional experiments are required for orientational analysis, there are clear
spectral changes upon activation. This chapter explores the structural differences between pristine
nickel phyllosilicate and its activated derivative.
In Chapter 6, the Inelastic Neutron Scattering (INS) spectra of nickel phyllosilicate and the
activated nickel phyllosilicate are compared. The activated sample hydrogenates CO2 to
hydrocarbons through the reverse water gas shift reaction followed by a Fischer-Tropsh reaction.
The theory behind INS and the spectral analysis will be provided. INS spectra were collected on
the following samples: nickel phyllosilicate, activated nickel phyllosilicate, activated nickel
8

phyllosilicate exposed to CO2, activated nickel phyllosilicate exposed to H2, and post-reaction
activated nickel phyllosilicate. The results of these experiments will be compared to
computationally calculated spectra. INS is used as a complementary technique to VSFG
spectroscopy because INS is a bulk measurement that is sensitive to vibrations involving hydrogen.
In Chapter 7, we will return to VSFG spectroscopy to study deuterated water at a charged
interface.
46
In this experiment, we use an electrochemical cell with graphene serving as the
electrode. As a voltage is applied to the graphene, the charge at the surface of the graphene is
modified resulting in a change in the orientation of the free-OD. We analyzed the orientation of
the free-OD as a function of charge at the interface. This system is unique in that it can be compared
to optical tweezers, which hold materials like proteins in states that are not a potential energy
minimum. At each charge studied, we calculated the Gibbs free energy potential curves for the
free-OD. This is the first report of the orientation of water at the graphene interface and is the first
time VSFG spectroscopy was used to determine free energy potential curves.
In Chapter 8, we look at how students in a flipped classroom for second semester general
chemistry view chemistry in the world around them. We modeled our assessment based on
evaluations used in informal science learning environments. This evaluation was provided at the
beginning of the semester and repeated at the end. Weekly, students are asked How have you seen
chemistry outside of class this week? These results are analyzed to determine how students see
chemistry around them and if thinking in this way changes their views on chemistry. The material
was analyzed to look at different categories of examples students provided, showing the diversity
in where they are seeing chemistry. Additionally, many of the responses included a detailed
explanation of why the example is chemistry. Several students explicitly commented on
9

completing external research to provide the explanation. There is a direct correlation between the
topic covered in class and the examples provided by many of the students on the assignment. These
results suggest that asking students weekly about chemistry outside of the classroom results in
increased engagement with the material and may impact how students view chemistry in the world
around them.

10

Chapter 2

Vibrational Sum Frequency Generation Spectroscopy

2.1: Introduction
Vibrational Sum Frequency Generation (VSFG) spectroscopy is a nonlinear surface-
specific technique first developed in the late 1980s by the Shen group.
40
To briefly describe the
process, a broadband femtosecond IR pulse excites the vibrations of interest and a narrow-band
visible pulse upconverts that response, inducing a second order polarization in the material. These
two pulses create the selection rule, according to which VSFG requires both an IR and Raman
active vibrational mode. These pulses are overlapped spatially and temporally at the interface and
the VSFG signal that is generated is detected in the phase-matched direction. The VSFG signal is
then frequency-resolved using a monochromator and a CCD detector. The spectral resolution is
determined by the narrow-band visible pulse.
47

A cartoon representation of VSFG spectroscopy is shown in Figure 2.1. Figure 2.1a shows
that the VSFG signal is only generated from the surface moieties in yellow. Figure 2.1b shows the
energy level diagram corresponding to this process. This clearly demonstrates why the technique
is referred to as sum frequency––the resulting frequency from this process is the sum of the two
incoming pulses. In this chapter, we will discuss potential applications of VSFG spectroscopy, our
experimental setup for VSFG spectroscopy, and the theoretical framework for the mathematics
11

behind orientational analysis. The tools discussed in this chapter will resurface in Chapters 3, 4, 5,
and 7 of this dissertation.

Figure 2.1. Cartoon representations of Vibrational Sum Frequency Generation (VSFG) Spectroscopy. 2.1a: Cartoon
of interface with E(ω IR) representing the IR pulse, E(ω VIS) representing the visible pulse, and E SFG representing the
generated VSFG signal. The signal comes from the chromophores on the surface in yellow. 2.1b: Sum Frequency
Generation (SFG) energy level diagram where ω IR interacts with the sample inciting a vibration. Then ω VIS interacts
with the sample to upconvert to induce a virtual state (dashed line). The resulting SFG signal is generated with ω SFG
= ω IR + ω VIS.

2.2: Applications of Vibrational Sum Frequency Generation (VSFG)
spectroscopy
VSFG spectroscopy was first applied to Coumarin 504,
35
but quickly expanded to other
interfaces such as monolayers of organics,
48
liquid/solid interfaces,
36
and adsorbed carbon
monoxide on platinum.
49

Since the early years, VSFG spectroscopy has been used extensively to study the
orientation of water molecules under a variety of conditions.
5, 6, 50-55
For example, the water/air
12

interface in water samples with dissolved ions has been studied to observe the impact of the
addition of ions on the water surface.
53
Recently, our group studied water at charged interfaces
using a graphene electrode (see Chapter 7).
46
Many studies have been conducted on the dynamics
of bulk water. Due to limited techniques, the dynamics of interfacial water have been minimally
explored and there is still much to learn about these systems.
4

Beyond systems such as water, VSFG spectroscopy has been applied to study a variety of
other systems. VSFG spectroscopy as a surface specific technique can be used to study structure-
property relationships that are otherwise inaccessible or less accessible. Polymers are widely used
in paints, coatings, packaging materials, skin-care products, biomedical implants, lubricants, etc.
56

These applications are greatly influenced by the structure of the polymer at the interface. In the
field of polymers, VSFG spectroscopy has been widely used to study things like the polymer/air
interface
57-59
and the polymer/liquid interface.
59-61
For example, the first VSFG spectroscopy study
of a polymer surface in water was reported in 1997 by Somorjai et. al.
62

The field of heterogenous catalysis has been the driving force for many of the
advancements in surface science.
16-20
VSFG spectroscopy was one of the first methods employed
for ambient pressure surface science and has been widely used in the field of catalysis.
16
VSFG
spectroscopy was modified to allow for UHV-compatible high pressure cells to study model
catalyst surfaces and either adsorption sites, orientations, or adsorbate structures.
63-65
These initial
studies were performed using small molecules such as CO or NO on single-crystal surfaces
including Pt(111),
66-68
Pd(111),
69
and Rh(111)
70
. Since then, VSFG spectroscopy has been
expanded to study adsorbates under ambient pressures on other surfaces such as thin films,
71

polycrystalline foils,
72, 73
and supported nanoparticles.
20, 69
This expansion has allowed for a
13

notable increase in the fundamental understanding of the structure-property relationships of
heterogeneous catalysts.
Some more recent advancements in VSFG spectroscopy include the application to
biological surfaces. For example, VSFG spectroscopy has been used to study proteins through
application of chiral VSFG.
74-76
Recently, studies looking at the chiral behavior of peptides
77
and
molecules that are crucial for the development of biomedical devices such as oligo(ethylene
glycol) and poly(ethylene glycol)
78
using VSFG spectroscopy have been completed. VSFG
spectroscopy has a wide range of applications which continuously expand as more systems are
explored.
2.3: Experimental setup
Our experimental setup for broadband VSFG spectroscopy consists of a femtosecond IR
pulse and a picosecond visible pulse, following the diagram in Figure 2.2. A Coherent Legend
Elite Duo (Ti: sapphire) is pumped with a Coherent Evolution (Nd: YLF) and a seed pulse from a
Coherent Micra. This pulse is amplified and split into two paths. The visible path is delivered to a
4f-stretcher where a narrow band is selected using a mechanical slit where we select the resolution.
Figure 2.3 shows an example of the visible profile from the 4f-stretcher. The second path passes
through a TOPAS-C Light Conversion and a NDFG Light Conversion to allow for a tunable IR
pulse. The visible and IR pulses (69˚ and 61˚, respectively) are overlapped spatially and temporally
onto the sample. The VSFG signal in the phase-matched direction is focused onto a Princeton
Instruments Acton SP2500 monochromator. The CCD detector records the spectra. Using half-
wave plates for each beam, the polarization of the beam can be selected. A dark current background
is collected and subtracted from the experimental data. Data analysis is completed using both
14

IgorPro
79
and MATLAB.
80
IgorPro
79
is utilized for the normalization, background subtraction, and
experimental fitting. MATLAB
80
is used for the calculations for orientational analysis.

Figure 2.2. Schematic of the Vibrational Sum Frequency Generation (VSFG) spectroscopy laser setup. Reproduced
from Dutta.
81

15

Figure 2.3. Visible profile showing gaussian profile with 3.6 cm
-1
full-width-half-maximum.

Within our lab, the experiments have been run in three different configurations shown in
Figure 2.4. The differences between these three configurations are discussed in more detail in
Chapter 3. The straight reflection geometry (Figure 2.4a) is the geometry traditionally used in the
Benderskii laboratory. The inverted geometry (Figure 2.4b) increases signal count due to a change
in the Fresnel factors. The prism geometry (Figure 2.4c) causes an increase in signal count due to
the Fresnel factors as well as total internal reflection. Analyzing the data in these configurations
allowed for orientational analysis of complex spectra, see Chapters 3, 4, 5, and 7.
16

Figure 2.4. Cartoon representation of experimental geometries used in the Benderskii lab. Sample in teal, calcium
fluoride window and prism in white, and red arrows represent experimental beams. Geometry: 2.4a: Straight
reflection. 2.4b: Inverted. 2.4c: Prism.

2.4: Orientational analysis
Included here are the mathematical equations used for the orientational analysis of the
VSFG experiments discussed in later chapters of this dissertation. The equations are following
Hong-Fei Wang’s formulation.
37
Please refer to this paper for a more detailed explanation of
orientational analysis.
The intensity of the VSFG signal in terms of 𝜒 𝑒𝑓𝑓 ( 2)
is
𝐼 ( 𝜔 ) =
8𝜋 3
𝜔 2
𝑠𝑒𝑐 2
𝛽 𝑐 0
3
𝑛 1
( 𝜔 ) 𝑛 1
( 𝜔 1
) 𝑛 1
( 𝜔 2
)
|𝜒 𝑒𝑓𝑓 ( 2)
|
2
𝐼 ( 𝜔 1
) 𝐼 ( 𝜔 2
)
In VSFG spectroscopy formalism, ω, ω1, and ω2 are the VSFG, visible, and IR beams,
respectively. The n1(ωi) is the refractive index if the bulk medium at frequency ωi at the interface.
17

β is the incident or reflection angle from the interface and I(ωi) is the intensity of the signal or
input beam. 𝜒 𝑒𝑓𝑓 ( 2)
is the 27 macroscopic susceptibility tensors 𝜒 𝑖𝑗𝑘 ( 2)
in the lab frame coordinate
system. Assuming an achiral rotationally isotropic interface, the 𝜒 𝑒𝑓𝑓 ( 2)
term is
𝜒 𝑒𝑓𝑓 ( 2)
= 𝑠𝑖𝑛 Ω𝑠𝑖𝑛 Ω
1
𝑐𝑜𝑠 Ω
2
𝐿 𝑦𝑦
( 𝜔 ) 𝐿 𝑦𝑦
( 𝜔 1
) 𝐿 𝑧𝑧
( 𝜔 2
) 𝑠𝑖𝑛 𝛽 2
𝜒 𝑦𝑦𝑧

+ 𝑠𝑖𝑛 Ω𝑐𝑜𝑠 Ω
1
𝑠𝑖𝑛 Ω
2
𝐿 𝑦𝑦
( 𝜔 ) 𝐿 𝑧𝑧
( 𝜔 1
) 𝐿 𝑦𝑦
( 𝜔 2
) 𝑠𝑖𝑛 𝛽 1
𝜒 𝑦 𝑧𝑦

+ 𝑐𝑜𝑠 Ω𝑠𝑖𝑛 Ω
1
𝑠𝑖𝑛 Ω
2
𝐿 𝑧𝑧
( 𝜔 ) 𝐿 𝑦𝑦
( 𝜔 1
) 𝐿 𝑦𝑦
( 𝜔 2
) 𝑠𝑖𝑛𝛽 𝜒 𝑧𝑦𝑦
− 𝑐𝑜𝑠 Ω𝑐𝑜𝑠 Ω
1
𝑐𝑜𝑠 Ω
2
𝐿 𝑥𝑥
( 𝜔 ) 𝐿 𝑥𝑥
( 𝜔 1
) 𝐿 𝑧𝑧
( 𝜔 2
) 𝑐𝑜𝑠𝛽𝑐𝑜𝑠 𝛽 1
𝑠𝑖𝑛 𝛽 2
𝜒 𝑥𝑥𝑧

− 𝑐𝑜𝑠 Ω𝑐𝑜𝑠 Ω
1
𝑐𝑜𝑠 Ω
2
𝐿 𝑥𝑥
( 𝜔 ) 𝐿 𝑧𝑧
( 𝜔 1
) 𝐿 𝑥𝑥
( 𝜔 2
) 𝑐𝑜𝑠𝛽𝑠𝑖𝑛 𝛽 1
𝑐𝑜𝑠 𝛽 2
𝜒 𝑥𝑧𝑥
+ 𝑐𝑜𝑠 Ω𝑐 𝑜𝑠 Ω
1
𝑐𝑜𝑠 Ω
2
𝐿 𝑧𝑧
( 𝜔 ) 𝐿 𝑥𝑥
( 𝜔 1
) 𝐿 𝑥𝑥
( 𝜔 2
) 𝑠𝑖𝑛𝛽 𝑐𝑜𝑠 𝛽 1
𝑐𝑜𝑠 𝛽 2
𝜒 𝑧𝑥𝑥
+ 𝑐𝑜𝑠 Ω𝑐𝑜𝑠 Ω
1
𝑐𝑜𝑠 Ω
2
𝐿 𝑧𝑧
( 𝜔 ) 𝐿 𝑧𝑧
( 𝜔 1
) 𝐿 𝑧𝑧
( 𝜔 2
) 𝑠𝑖𝑛𝛽𝑠𝑖𝑛 𝛽 1
𝑠𝑖𝑛 𝛽 2
𝜒 𝑧𝑧𝑧

The Ωi terms correspond to the polarization angles of the VSFG (i=0), visible (i=1), and
IR (i=2). The Fresnel factors (Lii) at a particular frequency are:
𝐿 𝑥𝑥
( 𝜔 𝑖 ) =
2𝑛 1
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛾 𝑖 𝑛 1
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛾 𝑖 + 𝑛 2
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛽 𝑖
𝐿 𝑦𝑦
( 𝜔 𝑖 ) =
2𝑛 1
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛽 𝑖 𝑛 1
( 𝜔 𝑖 ) 𝑐 𝑜 𝑠 𝛽 𝑖 + 𝑛 2
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛾 𝑖
𝐿 𝑧𝑧
( 𝜔 𝑖 ) =
2𝑛 2
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛽 𝑖 𝑛 1
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛾 𝑖 + 𝑛 2
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛽 𝑖 (
𝑛 1
( 𝜔 𝑖 )
𝑛 ′
( 𝜔 𝑖 )
)
2

where n’(ωi) is the effective refractive index at the interface and γ i is the refractive angle into
medium 2.
18

We use polarization-selected VSFG spectroscopy, allowing us to select specific 𝜒 𝑒𝑓𝑓 ( 2)
terms
through the polarization of the beams. The polarization combinations used in this experiment are
PPP, SSP, and SPS, leading to the following 𝜒 𝑒𝑓𝑓 ( 2)
terms:
𝜒 𝑒𝑓𝑓 ,𝑝𝑝𝑝
( 2)
= − 𝐿 𝑥𝑥
( 𝜔 ) 𝐿 𝑥𝑥
( 𝜔 1
) 𝐿 𝑧𝑧
( 𝜔 2
) 𝑐𝑜𝑠𝛽𝑐𝑜𝑠 𝛽 1
𝑠𝑖𝑛 𝛽 2
𝜒 𝑥𝑥𝑧

− 𝐿 𝑥𝑥
( 𝜔 ) 𝐿 𝑧𝑧
( 𝜔 1
) 𝐿 𝑥𝑥
( 𝜔 2
) 𝑐𝑜𝑠𝛽𝑠𝑖𝑛 𝛽 1
𝑐𝑜𝑠 𝛽 2
𝜒 𝑥𝑧𝑥
+ 𝐿 𝑧 𝑧 ( 𝜔 ) 𝐿 𝑥𝑥
( 𝜔 1
) 𝐿 𝑥𝑥
( 𝜔 2
) 𝑠𝑖𝑛𝛽𝑐𝑜𝑠 𝛽 1
𝑐𝑜𝑠 𝛽 2
𝜒 𝑥𝑥𝑧

+ 𝐿 𝑧𝑧
( 𝜔 ) 𝐿 𝑧𝑧
( 𝜔 1
) 𝐿 𝑧𝑧
( 𝜔 2
) 𝑠𝑖𝑛𝛽𝑠𝑖𝑛 𝛽 1
𝑠𝑖𝑛 𝛽 2
𝜒 𝑧𝑧𝑧

𝜒 𝑒𝑓𝑓 ,𝑠𝑠𝑝
( 2)
= 𝐿 𝑦𝑦
( 𝜔 ) 𝐿 𝑦𝑦
( 𝜔 1
) 𝐿 𝑧𝑧
( 𝜔 2
) 𝑠 𝑖 𝑛 𝛽 2
𝜒 𝑦𝑦𝑧

𝜒 𝑒𝑓𝑓 ,𝑠𝑝𝑠 ( 2)
= 𝐿 𝑦𝑦
( 𝜔 ) 𝐿 𝑧𝑧
( 𝜔 1
) 𝐿 𝑦𝑦
( 𝜔 2
) 𝑠𝑖𝑛 𝛽 1
𝜒 𝑦𝑧𝑦
The lab frame macroscopic susceptibility tensor 𝜒 𝑖𝑗𝑘 ( 2)
is related to the ensemble average
molecule frame microscopic hyperpolarizability tensor 𝛽 𝑎𝑏𝑐 ( 2)
over all possible orientations through:
𝜒 𝑖𝑗𝑘 ( 2)
= 𝑁 𝑠 ∑〈𝑅 𝑖𝑎
𝑅 𝑗𝑏
𝑅 𝑘𝑐
〉𝛽 𝑎𝑏𝑐 ( 2)
𝑎𝑏𝑐
where Ns is the number density of the vibrating moiety and R ia is an element of the rotational
transformation matrix from the molecular frame (a, b, c) to the lab frame (i, j, k). As we assume
the twist and azimuthal angles are isotropically distributed, only the tilt angle θ needs to be
considered.
19

The microscopic hyperpolarizability tensor is related to the partial derivative of the Raman
polarizability tensor
𝜕 𝛼 𝑎𝑏
( 1)
𝜕 𝑄 𝑞 and IR transition dipole moment
𝜕 𝜇 𝑐 𝜕 𝑄 𝑞 of the qth vibrational mode where
Qq is the normal coordinates through:
𝛽 𝑎𝑏𝑐 𝑞 = −
1
2𝜖 0
𝜔 𝑞 𝜕 𝛼 𝑎 𝑏 ( 1)
𝜕 𝑄 𝑞 𝜕 𝜇 𝑐 𝜕 𝑄 𝑞
The molecular hyperpolarizability tensor can be fit with Lorentzian line shapes using the
following equation:
𝛽 ( 2)
= 𝛽 𝑁𝑅
( 2)
+ ∑
𝛽 𝑞 𝜔 𝐼𝑅
− 𝜔 𝑞 + 𝑖 Γ
𝑞 𝑞
where 𝛽 𝑁𝑅
( 2)
is the nonresonant contribution and β
q
is the sum frequency strength factor tensor, ωq
is the resonant frequency, and Γq is the dampening constant for the qth vibrational mode.
Molecular symmetry simplifies the 𝜒 𝑖𝑗𝑘 ( 2)
terms. For an OH stretch, the symmetry is assumed
to be C∞v and therefore,
𝜒 𝑥𝑥𝑧
( 2)
= 𝜒 𝑦𝑦𝑧
( 2)
=
1
2
𝑁 𝑠 𝛽 𝑐𝑐𝑐 [( 1 + 𝑅 ) 〈𝑐𝑜𝑠𝜃 〉 − ( 1 − 𝑅 ) 〈𝑐𝑜𝑠 3
𝜃 〉]
𝜒 𝑥𝑧𝑥 ( 2)
= 𝜒 𝑧𝑥𝑥 ( 2)
= 𝜒 𝑦𝑧𝑦 ( 2)
= 𝜒 𝑧𝑦𝑦 ( 2)
=
1
2
𝑁 𝑠 𝛽 𝑐𝑐𝑐 ( 1 − 𝑅 ) [〈𝑐𝑜𝑠𝜃 〉 − 〈𝑐𝑜𝑠 3
𝜃 〉]
𝜒 𝑧 𝑧 𝑧 ( 2)
= 𝑁 𝑠 𝛽 𝑐𝑐 𝑐 [𝑅 〈𝑐𝑜𝑠𝜃 〉 + ( 1 − 𝑅 ) 〈𝑐𝑜𝑠 3
𝜃 〉]
where θ is orientation angle and R is the hyperpolarizability ratio (R = β aac/βccc = βbbc/βccc). By
taking ratios of the polarizations, orientation angle can be determined.
20

To determine the orientation from the experiment, we use MATLAB
80
to calculate the
predicted experimental spectra using the above equations. We then make a curve of the ratios of
amplitudes as a function of orientation angle, θ. This curve can be compared to our ratios
determined from fitting our experimental data. This reported orientation is the orientation for the
ensemble average of our system.
2.5: Conclusion
VSFG spectroscopy is a powerful tool in the analysis of surfaces and interfaces. Being a
nonlinear technique, it is inherently surface-specific. Over the last 30 years, VSFG spectroscopy
has been widely used to study a variety of interfaces from proteins to water. The expansion of
VSFG spectroscopy to orientational analysis has drastically increased the amount of information
that can be obtained from VSFG spectra. Orientational analysis through VSFG spectroscopy is a
compelling tool for understanding the structure of a material at an interface. In Chapters 3, 4, 5,
and 7, VSFG spectroscopy will be applied to a variety of interfaces to understand the structure at
the surface.

21

Chapter 3

Application of Total Internal Reflection to Orientational Studies of
Vibrational Sum Frequency Generation Spectroscopy

3.1: Introduction
Vibrational Sum Frequency Generation (VSFG) spectroscopy is a powerful surface
specific technique. As the surface has significantly less chromophores contributing to the signal
compared to bulk techniques, VSFG spectroscopy tends to suffer from lower signal count and
signal-to-noise ratios. Mathematically speaking, the intensity of the VSFG signal (ISFG) scales
quadratically with the number of vibrating moieties (N) in the following equation:
𝐼 𝑆𝐹𝐺 ∝ |𝐸 𝑆𝐹𝐺 |
2
∝ |𝜒 ( 2)
|
2
= 𝑁 2
|〈𝛽 ( 2)
〉|
2

where 𝜒 ( 2)
= 𝑁 〈𝛽 ( 2)
〉 is the nonlinear susceptibility tensor expressed through the orientationally
averaged hyperpolarizability at the surface. This low signal count caused by the inherently small
number of chromophores at the surface has led to a number of groups attempting to increase VSFG
signal count, including the development of techniques such as heterodyne detection (HD-VSFG).
82

Total Internal Reflection (TIR) is one such technique applied to VSFG spectroscopy to increase
signal count.
22

TIR is the phenomenon when 100% of the light reaching the surface is reflected, with no
light refracted. There is a critical angle in which this phenomenon occurs. Through addition of a
prism, one can approach the TIR conditions for VSFG spectroscopy. TIR has been applied
previously to increase signal intensity in VSFG experiments,
57, 83-85
but to our knowledge, we are
the first group to apply this to orientational studies.
3.2: Parameters influencing the signal count
The 𝜒 ( 2)
dependance on Fresnel factors causes TIR and experimental configuration to have
an impact on VSFG signal (see Chapter 2 for a more detailed treatment). Briefly, the Fresnel
factors (𝐿 𝑖𝑗
) at a particular frequency are:
37

𝐿 𝑥𝑥
( 𝜔 𝑖 ) =
2𝑛 1
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛾 𝑖 𝑛 1
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛾 𝑖 + 𝑛 2
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛽 𝑖
𝐿 𝑦𝑦
( 𝜔 𝑖 ) =
2𝑛 1
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛽 𝑖 𝑛 1
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛽 𝑖 + 𝑛 2
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛾 𝑖
𝐿 𝑧𝑧
( 𝜔 𝑖 ) =
2𝑛 2
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛽 𝑖 𝑛 1
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛾 𝑖 + 𝑛 2
( 𝜔 𝑖 ) 𝑐𝑜𝑠 𝛽 𝑖 (
𝑛 1
( 𝜔 𝑖 )
𝑛 ′
( 𝜔 𝑖 )
)
2

where 𝑛 ′( 𝜔 𝑖 ) is the effective refractive index at the interface and 𝛾 𝑖 is the refractive angle into
medium 2. We can therefore predict, using MATLAB,
80
the impact of experimental configurations
on VSFG signal intensity.
Calcium fluoride is transparent in the frequency regions we access with our VSFG
spectroscopy experiments, robust, and readily available; therefore, it is our substrate of choice
experimentally. We selected an equilateral calcium fluoride prism to ensure phase-matching from
23

the refractive index and to most closely approach TIR. There is an air gap between the calcium
fluoride window and prism that will cause the VSFG signal to be generated at that interface. To
avoid this, a phase-matching liquid must be selected which has the same refractive index as
calcium fluoride in the frequency ranges experimentally probed: IR, visible, and VSFG.
Additionally, this liquid should be selected such that it absorbs minimally in the IR region of
interest. These experimental parameters have allowed us to apply TIR to VSFG spectroscopy and
to the orientational analysis of VSFG spectra.
3.3: Application to nickel phyllosilicate clays
Clays are naturally occurring layered materials with large surface areas and diverse
physical and chemical properties at their surfaces. In this study, we look at nickel phyllosilicate
(Figure 3.1) which belongs to the 1:1 phyllosilicate class. These correspond to a single sheet of
metal octahedra and a single sheet of silicon tetrahedra. The 1:1 phyllosilicates can form in either
the platelet or nanoscroll morphology. This particular sample of nickel phyllosilicate forms
nanoscrolls, as demonstrated by the TEM imaging seen in Figure 3.2. Nickel phyllosilicate has
been used extensively as catalyst supports
86
and adsorbates for wastewater remediation.
87
These
properties are greatly influenced by the structure at the surface. Due to the size of the nickel
octahedra, these scrolls form with nickel on the outside of the sheet; therefore, the surface of the
scrolls primarily consist of hydroxyl groups. According to the proposed crystal structure in Figure
3.1, there are at least three unique hydroxyl groups in this system, leading to complex spectra.
45

The application of TIR to this system was crucial in interpreting the spectral features, as discussed
in more detail in Chapter 4.
24

Figure 3.1. Proposed crystal structure of Ni 3Si 2O 5(OH) 4 with multiple layers with the following color scheme: nickel
teal, silicon grey, oxygen orange, and hydrogen black. Grey dashed lines represent the weak hydrogen-bonds holding
the layers together. Circles show the three different types of hydroxyls. Hydroxyls 1 and 3 are the “outward” hydroxyls
(1: free-hydroxyl and 3: weakly-hydrogen-bonded hydroxyl). Hydroxyl 2 is the “inward” hydroxyl. Structure modeled
using VESTA program.
88
Figure reproduced from Vaughn et al.
45

Figure 3.2. TEM of Ni 3Si 2O 5(OH) 4 showing multiwalled nanoscroll morphology of powder. Left panel inset: Cartoon
representation showing that the outer layer of the scroll is the nickel sheet (teal) and the inner layer is the silicate sheet
(grey) of the phyllosilicate layer. Figure modified from Vaughn et al.
45

25

For orientational analysis, we calculate the amplitudes of the VSFG signal in different
polarizations in MATLAB.
80
We can use this program to calculate the predicted spectral changes
as a function of experimental configuration. These calculations predict multiple orders of
magnitude changes as a function of configuration. Experimentally, we collected the spectra in all
three configurations using dimethylformamide as our phase matching liquid.
3.4: Results and discussion
This experiment is collected in multiple configurations. The changes in the spectra as a
function of configuration are seen in Figure 3.3. Figure 3.3a shows the experimental spectra of
nickel phyllosilicate in the OH-stretch frequency region in PPP polarization as a function of
experimental configuration. Figure 3.3b-d shows the experimental configurations comparatively.
Switching the experimental configuration from straight reflection (Figure 3.3b) to inverted
geometry (Figure 3.3c) shows a three-fold signal increase. The addition of the prism (Figure 3.3d)
to approach TIR geometry increases the signal 500x compared to the straight reflection geometry,
significantly decreasing our acquisition time as well as allowing us to identify spectral features
more clearly. Orientational analysis was compared in the three experimental configurations and
resulted in consistent angles for the nickel phyllosilicate. While this was not a previously reported
spectra, this demonstrates the power of TIR in orientational analysis.
26

Figure 3.3. 3.3a: VSFG spectra comparing straight reflection, inverted, and prism geometries. Inset shows straight
reflection and inverted geometry. 3.3b-d: Cartoon representation of experimental geometries. Nickel phyllosilicate in
teal, calcium fluoride window and prism in white, and red arrows represent experimental beams. Geometry: 3.3b:
Straight reflection. 3.3c: Inverted. 3.3d: Prism.

3.5: Conclusion
TIR is crucial for the analysis of complex VSFG spectra. The increase in signal intensity
allows for a decrease in acquisition time. This combined with the significant increase in the signal-
to-noise ratio, allows for more spectral features to be identified. As demonstrated here and in detail
27

in Chapters 4 and 7, this experimental geometry can be expanded to orientational analysis of the
VSFG spectra.
3.6: Acknowledgements
I would like to acknowledge Erica Howard for making the nickel phyllosilicate powders in
this study and Sabrina Falcon for making the thin films from the powders. I would like to thank
Muhammet Mammetkuliyev for creating the MATLAB
80
code to analyze the samples and Angelo
Montenegro for running the initial experiments with me. I would like to acknowledge the Tongva
people whose land the research was conducted on.

28

Chapter 4

Orientational Analysis and Vibrational Sum Frequency Generation
Spectroscopy of Nickel Phyllosilicate Clays

4.1: Introduction
Clays are layered materials that are naturally occurring. They have a wide variety of crystal
structures and particle morphologies. One class of clays are the phyllosilicates. The word
phyllosilicate means “sheet silicate” and is named after the alternating layered structure of these
materials.
89
They are defined by two attached sheets: a sheet of edge-sharing MO6 octahedra and
a sheet of corner-sharing SiO4 tetrahedra.
90
This results in a variety of crystal structures. For the
1:1 phyllosilicates, there is a single sheet of the metal octahedra and a single sheet of the silicon
tetrahedra, charged balanced by hydroxyls. There are two common morphologies for the 1:1
phyllosilicates: platelets (the serpentine class) and nanoscrolls (the chrysotile class).
91
The
nanoscrolls form when a size mismatch between the layers induces strain within the plane and the
material rolls into a scroll to relieve that strain.
92
This scrolling produces nanoscrolls that have
different inner and outer surface charges, making them unique compared to other commonly
studied nanostructures (e.g., carbon nanotubes). This effect has the potential to create novel
properties. The inner and outer charge differences drive the surface chemistry for these
29

nanoscrolls, such as adsorption properties, which greatly influence their applications e.g., in
wastewater remediation
87
and drug-delivery.
93

In this study we focus on nanoscrolls of nickel phyllosilicate, Ni3Si2O5(OH)4, which have
been historically studied as catalyst supports
86
and adsorbates for wastewater remediation.
87
Figure
4.1
45
shows the proposed crystal structure of nickel phyllosilicate where the sheet of teal octahedra
correspond to the nickel centered coordination polyhedral sheet. The metal sheet is attached to the
grey silicate ring of corner sharing tetrahedra. This layered structure is held together via weak
hydrogen-bonds between the layers. The metal octahedra are larger in size than the silica
tetrahedral rings. This size mismatch drives a buckling of the silica ring, driving the scroll to form
with the metal octahedra on the outer portion of the scroll. As the surface of the material is covered
by hydroxyls, it is safe to assume the hydroxyls are important in the surface adsorption properties.
In this work, we identify the hydroxyls on the surface of the nanoscrolls and characterize their
orientation using Vibrational Sum Frequency Generation (VSFG) spectroscopy. To the best of our
knowledge, this is the first time VSFG spectroscopy has been applied to a nanoscroll material.
4.2: Structural characterization of powders and thin films of nickel
phyllosilicate
4.2.1: Sample preparation
Nickel phyllosilicate was synthesized by combining stoichiometric amounts of nickel (II) nitrate
heptahydrate with silicic acid. This was dissolved in water and polymerized with sodium
hydroxide. The gel was aged for 2–10 days and put in an autoclave for 2 days at 200 °C.
94
The
resulting powder was dried and washed with water. Thin films were prepared by sonicating 0.01
30

g of nickel phyllosilicate in 1 mL of water for 2 hours then spin casting the material at 3000 RPM
for 30 seconds onto a calcium fluoride substrate. Samples were then left under vacuum at room
temperature for 24 hours to dry.

Figure 4.1. Proposed crystal structure of Ni 3Si 2O 5(OH) 4 with multiple layers with the following color scheme: nickel
teal, silicon grey, oxygen orange, and hydrogen black. Grey dashed lines represent the weak hydrogen-bonds holding
the layers together. Circles show the three different types of hydroxyls. Hydroxyls 1 and 3 are the “outward” hydroxyls
(1: free-hydroxyl and 3: weakly-hydrogen-bonded hydroxyl). Hydroxyl 2 is the “inward” hydroxyl. Structure modeled
using VESTA program.
88
Figure reproduced from Vaughn et al.
45

4.2.2: Experimental determination of the number of layers from Transmission
Electron Microscopy (TEM)
The Transmission Electron Microscopy (TEM) image of the powder in Figure 4.2a can be
used to estimate the number of layers in a given nanoscroll. This was completed by measuring the
31

difference between outer and inner diameter and dividing by 2 to account for both sides of the
scroll. Based on the proposed crystal structure (Figure 4.1), the top layer is roughly 5 Å and each
additional layer is roughly 7 Å. Examples of outer diameter, inner diameter, and the number of
layers associated can be seen in Table 4.1.

Figure 4.2. 4.2a: TEM of Ni 3Si 2O 5(OH) 4 showing multiwalled nanoscroll morphology of powder. Left panel inset:
Cartoon representation showing that the outer layer of the scroll is the nickel sheet (teal) and the inner layer is the
silicate sheet (grey) of the phyllosilicate layer. 4.2b: Atomic Force Microscopy topography image of Ni 3Si 2O 5(OH) 4
80 nm thin films used for vibrational spectroscopy analysis covering a 10 x 10 μm. Color scale in units of nm. Figure
reproduced from Vaughn et al.
45

Table 4.1. Example outer and inner diameters and layers determined from TEM. Table reproduced from the
supplemental information of Vaughn et al.
45

Outer diameter (nm)
Inner diameter (nm) Number of layers
20 5 11
25 12 9
20 8 8
25 5 14
20 16 3

32

The samples in this study are spin-cast 80 nm films of nanoscrolls lying flat on a calcium
fluoride substrate. TEM imaging (Figure 4.2a) shows that the scrolls are multiwalled with an
average outer diameter of 20 nm. TEM images show nanoscrolls roughly between 2 and 20 layers,
with the average being approximately 10 layers. The unit cell of a multilayer system should consist
of several environments including the free-hydroxyls, “inward” pointing hydroxyls, and weakly
hydrogen-bonded hydroxyls as shown in Figure 4.1. One-third of the hydroxyls in a given layer
should be weakly hydrogen-bonded.
95
For a given unit cell 10 layers thick, there would be 27
weakly hydrogen-bonded hydroxyls, 40 “inward” hydroxyls, and 63 free-hydroxyls. Differences
in the number density of these hydroxyls agree semi-qualitatively with the amplitude differences
in the VSFG spectra below, vide infra.
4.2.3: Thin film preparation
Figure 4.2 shows the nanoscale structure of the material as determined by TEM and the
morphological surface of the spin-coated thin films characterized by Atomic Force Microscopy
(AFM) which shows that the 80 nm thin films consist of nanoscrolls lying flat on a calcium fluoride
substrate with random azimuthal (in-plane) orientation. As evident from the proposed crystal
structure (Figure 4.1), the surface properties of these clays are largely determined by the hydroxyls
on the outer leaflet of the nanoscroll. This is particularly true for the adsorption properties of this
system in aqueous environments.
96
The hydroxyl stretch vibrational modes present a convenient
spectroscopic probe to study the structure and morphology of the nanoscroll surface. Furthermore,
the sensitivity of the OH-stretch frequency to the local environment may allow for operando
detection of the changes at the surface of the clay nanoscroll during, e.g., an adsorption process or
a catalytic reaction.
33

4.3: Experimental development of high-resolution spectroscopy
VSFG spectroscopy is a nonlinear surface-specific technique first developed in the late
1980s.
40
Details of our instrument can be found in Chapter 2 of this dissertation. Briefly, a
broadband femtosecond IR pulse excites the vibrations of interest and a narrow-band visible pulse
upconverts that response, inducing a second order polarization in the material. These pulses are
overlapped spatially and temporally at the interface and the VSFG signal that is generated is
detected in the phase-matched direction and frequency-resolved using a monochromator and a
CCD detector. The spectral resolution is determined by the narrow-band visible pulse.
47
We took
advantage of total internal reflection using a calcium fluoride prism to enhance the VSFG signal,
57,
83-85
as detailed in Chapter 3 of this dissertation.
The spectral resolution determined by the narrow-band visible pulse is selected using a 4f-
stretcher in our experimental setup. To briefly describe how this works, our visible pulse leaves
our amplifier as a ~200 picosecond pulse. This pulse is sent to the compressor which converts it
to a ~2 picosecond pulse. This compression of the pulse temporally results in a wider pulse
spatially. After the compressor, the pulse is directed to our 4f-stretcher which consists of a grating,
a collimating lens, a mechanical slit, a second collimating lens, and a second grating, all of which
are spaced a set focal distance, f, apart, see Figure 4.3. The size of the opening of the mechanical
slit determines the power and bandwidth or spectral resolution of the visible pulse.
34

Figure 4.3. Diagram of the 4f-stretcher. From left to right, the beam hits a diffraction grating and expands. The lens
collimates the beam. The collimated beam moves to the mechanical slit which was optimized in both directions as
indicated by the arrows. The slit selects a narrow portion of the beam. The beam then expands until the collimating
lens. The collimated beam reaches a diffraction grating and then continues through the path. Each optic is a focal
distance f from the others, hence the name 4f.

As discussed in detail in Chapter 3, VSFG spectroscopy suffers from low signal counts.
Therefore, careful selection of this narrow-band visible pulse is required. For samples where the
features have large full-width-half-maximums, this is less of an issue. In nickel phyllosilicate, there
are multiple features within a few wavenumbers of one another. To increase the resolution, you
must narrow the mechanical slit, letting a smaller amount of the pulse through; therefore, there is
a tradeoff between the resolution and the power of your pulse. For example, to go from a 9 cm
-1
to
5.8 cm
-1
resolution, your power decreases from 10 mW to 5 mW. The 4f-stretcher was carefully
aligned to complete these high-resolution experiments. To ensure the most power, the slit was
fixed at a set width and the entire slit was moved horizontally across the profile of the pulse while
measuring the power. The highest power reported became the fixed position of the slit. Then, the
slit was slowly closed while measuring the power and beam profile. After several iterations at
slightly different positions on the table, a resolution of 3.6 cm
-1
was achieved. This profile can be
seen in Figure 4.4. Note that the etaloning seen is a result of an optic on the table. This high
resolution was required for the analysis of the data as seen below.
35

Figure 4.4. Visible profile showing gaussian profile with 3.6 cm
-1
full-width-half-maximum defining the resolution
of the Vibrational Sum Frequency Generation spectra. Figure reproduced from the supplemental information of
Vaughn et al.
45

4.4: Vibrational spectroscopy characterization of hydroxyls of nickel
phyllosilicate
4.4.1: FTIR
VSFG requires both an IR and Raman active vibrational mode, as discussed in detail in
Chapter 2. Previous FTIR studies of the platelet and nanoscroll forms of the mineral have identified
an in-phase and out-of-phase hydroxyl stretch at approximately 3650 and 3610 cm
-1
,
respectively.
97, 98
These correspond to the coupled stretching modes of the three hydroxyls attached
to a single nickel on the nickel side of the sheet (Figure 4.1). FTIR on the 80 nm thin films used
in this study can be seen in Figure 4.5. The out-of-phase feature still exists in these samples, but
the in-phase feature appears to have a shoulder not previously observed in the bulk analysis. Due
36

to the low Raman cross-section for hydroxyl groups, the film thickness makes the Raman signal
too weak for comparison.

Figure 4.5. FTIR of the hydroxyl stretch region of the 80 nm thin film of Ni 3Si 2O 5(OH) 4. Figure reproduced from
Vaughn et al.
45

4.4.2: Vibrational Sum Frequency Generation (VSFG) spectroscopy
We select the polarization of the VSFG, visible, and IR pulses––either S or P polarized
light (e.g., SSP refers to S polarized VSFG, S polarized visible, and P polarized IR, respectively)
to selectively probe a specific combination of twenty-seven tensor elements of the nonlinear
susceptibility χ
(2)
. The analysis of spectra collected in different polarization combinations allows
for the orientation of the vibrating moiety relative to the surface normal to be determined. The
VSFG spectra in the PPP, SSP, and SPS polarization combinations are shown in Figure 4.6. Each
spectrum was fit with Lorentzian line shapes:
37, 99

𝜒 ( 2)
( 𝜔 𝐼𝑅
) = 𝐴 𝑁𝑅
𝑒 𝑖𝜙
+ ∑
𝐵 𝑗 Γ
𝑗 ( 𝜔 𝐼𝑅
− 𝜔 𝑗 )+ 𝑖 Γ
𝑗 𝑁 𝑗 =1

37

where 𝜒 ( 2)
is the nonlinear susceptibility, ANR is the non-resonant contribution, and ω IR is the IR
frequency. The resonant contribution is a Lorentzian for vibrational mode j with amplitude Bj,
linewidth Γj, and vibrational frequency ωj. The resonant vibrational mode frequencies ωj are forced
to be the same for the PPP, SSP, and SPS spectra.

Figure 4.6. Vibrational Sum Frequency Generation (VSFG) spectra of the hydroxyl stretch region of Ni 3Si 2O 5(OH) 4
in each polarization combination. Top portion of graph shows raw data in black hollow circles, overall fit in colored
solid line, and each individual Lorentzian contributing to signal in thin black lines. Bottom portion shows residual of
the fit in units of percent. 4.6a: VSFG spectrum of the hydroxyl stretch region of Ni 3Si 2O 5(OH) 4 in PPP polarization.
Fit presented in blue. 4.6b: VSFG spectrum of the hydroxyl stretch region of Ni 3Si 2O 5(OH) 4 in SSP polarization. Fit
presented in red. 4.6c: VSFG spectrum of the hydroxyl stretch region of Ni 3Si 2O 5(OH) 4 in SPS polarization. Fit
presented in green. Figure reproduced from Vaughn et al.
45

The experimental data, the overall fit, the residual of the fit, as well as each Lorentzian that
contributed to the fit are shown in Figure 4.6. The in-phase and out-of-phase features identified in
the FTIR are present. The out-of-phase mode, ω1 = 3614 cm
-1
, appears as a shoulder on the lower-
frequency side, similar to the out-of-phase feature identified in the FTIR analysis of the natural
mineral and synthesized powder.
97, 98
Below, we focus on the in-phase hydroxyl stretches in the
3640–3655 cm
-1
range. The FTIR spectrum found in literature
97, 98
reports a single in-phase feature
as discussed earlier, whereas the VSFG data distinguishes three in-phase features. Unlike FTIR,
VSFG is a coherent optical technique and can therefore distinguish between hydroxyls pointed in
38

opposite directions due to constructive and destructive interference of the signals. Briefly, each
spectrum was originally fit with three Lorentzian profiles, but this led to inconsistent frequencies
across the three data sets. Four Lorentzian profiles were required to allow for consistent fitting of
all parameters. Based on the experimental fitting, two hydroxyls are oriented “outward” and one
“inward” in this system. A table with the fitting parameters can be seen in Table 4.2.
Table 4.2. Experimental fitting parameters from IgorPro
79
fits. Table reproduced from the supplemental information
of Vaughn et al.
45

Parameter
PPP polarization SSP polarization SPS polarization
𝜔 1
3614 ± 0 3614 ± 0 3614 ± 0
𝜔 2
3642 ± 0 3642 ± 0 3642 ± 0
𝜔 3
3645 ± 0 3645 ± 0 3645 ± 0
𝜔 4
3653 ± 0 3653 ± 0 3653 ± 0
𝐵 1
70 ± 20 18 ± 9 15 ± 2
𝐵 2
100 ± 40 11 ± 16 130 ± 30
𝐵 3
-180 ± 40 -110 ± 20 -190 ± 50
𝐵 4
520 ± 40 240 ± 20 70 ± 10
Γ
1
12 ± 2 9 ± 3 25 ± 3
Γ
2
8 ± 2 9 ± 8 8.2 ± 0.5
Γ
3
4.1 ± 0.4 3.5 ± 0.3 8.3 ± 0.9
Γ
4
11.3 ± 0.4 11.7 ± 0.4 11.5 ± 0.4
𝐴 𝑁𝑅
10 ± 17 9 ± 6 1.6 ± 0.3
φ -16.9 ± 0.7 2.1 ± 0.4 12 ± 1

The two different “outward” and one “inward” hydroxyls in the VSFG spectra correspond
to the positive and negative amplitudes of the experimental fit, respectively. Assuming a multi-
layer crystal structure model, as shown in Figure 4.1, there are three unique hydroxyl features. The
two “outward” hydroxyls are the weakly hydrogen-bonded hydroxyls that hold the layers together
and the free-hydroxyl, the hydroxyls on the outermost layer and the non-hydrogen-bonded
hydroxyls on the lower layers. The “inward” hydroxyl points into the center of the silicate rings.
The free-hydroxyls correspond to the ω4 = 3653 cm
-1
feature in our VSFG spectra. Analysis of the
spectral fits reveals that the ω3 = 3645 cm
-1
feature must destructively interfere with the other two
39

features, therefore this moiety must be oriented in the opposite direction of the other two features.
Based on the proposed crystal structure, this feature can be identified as the “inward” hydroxyl.
The weakly hydrogen-bonded hydroxyl is assigned as the ω2 = 3642 cm
-1
for two reasons: 1) a
hydrogen-bond would red-shift the frequency of the stretch and 2) the transition dipole is oriented
in the same direction as the free-hydroxyl. VSFG spectroscopy has allowed us to identify three
unique in-phase hydroxyls in this system, which would not be possible using FTIR alone.
4.5: Orientational analysis of nickel phyllosilicate
Orientational analysis was performed on the hydroxyl stretch of the clays and a detailed
description of this can be found in Chapter 2. Briefly, we calculate amplitudes of the OH-stretch
peak in SSP, PPP, and SPS spectra as a function of its tilt angle.
37
More specifically, we calculate
the macroscopic susceptibility χ
(2)
by calculating the orientational average of the molecular
hyperpolarizability β
(2)
over the assumed orientational distribution of the hydroxyls. An
orientational curve generated in this way allows us to map the orientation angle (θ) to a theoretical
ratio of amplitudes using the parameters of the assumed orientational distribution as discussed
below. These calculated ratios are then compared to the experimental values. The amplitudes of
each Lorentzian determined from fitting the experimental VSFG spectra are converted into ratios.
For all three polarization combinations (SSP/PPP, SPS/SSP, and SPS/PPP), the experimental ratios
of the amplitudes are plotted onto the calculated orientational curves.
Orientational analysis requires several assumptions to be made. Since the individual
particles on the films exhibited a nanoscroll morphology, the distribution function of the hydroxyls
was treated as a step function where θ0 is the cut-off angle (Figure 4.7e).
100
Physically, this
corresponds to all hydroxyls on the nanoscroll contributing equally to VSFG signal until the cut-
40

off angle, θ. In this treatment, the tilt angle θ varies while the other two Euler angles corresponding
to twist and azimuthal (𝜑 and 𝜓 ) are assumed to be isotropically distributed. While systems such
as mica have shown strong anisotropy in the hydroxyls, anisotropic measurements of these films
show no effect on signal.
101
By spin casting this thin film, we would expect a random distribution
azimuthally and therefore, would not expect anisotropy in this system. This distribution function
also assumes the scrolls are round, see Figure 4.7d.

Figure 4.7. Calculated orientation curves mapped onto orientation angle (𝜃 ) are shown in blue. Experimentally
determined ratios are shown in red. Dashed red shows orientation for given experimental ratios. 4.7a: SSP/PPP ratio
corresponds to 140–164  of uncompensated scroll. 4.7b: SPS/SSP ratio corresponding to 146–152  of uncompensated
scroll. 4.7c: SPS/PPP ratio resulting in 146–151  of uncompensated scroll. 4.7d: Cartoon representation of scrolling.
Teal shows inner compensated portion of scroll. Purple shows uncompensated portion of scroll contributing to VSFG
signal. 𝜃 ∘
is the orientation angle depicted on the x-axis of the orientation curves. 4.7e: Step-distribution function
considered for orientational analysis where 𝜃 ∘
is the cut-off angle for the step function. Figure reproduced from
Vaughn et al.
45

41

In these nanoscrolls, the only portion contributing to the VSFG signal is the uncompensated
portion of the scroll in excess of the integral number of full turns (Figure 4.7d). The molecular
hyperpolarizability of the free-hydroxyls (ω4) was assumed to be the same as that of the free-OH
stretch at the air-water interface (βaac = 0.32, βbbc = 0.32, and βccc = 1).
6
The TEM in Figure 4.2a
shows nanoscrolls with an average diameter of 20 nm and analysis of the AFM shows that the film
thickness is 80 nm, both of which are significantly smaller than the wavelength of light; therefore,
the refractive index of the interface was treated as the calcium fluoride and air interface, as
previously reported with other nanosized materials.
100, 102
As the “inward” hydroxyl (ω3) and the
weakly hydrogen-bonded hydroxyls (ω2) have opposite amplitude signs and are only three
wavenumbers apart, orientational analysis can only be completed for the free-hydroxyl (ω4)
centered at 3653 cm
-1
.
The orientational analysis was completed using the following three ratio combinations:
SSP/PPP, SPS/SSP, and SPS/PPP. Figure 4.7 shows all three orientation curves with ratios plotted
as a function of tilt angle θ. Using the error bars determined experimentally, the orientations
according to the data sets are SSP/PPP: 140–164°, SPS/SSP: 146–152°, and SPS/PPP: 146–151°.
All three data sets yield a consistent range of tilt angles. We assume for a multiwalled scroll, the
inner portions of the scroll (whole number of full turns) will not yield VSFG signal. The transition
dipole moments for a complete rotation of the inner scroll will cancel; therefore, only the
outermost, uncompensated portion of the nanoscroll will contribute to the VSFG signal. In Figure
4.7d, a cartoon representation of the scrolls shows that this orientation corresponds to the amount
of uncompensated scroll in these systems. In a theory paper by Krasilin et. al., Ni3Si2O5(OH)4
nanoscrolls were predicted to scroll 25.5 times, corresponding to 180˚ of uncompensated scroll.
103

Our experimental results show 140–164° for the ensemble of synthetic nanoscrolls.
42

4.8: Conclusion
Operando observation of the surface hydroxyls provides a powerful molecular-level
insight into the structure, morphology, and surface chemistry of clay materials. Using VSFG
spectroscopy, we identified four hydroxyl stretch modes––one out-of-phase and three in-phase––
for nickel phyllosilicate and assigned them to specific crystalline sites. Furthermore, the VSFG
orientational analysis allowed us to determine the amount of uncompensated nanoscroll (the
section rolled in excess of a whole number of turns). The out-of-phase hydroxyl ω1 = 3614 cm
-1

matched the previously reported FTIR results. A significant advantage of VSFG (a coherent
spectroscopic technique) over the incoherent FTIR spectroscopy is the ability to detect the sign of
the vibrational mode, indicating “outward” vs. “inward” orientation of chemical moieties. Using
the crystal structure and signs of the peaks, the ω2 = 3642 cm
-1
mode was assigned to the weakly
hydrogen-bonded hydroxyl group holding the layers together. The “inward” hydroxyl pointed into
the silicate ring was found at ω3 = 3645 cm
-1
and the free-hydroxyl was identified at ω4 = 3653
cm
-1
. Orientational analysis shows that the surface of the nanoscroll had 140–164° of
uncompensated scroll. This is the first example where VSFG spectroscopy was used to analyze
the uncommon morphology of a nanoscroll. The observation of four distinct hydroxyl modes
illustrates the sensitivity of the OH-stretch to the local environment of the clay surface. Principally,
this can now be extended to all 1:1 phyllosilicate systems and potentially to other nanoscrolls of
interest.

43

4.9: Acknowledgements
I would like to acknowledge my co-authors for the orientational analysis paper Angelo
Montenegro, Erica S. Howard, Muhammet Mammetkuliyev, Sabrina Falcon, Matthew
Mecklenburg, Brent C. Melot, and Alexander V. Benderskii. I would like to acknowledge Erica
Howard for making the nickel phyllosilicate powders in this study and Sabrina Falcon for making
the thin films from the powders. I would like to thank Muhammet Mammetkuliyev for creating
the MATLAB
80
code to analyze the samples and Angelo Montenegro for running the initial
experiments with me. The Benderskii group acknowledges the Air Force Office of Scientific
Research under Grant No. FA9550-15-1-0185. The Melot group acknowledges the Research
Corporation for Science Advancement for support through a Cottrell Scholar award. I
acknowledge support from the GRFP from the National Science Foundation under Grant No.
DGE-1418060 and the National GEM Consortium with Los Alamos National Laboratory. TEM
samples were prepared at the University of Southern California’s CNI with help from Carolyn
Marks. I would like to acknowledge the Tongva people whose land the research was conducted
on.

44

Chapter 5

Characterization and Vibrational Sum Frequency Generation
Spectroscopy of the Thermally Treated Nickel Phyllosilicate Analog

5.1: Introduction
Clays are layered materials that are naturally occurring and have a wide variety of crystal
structures and particle morphologies. One class of clays are the phyllosilicates. The word
phyllosilicate means “sheet silicate” and is named after the alternating layered structure of these
materials.
89
They are defined by two layers: a sheet of edge-sharing MO6 octahedra and a sheet of
corner-sharing SiO4 tetrahedra.
90
These can combine in multiple ways. For the 1:1 phyllosilicates,
there is a single sheet of the metal octahedra and a single sheet of the silicon tetrahedra, charged
balanced by hydroxyls. These structures can form in two particle morphologies: platelets and
nanoscrolls. The nanoscrolls form when a size mismatch between the layers induces strain within
the plane and the material rolls into a scroll to relieve that strain.
92
In Chapter 4, we looked at
nickel phyllosilicate nanoscrolls. In this chapter, we will look at the thermally treated analog of
the nickel phyllosilicate nanoscrolls.
Phyllosilicates have been historically used as catalysts.
104-108
These have primarily been
the aluminosilicate clays. Zeolites replaced phyllosilicates commercially due to increased thermal
45

stability and higher surface areas.
109
Despite this, phyllosilicates are still relevant in catalysis. This
is because the layered structure of the phyllosilicates makes them ideal candidates for studying the
impact of structure on the properties of the material. They are easy to synthesize and you can
synthesize a variety of mixed metals of these materials to study how changing the metal influences
the morphology of these systems.
110
For uses in catalysis, this makes the phyllosilicates a model
system for studying bimetallic synergy. In addition, thermal treatment to induce surface defects
can be completed to create new active sites for catalysis.
The proposed crystal structure of nickel phyllosilicate is shown in Figure 5.1. This system
is discussed in detail in Chapters 3 and 4. The thermal treatment of nickel phyllosilicate involves
heating the phyllosilicate in air at 500 ˚C for 6 hours which activates the sample catalytically.
During this process, water and hydrogen are evolved. This evolution induces defect sites as shown
in Figure 5.2. The loss of hydrogen induces Lewis base sites, while the loss of water creates
frustrated Lewis acid-base pairs across the unsaturated metal and the neighboring oxygen. We
believe that this frustrated Lewis acid-base pair is responsible for the catalytic activities of the
activated form. Specifically, these sites hydrogenate CO2 through the Reverse Water Gas Shift
reaction followed by a Fischer-Tropsch reaction to produce a mixture of hydrocarbons and
oxygenates. Throughout this chapter, the nickel phyllosilicate as synthesized will be referred to as
pristine and the thermally treated analog will be referred to as activated. The goal of this study is
to understand the structural differences between pristine nickel phyllosilicate and its activated
analog.
46

Figure 5.1. Proposed crystal structure of Ni 3Si 2O 5(OH) 4 with multiple layers with the following color scheme: nickel
teal, silicon grey, oxygen orange, and hydrogen black. Grey dashed lines represent the weak hydrogen-bonds holding
the layers together. Circles show the three different types of hydroxyls. Hydroxyls 1 and 3 are the “outward” hydroxyls
(1: free-hydroxyl and 3: weakly-hydrogen-bonded hydroxyl). Hydroxyl 2 is the “inward” hydroxyl. Structure modeled
using VESTA program.
88
Figure reproduced from Vaughn et al.
45

Figure 5.2. Proposed activation mechanism for nickel phyllosilicate. Left: Dehydroxylation where water is off-
gassed. A frustrated Lewis acid-base pair is made where the unsaturated metal is the acid and the oxygen is the base.
Right: Deprotonation where hydrogen gas is evolved, and two oxygens (basic sites) remain. To maintain charge, this
corresponds to an oxidation state change in the nickel from Ni
2+
to Ni
3+
.
47

5.2: Characterization of activated nickel phyllosilicate
5.2.1: Synthesis of activated nickel phyllosilicate
The pristine nickel phyllosilicate was activated by heating for 6 hours at 500 °C.
94
The
resulting powder is considered to be activated nickel phyllosilicate. This is confirmed by a color
change from teal to black. Thin films were prepared by sonicating 0.01 g of activated nickel
phyllosilicate in 1 mL of water for 1 hour then spin casting the material at 3000 RPM for 30
seconds onto a calcium fluoride substrate. Samples were then left under vacuum at room
temperature for 24 hours to dry.
5.2.2: Structural differences as determined by X-ray diffraction, Pair Distribution
Function (PDF), X-ray photoelectron spectroscopy, and Transmission Electron
Microscopy (TEM)
The pristine nickel phyllosilicate and the activated nickel phyllosilicate structures have
been extensively characterized by the Melot group, primarily by Dr. Howard. Figures 5.3 and 5.4
show the characterization of the pristine and activated forms of nickel phyllosilicate through
powder X-ray diffraction, Pair Distribution Function (PDF) and X-ray photoelectron spectroscopy
as completed by Howard.
94
The powder X-ray diffraction shows that crystallinity is maintained
upon activation. The broad peaks arise from the small particle size, disorder, and strain within the
system.
111
The [002] and [004] peaks show a loss of intensity, implying a loss of coherence upon
activation.
94
One potential cause for this is the increase in stacking faults upon activation.
111

48

Figure 5.3. Laboratory powder X-ray diffraction of pristine nickel phyllosilicate and activated nickel phyllosilicate.
Miller indices are based on the reported monoclinic structure (Cc). The two peaks that reduce in intensity upon
activation are the [002] and [004].
94

Figure 5.4. 5.4a-d: Least-squares fits to the experimental Pair Distribution Function (PDF). 5.4a: synchrotron X-ray
PDF and 5.4b: spallation neutron PDF for pristine nickel phyllosilicate. 5.4c: synchrotron X-ray PDF and 5.4d:
spallation neutron PDF for activated nickel phyllosilicate. 5.4e: Oxygen 1S core level X-ray photoelectron
spectroscopy of pristine (light blue) and activated (dark blue) nickel phyllosilicate. 5.4f: Fits for the pristine nickel
phyllosilicate. 5.4g: Fits for the activated nickel phyllosilicate. Shifts between 5.4f and 5.4g correspond to a decrease
in hydroxyl environments. Figure modified from Howard.
94

The X-ray PDF and neutron PDF confirm the loss of coherence and establish the presence
of five-coordinate nickel throughout the structure.
94
The X-ray photoelectron spectroscopy
49

suggests a loss of 30% of the hydroxyls upon activation and the presence of Ni
2+
and Ni
3+
in the
system.
94
The presence of Ni
3+
supports our activation mechanism as the evolution of H2 would
require an oxidation state change in the nickel. Transmission Electron Microscopy (TEM) data
shown in Figure 5.5 show that the nanoscrolls maintain their morphology upon activation.

Figure 5.5. TEM of the 5.5a: pristine nickel phyllosilicate and 5.5b: activated nickel phyllosilicate. Scrolls maintain
shape after thermal treatment.

5.2.3: Differences in thin film as demonstrated by Atomic Force Microscopy (AFM)
For Vibrational Sum Frequency Generation (VSFG) spectroscopy studies, thin films of the
powder must be made. Atomic Force Microscopy (AFM) comparing the two samples can be seen
in Figure 5.6. The pristine samples form films with larger surface coverage whereas the activated
samples form films with lesser surface coverage. The pristine sample has film thicknesses of
roughly 80 nm and the activated has a film thickness of roughly 40 nm, though there are more
fluctuations in the surfaces of the pristine films. This fluctuation is shown in the height profile for
50

the pristine sample in Figure 5.6b. This lesser surface coverage will result in differences in the
optical spectroscopy, particularly with regards to signal counts.

Figure 5.6. Atomic Force Microscopy (AFM) of the pristine (5.6a-b) and activated (5.6c-d) thin films used in this
study. Color scale is in units of nm. Topography vs distance height profiles (5.6b,d) are in nm on the y-axis and
microns on the x-axis. Yellow line represents the position of the height profile scan. 5.6a: AFM topography image of
pristine 80 nm thin films used for vibrational spectroscopy analysis covering a 10 x 10 μm. 5.6b: Height profile scale
of the pristine thin films showing variations in the height across the scanning area. 5.6c: AFM topography image of
activated 40 nm thin films used for vibrational spectroscopy analysis covering a 10 x 10 μm. 5.6d: Height profile scale
of the activated thin films showing variations in the height across the scanning area.

5.3: Vibrational spectroscopy comparison of the pristine and activated films
5.3.1: FTIR
FTIR of the thin films used in the VSFG experiments can be seen in Figure 5.7. The thin
films required 3 hours and 10 hours of collection time under vacuum for the pristine and activated
films, respectively. The 10-hour collection time and low signal intensity of the activated film
51

resulted in small amounts of water in the air interfering with the spectra. A background subtraction
was completed but the amount of water that entered the cell during the background collection was
not identical to the amount that entered during the scan.

Figure 5.7. FTIR of the hydroxyl region in units of absorbance for the thin films. 5.7a: Pristine thin film. Data
collected over 3 hours. 5.7b: Activated thin film. Data collected over 10 hours. Oscillations are interference from
water in the air.

The FTIR of the pristine powder form of nickel phyllosilicate has been reported
previously.
97, 98
The details of the thin film identification were discussed in detail in Chapter 4.
Thermal treatment causes significant changes to the spectra. The interlayer water is decreased upon
activation, as seen by the decrease in intensity of the hydrogen-bonded OH region from 3200–
3400 cm
-1
.
112
The hydroxyl stretching region of nickel phyllosilicate has been identified as an in-
phase inner-surface Ni3-OH stretching at 3648 cm
-1
and a doubly degenerate out-of-phase
stretching at 3610 cm
-1
.
97

52

5.3.2: Vibrational Sum Frequency Generation (VSFG) spectroscopy
In Chapter 4, we demonstrated that while FTIR can only identify two hydroxyl features
(the in-phase and out-of-phase), VSFG spectroscopy can identify four with the three in-phase
features corresponding to Hydroxyls 1–3 in Figure 5.1. VSFG spectroscopy is a nonlinear surface-
specific technique that was developed in the late 1980s by Shen.
40
Chapter 2 of this dissertation
covers the details of our instrument and the technique. Briefly, a broadband femtosecond IR pulse
excites the vibrations of interest and a narrow-band visible pulse upconverts that response,
inducing a second order polarization in the material. The pulses are overlapped spatially and
temporally at the interface, generating VSFG signal that is detected in the phase-matched direction
and frequency-resolved using a monochromator and a CCD detector. The spectral resolution is
determined by the narrow-band visible pulse.
47
Implementation of a high-resolution setup was
used in this experiment, the details of which can be found in Chapter 4. We took advantage of total
internal reflection using a calcium fluoride prism to enhance the VSFG signal,
57, 83-85
as detailed
in Chapter 3 of this dissertation.
The VSFG spectra of the pristine and activated forms of nickel phyllosilicate in PPP, SSP,
and SPS polarizations can be seen in Figure 5.8. There are some obvious changes in the spectra.
For one, the signal count in the activated sample is significantly less than in the pristine sample as
seen in the scale bars. This occurs for two reasons: 1) activation results in the loss of hydroxyl
groups as shown in Figure 5.2, and 2) the surface coverage of the activated thin films is
significantly less than the pristine thin films as shown in Figure 5.6. Unfortunately, this means you
cannot relate the loss of signal to the loss of hydroxyls in this system. Further studies making thin
films with similar surface coverage are currently underway. In Figure 5.8a-b, the shape of the
53

VSFG spectra are very similar, though there may be some broadening or a new feature in the
activated SSP spectra on the higher frequency side. The most noticeable difference is the loss of
intensity on the lower frequency feature upon activation in the SPS spectra in Figure 5.8c. This
could potentially occur if there is a loss of coherence that decreases the hydrogen-bonding that
holds the layers together, as predicted by the powder X-ray diffraction.

Figure 5.8. Comparison of pristine and activated VSFG signals. Raw data is represented in solid lines to make the
spectral differences clearer. 5.8a: PPP polarization comparison of pristine and activated samples. Activated samples
show over an order of magnitude decrease in signal intensity. There also appears to be a slight frequency shift upon
activation. 5.8b: SSP polarization comparison of pristine and activated samples. Activated samples show an one order
magnitude decrease in intensity. There appears to be a slight broadening on the high frequency side upon activation.
5.8c: SPS polarization comparison of pristine and activated samples. The activated samples show a decrease in signal
by a factor of 2. Additionally, the lower frequency peak has decreased in intensity relative to the higher frequency
feature.

Another major qualitative difference is the uneven loss of intensity across the three
polarizations, this is most easily seen in Figure 5.9. Going from the pristine to activated, the PPP
and SSP intensity scale in the figure decreases by a factor of 50, while the SPS intensity scale in
the figure decreases by a factor of 2. To emphasize this, the PPP signal decreases by over an order
of magnitude by comparing pristine and activated samples. The SSP signal decreases by an order
of magnitude whereas the SPS signal decreases by a factor of 2. This results in the PPP and SSP
intensity being much closer in value in the activated form compared to the pristine. In Chapter 2,
54

the theory behind orientational analysis was discussed in detail. Chapter 4 showed a direct
application of orientational analysis to the pristine nickel phyllosilicate clay nanoscroll. The ratio
of the intensities or amplitudes of the signals in different polarizations relates to the orientation of
the molecule (see Chapter 4, Figure 4.7). As the ratio relates to the orientation of the species, the
uneven loss of intensity in the signal upon activation implies that the orientation of the nanoscrolls
has changed. There appears to be minor shifts in the vibrational frequencies upon activation,
though careful fitting is required to confirm this. Further orientational analysis and data collection
are required to make definitive statements about these changes.

Figure 5.9. 5.9a: PPP, SSP, and SPS polarization VSFG signal intensity for the pristine sample. 5.9b: PPP, SSP, and
SPS polarization VSFG signal intensity for the activated sample. Comparison shows that the relative ratios of
SSP/PPP, SPS/PPP, and SPS/SSP are significantly different upon activation.

5.4: Conclusion
Thermal treatment of nickel phyllosilicate induces defect sites that increase the catalytic
activity of the material. Characterization of the pristine and activated forms of nickel phyllosilicate
55

confirmed that the activation process is a combination of dehydroxylation and deprotonation.
Vibrational Sum Frequency Generation (VSFG) spectroscopy is a powerful tool for the
identification of surface hydroxyls. VSFG spectroscopy can also identify structural differences
between the pristine and activated forms of the clay. Additional experiments on the activated form
are required for a complete analysis of the orientation of the hydroxyls. This will allow us to
determine if the scroll loosens upon activation. Using the hydroxyl as a probe, studies focused on
hydrophilicity of the surface, adsorption, surface charges, and catalysis can be monitored operando
with VSFG spectroscopy.
5.5: Acknowledgements
I would like to acknowledge Erica Howard for making the activated nickel phyllosilicate
powders in this study and Sabrina Falcon for making the thin films from the powders. I would like
to thank Muhammet Mammetkuliyev for creating the MATLAB
80
code to analyze the samples and
Angelo Montenegro for running the initial experiments with me. The Benderskii group
acknowledges the Air Force Office of Scientific Research under Grant No. FA9550-15-1-0185.
The Melot group acknowledges the Research Corporation for Science Advancement for support
through a Cottrell Scholar award. I acknowledge support from the GRFP from the National Science
Foundation under Grant No. DGE-1418060 and the National GEM Consortium with Los Alamos
National Laboratory. TEM samples were prepared at the University of Southern California’s CNI
with help from Carolyn Marks. I would like to acknowledge the Tongva people whose land the
research was conducted on.

56

Chapter 6

Inelastic Neutron Scattering of Pristine and Activated Nickel
Phyllosilicate Clays

6.1: Introduction to Inelastic Neutron Scattering (INS)
Inelastic neutron scattering (INS) is an experimental technique used to study the molecular
motion in systems.
113, 114
It can be used to observe the motions of materials, diffusion, and
vibrations. The field emerged from physicists Bertram Brockhouse’s research and resulted in him
winning the 1994 Nobel Prize.
115
Neutrons are used to study both structure and dynamics of
molecules, in the forms of elastic and inelastic neutron scattering. They are so widely used that
Brockhouse was quoted saying “If the neutron did not exist, it would need to be invented.” Since
its inception, INS has become an established technique for low-temperature measurements of
liquids, crystals, amorphous solids, and adsorbed molecules.
116-127
This makes this technique
particularly well-suited for computational comparison.
114, 128-131

INS as a vibrational spectroscopy is most similar conceptually to Raman scattering in the
sense that what is measured is the change in energy of radiation after interaction with the
material.
128
Despite some of their similarities, INS is a very different spectroscopy tool compared
to FTIR and Raman spectroscopies. FTIR and Raman spectroscopy have selection rules,
57

specifically a change in transition dipole moment and a change in the polarizability as the motion
occurs, respectively. These rules result because photons interact with electrons. INS spectroscopy
is different in that neutrons interact with nuclei. How the nuclei scatter depends on the nuclear
forces involved and therefore are different for each nuclei in question with no obvious trend on the
periodic table.
128
Each nuclei has a scattering cross section which determines how readily it scatters
the neutrons. Hydrogen has the largest cross section of any nuclei, making INS sensitive to
hydrogen-related vibrations.
128, 132

Because INS spectroscopy depends on cross sections and not transition dipole moments or
polarizability changes, it is mathematically easier to model using computational methods.
114, 127,
133
Using normal mode analysis, most INS spectra can be reproduced,
128
significantly decreasing
the computational costs associated with the calculations. The increased sensitivity to hydrogen and
easier modeling make INS an attractive vibrational spectroscopy technique.
Another major difference between INS and other optical spectroscopies is that high energy
neutrons penetrate samples, whereas photons require the sample to be optically transparent to pass
through.
128, 132
This leads to drastically different experimental configurations in the experiments.
Additionally, it leads to INS having a wider spectral range in the lower frequency modes. In optical
spectroscopy, you are limited by the absorbance of your optics, which constrain your frequency
region. While the experimental sample holder or “can” will absorb some of your neutrons, this
happens at a lower frequency than with optical spectroscopy.
132
The differences between INS and
optical spectroscopies discussed above make INS vibrational spectroscopy an ideal
complementary technique to the Vibrational Sum Frequency Generation (VSFG) spectroscopy of
the nickel phyllosilicate and its analog discussed in Chapters 3–5 of this dissertation.
58

INS has been used extensively to study catalysts.
116, 123, 134-139
Hydrogen is involved in
many catalytic reactions.
140
The sensitivity of INS to hydrogens make it ideal for studying
hydrogen in catalytic systems such as chemisorbed hydrogen,
136, 141-143
acidity in zeolites,
144
and
adsorbed water.
138
INS can also be used to study spillover of hydrogen onto catalyst supports,
116,
145-147
the effect of coking and deactivation,
148-151
and identification of reaction intermediates.
152,
153
There is active research into building apparatuses for heterogeneous catalysis studies.
134

6.2: Nickel phyllosilicate catalytic activity
Phyllosilicates have been traditionally used as catalysts.
104, 105
They exhibit Lewis acidic
sites
154-158
and have been used in a variety of reactions including the cracking of paraffins to shorter
hydrocarbons.
109
While zeolites mostly replaced these catalysts commercially due to increased
thermal stability and higher surface areas, phyllosilicates are still of interest in the field of catalysis.
Nickel phyllosilicate is a clay mineral belonging to the 1:1 phyllosilicate class, shown in
Figure 6.1a.
45
It can form in the platelet or nanoscroll morphology. For these studies, we are
looking specifically at nanoscrolls of the material, demonstrated in Figure 6.1b.
45
The synthesis of
nickel phyllosilicate and it’s structure are discussed in detail in Chapters 4 and 5 of this
dissertation. Upon thermal treatment at 500 ˚C for 6 hours in air, the nickel phyllosilicate
undergoes a structural change to form the thermally treated or “activated” form of the material.
Throughout this chapter, “pristine” will be used to reference nickel phyllosilicate and “activated”
will be used to reference the thermally treated analog. Upon activation, water and hydrogen gas
evolve. The proposed structural changes are shown in Figure 6.2. The formation of defect sites
induces different catalytic activity in the activated form. The loss of water leads to the formation
of a frustrated Lewis acid-base pair with the unsaturated metal and the exposed oxygen.
59

Figure 6.1. 6.1a: Proposed crystal structure of Ni 3Si 2O 5(OH) 4 with multiple layers with the following color scheme:
nickel teal, silicon grey, oxygen orange, and hydrogen black. Grey dashed lines represent the weak hydrogen-bonds
holding the layers together. Circles show the three different types of hydroxyls. Hydroxyls 1 and 3 are the “outward”
hydroxyls (1: free-hydroxyl and 3: weakly-hydrogen-bonded hydroxyl). Hydroxyl 2 is the “inward” hydroxyl.
Structure modeled using VESTA program.
88
6.1b: TEM of Ni 3Si 2O 5(OH) 4 showing multiwalled nanoscroll
morphology of powder. Left panel inset: Cartoon representation showing that the outer layer of the scroll is the nickel
sheet (teal) and the inner layer is the silicate sheet (grey) of the phyllosilicate layer. Figure modified from Vaughn et
al.
45

Figure 6.2. Proposed activation mechanism for nickel phyllosilicate. Left: Dehydroxylation where water is off-
gassed. A frustrated Lewis acid-base pair is made where the unsaturated metal is the acid and the oxygen is the base.
Right: Deprotonation where hydrogen gas is evolved and two oxygens (basic sites) remain. To maintain charge, this
corresponds to an oxidation state change in the nickel from Ni
2+
to Ni
3+
.

60

The Melot group is interested in studying the relationship between the structure of a
material and the material’s properties. Based on our proposed mechanism from our catalytic
studies on the activated nickel phyllosilicate, we see CO2 hydrogenated through the Reverse Water
Gas Shift reaction followed by a Fischer-Tropsh reaction to create a mixture of hydrocarbons.
These catalyst systems exhibit the potential to shift the reactions towards selectivity of short chain
olefins.
159-161
Here, we will look at the reaction of CO2 and H2 over the activated nickel
phyllosilicate to attempt to identify active sites on the material and identify adsorbed species.
In this study, we aim to characterize the structural differences between the nickel
phyllosilicate (pristine) and its thermally treated analog (activated). For the activated sample, we
look to identify adsorbates on the surface by studying the sample exposed to CO 2 and H2
individually. Finally, we run the catalytic cycle, pausing the experiment midway through to
attempt to identify reaction intermediates.
6.3: Experimental design for Inelastic Neutron Scattering (INS) experiments
The pristine nickel phyllosilicate was synthesized at the University of Southern California
using the synthetic method referenced in Chapter 4. The ~20 g sample was mailed to the facility
for experimental analysis. Upon arrival, the sample was split into two batches: A and B. The two
batches were either under reaction or on the neutron beam, alternating between each sample. The
reaction was completed on a catalysis rig at the facility. This rig allowed for control over the
sample temperature as well as the gas flow to the sample.
Figure 6.3 shows a flow chart for the sample preparation. The numbers represent the order
in which the data was collected on the neutron beam. Batch A was used to collect the pristine
61

nickel phyllosilicate spectrum. Afterwards, Batch A was thermally treated in a high temperature
flow cell to form activated nickel phyllosilicate. Batch A was immediately used to catalytically
hydrogenate CO2. This reaction was run under a 4:1 H2:CO2 ratio at 350 ˚C with He as a carrier
gas. This corresponded to 33 mL/min of He, 100 mL/min of H2 and 25 mL/min of CO2. This
reaction ran for 1 hour and 15 minutes, then the flow can was sealed, and the sample was
transferred to an aluminum can sample holder in a glove box. Batch A was then used to collect the
post-reaction spectrum.

Figure 6.3. Flow chart for experimental process for INS data collection. Nickel phyllosilicate sample was split into
two batches: A and B, each ending with activated nickel phyllosilicate exposed to gases. INS data was collected in
numerical order.

Meanwhile, Batch B was thermally treated to form the activated nickel phyllosilicate in a
high temperature flow cell. This was transferred to an aluminum flow can in a glove box and data
on Batch B was collected. Then, Batch B was exposed to CO2 gas at room temperature at a rate of
62

20 mL/min for 35 minutes. The flow cell was closed and data on Batch B was collected. This
sample was then transferred to a high temperature flow cell in a glove box and dosed with H2 gas
at 350 ˚C for 30 minutes with a flow rate of 20 mL/min. The flow can was sealed, cooled, and the
sample was transferred to an aluminum can in a glove box. Data on Batch B was then collected.
INS experiments were completed at the MAPS instrument at the ISIS facility at the
Rutherford Appleton Laboratory, Chilton, Didcot, UK. For these experiments, we were interested
in the frequency region from 100–4000 cm
-1
. MAPS allows you to vary the incident energy to get
increased resolution over a range of frequencies. These experiments were run with the following
energy ranges: 120 meV, 250 meV, and 650 meV which correspond to an increased resolution in
the 100–800 cm
-1
, 800–1800 cm
-1
, and 1800–4000 cm
-1
ranges, respectively. The sample holders
were either an aluminum can or an aluminum flow can. For the aluminum can, an aluminum foil
packet was made for each sample and the powder was poured into the packet. The packet was then
placed in the aluminum can. For the aluminum flow can, the sample sat directly inside the can.
There was a total of 5 data sets (pristine, activated, activated CO2 exposed, activated H2 exposed,
and post-reaction activated) collected at three different energies. Data was analyzed in Mantid
162

and exported to MATLAB
80
for background subtractions where available and graphing.
Background subtractions were scheduled to be completed before the COVID shutdown, which has
delayed the collection of some of the spectra. Therefore, background subtraction will only be
shown for one sample set.

63

6.4: Computational results
6.4.1: Description of the computational methods used
Calculations were performed in the Vienna Ab Initio Simulation Package (VASP5)
163

using a plane-wave basis set with a cutoff energy of 520 eV. The core electrons were described by
the projector augmented wave method
164
and the D3 correction of Grimme was applied to account
for the van der Waals interactions.
165
The frequencies were calculated using the finite-difference
method. The convergence of the self-consistency cycles (SCF) was set to 10
̶ 5
eV and during
geometry optimization atomic forces were converged to within 0.01 eV Å
-1
. These Density
Functional Theory (DFT) results were used as the input into the abINS
166
program and the resulting
output file was the calculated INS spectra. There are two model systems considered in this analysis.
Layered clays, such as the phyllosilicates, are known for intercalating water into their structures,
either between the layers or within the nanoscrolls.
167, 168
Therefore, we modeled the pristine and
activated samples two ways, with water and without water. We then compared the models to the
experimental results, see section 6.5.1. All calculations below resulted from the model assuming
water is not present.
6.4.2: Comparison of the pristine and activated calculations
The comparison of the calculated INS spectra for the pristine and activated samples are
shown in Figures 6.4 and 6.5. Across the entire frequency region, there is a clear loss of intensity
upon activation (Figure 6.4). We anticipate a decrease in signal upon activation as the activation
process results in dehydroxylation and deprotonation as seen in Figure 6.2. When utilizing a
technique that measures hydrogen vibrations, it follows that an appreciable signal intensity drop
64

should be observed after activation due to the loss of hydrogens. Figure 6.5 shows the calculated
spectra in regions matching the experimental results. This simplified the observation of frequency
shifts upon activation.

Figure 6.4. Calculated INS spectra using D3 functional for the pristine (blue) and activated (black) samples. Data on
same scale to show relative loss of intensity upon activation.

Figure 6.5. Calculated INS spectra using D3 functional for the pristine (blue) and activated (black) samples.
Frequency regions blocked to represent those covered experimentally. Left: 100–900 cm
-1
. Center: 600–1800 cm
-1
.
Right: 1800–4000 cm
-1
.

For the 100–900 cm
-1
region shown in the right panel of Figure 6.5, there is a loss of
intensity in the 300–500 cm
-1
region. Vibrations in this region correspond to rocking modes of the
65

frame. As these motions also move the hydrogen, they are referred to as “riding modes.”
116
These
regions are known to be heavily influenced by the hydrogen-bonding between the layers.
169
The
activation process disrupts the hydrogen-bonding network (see Chapter 5) and there should be
changes in response. From the 600–900 cm
-1
, there is the intensity drop upon activation as well as
a red-shift and splitting of the associated features. This region corresponds to hydrogen wagging
modes, coupled with frame movement. These would be anticipated to produce spectral changes
upon activation because the loss of hydrogens upon activation and presence of defect sites should
influence the frequencies of these wagging modes.
In the 600–1800 cm
-1
region shown in the center panel of Figure 6.5, we see markedly
different results with clear changes beyond the signal intensity loss. Upon activation, there are
changes to the vibrational features in the 1000–1400 cm
-1
region. Unfortunately, the features
between 1000–1800 cm
-1
cannot be viewed in our vibrational modelling software. Our modelling
software shows only first-order vibrations, but the calculations can also consider the higher order
transitions, such as phonon-to-phonon transitions. According to the first-order vibrational
calculation, there are no vibrations between 1024–3136 cm
-1
; therefore, the changes in this region
must correspond to higher order transitions. While these can be modelled, we cannot speak to what
these changes look like vibrationally.
The hydroxyl region in the 1800–4000 cm
-1
region shown in the left panel in Figure 6.5 is
of particular interest as discussed in detail in Chapters 4 and 5. Figure 6.6 shows a zoomed in
graphic to allow for more careful analysis of these changes.
66

Figure 6.6. Calculated INS spectra using D3 functional for the pristine (blue) and activated (black) samples.
Frequency regions covering the hydroxyl stretches.

Upon careful observation of this region, changes beyond the loss of intensity include clear
frequency shifts. The feature above 3700 cm
-1
clearly broadens and red-shifts while the lower
frequency region red-shifts. INS is a bulk technique and the DFT is completed by assuming an
extended solid system; therefore, it will not identify the Hydroxyl 1 feature from Figure 6.1.
Computation should be able to distinguish between Hydroxyls 2 and 3 and experiment can also do
this if the vibrational frequencies are far enough away from one another. In Table 6.1, the identified
hydroxyl stretches with corresponding frequencies for the pristine and activated calculations are
shown.

67

Table 6.1. Description of the hydroxyl identified by INS computation and corresponding frequency for the pristine
and activated calculations.
Description of Hydroxyl (reference Fig.
6.1)
Pristine Frequencies
(cm
-1
)
Activated Frequencies
(cm
-1
)
#2 3727, 3726 3717, 3690
#3 in-phase stretch 3647 3625
#3 out-of-phase stretch 3638, 3635, 3632, 3631 3616, 3614, 3600

As seen clearly in Table 6.1, all frequencies red-shift upon activation. For Hydroxyl #2,
the two vibrational frequencies in the pristine sample shift unevenly upon activation. This is due
to the presence of the active site. The Hydroxyl #2 stretches shifted 10 and 36 cm
-1
, respectively.
The larger splitting between the features upon activation results in the broadening seen clearly in
Figure 6.6. The Hydroxyl #3 in-phase stretch shifted 22 cm
-1
and the corresponding out-of-phase
stretches shifted between 38–22 cm
-1
. The lower frequency feature in Figure 6.6 covers both the
in-phase and out-of-phase stretches of Hydroxyl #3. According to the calculated frequencies, there
is a broadening of this feature as well, though it is less pronounced than the broadening for the
higher frequency feature. The Appendix shows still frames of the represented vibrations as
modelled in ChemCraft.
170
These calculations show us that upon activation, we should see a
decrease in signal, a red-shift in frequency, and a broadening in the spectral shape within the
experimental results.
6.4.3: Comparison of the activated, CO
2
dosed, and H
2
dosed calculations
The comparison of the calculated INS spectra for the activated and the CO2 dosed activated
samples can be seen in Figure 6.7. A cartoon representation, from ChemCraft
170
, of the CO2
68

adsorbed on the activated surface can be seen in Figure 6.8. The CO2 bridges over the frustrated
Lewis acid-base pair, forming a carbonate-like species with the oxygen and the unsaturated metal
on the surface. In the 100–900 cm
-1
spectral region on the left side of Figure 6.7, there are two
areas where changes occur. There is a slight suppression of the feature around 400 cm
-1
and a
substantial suppression of the features between 800–900 cm
-1
. In the activated sample, the feature
around 400 cm
-1
corresponds to “riding modes” of hydrogen. The addition of CO2 onto the surface
results in less of this coupled motion. The 800–900 cm
-1
region corresponds to wagging modes of
the hydrogen coupled with frame motion. Again, the addition of CO2 onto the surface limits some
of the wagging modes available to the hydrogen.

Figure 6.7. Calculated INS spectra using D3 functional for the activated (black) and activated with adsorbed CO 2
(purple) samples. Frequency regions blocked to represent those covered experimentally. Left: 100–900 cm
-1
. Center:
600–1800 cm
-1
. Right: 1800–4000 cm
-1
.
69

Figure 6.8. Computed structure for activated sample with CO 2 adsorbed. CO 2 on defect site circled in red. Figure
created in ChemCraft.
170

For the 600–1800 cm
-1
spectra in the center panel of Figure 6.7, the new changes
correspond to the suppression of the 1400–1800 cm
-1
features. As discussed in section 6.4.2, these
features correspond to higher order phonon modes and therefore cannot be modelled. The right
panel of Figure 6.7 shows the hydroxyl stretch region. As seen here, there are significant changes
to the spectral region. Table 6.2 shows the description of the hydroxyl and the corresponding
vibrational frequencies as determined by the INS calculations.
Table 6.2. Description of the hydroxyl identified by INS computation and corresponding frequency for the activated
and CO 2 dosed activated calculations.
Description of Hydroxyl
(reference Fig. 6.1)
Activated
Frequencies (cm
-1
)
CO 2 Dosed Activated Frequencies (cm
-1
)
#2 3717, 3690 3708, 3702, 3692
#3 in-phase stretch 3625 None
#3 out-of-phase stretch 3616, 3614, 3600 3755, 3752, 3749, 3743, 3735, 3734, 3729, 3668
70

There are significant changes in the hydroxyl region. The most noticeable is the net blue-
shift in the features, which appears to be an artifact of the calculations. Addition of the CO2 spaces
the layers out such that hydrogen-bonds no longer hold the structure together; therefore, the
features that were once classified as Hydroxyl #3, now appear to be more like the free-OH or
Hydroxyl #1. Realistically, the CO2 would be more likely to bind on the surface of these
nanoscrolls; therefore, the hydrogen-bonding holding the layers together would be expected to be
maintained. This means the disruption of the hydrogen-bonding of the lower layers seen in the
calculation is an artifact of the extended solid system calculation. The in-phase feature for
Hydroxyl #3 has completely disappeared due to the addition of the CO2 into the active site. The
out-of-phase stretches for Hydroxyl #3 show significant splitting, going from three frequencies in
the activated to eight frequencies upon dosing. For Hydroxyl #2, the frequencies red-shift and there
is a splitting.
Overall, dosing the activated sample with CO2 should impact three general areas of the INS
spectrum. There should be a slight suppression of the feature around 400 cm
-1
and a substantial
suppression between 800–900 cm
-1
. In the hydroxyl region, a broadening associated with the
splitting is anticipated.
The comparison of the calculated INS spectra for the activated and the H2 dosed activated
samples can be seen in Figure 6.9. A cartoon representation, from ChemCraft
170
, of the H2
adsorbed on the activated surface can be seen in Figure 6.10. In the 100–900 cm
-1
spectral region
on the left side of Figure 6.9, there are two areas where changes occur. There is a slight red-shift
of the feature around 400 cm
-1
, as well as a substantial shift and suppression of the features between
800–900 cm
-1
. In the activated sample, the feature around 400 cm
-1
corresponds to “riding modes”
71

of hydrogen. The addition of H2 onto the surface results in a shift of this coupled motion. The 800–
900 cm
-1
region corresponds to wagging modes of the hydrogen coupled with frame motion.
Unlike in the CO2 addition where this mode is only suppressed, there also appears to be new modes
between 500–600 cm
-1
corresponding to the wagging motion of the adsorbed H2.

Figure 6.9. Calculated INS spectra using D3 functional for the activated (black) and activated with adsorbed H 2 (light
blue) samples. Frequency regions blocked to represent those covered experimentally. Left: 100–900 cm
-1
. Center:
600–1800 cm
-1
. Right: 1800–4000 cm
-1
.

Figure 6.10. Computed structure for activated sample with H 2 adsorbed. H 2 on defect site circled in red. Figure created
in ChemCraft.
170

72

For the 600–1800 cm
-1
spectra in the center for Figure 6.9, the new changes correspond to
the suppression of the 1400–1800 cm
-1
features. As discussed in section 6.4.2 and above with the
CO2 dosed spectra, these features correspond to higher order phonon modes and therefore cannot
be modelled. The right panel of Figure 6.9 shows the hydroxyl stretch region. As seen here, there
are significant changes to the spectral region. Table 6.3 shows the description of the hydroxyls and
the corresponding vibrational frequencies as determined by the INS calculations.
Table 6.3. Description of the hydroxyl identified by INS computation and corresponding frequency for the activated
and H 2 dosed activated calculations.
Description of Hydroxyl
(reference Fig. 6.1)
Activated Frequencies
(cm
-1
)
H 2 Dosed Activated Frequencies (cm
-1
)
#2 3717, 3690 3711, 3710, 3694, 3685
#3 in-phase stretch 3625 None
#3 out-of-phase stretch 3616, 3614, 3600 3754, 3752, 3749, 3746, 3745, 3728, 3723

There are significant changes in the hydroxyl region, quite similar to that seen in the CO2
dosing. The most noticeable is the net blue-shift in the features. This appears to be an artifact of
the calculations. Addition of the H2 spaces the layers out such that hydrogen-bonds no longer hold
the structure together; therefore, the features that were once classified as Hydroxyl #3, now appear
to be more like the free-OH or Hydroxyl #1. Realistically, the H2 would be more likely to bind on
the surface of these nanoscrolls; therefore, the hydrogen-bonding holding the layers together would
be expected to be maintained. This means the disruption of the hydrogen-bonding of the lower
layers seen in the calculation is an artifact of the extended solid system calculation. Just like with
the CO2 dosing, the in-phase feature for Hydroxyl #3 has completely disappeared due to the
addition of the H2 into the active site. The out-of-phase stretches for Hydroxyl #3 show significant
73

splitting, going from three frequencies in the activated to seven frequencies upon dosing. For
Hydroxyl #2, the frequencies red-shift and there is a splitting. In this 1800–4000 cm
-1
region, there
is one new feature. This corresponds to the stretching mode of the adsorbed H2 and is seen at 3317
cm
-1
. Figure 6.11 shows the vibration using five frames. This motion appears to be a stretch along
the H-H bond, inducing a stretch in the Ni-H bond.

Figure 6.11. Still frames from ChemCraft
170
showing 3317 cm
-1
vibrational motion of the adsorbed H 2 on the activated
sample defect site. Each number corresponds to the individual frame. Red circle draws attention to the adsorbed H 2
motion.

Overall, dosing the activated sample with H2 should impact four general areas of the INS
spectrum. There should be a slight frequency shift of the feature around 400 cm
-1
and a substantial
suppression and red-shift between 800–900 cm
-1
. The adsorbed H2 should have a hydride like
stretch at 3317 cm
-1
. In the hydroxyl region, a broadening associated with the splitting is
anticipated. Below, we will compare our calculations to the experimental results.
6.5: Results and discussion
6.5.1: Comparison of pristine and activated samples to computational models
We will now compare the pristine and activated samples’ experimental results to the
computational models to determine which model is the correct fit. As clays are layered systems,
74

they often intercalate water into the layers.
167, 168
Therefore, we created two computational models
for comparison, one with water and one without water for both the pristine and activated samples.
In Figure 6.12, we look at the pristine nickel phyllosilicate determined experimentally and compare
it to the two models. In Figure 6.12a, we compare to the with water model and in Figure 6.12b, the
without water model.

Figure 6.12. Comparison of experimental INS spectra and the computational spectra using D3 functional for the
pristine (blue) comparing two models. Pristine sample was collected in the aluminum can. Left: Data collected at 120
meV, corresponding to increased resolution from 100–900 cm
-1
. Center: Data collected at 250 meV, corresponding
to increased resolution from 600–1800 cm
-1
. Right: Data collected at 650 meV, corresponding to increased resolution
from 1800–4000 cm
-1
. 6.12a: Pristine (blue) experimental results compared to the computational model including
water between the layers (green). 6.12b: Pristine (blue) experimental results compared to the computational model
excluding water between the layers (red).

For the frequency region from 100–900 cm
-1
, the without water model is clearly a closer
fit to the features around 400 cm
-1
and from 600–900 cm
-1
. From 1000–1800 cm
-1
, neither models
75

account for all of the features and both are comparable in accuracy. In the higher region frequency
of the hydroxyl stretch, discussed in detail in Chapters 4 and 5, the without water model is clearly
closer in frequency, though it does show a red-shift relative to the experimental spectra. For the
pristine model, we used the without water calculations for all comparisons.

Figure 6.13. Comparison of experimental INS spectra and the computational spectra using D3 functional for the
activated (black) comparing two models. Activated sample was collected in the aluminum flow can. Left: Data
collected at 120 meV, corresponding to increased resolution from 100–900 cm
-1
. Center: Data collected at 250 meV,
corresponding to increased resolution from 600–1800 cm
-1
. Right: Data collected at 650 meV, corresponding to
increased resolution from 1800–4000 cm
-1
. 6.13a: Activated (black) experimental results compared to the
computational model including water between the layers (green). 6.13b: Activated (black) experimental results
compared to the computational model excluding water between the layers (red).

The activated sample is synthesized by heating at 500 ˚C for 6 hours and evolves water and
hydrogen (see Figure 6.2). It would not be anticipated that the activated samples would have any
water remaining. Therefore, we anticipate the no water model to be the more accurate one. In
Figure 6.13, we examine the activated experimental spectrum and compare to the with water model
76

(Figure 6.13a) and the without water model (Figure 6.13b). The comparison is not as clear as in
the pristine.
In the 100–900 cm
-1
spectra, the with water appears to be a better model for the feature
from 600–900 cm
-1
while the without water is a better model for the 200–600 cm
-1
region. Looking
at the 600–1800 cm
-1
spectrum, the 1000–1400 cm
-1
region appears to be a better fit in the with
water model. As seen with the pristine sample, the hydroxyl region in the 1800–4000 cm
-1

spectrum agrees more readily with the without water. As we wouldn’t anticipate water in the
system and several areas match better with the without water model, we used the without water
model for the below analysis.
It has been shown for clays such as kaolinite, that the stacking faults greatly influence the
hydrogen-bonding between the layers.
169
This leads to disagreement between the computation and
the experimental results in the lower frequency regions from 200–400 cm
-1
.
169, 171, 172
Stacking
faults could be contributing to some of our discussed changes. This is particularly true in the
activated spectrum as activation leads to a loss of coherence between the layers (see Chapter 5).
This disruption is highly influenced by the hydrogens in this material and therefore should be
reflected in the INS data. Additionally, there are further complications in exactly how the defect
sites would be modelled in these types of experiments as the number of defect sites has a large
impact on the computational modelling. These models were also completed using the smallest unit
cell possible, which could lead to the defect sites being overrepresented in our model. Therefore,
the exact intensities of these features are questionable.
77

Overall, the without water model was used to analyze the results of this experiment. Across
the pristine and activated samples, this model appears to be the most accurate for both. Therefore,
all comparisons to computational modelling assume that there is no water in between the layers.
6.5.2: Comparison of pristine and activated samples to one another
The experimental comparison of the INS spectra for the pristine and activated samples can
be seen in Figure 6.14. There are clear spectral differences between the two samples beyond the
obvious loss of intensity upon activation. This was predicted in section 6.4.2. For the lower
frequency region from 100–900 cm
-1
in the left panel of 6.14, there are significant changes in the
spectra. From 100–300 cm
-1
, the activated sample shows a spectral feature not observed in the
pristine spectra. Unfortunately, this appears to be a contribution associated with the aluminum
flow can. Figure 6.15 shows this frequency region collected with the 250 meV neutrons. This was
the only sample with a background that could be subtracted. As seen in this figure, the background
accounts for a majority of that feature. Careful background subtraction of both cans collected at
120 meV is required to analyze this data fully.
The changes in the frequency region between 600–900 cm
-1
can be seen in both the left
and center panels of Figure 6.14. The feature between 600–800 cm
-1
blue-shifts slightly.
Additionally, while there is a net decrease in the signal intensity, the intensity across the 600–900
cm
-1
does not decrease evenly. The feature between 800–900 cm
-1
decreases significantly in
intensity, relative to the 600–800 cm
-1
feature. As discussed in the section 6.4.2, these correspond
to the hydrogen wagging modes coupled to the frame motion. The changes seen experimentally
do not reflect the anticipated changes as determined computationally. Based on the computation,
between the 600–900 cm
-1
region, the blue side is anticipated to be higher intensity than the red
78

side. This is the opposite of what is seen experimentally. If the computation is anticipating more
active sites than the number experimentally, this could account for differences in the spectra.

Figure 6.14. Comparison of the experimental INS spectra for the pristine (blue) and activated (black) samples. Pristine
sample was collected in the aluminum can. Activated sample was collected in the aluminum flow can. Left: Data
collected at 120 meV, corresponding to increased resolution from 100–900 cm
-1
. Center: Data collected at 250 meV,
corresponding to increased resolution from 600–1800 cm
-1
. Right: Data collected at 650 meV, corresponding to
increased resolution from 1800–4000 cm
-1
.

Figure 6.15. Experimental INS spectra for the activated (black) and background subtracted activated (grey) samples.
Samples were collected in the aluminum flow can. Demonstrates that the low frequency contributions may come from
the can. Data collected at 250 meV. This is not ideal for resolution, but was the only sample in which we had a
background run.
79

The hydroxyl region is of particular importance for these clays, discussed in detail in
Chapters 4 and 5. As seen in the right panel of Figure 6.14, there is a loss of intensity in the
hydroxyl stretching region upon activation. This is anticipated due to the dehydroxylation and
deprotonation upon activation, both of which contribute to the loss of NiOH species. In Figure
6.16, we see the hydroxyl region of interest for the experimental INS. This can be compared
directly to the computational results from Figure 6.6. The computational results predicted the
presence of two distinct hydroxyl features. It also predicted that upon activation the frequencies
would red-shift and broaden slightly, particularly in the higher frequency feature. Experimentally,
it is unclear if there is broadening or frequency shifting. If it does occur, it is not as pronounced as
anticipated in the calculations. Additionally, the INS data only shows a single hydroxyl feature
compared to the two predicted by the calculations.

Figure 6.16. Experimental INS spectra using 650 meV for the pristine (blue) and activated (black) samples. Frequency
region covering the hydroxyl stretches.

80

Theoretically, the net loss under the area of the curve upon activation can be contributed
to the number of hydroxyls lost during this process. A back-of-the-envelope calculation shows
closer to 40% of the hydroxyls were lost upon activation. Once background subtractions
accounting for differences in the can are completed, this number can be formally calculated. The
Pair Distribution Function and X-ray photoelectron spectroscopy in Chapter 5 anticipated a loss
of 30%. Comparison of the INS results to this and other results discussed in Chapters 4 and 5 will
be completed after backgrounds are obtained.
6.5.3: Comparison of the activated sample to the CO
2
dosed, H
2
dosed, and post-
reaction activated samples
Unfortunately, exposure of the activated sample to CO2 and H2 showed no new features as
seen in Figures 6.17 and 6.18, respectively. The differences in the low frequency region of 6.17
are likely can contributions from the aluminum flow cell (see Figure 6.15). The anticipated
changes discussed in 6.4.3 were not observed. We presume that either our experimental setup did
not allow for the adsorbed species to remain on the surface, or that the activation process resulted
in a different product. Similarly, the post-reaction activated in Figure 6.19 showed no differences
beyond the can contributions.
81

Figure 6.17. Comparison of the experimental INS spectra for the activated (black) and activated dosed with CO 2
(purple) samples. Both samples were collected in the aluminum flow can. Left: Data collected at 120 meV,
corresponding to increased resolution from 100–900 cm
-1
. Center: Data collected at 250 meV, corresponding to
increased resolution from 600–1800 cm
-1
. Right: Data collected at 650 meV, corresponding to increased resolution
from 1800–4000 cm
-1
.

Figure 6.18. Comparison of the experimental INS spectra for the activated (black) and activated dosed with H 2 (light
blue) samples. Activated sample was collected in the aluminum flow can. H 2 dosed sample was collected in the
aluminum can. Left: Data collected at 120 meV, corresponding to increased resolution from 100–900 cm
-1
. Center:
Data collected at 250 meV, corresponding to increased resolution from 600–1800 cm
-1
. Right: Data collected at 650
meV, corresponding to increased resolution from 1800–4000 cm
-1
.
82

Figure 6.19. Comparison of the experimental INS spectra for the activated (black) and activated post-reaction (pink)
samples. Activated sample was collected in the aluminum flow can. Post-reaction sample was collected in the
aluminum can. Left: Data collected at 120 meV, corresponding to increased resolution from 100–900 cm
-1
. Center:
Data collected at 250 meV, corresponding to increased resolution from 600–1800 cm
-1
. Right: Data collected at 650
meV, corresponding to increased resolution from 1800–4000 cm
-1
.

While we did not see any adsorbed species, the comparison of the pristine and activated
samples were successful. This comparison demonstrates that INS can tell the differences spectrally
between the two samples. This data is currently being compared to the Vibrational Sum Frequency
Generation (VSFG) spectroscopy discussed in Chapters 4 and 5. We intend to repeat this procedure
at an INS facility using a different catalysis setup in an attempt to identify adsorbed species and
reaction intermediates.
6.6: Conclusion
In this study, we looked at the Inelastic Neutron Scattering (INS) spectra for the pristine,
the activated, the activated dosed with CO2, the activated dosed with H2, and the post-reaction
activated clays. These were compared to the computational predictions for the INS spectra. The
computational results predicted distinct changes upon activation and upon dosing. By comparing
the models to the experimental results for the pristine and activated samples, we determined that
83

there is no interlayer water in our system. All following calculations compared assumed the no
water model. Unfortunately, we were unable to see any spectral changes experimentally upon
dosing or post-reaction. The pristine versus activated experimental results showed changes in the
hydrogen wagging modes and the hydroxyl stretching modes. The computational results predicted
more differences than were seen experimentally, but careful background subtractions may reveal
more changes. Additional experiments are required to identify adsorbed hydrogen and carbon
dioxide as well as reaction intermediates on the surface.
6.7: Appendix
This appendix contains figures showing the hydroxyl stretching modes discussed in section
6.4.2. Figure 6.20 is one of the pristine Hydroxyl #2 stretches. Figure 6.21 is the pristine Hydroxyl
#3 in-phase stretch. Figure 6.22 is one of the pristine Hydroxyl #3 out-of-phase stretches. Figure
6.23 is one of the activated Hydroxyl #2 stretches. Figure 6.24 is the activated Hydroxyl #3 in-
phase stretch. Figure 6.25 is one of the activated Hydroxyl #3 out-of-phase stretches.

Figure 6.20. Still frames from ChemCraft
170
showing the 3726 cm
-1
vibrational motion of Hydroxyl #2 on the pristine
sample. Each number corresponds to the individual frame. Red circle draws attention to the hydroxyl motion.

84

Figure 6.21. Still frames from ChemCraft
170
showing the 3647 cm
-1
in-phase vibrational motion of Hydroxyl #3 on
the pristine sample. Each number corresponds to the individual frame. Red circle draws attention to the hydroxyl
motion.

Figure 6.22. Still frames from ChemCraft
170
showing the 3631 cm
-1
out-of-phase vibrational motion of Hydroxyl #3
on the pristine sample. Each number corresponds to the individual frame. Red circle draws attention to the hydroxyl
motion.

Figure 6.23. Still frames from ChemCraft
170
showing the 3717 cm
-1
vibrational motion of Hydroxyl #2 on the activated
sample. Each number corresponds to the individual frame. Red circle draws attention to the hydroxyl motion.
85

Figure 6.24. Still frames from ChemCraft
170
showing the 3625 cm
-1
in-phase vibrational motion of Hydroxyl #3 on
the activated sample. Each number corresponds to the individual frame. Red circle draws attention to the hydroxyl
motion.

Figure 6.25. Still frames from ChemCraft
170
showing the 3614 cm
-1
out-of-phase vibrational motion of Hydroxyl #3
on the activated sample. Each number corresponds to the individual frame. Red circle draws attention to the hydroxyl
motion.

6.8: Acknowledgments
I would like to acknowledge Erica Howard for making the nickel phyllosilicate powders in
this study. The Melot group acknowledges the Research Corporation for Science Advancement
for support through a Cottrell Scholar award. I acknowledge support from the GRFP from the
National Science Foundation under Grant No. DGE-1418060 and the National GEM Consortium
with Los Alamos National Laboratory. I would like to thank the Rutherford Appleton Laboratory
for neutron beam time as well as everyone at the ISIS facility for their help and support in the data
collection and analysis, particularly Jeff Armstrong and Patrick Stewart. I would like to thank my
86

computational collaborators Richard Catlow, Keith Butler, and Matthew Quesne for completing
the INS calculations discussed in this chapter.

87

Chapter 7

Orientation of Water at a Charged Interface and Expansion to Gibbs
Free Energy Potentials

7.1: Introduction
Force modulation spectroscopy covers a series of techniques (e.g., atomic force
microscopy and optical tweezers) where the behavior of a molecule is studied as a mechanical
force is applied.
173
Studying molecular behavior as a function of length allows for key insights into
molecular dynamics. Atomic Force Microscopy (AFM) can be used to modulate forces on the
order of pN.
174
In this range, AFM has been used to study both intramolecular processes (e.g.,
polymer chain interactions
175
) and intermolecular processes (e.g., protein folding
176, 177
and
covalent bond rupture
178
). One disadvantage to AFM is that it is limited to processes external to
cells, narrowing what can be studied in the field of biophysics.
173
Additionally, the stiff cantilevers
in AFMs can damage soft surfaces including cells.
179

The development of optical tweezers expanded the range force modulation spectroscopy
to nN and provided a more flexible technique for soft surfaces compared to AFM.
179
Ashkin’s
optical tweezers first reported in 1986
180
and first applied to trapping in 1970,
181
resulted in a Nobel
Prize in Physics in 2018.
182
Optical tweezers have been used in biophysics to study systems
88

ranging from DNA,
183, 184
to the folding and unfolding mechanisms in proteins (e.g., tintin
185
), and
even to whole cells.
186

Single molecule studies using force modulation spectroscopy have also directly influenced
the field of thermodynamics. Force modulation spectroscopy allows systems to be carefully held
in states well beyond equilibrium. Hummer and Szabo have been extracting Potential Energy
Curves (PECs) from single-molecule pulling experiments since 2001,
187, 188
but in 2010, the
application of a quasi-harmonic approximation simplified the process on the unfolding of RNA.
189

This approximation has allowed for a variety of other PECs to be extracted from force modulation
spectroscopy.
190-192
In this study, we use a graphene electrode to have strict control over the angle
of the free-OD in deuterated water. This new method is reminiscent of the length control in force
modulation spectroscopy, allowing us to extract Gibbs free energy potentials from a Vibrational
Sum Frequency Generation (VSFG) spectroscopy experiment for the first time.
Water at charged interfaces is an important component in a variety of chemical and
biological processes ranging from electrocatalysis to biomembranes.
193-196
Through the use of
surfactants, the orientation of water at charged interfaces has been studied extensively using VSFG
spectroscopy.
196-200
However, these studies require either multiple experiments, or changing the
pH of the solution to modify the charged interface, making the process more difficult and calling
into question the effects of other parameters such as ions in the solutions. Using electrodes, the
ordering of water has been studied at different voltages using X-ray scattering.
195
To our
knowledge the orientation of water as a function of applied voltage has not been previously
reported. In this study, we use VSFG spectroscopy to study the orientation of the free-OD in
89

deuterated water as a function of applied voltage and extract the free energy potentials for this
nonequilibrium system.
7.2: Previous results
Previously, our group reported the structure of D 2O at the charged graphene interface.
201

We found that the VSFG spectra shows a pronounced asymmetry in the presence of a positive
versus negative charge. As interfacial water is treated like a dielectric medium, our basic
understanding relies on the assumption that water has a linearly proportional response to an
external field. Therefore, the response is expected to have the same magnitude and opposite sign
for positive and negative fields of the same strength. Miller’s rule states that the VSFG spectral
response as a function of the external field is connected to the dielectric constant and linear
susceptibility.
202

Our experimental voltage-dependent VSFG spectra show a nonlinear response to the
external field.
201
At the surface, the hydrogen-bonding network dominating bulk water is broken.
This results in the presence of a non-hydrogen-bonded species referred to as the free-OH (free-
OD), as shown in Figure 7.1. In deuterated water, this is a narrow and spectrally isolated peak at
2700 cm
-1
.
6, 203
This species exists in the top monolayer of D2O, where the break in symmetry
permits a hydrogen-bond deficiency. Comparatively, it is much simpler to interpret
204
relative to
the broad hydrogen-bonded OD-stretch peaks spanning from approximately 2200 to 2600 cm
-1
.
201,
205, 206

Traditionally, the free-OD species is absent at typical charged interfaces, since water
hydrogen-bonds with these interfaces (e.g., surfactants with charged headgroups and mineral
90

surfaces). Graphene is hydrophobic in nature and therefore repels the water, resulting in a ~3 Å
vacuum gap between the graphene and the water (see Figure 7.1).
206, 207
This vacuum gap allows
for the presence of the free-OD at the graphene interface upon addition of negative charge.
201
This
free-OD response was found to be asymmetric with respect to the applied field, and absent when
the graphene electrode was neutral and positively charged. The asymmetry was rationalized
through a mechanism of field-induced molecular reorientation.
201
In the presence of negative DC
fields, the OD bond orients toward the graphene interface and into a ~3 Å vacuum gap,
206, 207

rendering it unable to hydrogen-bond. Whereas, for positive fields, the OD orients toward the bulk
phase of water, forming hydrogen-bonds and leading to the suppression of the free-OD signal.
198,
201, 205, 208

Figure 7.1. Cartoon representing the free-OD orientation at the graphene interface. Figure modified from Montenegro
et. al.
46

This pivotal paper called into question the treatment of interfacial water as a simple
dielectric medium. Missing from our previous study was the orientational analysis of the free-OD.
In this work, we determine the orientation of the free-OD bond as a function of charge density on
91

the graphene electrode as determined by the applied voltage. We further expand this to calculate
the Gibbs free energy potentials at each charge.
7.3: Experimental setup
7.3.1: Preparation of graphene electrode
Two strips of 50 nm thick gold were deposited onto a calcium fluoride substrate. Then, a
strip of monolayer graphene was grown by chemical vapor deposition and transferred onto the
substrate. The gold strips are in electrical contact with the graphene sample and serve as contacts
on the graphene to allow for application of voltage. The resistance across the plane of graphene is
monitored periodically to ensure sample quality.
7.3.2: Electrochemical cell assembly
A diagram of the electrochemical cell we used for our experiment can be seen in Figure
7.2. The graphene on the calcium fluoride substrate acts as the top window of the flow cell. It is
spectroscopically transparent in the region of interest. The addition of the calcium fluoride prism
increases the signal counts through total internal reflection as discussed in detail in Chapters 3–5
of this dissertation and in Vaughn et al.
45
More details on the importance of this are included in
section 7.3.4. Dimethylformamide is used as our index matching fluid.
The electrochemical cell consists of the graphene working electrode on the top of the cell
and the glassy carbon counter electrode on the bottom of the cell. They are separated using a Teflon
spacer which the D2O in this study flows through. The constant water flow reduces the laser-
induced heating of the graphene electrode. Some measurements were completed using a two-
92

terminal configuration in which the graphene and glassy carbon electrodes are connected to the
positive and negative terminals of a Keithley Instruments Model 2400 voltage source. When
applying the three-terminal setup, an Ag/AgCl reference electrode is inserted through the side wall
of the Teflon spacer. A Gamry Instruments Reference 600 potentiostat is used to connect the
working, counter, and reference electrodes.

Figure 7.2. Electrochemical flow cell used in VSFG spectroscopy experiments. 7.2a: The calcium fluoride prism,
index-matching fluid, calcium fluoride window, and graphene electrode are nearly transparent to the IR, visible, and
SFG beams. The cell is shown in the three-terminal configuration; graphene, the Ag/AgCl reference electrode, and
the glassy carbon counter electrode were connected to a potentiostat. In the two-terminal configuration, the Ag/AgCl
reference electrode is absent; graphene and glassy carbon electrodes were connected to a two-terminal voltage source.
7.2b: Detailed view of the graphene electrode (labels in grey); it is inverted (so that graphene faces D 2O) when placed
in the electrochemical flow cell. Figure from Montenegro et al.
46

7.3.3: Raman spectroscopy
In a method similar to that described in our previous publication,
201
we collected voltage-
dependent Raman spectra using a Renishaw micro-Raman spectrometer. This was completed using
both the two-terminal and three-terminal configurations and were used to determine the charge
93

density on the graphene. By observing the graphene electrode’s G-band at ~1585 cm
-1
, the relative
shifts can be determined and used to calculate the charge density. The G-band is known to respond
linearly to excess charge density through the following equations:
209, 210

𝐸𝑙𝑒𝑐𝑡𝑟𝑜𝑛𝑠 : 𝐸 𝑓 = 21∆𝜔 𝐺 + 75 [𝑐𝑚 −1
]
𝐻𝑜𝑙𝑒𝑠 : 𝐸 𝑓 = −18∆𝜔 𝐺 − 83 [𝑐𝑚 −1
]
𝑛 = (
𝐸 𝑓 11.65
)
2
10
10
[𝑐𝑚 −2
]
where the G-band frequency shift ∆𝜔 𝐺 can be used to determine the Fermi energy 𝐸 𝑓 . The Fermi
energy is related to the doping concentration n. The surface charge density is given by the
following equation:
211

𝜎 = 𝑛𝑒
This allows the G-band shift to serve as an internal standard to connect the surface charge
density to the two and three terminal voltages. The magnitude of the applied field at the interface,
Ɛ0(0), was estimated by treating the graphene electrode as an infinite plane of charge that is
separated from water by a ~3 Å vacuum gap.
206, 207

Ɛ
0
( 0) =
𝜎 2𝜀 0
𝜀 (with ε = 1)
The electron doping on the graphene as a function of G-band Raman shift is shown in
Figure 7.3. This shows the relationship between the applied voltage and the doping concentrations.
As the free-OD is only present at negative voltages, this figure only looks at the electron doping,
94

not hole doping (positive charges). These numbers were used to determine the charge on graphene
in the VSFG spectroscopy experiments.

Figure 7.3. Electron doping of graphene using the G-band shifts from the Raman mode. Demonstrates the relationship
between the applied voltage and the electron density at the surface. Figure from Montenegro et. al.
46

The Raman measurements were collected using a backscattering geometry with a 50 μW,
linearly polarized 532 nm laser beam focused to 1 μm in diameter. The collected scattered light
was dispersed onto a CCD detector by a single-grating monochromator with a spectral resolution
of approximately 0.5 cm
-1
.
7.3.4: Vibrational Sum Frequency Generation (VSFG) spectroscopy
As discussed in section 7.3.2, the experimental setup we use for VSFG spectroscopy
collection takes advantage of total internal reflection to increase the signal counts. With the prism
95

geometry, the graphene is inverted, meaning the beams pass through the calcium fluoride to get to
the graphene/D2O interface. This has a huge benefit in that it avoids the signal loss from passing
through water which absorbs the IR strongly. In addition, as discussed in detail in Chapter 3, this
significantly increases the signal counts due to approaching total internal reflection geometry. This
was crucial for the collection of this data as orientational analysis requires the collection of both
SSP and PPP. The voltage ranges used in this study result in very weak signal, particularly as the
free-OD first emerges. Without the prism geometry method, we would not have been able to
measure the orientation at these voltages. This is another example of how total internal reflection
can greatly expand the experimental reach of VSFG spectroscopy (see Chapters 3 and 4 for other
examples).
The details of our experimental setup are found in Chapter 2. Briefly, a 100-femtosecond
broadband pulse centered at 2700 cm
-1
with a full-width-half-maximum of 300 cm
-1
was used to
excite the free-OD stretch at the graphene/D2O interface. This induces a first-order polarization
that is upconverted to a second-order polarization with the use of a picosecond narrow band visible
pulse at 800 nm. This results in VSFG signal which propagates in the phase-matched direction and
is detected using a CCD detector. The IR and visible beams co-propagate in the plane of incidence
with an angle of 60˚ and 68˚ with respect to the surface normal. Spot sizes of the IR and visible
beams were focused to 200 μm and 250 μm at the graphene/D2O interface. The average powers of
the incoming beams were 12 mW and 25 mW, respectively. The raw VSFG spectra were
background subtracted and normalized by the nonresonant PPP spectrum of gold. Voltage was
applied at random (not linearly) to account for any drift in the system.
96

7.4: Vibrational Sum Frequency Generation (VSFG) spectroscopy at the
graphene/D2O interface
7.4.1: Vibrational Sum Frequency Generation (VSFG) spectra of D
2
O at the
graphene interface
The electric field-dependent VSFG spectra of the graphene/D2O interface were collected in two
polarization combinations: SSP and PPP. These results are shown in Figure 7.4. The tunable
electric field is generated by applying an electrochemical potential. This dopes the graphene
electrode with excess negative charge as seen in Figure 7.3.

Figure 7.4. Electric-Field dependent VSFG spectra of the graphene/D 2O interface collected in the 7.4a: SSP
configuration and 7.4b: PPP configuration. The amplitudes generally increase linearly with the applied electric-field
magnitude. Shape corresponds to raw data and line corresponds to mathematical fit. Figure modified from Montenegro
et. al.
46

97

The free-OD mode is assigned to the narrow and spectrally isolated peak at 2700 cm
-1
in
Figure 7.4.
6, 203
This occurs because under a large negative applied electric field, the topmost OD
bond rotates up out of the bulk solution towards the electrode, creating the free-OD bond. As the
applied electric field increases in intensity, the free-OD signal becomes stronger. The amplitudes
in the spectra generally increase linearly with the applied field. In Chapter 2 of this dissertation, it
was discussed that the ratio of amplitudes determines the orientation of the molecule.
7.4.2: Selection of orientational analysis parameters through selection of distribution
function and distribution width
For orientational analysis, we must select a model for the orientational distribution (see
Chapters 2 and 4 for more details) to determine the angular dependence. In this study, we assume
reorientation is the dominant mechanism for the observed field-dependence in the VSFG spectra
in Figure 7.4. The orientational analysis method developed by H-F Wang et. al.
37
(see Chapter 2)
depends on the orientational distribution function chosen to model the behavior of water at the
interface. This is a subject of active research.
212-214
Intuitively, this distribution width is expected
to narrow as the electric field magnitude increases. This is because the system is expected to
become more rigid. The extent of this effect is currently unknown and therefore, this analysis
assumes a static distribution width with respect to the applied field. This assumption is valid if the
extent of the narrowing is negligible with respect to the range of fields that were applied.

98

A Gaussian distribution has been historically used.
197, 198, 200, 206, 213
The Gaussian
distribution function is shown below.
𝑓 ( 𝜃 ) =
1
√2𝜋 𝜎 2
𝑒 ( 𝜃 −𝜃 0
)
2
2𝜎 2

Recently, Nagata and coworkers reported that an exponential decay distribution was the
more accurate model for their system.
212
The exponential decay distribution function is shown
below.
𝑓 ( 𝜃 ) = 𝑒 −
𝜃 𝜃 0

Below, we look at both the Gaussian distributions with varying distribution widths and the
exponential decay to determine the best model for the orientational analysis of the free-OD at the
charged graphene interface.
First, the experimental SSP and PPP spectra from Figure 7.4 were fit with a Lorentzian.
These fits are represented by the lines in the figure. Based on these fits, the PPP amplitude, SSP
amplitude and the PPP/SSP ratio can be calculated as a function of charge density on the graphene.
These results are shown in Figure 7.5a. In Figure 7.5b, we compare these results to the exponential
decay function model. The calculations for determining this type of orientation curve are discussed
in Chapter 2. Although the exponential distribution may be attractive from a thermodynamic
perspective,
212
due to the restrictive orientational freedom that it provides, our analysis supports
that it does not faithfully represent water at the graphene interface. Our experimental ratios of PPP
to SSP range from approximately 1.0 to 1.4. The corresponding exponential decay distribution
model results in ratios ranging from approximately 1.3 to 3.0. This means that a majority of our
99

experimentally determined ratios do not fall onto the ratio curve; therefore, we assume that the
exponential decay distribution model is not an accurate model for our system. Due to this, we will
look exclusively at the Gaussian distribution model.

Figure 7.5. 7.5a: Experimental PPP (black) and SSP (red) amplitudes, and their ratio (blue), for a 7.5b: decaying
exponential distribution of orientation angles. The experimental amplitude ratio of PPP to SSP, ranges from about 1.0
to 1.4, points that do not exist on the calculated blue curve in 7.5b. Thus, the distribution of free-OD angles at the
graphene/water interface is not well described by a decaying exponential. Figure modified from Montenegro et al.
46

Figure 7.6. Gaussian distribution of orientation angles with a width of σ = 15˚. Calculation corresponds more closely
to the experimental spectra in Figure 7.5a compared to the model in Figure 7.5b. Figure modified from Montenegro
et al.
46

100

The Gaussian distribution of width 𝜎 = 15° was selected for this system. Details
explaining this determination are provided below. Figure 7.6 shows the Gaussian distribution
function. It is clear by comparing this to Figure 7.5a, that the Gaussian distribution function
qualitatively provides much better agreement with the experimental data, compared to the
exponential decay function. Specifically, the PPP and SSP amplitudes both increase
monotonically. The PPP amplitude increases at a faster rate than the SSP amplitude as the angle
gets smaller. Additionally, the experimental ratios fall onto the calculated curve, unlike in the
exponential decay distribution model. These qualitative features consistently emerge for a range
of selected Gaussian distribution widths.
In the Gaussian orientation model, one of the parameters we must select is the distribution
width σ for the Gaussian function. To determine this, we used two complimentary methods and
selected the width associated with the most consistent results between the two. Both methods are
dependent on the width of the orientational distribution. Since narrower widths describe a more
rigid system, the width of the orientational distribution can be interpreted as a susceptibility to
reorientation of the molecule. Near the point of zero charge, the PPP and SSP amplitudes of the
free-OD peak are approximately equal, which translates to an average free-OD orientation of about
45˚ with respect to the surface normal. Note that this deviates from the average orientation of the
free-OD species at the air/water interface, which orients at 30-40˚ with respect to the surface
normal,
212, 215, 216
and may be due to the hydrophobic nature of the neutral graphene interface.
206

This is modelled in Figure 7.7. This dependence shows the importance of selecting the proper
distribution width.
101

The two complimentary method procedure involved the repetitive application of both
methods to obtain field dependent average free-OD orientation, <𝜃 >, for each of the following
distribution widths: 𝜎 = 5˚, 10˚, 15˚, 20˚, 25˚, and 30˚.

Figure 7.7. Orientational distribution width and average orientation angle. The calculated average orientation angle
depends on the orientational distribution width. Narrower widths correspond to a more rigid free-OD ensemble with
respect to reorientation. Figure from Montenegro et al.
46

The first method is based on a quantitative formalism of H-F Wang et. al.
37
This was
discussed in detail in Chapter 2. Briefly, in this method, the amplitudes of the free-OD peak are
calculated in the PPP and SSP polarization configurations as a function of the average orientation
angle. In these calculations, we assumed that the hyperpolarizability of the OD-stretch at the
graphene and air interfaces are the same (βaac = 0.26, βbbc = 0.26, and βccc = 1), though a range of
0.23–0.28 has been reported.
217
The second method for selection of orientational analysis
parameters is through the calculation of Gibbs free energy potentials for this system, reminiscent
102

of the Potential Energy Curves from the optical tweezers experiments discussed in the
introduction.
7.4.3: Selection of Gaussian distribution function orientational analysis parameters
through calculation of Gibbs free energy potential curves
The second method for the determination of the orientational analysis parameters assumes
that the dominant mechanism for reorientation is the field-induced torque on the dipole of water.
To determine this, we generate the field-dependent free energy orientating potential. This is based
on the orientational distribution function, in which we locate its minima. This minimum
corresponds to the average orientation angle. Below is the mathematical formalism for completing
these calculations.
𝐺 ( 𝜃 ) is the orientational free energy potential for a Gaussian distribution, centered at the
orientation angle of the free-OD with respect to the surface normal, at the point of zero charge
(pzc).
𝑒 −𝐺 ( 𝜃 )
𝑘𝑇
=
1
𝜎 √2𝜋 𝑒 −
( 𝜃 −𝜃 𝑝𝑧𝑐
)
2
2𝜎 2

This corresponding free energy potential, 𝐺 ( 𝜃 ) , is harmonic with respect to molecular
orientation. The free energy orienting potential, 𝑂 ( 𝜃 ) , of water is:
𝑂 ( 𝜃 ) = 𝐺 ( 𝜃 )− 𝜇 ∙ 𝐸 = 𝑘𝑇 [𝑙𝑛 ( 𝜎 √2𝜋 )+
( 𝜃 −𝜃 𝑝𝑧𝑐
)
2𝜎 2
2
] − 𝜇𝐸𝑐𝑜𝑠 ( 𝜃 0
)
103

where 𝜃 0
is the angle between the permanent dipole of water and the surface normal. The addition
of the term 𝜇 ∙ 𝐸 is the potential energy of a dipole in a DC-field. This imparts anharmonicity onto
the orienting potential and shifts its minimum to narrower orientation angles, representing the
effect of the field-induced reorientation of water.

Figure 7.8. Top: Cartoon representing the orientation angle at the surface of graphene. 7.8a: Calculated free energy
potential curves, where the observed shift to steeper angles (with respect to the surface normal) is modeled solely
through reorientation of water in the applied field. This reorientation mechanism accounts for a shift in orientation
angle from about 45˚ to 30˚. 7.8b: The Gaussian orientational distribution (𝜎 = 15˚) shifts with the applied field.
Figure modified from Montenegro et al.
46

104

In section 7.4.2, it was mentioned that the Gaussian distribution of width 𝜎 = 15° was
selected for this system. The corresponding Gibbs free energy potentials for this distribution width
are shown in Figure 7.8. This calculation was completed for each of the distribution widths and
charges studied in this system. A comparison of the two models was used to determine the ideal
width. The discussion of these parameters continues in the following section.
As discussed in the introduction, this method has allowed us to determine the Gibbs free
energy potential curves for the D2O at a charged interface where we are not at a potential energy
minimum. To our knowledge, this is the first time VSFG spectroscopy has been applied in this
way. Here, we have proposed a new way to model this behavior without the use of optical tweezers.
7.4.4: Combination of the two methods to determine orientational analysis
parameters
The details for the comparison of the two methods for determining the distribution width
for the Gaussian function are below. The Gaussian distribution widths considered in this study are
the following: 𝜎 = 5°, 10°, 15°, 20°, 25°, 30°.
First, the amplitudes and ratios for the Gaussian distribution functions described above
were calculated and plotted as a function of the average free-OD orientation angle via the first
method detailed by H-F Wang et al.
37
Then, a plot of the average orientation versus the applied
field was generated for each distribution width. Although the free-OD response is absent for
neutral graphene, an estimate of the free-OD angle at this point, θ
pzc
, is obtained through
extrapolation of the linear fit of this plot to zero field. Corresponding to θ
0
= θ + 52.5
o
where θ is
the angle between the surface normal and the free-OD moiety, and θ
0
is the angle between the
105

surface normal and the permanent dipole moment of water. 𝜇 is the permanent dipole moment of
water, which was taken to be 2.5 D, where D is the electric dipole in units of Debye.
218, 219

Next, the free energy orienting potential energy curves were plotted for each width of the
Gaussian distribution function and each applied field. The minima of the field-dependent free-
orienting potentials were located. These correspond to the average free-OD orientation angles.
These orientation angles were plotted against the applied field and superimposed onto the plots
generated from method 1. These were organized by the width of the orientational distribution.

Figure 7.9. Determination of the orientational distribution width. 7.9a-f: Orientation of D 2O as a function of charge
density on graphene for each distribution width. Widths corresponding to 7.9a: 5˚, 7.9b: 10˚, 7.9c: 15˚, 7.9d: 20˚,
7.9e: 25˚, and 7.9f: 30˚. The orientational distribution width that yields orientation angles that are consistent between
the orienting potential and H-F Wang methods. 7.9g: The sum of the residuals squared is calculated on the right; the
shown polynomial fit suggests that the optimal distribution width is 𝜎 = 16˚. This is closest to our 𝜎 = 15˚ model.
Figure modified from Montenegro et al.
46

The corresponding orientation as a function of charge on the graphene for each distribution
function can be seen in Figure 7.9. Consistency between the two complementary methods was
106

evaluated by calculating the sum of the squared difference between the two orientations for each
applied field and distribution width. Figure 7.9g shows the sum of the residuals between the two
models calculated for each Gaussian distribution width. The polynomial fit reveals that the 𝜎 =
15° model results in the lowest residual. This corresponds to a Gaussian with a full-width-half-
maximum ≈ 35˚. Therefore, the 𝜎 = 15° width will be used for the orientational analysis
calculations.
7.4.5: Calculated orientation for D
2
O at the graphene interface as a function of
charge density
From the analysis in section 7.4.4, the Gaussian distribution width of 𝜎 = 15° was used in
the orientational analysis. Experimentally, the applied fields ranged from 0 to -0.2 V/Å. The
average free-OD orientation angle as a function of applied charge density on the graphene is shown
in Figure 7.10. As seen in the figure, the orientation angle shifts from 45˚ at more neutral voltages
to 30˚ at the most negative applied voltage. This is the first time that orientational analysis of water
as a function of applied charge has been completed.
107

Figure 7.10. Top: Cartoon representing the orientation angle at the surface of graphene. Bottom: Experimental
electric-field dependent average orientation angle of free-OD for a Gaussian distribution width of 𝜎 = 15˚.

7.6: Conclusion
In this study, we determined the orientation of water as a function of charge density at the
graphene electrode interface. To do this, we explored the Gaussian and exponential decay
orientational distribution models. We calculated the corresponding Gibbs free energy potential
curves for the systems and compared these to the distribution models. By a systematic comparison
of the two, we determined the Gaussian distribution width to be 𝜎 = 15°. This allowed us to
determine that the free-OD at the interface goes from an angle of 45˚ at more neutral charges to an
108

angle of 30˚ at the most negative charge. This is the first time that the orientation of the free-OD
as a function of charge density has been determined. This is also the first time VSFG spectroscopy
has been used to derive free energy potential curves. This is reminiscent of the Potential Energy
Curves from optical tweezer experiments.
7.7: Acknowledgements
I would like to acknowledge my co-authors on this paper: Angelo Montenegro, Muhammet
Mammetkuliyev, Haotian Shi, Dhritiman Bhattacharyya, Bofan Zhoa, Stephen Cronin, and
Alexander Benderskii. The Benderskii group and Cronin group would like to acknowledge that
the research was supported by Air Force Office of Scientific Research (AFOSR) grant No.
FA9550-15-1-0184 and FA9550-19-1-0115, the Army Research Office (ARO) Award No.
W911NF-17-1-0325, the U.S. Department of Energy (DOE), Office of Science, Office of Basic
Energy Sciences (BES), under Award DE-SC0019322, and the National Science Foundation
(NSF) Award No. CBET-1512505. I acknowledge support from the GRFP from the National
Science Foundation under Grant No. DGE-1418060 and the National GEM Consortium with Los
Alamos National Laboratory. I would like to acknowledge the Tongva people whose land the
research was conducted on.

109

Chapter 8

Science Education in the Real-World

8.1: Introduction to chemical education research
There is a disconnect between real-world political, economic, and social issues and the
topics taught in secondary and higher education science classrooms.
220, 221
This is a major concern
of contemporary chemical education. We want students to understand the everyday applications
of the science we are teaching in the classrooms. Despite this, the education provided at the
university-level is more focused on skill training, content coverage, and exam preparation.
222
In
the last two decades, there has been a shift towards teaching chemistry with an emphasis on
connecting everyday life to the science in the classroom.
223, 224

There are a variety of ways this has been employed in the classroom including the
implementation of mini-lab activities which ask students to predict the outcome of the experiment
based on a series of guided questions applying real-world knowledge.
225
These inquiry-based
teaching methods have been promoted by the National Research Council and the National
Academy of Sciences.
226, 227
The expansion into both lecture and laboratories has been more
common in the last two decades.
228
Another method aimed at everyday connections has been the
implementation of newspaper articles into the classroom.
221, 222, 229
Developing this awareness for
110

students is important and impacts their thinking capacity and social accountability.
221, 222, 230, 231

Some more creative avenues include exploring chemistry in popular books,
232-234
television
shows,
235
and movies.
236, 237

Other methods include completely revamping the way in which chemistry is taught. This
includes having courses specific for a topic, for example “citizen” chemistry
238-247
or for a specific
“issue” such as business or art.
248-253
There are a wide variety of diverse methods used for this
application.
223, 254-268
Some included a suggestion box for students to ask about real-world
applications for the following class.
267
Students have also written fake letters to relatives answering
chemistry questions.
263
Additionally, courses, experiments, or lectures have been designed to
incorporate topics such as forensic science as an application based teaching method.
255, 269-276

In this study, we apply our own approach to this problem. Using evaluations and weekly
assignments, we analyze students' responses to see how they view chemistry in the world around
them. The goal of this research is to see if asking students weekly to describe the most recent time
they saw chemistry outside of the classroom overall influences their ability to identify everyday
activities as chemistry. This preliminary study shows that individual students identify diverse
examples of chemistry. A majority of the responses went beyond the basic prompt to describe the
chemistry behind their observations. Additionally, 29.5% of students admitted to doing external
research to fully understand the chemistry at work. The increased engagement of the students was
a surprising but welcomed result. The following details our classroom model and theoretical
framework.

111

8.2: Justification and parameters for our flipped classroom model
Our classroom is a flipped model for the second semester of general chemistry. First
established in the early 2000s, the flipped classroom model allows for an increased opportunity
for students to participate in active learning.
277, 278
Active learning activities show an increase in
student success and improved attitudes about the subject matter.
279-284
Further studies from flipped
classrooms in science and engineering report higher test scores, lower failure rates, and lower
DFW (Ds, Fs, withdrawal) rates.
281, 285

A variety of chemistry flipped classroom models have been studied and analyzed for
trends. For organic chemistry, the flipped model showed a reduction in the DFW rate and an
increase in the student grade point average for the class.
285, 286
The general chemistry results have
shown an increase in performance for at least one demographic of students and a lower DFW
rate.
286, 287
While not all studies show an increase in student performance,
288
a majority of the
studies support that the flipped classroom model benefits students who perform lower at the
beginning of the class.
282
DFW rates in chemistry courses are some of the highest in science
classes;
289
therefore, application of the flipped course model to general chemistry could potentially
increase student retention rates.
Our flipped model classroom consisted of 105 students in Spring 2021 with no students
dropping. Students registered for the class knowing that it was a flipped classroom. In this study,
we are looking at student attitudes towards chemistry in the world around them. We hoped to
change their views of chemistry by asking them weekly to describe the most recent time they saw
chemistry outside of class. The results from these questions could be applied to formative
assessments
290-293
as well as for a measurement of the chemical thinking framework.
294-300

112

8.3: Theoretical framework
We specifically wanted to see how students saw chemistry in the world around them. To
do this, we decided to design a two-part evaluation, one to be completed at the beginning of the
semester and the other at the end of the semester. This would be our comparison to determine the
net impact. The evaluation was based off of learning evaluations in informal learning settings.
301

In addition to the evaluations, students were asked a question pertaining to this study on their
weekly summary assignments. This prompted students to think about chemistry outside of the
classroom every week of the class. Their responses were surveyed and analyzed.
The evaluation at the start and end of the semester were identical. They asked the following
prompts: 1. Describe the most recent time you saw chemistry in action. (1–2 sentences) 2. How
long ago was that experience? (a) within the last week, (b) within the last month, (c) last semester,
(d) longer. The first evaluation was to serve as a benchmark to see what percentage of students
identified the most recent time that they saw chemistry as the previous semester's class. The hope
was that the students who identified chemistry as a phenomenon in the classroom would shift
perspectives by the end of the class.
The weekly summary assignment students had each week consisted of the following
questions: 1. What is the most important thing that you learned in class this week? 2. How have
you observed chemistry outside of class this week? 3. What questions do you have remaining from
this week? The question used specifically for this study is question 2. We wanted to see how
students identified chemistry in the world around them and if this changed as the semester
progressed.
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The data from this study was analyzed using NVivo Pro
302
software and the answers to the
question were treated as survey responses. Each student was provided with a number (1–105) to
remain anonymous by a third party. Data was categorized into a variety of topics including student,
week, type of chemistry example, type of language used, etc. We looked specifically for trends
within the class as a whole and within individual students. As with this qualitative type of analysis,
we did our best to categorize with repeated review of our category descriptions. Below are the
results and discussion of this study.
8.4: Results and discussion
Within the NVivo Pro
302
program, we categorized the students’ responses to look at
individual students, individual weeks, the types of examples provided, the language used in the
answer, the presence of a description, and how the example related to a topic from the class. The
following will look at the individual categories for which we analyzed the results.
8.4.1: Type of example provided
Chemistry is everywhere in the world around us. To see if students also noticed this, we
looked specifically at the types of examples students provided. This was done by first skimming
through the responses for weeks 1–3. The topics were first decided based on this evaluation.
Following the first round of coding, the examples were expanded to account for more variations
in the students’ examples. In the end, 15 topic categories were selected for analysis of the
responses.
114

To provide context, the following are three types of examples and a corresponding sample
student response. Animal or plant related: The water in my fish tank evaporated and I needed to
add water back to the top. Using concentration (cv=cv), I calculated the amount of chemicals I
needed to add back into the tank to keep the environment at equilibrium. Cooking (water-related):
My family recently purchased an Instant Pot that is essentially a pressure cooker. While
experimenting with the controls and looking up recipes that all seemed to contain one cup of water,
I researched and discovered that the high pressure comes from the machine's ability to confine the
steam that arises (no pun intended) from the boiling water evaporating. This endothermic process
allows the Instant Pot to cook its contents at a rapid rate. Personal hygiene, bodily functions, and
medicine: I used micellar water to remove my makeup. I learned a while ago that micellar water
is a surfactant and removes makeup differently than regular water because of the
hydrophobic/hydrophilic structure of the micelles. This isn’t a chemical reaction, but an example
of intermolecular forces and their uses. All three of these examples are clearly related to general
chemistry in very different ways. Table 8.1 shows the results from the analysis of the type of
examples provided for all of the student responses.
As demonstrated by the table, the most frequently related examples involve cooking. A
total of 38.4% of the types of examples provided were cooking related. Overall, 99.0% of students
provided at least one cooking related example. This makes sense given that in the first week of
class, we provided a baking example to explain reaction kinetics. Additionally, cooking and baking
are commonly referenced as an example of chemistry.
We were also interested in looking at the types of examples from individual students. The
question we posed was Are the majority of students identifying chemistry in the world around them
115

in a variety of contexts? To analyze this, the number of types of examples provided by each student
was tallied. Figure 8.1 is a graph showing the number of students versus the number of categories
for the responses. For example, twenty students reported chemistry that falls into six different topic
categories for the types of examples discussed in Table 8.1.
Table 8.1. Type of example from student responses categorizing the type of chemistry students identified. The number
of responses for each example and the corresponding percentages are provided.
Type of example
Number of Responses
Percentage of total (%)
Cooking (everything else)
515
29.3
Personal hygiene, bodily functions, and
medicine
215
12.3
Cooking (water related)
161
9.2
Cleaning
134
7.6
Not an example of chemistry
121
6.9
Combustion
109
6.2
Class or another class
105
6.0
Weather
86
4.9
Oxidation of metals
71
4.0
Outdoors/nature
55
3.1
Electronics related
53
3.0
Animal or plant related
41
2.3
Other
36
2.0
YouTube
29
1.6
Crafts
27
1.5

116

Figure 8.1. The number of different example types provided and the corresponding number of students who gave
those responses.

A total of 91.4% of the students provided examples that fell into five or more topic
categories. The largest percentage of students (23.8%) provided examples corresponding to eight
different categories, making it the mode. This shows that the students are seeing a variety of
examples of chemistry in the world around them. This diversity is promising in that students are
seeing chemistry outside of the classroom in more than just cooking or medicine.
8.4.2: Use of scientific language in the provided response
Another aspect we studied was the use of scientific language. The assignment did not
specify the language to be used in providing a chemistry example. The results were broken up into
three different categories: clearly scientific language, some scientific language, and no scientific
language. Table 8.2 provides the number of responses and corresponding percentages for these
categories.
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Table 8.2. Responses categorizing the type of language used in student responses and corresponding percentages.
Categories include clearly scientific language, some scientific language, and no scientific language.
Language usage
Number of responses
Percentage of responses (%)
Clearly scientific language
562
33.5
Some scientific language
572
34.0
No scientific language
546
32.5

To demonstrate how these categories were determined, the following are representative
examples for each category classified. Clearly scientific language: The last time I saw (or really
experienced) chemistry in action was driving my car two hours yesterday. While I could not visibly
see the combustion reaction occurring in the engine of the vehicle, I could hear and feel the process
taking place as well as see the resulting energy output of that reaction in the form of movement of
the car as a result of the spinning of the axle. Some scientific language: I was lighting a birthday
candle using matches, and the match needs to strike against the red strip on the box. There is a
reaction caused by the friction between the chemicals in the match and the strip on the box. No
scientific language: I used soap to wash my hands before dinner. Overall, a total of 67.5% of the
responses included some level of scientific language.
We were interested in looking for any trends in scientific language used as a function of
time. The thought being, as students become more comfortable with the scientific language that
they would be more likely to use it. As seen in Figure 8.2, there are no trends in the scientific
language used as a function of time. Presumably, students already have the fundamental chemistry
vocabulary from first semester. It would be interesting to repeat this experiment in a first semester
chemistry course to see if the trend occurs there.
118

Figure 8.2. Graph showing the number of students for a given week whose student responses fell into the following
description categories: clearly scientific language (clearly), some scientific language (some), and no scientific
language (none).

A word cloud showing the most common words in the responses across all weeks is shown
in Figure 8.3. Water was the most frequently used word with outside, class, and reaction the next
most commonly used words. Within the cloud, there are chemistry specific words including
entropy, molecules, bonds, and combustion. There are also words that have social context and
chemistry meaning including acid, energy, solution, chemicals, carbon, and oxygen. While the
language used did not have any trends, two different notes remain. Students felt comfortable
explaining chemistry in the world around them in both a scientific context, including language and
descriptions, as well as in ‘layman’s’ terms.
119

Figure 8.3. Word cloud from NVivo Pro
302
for the data corresponding to all sixteen weeks analyzed in this chapter.
The frequency of the word is directly associated with the size in the figure.

8.4.3: Additional explanation provided
This was not something we anticipated finding. The question provided to the students was
How have you observed chemistry outside of class this week? This question does not explicitly ask
for an explanation of why this response is an example of chemistry. Despite this, most of the
student responses included an explanation. The most surprising finding was the number of
responses in which students referenced researching the topic to find the explanation. After reading
through the first few weeks of responses, we discovered this was something we should analyze.
Table 8.3 shows the number of responses and corresponding percentages for the three categories:
explanation provided, external research completed to provide the explanation, and no explanation
provided.
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Table 8.3. Table showing the number of responses and corresponding percentages for the number of students who
provided an explanation of why their example was chemistry, referenced researching the explanation, or provided no
explanation of why the example was chemistry.
Description of explanation provided
Number of responses
Percentage of responses (%)
Explanation
917
54.6
Researched the explanation
66
3.9
None
697
41.5

To demonstrate how these were categorized, the following are representative examples for
each of the categories. Explanation: Living in a snowy climate, the roads are covered in salt. This
lowers the freezing point of water, helping drivers reach their destination safely. Researched the
explanation: Outside of class this past week, I observed chemistry as I was cooking seeing chicken
breast browning in the pan. After some googling, I found that it was called the maillard[sic]
reaction which takes place between amino acids and sugars which gives food color when cooking.
None: I was making dinner and needed to boil water for pasta. These three provide very different
amounts of information with regards to the chemistry example provided.
A total of 58.5% of the responses included a detailed explanation with 29.5% of the
students admitting to doing external research to understand why this was chemistry. As we found
these results interesting, we wanted to look for any trends in the results as the class progressed.
Figure 8.4 shows the number of responses as the weeks progress. As seen in the figure, there
appears to be no trend in whether an explanation is provided for the example.
121

Figure 8.4. Graph showing the number of students for a given week whose student responses fell into the following
categories: explanation provided (explanation), researched the explanation (research), and none.

8.5: Analysis still being processed
8.5.1: Evaluation
Unfortunately, we are unable to analyze the evaluation provided at the beginning and end
of class. A glitch in Blackboard prevented the second evaluation from releasing to the students.
Initial responses from the first evaluation showed a number of students providing examples related
to the first semester of general chemistry. For analysis, we planned to look specifically at what
types of examples the students provide as defined by section 8.4. Additionally, we wanted to
examine the second question in the evaluation, specifically, how long ago was the instance
described. As students are identifying new examples of chemistry in the world around them each
week, we anticipated the results from the second evaluation to mostly show chemistry examples
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that have occurred within the last week. This would have demonstrated that this assignment has
influenced how students view chemistry in the world around them, specifically shifting their
perspectives from seeing chemistry as something that exists in the classroom, to something that is
everywhere around them. Even without the results from the evaluation, this study provided
valuable insights into how students see chemistry in the world around them.
8.5.2: Comparison to topics discussed in class
We are in the process of analyzing each student response and grouping it by category into
when the topic was covered, either in the first semester or the second semester of general
chemistry. Each class has subcategories based on the topic. For example, the first semester includes
topics such as gases, intermolecular forces, and thermochemistry (without entropy or spontaneity).
The second semester class includes topics such as acids and bases, coordination/inorganic
chemistry, and entropy and thermodynamics. We are interested in seeing if there is a correlation
between the examples students provide and the section covered in class.
Figure 8.5 shows two different word clouds from the student responses corresponding to
Weeks 10 and 12. The material covered in Week 10 in class is free energy and spontaneity,
standard and non-standard conditions, and temperature dependence of an equilibrium constant.
These are under the topic of thermochemistry and entropy. Looking at the left side of Figure 8.5,
the top three words this week were water, class, and outside. Beyond these three, the most
commonly used words that week were entropy, spontaneous, energy, and reaction. Clearly, a large
number of the student examples provided for this assignment are related to the material discussed
in class on Week 10. Another example in which this is clear is in Week 12. For Week 12, the class
discussed the Nernst equation, concentration cells, and electrolysis. These all correspond to
123

electrochemistry in which batteries and redox reactions are covered. On the right side of Figure
8.5, the word cloud shows water, outside, class, and reaction as the top words. Beyond these words,
battery and batteries are also very commonly used. Other examples in this word cloud include iron,
redox, and electrons. Week 12 also shows a clear correlation between some of the student
responses and the topic discussed in class.

Figure 8.5. Associated word clouds for Weeks 10 and 12, demonstrating that specific language tied to the topic in
class is referenced by students in the assignment.

A detailed analysis categorizing the student responses by topics discussed in each class
(first and second semester) is currently underway. We will be looking to see if this trend holds
across all topics in the class or if there are specific topics that translate to the real world better for
students. These preliminary results imply that the students are seeing the topics we discuss in class
in the world around them.
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8.5.3: Demographics and student backgrounds
There are three topics we want to look at with regards to student responses: majors,
previous chemistry experience, and demographics. In terms of majors, we are interested in
studying if there are any trends in the types of examples provided depending on the declared major
of the individual student. For previous chemistry experience, we would like to see if where the
students took first semester general chemistry influences the results. An example question for this
analysis is Do students who took AP chemistry in high school respond differently than students
who took the first semester at USC or another college? More importantly, we would like to look
at how these results compare among demographics of students, particularly with regards to gender
and race. The introduction of this chapter discussed how the pedagogical research shows that these
teaching techniques help minority students bridge the gap. We would like to see if there is any
evidence that our technique could benefit any particular group of students.
8.6: Conclusions and future work
In this study, we are analyzing student responses to the question How have you observed
chemistry outside of class this week? We analyzed the types of examples the students provided
and found that students see chemistry in diverse ways in the world around them with 91.4% of
students providing responses that fall into at least five different categories. While there were no
clear trends in the descriptions provided or language students used, students went beyond the
requirements for the assignment by providing additional explanations of why the topic was
chemistry. A total of 58.5% of the responses included this additional explanation with 29.5% of
the students doing external research to answer why this is chemistry. In total, 66.0% of the
125

responses included some scientific language. Analysis of the data is still underway. The evaluation
still needs to be analyzed along with the comparison of the responses to the topics covered in class.
Ideally, this will be a two-year study to look at how the responses relate to the demographics of
students.
8.7: Acknowledgements
I would like to thank Dr. Jessica Parr for allowing me to help teach her second semester
general chemistry course under her mentorship. This study would not have been possible without
her time, effort, and guidance. I would also like to thank all of my students in the class for
participation in this study. Lastly, I would like to acknowledge the Tongva people whose land the
research was conducted on.

126

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Abstract (if available)

Abstract Interfaces are the boundary in which two surfaces meet i.e., a gas and a solid or a solid and a liquid. Surfaces are where the vast majority of fundamental chemistry happens. This makes interfaces of particular interest for chemical applications from adsorption to reactivity. Therefore, understanding how the structure of a material’s surface correlates to its’ properties is key to fields like materials design. Even for molecules like water, the dynamics at the interface are still a topic of active research. ? A variety of linear and nonlinear techniques aimed to determine what is happening at the interface have been developed over the last few decades. Vibrational Sum Frequency Generation (VSFG) spectroscopy is one inherently surface-specific nonlinear optical technique that looks at the IR and Raman active vibrational modes on the surface of a material. The application of polarization-selected VSFG spectroscopy allows the molecular orientation of the vibrating moiety to be determined. Looking at molecular orientation provides a deeper level of understanding of the dynamics at the interface. ? By developing and applying high-resolution VSFG spectroscopy to clay catalysts, we looked at the role of the hydroxyl structure in the adsorption properties and catalytic activity of these systems. This was done for a nickel phyllosilicate catalyst and its thermally treated analog. Complimentary techniques such as Inelastic Neutron Scattering were applied to help understand the role of the hydroxyls in the catalytic properties of the material. ? We also looked at the structure of water at the interface of a charged graphene electrode. Specifically, we studied the orientation of water as a function of charge density on the graphene. This broadens our understanding of water’s structure for applications ranging from atmospheric sciences to biological processes. In this dissertation, we look at how the structure of a material at the interface relates to its’ properties. From catalysis to water, this expands our fundamental knowledge of the dynamics at the surfaces of materials. ? Understanding how students learn is crucial in influencing their perceptions of chemistry in the world around them. Students struggle with understanding the real-world applications of the chemistry they learn in the introductory general chemistry courses. Many innovative teaching techniques have been applied to change the way students view chemistry in their daily lives, but these oftentimes require drastic changes to lesson plans and significant overhauls to the course content. It is my goal to modify these perceptions with smaller scale assignments, requiring less modifications on the professor’s lesson plans and leaving the learning in the hands of the students. In the last chapter of this dissertation, we look at answering the question Does asking students to identify chemistry in the world around them change their perceptions of the role of chemistry in their daily lives? By quantifying the responses to the question “How have you observed chemistry outside of class this week?”, we can determine if asking this question weekly alters students’ views. Modifying the way students see chemistry will allow them to reach a deeper understanding of the world around us and chemistry’s role in that world.

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Asset Metadata

Creator Vaughn, Ariel Elizabeth (author)

Core Title Vibrational spectroscopy and molecular orientation at interfaces: correlating the structure of a material's surface to its properties

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Chemistry

Degree Conferral Date 2021-08

Publication Date 07/22/2021

Defense Date 06/16/2021

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

Tag catalysis,chemical education,clays,molecular orientation,nonlinear spectroscopy,OAI-PMH Harvest,sum frequency generation,surface science

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Benderskii, Alexander (committee chair), El-Naggar, Moh (committee member), Melot, Brent (committee member)

Creator Email aevaughn@usc.edu,arielelizabethvaughn@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC15614229

Unique identifier UC15614229

Legacy Identifier etd-VaughnArie-9816

Document Type Dissertation

Format application/pdf (imt)

Rights Vaughn, Ariel Elizabeth

Type texts

Source 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. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

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