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UC Irvine Electronic Theses and Dissertations UC Irvine UC Irvine Electronic Theses and Dissertations Title Understanding the State of Bacterial Systems with Machine Learning-Enabled Interpretation of Surface Enhanced Raman Scattering Spectra Produced by Novel Nanomanufacturing Permalink https://escholarship.org/uc/item/1wr5x8tf Author Thrift, William John Publication Date 2019 License https://creativecommons.org/licenses/by-nc-nd/4.0/ 4.0 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, IRVINE Understanding the State of Bacterial Systems with Machine Learning-Enabled Interpretation of Surface Enhanced Raman Scattering Spectra Produced by Novel Nanomanufacturing Dissertation submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in Materials Science and Engineering by William John Thrift Dissertation Committee: Professor Regina Ragan, Chair Professor Filippo Capolino Associate Professor Allon Hochbaum 2019 Portions of Chapter 3 and 10 reprinted with permission from Thrift, W. J. et al. ACS Nano 2017, 11 (11), 11317–11329. ©2017 American Chemical Society Portions of Chapter 4 and 10 reprinted with permission from Thrift, W. J. et al. Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XV; International Society for Optics and Photonics, 2017; Vol. 10346, p 103461M. ©2017 International Society for Optics and Photonics Portions of Chapter 5 and 10 reprinted with permission from C. Q. Nguyen, et al. ACS Appl. Mater. Interfaces, 2018, 10, 12364–12373. ©2018 American Chemical Society Portions of Chapter 6 and 10 reprinted with permission from Thrift, W. J. et al. ACS sensors, 2019. ©2019 American Chemical Society All Other Materials © 2019 William John Thrift DEDICATION To the people who made this dissertation possible: my family, Regina, and Hurik. Also Roger Penrose, whose book, “The Road to Reality” inspired me to go to get a PhD. ii TABLE OF CONTENTS Page LIST OF FIGURES ix ACKNOWLEDGMENTS xii CURRICULUM VITAE xiv ABSTRACT OF THE DISSERTATION xix PART ONE: Introduction 1 CHAPTER 1: Introduction 2 1.1 The History of Analytical Chemistry and Chemical Sensing 2 1.2 The Development of Modern Chemical Sensors 4 1.2.1 Light Spectroscopy 4 1.2.2 Mass Spectrometry 7 1.2.3 Nuclear Magnetic Resonance Spectroscopy 9 1.2.4 Electrochemical Sensors and Immunoassays 10 1.3 Chemical Sensing by Bacteria 12 1.4 Olfaction as a Medical Device 14 1.5 Metabolomics 16 1.6 Surface Enhanced Raman Scattering (SERS) 19 1.7 Chemical Sensing with SERS 22 1.8 Thesis Outline 24 iii CHAPTER 2: Background 29 2.1 Localized Surface Plasmon Resonance 29 2.2 SERS Spectroscopy 30 2.3 Electrohydrodynamic (EHD) Flow 33 2.4 Machine Learning 34 PART TWO: Nanomanufacturing Large-Area, High Performance Optical Devices 38 CHAPTER 3: Driving Chemical Reactions in Plasmonic Nanogaps with 39 Electrohydrodynamic Flow 3.1 Introduction 39 3.2 Electrohydrodynamic (EHD) Flow Driving Chemical Crosslinking for 42 Assembly of Oligomers on Surfaces 3.3 EHD Driving Forces Influencing Oligomer Assembly 50 3.4 Plasmon Resonance Response of EHD-Anhydride Substrates 55 3.5 SERS Response 58 3.6 Conclusion 60 3.7 Methods 61 3.7.1 Materials 61 3.7.2 Nanoantenna Oligomer Substrate Fabrications 62 3.7.3 Assembly on Alternative Substrates 63 3.7.4 Characterization 63 3.7.5 Finite Element Simulations 64 3.7.6 Atomistic Simulation 65 3.8 Supplemental Information 68 iv CHAPTER 4: Templated Electrokinetic Directed Chemical Assembly for the 76 Fabrication of Close-Packed Plasmonic Metamolecules 4.1 Introduction 76 4.2 Results and Discussion 78 4.3 Conclusion 83 4.4 Methods 83 4.4.1 Chemically Functionalized Template Fabrication 83 4.4.2 EHD Flow Assisted Chemical Assembly of Nanoparticles 85 onto Pillar Templates 4.4.3 Characterization 86 PART THREE: Machine Learning Enabled Biosensing with Surface Enhanced 87 Raman Scattering Spectroscopy CHAPTER 5: Longitudinal Monitoring of Biofilm Formation via Robust 87 Surface-Enhanced Raman Scattering Quantification of Pseudomonas aeruginosa - Produced Metabolites 5.1 Introduction 88 5.2 Large-Area Uniformity of SERS Substrates 92 5.3 Quantification and Detection of Pyocyanin in Aqueous Media 96 5.4 Training Data Acquisition and Building Multivariate Predictive Model 97 5.5 Pyocyanin Quantitative Detection in Complex Media 99 5.6 Monitoring Biofilm Formation via Pyocyanin Quantification 101 5.7 Conclusion 104 5.8 Methods 105 v 5.8.1 Materials 105 5.8.2 Self-Assembly of SERS Substrates 106 5.8.3 Characterization 107 5.8.4 P. aeruginosa cell free supernatant preparation 107 5.8.5 Fluidic Device Fabrication and Biofilm Growth 108 5.8.6 Antibiotic Susceptibility Measurements 108 5.8.7 Spectroscopic Measurements, Instrumentation, and Procedure 109 5.8.8 Spectra Processing and Analysis 110 5.8.9 Simulations 111 CHAPTER 6: SERS-based Odor Compass: Locating Multiple Chemical Sources 113 and Pathogens 6.1 Introduction 113 6.2 SERS Sensor Array Fabrication and Validation 115 6.3 Visualization of Odorant Chemisorption on SERS Surfaces 119 6.4 Model Performance on Multi-Source Task 121 6.5 Conclusion 126 6.6 Methods 127 6.6.1 Materials 127 6.6.2 SERS Sensor Fabrication 128 6.6.3 Analyte Deposition 129 6.6.5 Spectra Preprocessing 129 6.6.6 Compass Models 130 6.6.7 Static Biofilm Preparation and Characterization 131 vi CHAPTER 7: Improved Concentration Regressions with Convolutional Neural 133 Networks for Surface Enhanced Raman Scattering Sensing 7.1 Introduction 133 7.2 Results and Discussion 135 7.3 Conclusion 142 7.4 Methods 143 7.4.1 Materials 143 7.4.2 2-Dimensional Physically activated Chemical (2PAC) Assembly 144 7.4.3 Characterization 144 CHAPTER 8: Quantification of Analyte Concentrations in the Single Molecule 145 Regime Using Convolutional Neural Networks 8.1 Introduction 145 8.2 Discussion 148 8.3 Experimental System 149 8.4 CNN for Single Molecule SERS Quantification 152 8.5 Transfer Learning for New Analyte Molecule 153 8.6 Conclusion 154 8.7 Methods 155 8.7.1 Materials 155 8.7.2 2PAC Assembly 155 8.7.3 Characterization 156 8.7.4 Data Analysis and Models 156 vii CHAPTER 9: Semi-Supervised and Unsupervised Rapid Antimicrobial 158 Susceptibility Testing (AST) with SERS 9.1 Introduction 158 9.2 Experimental System 161 9.3 Semi-Supervised Learning for SERS AST 166 9.4 Transfer Learning for SERS AST 171 9.5 Conclusion 175 9.6 Methods 175 9.6.1 Materials 175 9.6.2 SERS Sensor Fabrication 176 9.6.3 Bacterial Cell Culture and Antibiotic Treatments 177 9.6.4 Metabolite Mixture Preparation 178 9.6.5 SERS Spectroscopy 178 9.6.6 Spectra Preprocessing 179 9.6.7 Unsupervised Data Visualization 179 9.6.8 Variational Autoencoder Implementation 179 9.6.9 Semi-Supervised Learning 181 9.6.10 Transfer Learning 181 9.7 Supplemental Information 182 CHAPTER 10: Conclusion 186 REFERENCES 191 viii LIST OF FIGURES Page Figure 2.1 Sketch of the Physical System considered in the derivation of LSPR 29 Figure 2.2 Diagram of various light scattering phenomenon 32 Figure 2.3 EHD flow streamlines 34 Figure 2.4 Components of an artificial neuron 35 Figure 2.5 Schematic representation of a fully connected neural network 36 Figure 3.1 Schematic of EHD attractive forces and carbodiimide chemistry 43 Figure 3.2 Transmission electron microscope (TEM) image of EHD assembled Au 46 nanospheres, SERS spectra of EHD sample and various chemical treatments, and SERS spectra of benzenethiol Figure 3.3 Simulated effects of gap spacing and ligand density on C-O distance 48 Figure 3.4 Minimum energy path of anhydride crosslinking 50 Figure 3.5 Scanning electron microscopy (SEM) images of surfaces with various 52 assembly conditions and the observed oligomerization distributions Figure 3.6 SEM image and oligomerization distributions of 20 nm diameter Au 55 nanospheres Figure 3.7 Optical properties of assembled surfaces 57 Figure 3.8 SERS enhancement factor map 60 Figure 3.9 TEM image of control surface with SERS spectra 68 Figure 3.10 Schematic of method to determine oligomerization 70 Figure 3.11 TEM image of 20 nm EHD-anhydride substrate 71 Figure 3.12 SEM image of EHD-anhydride substrate with four depositions 72 ix Figure 3.13 SEM image highlighting block copolymer template 73 Figure 3.14 SERS spectra showing sample to sample uniformity 74 Figure 3.15 SERS spectra showing spectra stability 75 Figure 4.1 Schematic of EHD nanoparticle assembly and SEM image of 78 as-assembled surface Figure 4.2 SEM images of close-packed metamolecules 80 Figure 4.3 Simulation of optical response of close-packed metamolecules 81 Figure 4.4 Schematic of fabrication process 85 Figure 5.1 Schematic overview of EHD-anhydride assembly 93 Figure 5.2 Characterization of EHD-anhydride assembled surface 95 Figure 5.3 SERS spectra of pyocyanin and dose-response relationship 97 Figure 5.4 PLSR model of pyocyanin concentration and evaluation of bacteria-free 101 supernatant Figure 5.5 Longitudinal monitoring of biofilm formation and antibiotic treatment 104 Figure 6.1 Odor compass diagram, SEM image, and SERS spectra 118 Figure 6.2 Non-negative matrix factorization scores and components 121 Figure 6.3 Schematic of label generation 123 Figure 6.4 Odor compass model performance 125 Figure 6.5 Biofilm location identification with odor compass 126 Figure 7.1 Schematic of 2-dimensional physically activated chemical (2PAC) 136 self- assembly Figure 7.2 SERS spectra of Pyocyanin 137 x Figure 7.3 Comparison of predictions made by multilinear regression and multilayer 139 perceptron Figure 7.4 Schematic of convolutional neural
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