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Title Page

Contents

Abstract 18

Chapter 1. INTRODUCTION 20

1.1. Machine learning in electrochemical sensors 20

1.1.1. Concentration prediction with machine learning 21

1.1.2. Chemical identification with machine learning 23

1.1.3. Analysis of electrochemical data using convolutional neural network 23

1.1.4. Generative model for data exploration and optimization 24

1.1.5. Smart point-of-care detection 25

1.1.6. Materials for electrochemical sensing platforms 26

1.2. Research objective and outline 32

Chapter 2. BASIC PRINCIPLES AND TERMINOLOGIES, AND FUNDAMENTAL CALCULATIONS 34

2.1. Electroanalytical methods 34

2.1.1. Cyclic voltammetry (CV) 35

2.1.2. Electrochemical impedance spectroscopy (EIS) 39

2.2. Machine Learning for data analysis 44

2.2.1. Machine learning algorithm 44

2.2.2. Convolutional Neural Network (CNN) 50

2.2.3. Variational autoencoder (VAE) 52

Chapter 3. ELECTROCHEMICALLY EXFOLIATED WS₂ NANOSHEETS FOR THE ELECTROCHEMICAL IMPEDIMETRIC DETECTION OF NADH 55

3.1. Introduction 55

3.2. Experimental Section 59

3.2.1. Reagents 59

3.2.2. Electrochemical exfoliation of bulk WS₂ 59

3.2.3. Bulk WS₂ electrode was preparation 60

3.2.4. Electrochemical measurements 60

3.3. Results and Discussion 61

3.3.1. Morphological and structure analysis 61

3.3.2. Cyclic voltammetry characteristic of the ex-WS₂/SPCE 66

3.3.3. Electrochemical impedimetric detection of NADH on ex-WS₂/SPCE 70

3.4. Conclusion 83

Chapter 4. ANALYSIS OF ELECTROCHEMICAL IMPEDANCE DATA: USE OF MACHINE LEARNING AND DEEP NEURAL NETWORKS 84

4.1. Introduction 84

4.2. Experimental Section 88

4.2.1. EIS data simulation 88

4.2.2. Deep neural network building and training 89

4.3. Results and discussion 90

4.3.1. EIS dataset 90

4.3.2. Deep neural network construction for the EIS model classification 95

4.3.3. Deep neural network construction for the EIS parameters regression 101

4.3.4. Training and evaluation of the EIS model classification 102

4.3.5. Training and evaluation of the EIS parameters regression 115

4.4. Conclusions 121

Chapter 5. DEEP GENERATIVE LEARNING FOR EXPLORATION IN LARGE ELECTROCHEMICAL IMPEDANCE DATASET 122

5.1. Introduction 122

5.2. Experimental Section 129

5.2.1. EIS data acquisition 129

5.2.2. Model building and training process 130

5.3. Results and discussion 135

5.3.1. Exploration of synthetic EIS dataset with a VAE model 135

5.3.2. Visualization and prediction of EIS parameter with a VAE model 145

5.4. Conclusions 152

Chapter 6. CONCLUSION 153

6.1. Conclusion 153

References 156

논문요약 174

List of Tables

Table 1-1. Performance of sensor electrode based on MXene, g-C3N4, and h-BN materials. 28

Table 1-2. Performance of sensor electrode based on MOFs, and TMDs materials. 31

Table 3-1. Comparison of performance of the sensor developed in the present study with other reported electrochemical NADH sensor. 78

Table 4-1. Electrochemical and physical parameters and range. 94

Table 4-2. List of hyperparameters with the best fit values 104

Table 4-3. Comparison of R-squared of the predicted EIS parameters on the different circuit models. 118

Table 5-1. Comparison of R-squared of the predicted EIS parameters on the different circuit models. 149

List of Figures

Figure 1-1. Illustration of (a) conventional approach for electrochemical sensor and (b) ML approach for electrochemical sensor. 22

Figure 2-1. Four type of commonly used detection methods in electrochemical sensor. (a) cyclic voltammetry, (b) differential pulse... 38

Figure 2-2. Illustration of voltammogram of the redox reaction of Fc⁺ solution. 38

Figure 2-3. Illustration of (a) Nyquist plot and (b) Bode plot for EIS data. 40

Figure 2-4. EIS spectrum simulate form the respective equivalent circuit. 43

Figure 2-5. (a) The linear regression algorithm and (b) the k-nearest neighbor algorithm. 45

Figure 2-6. Performance comparison between modern ML and classical ML. 47

Figure 2-7. Performance comparison between modern ML and classical ML. 47

Figure 2-8. Illustration of convolution algorithm in the CNN. 49

Figure 2-9. Illustration of the pooling method. 49

Figure 3-1. FESEM images of (a) bulk WS₂ and (b) ex-WS₂. HRTEM images of (c, e) bulk WS₂ and (d, f) ex-WS₂. (g) XRD pattern of bulk-WS₂ and ex-WS₂ 64

Figure 3-2. (a) EDX spectrum of bulk-WS₂, (a) EDX spectrum of ex-WS₂, and (c) UV-Vis spectrum of bulk-WS₂ and ex-WS₂ 65

Figure 3-3. (a) CV response of ex-WS₂/ SPCE, bulk WS₂/SPCE and bare SPCE measured in 0.1M PBS solution containing 1mM NADH. (b) The dependance of... 67

Figure 3-4. Baseline corrected CV data showing the oxidation peak of bare SPCE and ex-WS₂/SPCE measured in 0.1 M PBS solution containing 1 mM NADH. 68

Figure 3-5. CV response data of ex-WS₂/SPCE in 0.1 M PBS solution containing different concentrations of NADH (0 mM to 5 mM). 69

Figure 3-6. CV data, effect of NADH concentration on Bulk WS₂/SPCE electrode. 69

Figure 3-7. (a) A plot between -Z" and log (frequency) measured for ex-WS₂/SPCE in 0.1 M PBS solution containing 1mM NADH at various applied DC... 72

Figure 3-8. (a) Nyquist plot measured for ex-WS₂/SPCE in different concentrations of NADH viz. 0 mM to 5 mM. (b) Their respective Bode plot representation. 73

Figure 3-9. (a) Bode plot measured for ex-WS₂/SPCE in 0.1 M PBS containing wide range of NADH concentrations (2 µM to 2048 µM). (b) Calibration plot... 75

Figure 3-10. (a) Schematic representation of the NADH sensing mechanism of ex-WS₂/SPCE. (b) Interference study showing the value of mod |Z| versus... 82

Figure 4-1. Five different equivalent circuit models. 93

Figure 4-2. The example of the EIS spectrum from five equivalent circuits. The orange and blue lines present possible EIS spectrum patterns for the respective circuits. 93

Figure 4-3. Input data for DNN models; (a) imaginary part of impedance (Z"), (b) phase angle (φ), and (c) magnitude of the impedance (|Z|). (d-f) show... 97

Figure 4-4. Illustration of the feature extraction of six input data using 1D convolution with a kernel size of 32. The solid red box shows the size of the... 98

Figure 4-5. The scheme of the neural networks used for (a) the EIS circuit classification and (b) parameters regression. 99

Figure 4-6. The effect of hyperparameters; (a) effect of the number of data on the accuracy, (b) effect of batch size and dropout rate on the accuracy, and (c)... 104

Figure 4-7. The training curve comparison of the DNN model with and without flipping features. The red color presents a DNN model without flipping features... 106

Figure 4-8. The training result of the validation set. (a) Confusion matrix for the DNN classification model using the optimal hyperparameter evaluated on the... 106

Figure 4-9. (a) The confusion matrix and (b) receiver operating characteristic (ROC) curve for the DNN classification were evaluated on the test set, yielding... 107

Figure 4-10. The example of correct classification results from the DNN model with the probability distribution on the right-hand side, in the case of (a) C3,... 110

Figure 4-11. The example of misclassification results from the DNN model with the probability distribution. (a) C3, (b) C4, and (c) C5. 111

Figure 4-12. The noise dataset with various signal-to-noise ratios (SNR) of (a) SNR 80 dB, (b) SNR 40 dB, and (c) SNR 20 dB, and their confusion matrix... 114

Figure 4-13. A demonstration using the DNN model for real EIS data prediction. 114

Figure 4-14. The prediction of C3 parameters for (a) Rs, (b) R1, (c) Q1, and (d) σ. Presented by first 100 spectra with R-squared and MAE value. The... 117

Figure 4-15. A demonstration using the DNN model for NADH concentration prediction; (a) EIS spectrum of NADH with different concentration, (b) The... 119

Figure 5-1. The schematic of (a) machine learning "black box" model and (b) conventional interpretation method for the classification EIS data. 125

Figure 5-2. The schematic of a deep generative model (VAE) for the EIS data classification. 128

Figure 5-3. Five different equivalent circuit models. 128

Figure 5-4. Schematic diagram of (a) VAE with classification model (b) VAE with regression model. 133

Figure 5-5. The schematic of a supervised VAE model for the EIS data classification. 134

Figure 5-6. The classification result with the confusion matrix and ROC curve on (a) circuit C1 to C4 and (b) circuit C1 to C5. 137

Figure 5-7. The classification result of the VAE model on the test set with the confusion matrix. 137

Figure 5-8. The classification result of (a) K-Nearest Neighbor model, (b) shallow neural network model, (c) deep neural network model, and (d) VAE model. 139

Figure 5-9. (a) the visualization of latent space in the EIS dataset and (b) the EIS reconstruction result using the respective latent variables (in the red... 142

Figure 5-10. Beeswarm plot of (a) feature impact on L1 and (c) feature impact on L2. Input data with colored the feature impact to the value of (b) L1 and (d) L2. 143

Figure 5-11. Illustration of the effect of the feature on the out value of L₁ and L₂. 146

Figure 5-12. The prediction of C3 parameters for (a) Rs, (b) R1, (c) Q1, and (d) σ. The first 50 spectra with an R-squared value are displayed. The orange... 148

Figure 5-13. Latent space with the mapping of (a) Rs, (b) R1, (c) Q, and (d) σ. 151

Figure 5-14. The reconstructed EIS spectrum from the latent variable. 151

초록보기

 전기화학 센서는 오염물질 모니터링, 약물 검출, 생물학 및 식품영양학 등의 너른 분야에서 활용되고 있다. 기존 연구의 궁극적인 목표는 높은 분석 성능을 가지는 물질을 합성하여 신호 대 잡음비를 향상시키는 것이었다. 이를 달성하기 위한 물질 디자인에는 빠른 전자 전달, 넓은 비표면적, 그리고 분석물에 대한 높은 활성도와 같은 물성이 포함되기 때문에, 제조과정에 많은 시간과 금액이 필요하다. 따라서 물질의 성능을 높이고 제조과정에 필요한 가격과 시간을 줄이기 위해, 물질 분석 성능과 데이터 분석의 최적화를 위한 머신 러닝(Machine learning, ML)의 도입을 고려할 수 있다.

본 논문에서는 우선 전기화학 센서, 최근 개발되고 있는 전극 물질, 전기화학 분석 방법의 기본 원리 및 ML 의 기초적인 방법론에 대한 개요를 제시한다. 또한, NADH 농도를 높은 성능으로 감지할 수 있도록 screen-printed carbon electrode 에 박리된 WS₂ 나노구조체를 도입하는 통상적인 방식으로 제작된 센서 전극(ex-WS₂/SPCE)을 제시하였다. WS₂ 나노구조체는 전기화학적 박리 방법을 사용하여 제작하였으며, ex-WS₂/SPCE 는 단순 SPCE 에 비해 큰 산화환원 전류 응답(55 %)과 함께 높은 전기화학촉매 활성도를 보였다. 이 ex-WS₂/SPCE 센서 전극은 전기화학 임피던스 분광법(EIS)을 사용하였을 때 2 M 에서 2048 M 에 달하는 영역에서 선형의 응답을 보였으며, 검출 한계는 57 nM 에 달하는 성능을 보였다.

이어서, ML 을 사용하여 EIS 데이터를 사람의 손을 거치지 않고 분석함으로써, 시간이 소요되는 기존의 과정과 달리 더욱 엄밀하게 결과를 해석하는 방식을 제시하였다. 이 섹션에서는 EIS 회로 모델과 변수 예측을 분류하기 위한 딥 러닝(deep neural network, DNN)을 사용하는 ML 전략을 제시한다. DNN 모델은 수신기 작동 특성 곡선(Receiver operating characteristic curve, AUC) 면적이 0.95 이상인 통상적인 EIS 회로에 대한 분류에 있어 높은 정확도를 보였다. 뿐만 아니라, 이 모델은 복잡한 EIS 시스템의 변수들에 대해서도 R-square 값이 0.999 에 달하는 높은 정확도를 가진다. 이상의 결과를 토대로, ML 을 사용한 방식이 사람의 개입 없이도 분석물의 농도를 예측할 수 있는 전기화학 센서 시스템 개발을 위한 방식이 될 수 있을 것으로 기대된다.

추가적으로, EIS 스펙트럼을 학습할 수 있는 기계 학습 알고리즘에 있어, 그 내부의 생성 모델을 제시하기 위한 semi-supervised 접근 방식을 도입하였다. 생성된 모델은 다차원 데이터에 대한 더 나은 시각화된 정보를 제공하며, 이를 더 낮은 차원의 데이터(latent space)로 압축한다. 여기서 physical descriptor 는 생성된 모델을 통해 latent space 에 매핑될 수 있고, 이로부터 latent space 가 property space 로 변환된다. 이러한 과정을 통해 데이터 세트 내에서 샘플 탐색, interpolation 및 최적화가 용이해진다. 따라서 EIS 분석을 위해 생성한 모델은 전기화학 시스템의 자동 분석을 발전시키기 위해 필수적인 도구로 활용될 수 있다.

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