Date of Award
5-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Chemistry
Committee Chair/Advisor
Carlos D. Garcia
Committee Member
George Chumanov
Committee Member
Daniel Whitehead
Committee Member
Jeb Linton
Abstract
This dissertation is a multidisciplinary effort that integrates low-cost analytical instrumentation, redox chemistry, and artificial intelligence to overcome existing limitations in the fields of wearable sensing technology, Deep Eutectic Solvents (DES), and antioxidant chemistry. The overall goal behind each implemented strategy is to enhance the accuracy, efficiency, and accessibility of analytical processes and technologies. A general overview of the thesis, along with the research outcomes is included in Chapter One. The theoretical framework of this dissertation is presented in Chapter Two. Chapter Three describes the development of a wearable platform (sensor and instrumentation) to rapidly detect (~20 minutes) S. aureus in skin infections. Chapter Four describes the utilization of an array of microelectrodes to detect the same pathogen in a high throughput fashion (15 samples analyzed in 13 minutes), leveraging the proposed sensing strategy for clinical settings. Chapters Five, Six, and Seven explore the use of computational methods to advance the development and applications of DES. To achieve this goal, Chapter Five demonstrates the use of RDKit to grasp the complexity of the formation and characteristics of Natural DES, providing a comprehensive overview of their physicochemical properties. Chapter Six introduces the first example of an artificial intelligence approach based on a large language model to predict the probability of formation of NADES in a high throughput manner (1M combinations in less than 30 minutes). Furthering these developments, Chapter Seven describes the use of machine learning to predict the melting point of various DES. Lastly, Chapter Eight explores the use of AI to predict the behavior (synergistic, additive, and antagonistic) and the level of interaction of phenolic antioxidants in antioxidant mixtures.
Recommended Citation
Ayres, Lucas B., "Design and Application of Smart Systems to Address Analytical Problems" (2024). All Dissertations. 3629.
https://tigerprints.clemson.edu/all_dissertations/3629
Author ORCID Identifier
https://orcid.org/0000-0002-9843-520X
Included in
Analytical Chemistry Commons, Computational Chemistry Commons, Data Science Commons, Food Chemistry Commons, Other Computer Engineering Commons, Other Engineering Science and Materials Commons