Author ORCID Identifier
0000-0001-7537-2173
Document Type
Dissertation
Date of Award
12-31-2024
Degree Name
Doctor of Philosophy in Materials Science and Engineering - (Ph.D.)
Department
Physics
First Advisor
Omowunmi A. Sadik
Second Advisor
Boris Khusid
Third Advisor
Hao Chen
Fourth Advisor
N. M. Ravindra
Fifth Advisor
Michael Scott Eberhart
Abstract
This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine critical kinetic parameters such as diffusion coefficients, rate constants, and transfer coefficients for DMAB reactions. Simulation results from KISSA-1D software confirmed the experimental findings, providing robust insights into the multi-step and complex mechanisms of electrochemical behavior.
The second part addresses the challenge of pain assessment by establishing ground truth methodologies using biomarkers such as COX-2 and iNOS, alongside functional MRI (fMRI) data. A Bayesian network was developed to correlate these biochemical markers with pain perception, offering a simulation-based framework to analyze neural responses to pain stimuli. This research integrates electrochemical detection techniques for pain biomarkers, such as dopamine and serotonin, with fMRI simulations to identify pain-related brain regions and validate predictive models. The hypothesis tested is that combining biochemical and neuroimaging data enhances the accuracy and reliability of pain assessments.
Overall, this dissertation contributes to the field of electrochemical kinetics with the development and validation of Envismetrics, while also advancing pain assessment methodologies through innovative integration of biochemical and neuroimaging data. These findings have significant implications for applications in energy storage, sensor technology, and medical diagnostics.
Recommended Citation
Xue, Huize, "Determination of electrochemical parameters for predicting reaction mechanism and algorithmic approaches to pain assessment" (2024). Dissertations. 1810.
https://digitalcommons.njit.edu/dissertations/1810
Included in
Biomedical Engineering and Bioengineering Commons, Categorical Data Analysis Commons, Chemical Engineering Commons, Materials Science and Engineering Commons, Neuroscience and Neurobiology Commons
