Monash University
Browse
Luke Gundry Thesis FINAL online version.pdf (35.95 MB)

Development of Artificial Intelligence Methods for Dynamic Electrochemical Studies of Complex Reaction Mechanisms

Download (35.95 MB)
thesis
posted on 2022-11-24, 04:24 authored by LUKE WILLIAM GUNDRY
The identification of electrochemical systems is generally a complex process with many difficulties. In this thesis a range of artificial intelligence methods are developed for the analysis of cyclic DC voltammetry and Fourier transformed AC voltammetry, these automated methods are then applied to interesting electrochemical systems. A range of artificial intelligence methods are based on techniques of statistics and machine learning with the specific methods being Optimisation, Bayesian inference, Supervised learning, and Unsupervised learning. In this thesis these methods show promise at extracting and identifying electrochemical information that was previously not possible by manual methods of analysis.

History

Campus location

Australia

Principal supervisor

Jie Zhang

Additional supervisor 1

Alan M. Bond

Additional supervisor 2

Gareth Kennedy

Additional supervisor 3

Jonathan Keith

Year of Award

2022

Department, School or Centre

Chemistry

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Science

Usage metrics

    Faculty of Science Theses

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC