Empirical models for cyclic voltammograms

Abstract

Technological devices such as mobile phones and laptop computers have created an
immense demand for efficient and long lasting power sources such as Lithium-ion
batteries. Key to improving the current generation of batteries is the understanding
of Lithium based materials that are suitable for use in batteries. Researchers investigating
battery materials often plot the output from their experiments as a cyclic
voltammogram. A voltammogram is simply a plot of Current against Potential.
In this thesis we investigate a range of empirical models for cyclic voltammograms
with a Bayesian perspective, using data from experiments carried out in the School
of Chemistry, University of Southampton. This work is motivated by the lack of well
formulated mathematical models for cyclic voltammograms involving a Lithium-ion
compound. By setting the models within a Bayesian framework, we are able to
obtain posterior predictive distributions for characteristics of the voltammogram of
interest to chemists.

Markov Chain Monte Carlo sampling methods are used to explore the posterior
distribution of the model parameters and to estimate the posterior predictive distributions.
We investigate four methods of modelling the experimental data: multiple
regression models for summary statistics, autoregressive models, sinusoidal models
and stochastic volatility models. The application of Bayesian model choice techniques
showed that the sinusoidal model provided the best description of the data.

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Identifiers

ORCID for J.K. Sahu: orcid.org/0000-0003-2315-3598

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