{"id":{"repo_id":"soton","oai_identifier":"oai:eprints.soton.ac.uk:167559"},"canonical_url":"https://search.dev.ndltd.org/etd/soton/oai:eprints.soton.ac.uk:167559","repository":{"repo_id":"soton","name":"University of Southampton","base_url":"https://eprints.soton.ac.uk/cgi/oai2"},"display":{"title":"Empirical models for cyclic voltammograms","abstract":"Technological devices such as mobile phones and laptop computers have created an<br/>immense demand for efficient and long lasting power sources such as Lithium-ion<br/>batteries. Key to improving the current generation of batteries is the understanding<br/>of Lithium based materials that are suitable for use in batteries. Researchers investigating<br/>battery materials often plot the output from their experiments as a cyclic<br/>voltammogram. A voltammogram is simply a plot of Current against Potential.<br/>In this thesis we investigate a range of empirical models for cyclic voltammograms<br/>with a Bayesian perspective, using data from experiments carried out in the School<br/>of Chemistry, University of Southampton. This work is motivated by the lack of well<br/>formulated mathematical models for cyclic voltammograms involving a Lithium-ion<br/>compound. By setting the models within a Bayesian framework, we are able to<br/>obtain posterior predictive distributions for characteristics of the voltammogram of<br/>interest to chemists.<br/><br/>Markov Chain Monte Carlo sampling methods are used to explore the posterior<br/>distribution of the model parameters and to estimate the posterior predictive distributions.<br/>We investigate four methods of modelling the experimental data: multiple<br/>regression models for summary statistics, autoregressive models, sinusoidal models<br/>and stochastic volatility models. The application of Bayesian model choice techniques<br/>showed that the sinusoidal model provided the best description of the data.","abstract_html":"Technological devices such as mobile phones and laptop computers have created an&lt;br/&gt;immense demand for efficient and long lasting power sources such as Lithium-ion&lt;br/&gt;batteries. Key to improving the current generation of batteries is the understanding&lt;br/&gt;of Lithium based materials that are suitable for use in batteries. Researchers investigating&lt;br/&gt;battery materials often plot the output from their experiments as a cyclic&lt;br/&gt;voltammogram. A voltammogram is simply a plot of Current against Potential.&lt;br/&gt;In this thesis we investigate a range of empirical models for cyclic voltammograms&lt;br/&gt;with a Bayesian perspective, using data from experiments carried out in the School&lt;br/&gt;of Chemistry, University of Southampton. This work is motivated by the lack of well&lt;br/&gt;formulated mathematical models for cyclic voltammograms involving a Lithium-ion&lt;br/&gt;compound. By setting the models within a Bayesian framework, we are able to&lt;br/&gt;obtain posterior predictive distributions for characteristics of the voltammogram of&lt;br/&gt;interest to chemists.&lt;br/&gt;&lt;br/&gt;Markov Chain Monte Carlo sampling methods are used to explore the posterior&lt;br/&gt;distribution of the model parameters and to estimate the posterior predictive distributions.&lt;br/&gt;We investigate four methods of modelling the experimental data: multiple&lt;br/&gt;regression models for summary statistics, autoregressive models, sinusoidal models&lt;br/&gt;and stochastic volatility models. The application of Bayesian model choice techniques&lt;br/&gt;showed that the sinusoidal model provided the best description of the data.","abstract_has_math":false,"creators":["Samuel, Jeffrey J."],"institution":"University of Southampton","degree_name":"Ph.D.","degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Sahu, J.K."],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-08","date_published":"2010-08","updated_at":"2026-07-24T04:36:17Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Sahu, J.K."]},{"key":"dc:creator","label":"Author","values":["Samuel, Jeffrey J."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2010-08"]},{"key":"dc:date.issued","label":"Date","values":["2010-08"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Mathematics (pre 2011 reorg)","School of Mathematics"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Southampton"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://eprints.soton.ac.uk/167559/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Ph.D."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://eprints.soton.ac.uk/167559/1/Thesis_accepted.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Technological devices such as mobile phones and laptop computers have created an<br/>immense demand for efficient and long lasting power sources such as Lithium-ion<br/>batteries. Key to improving the current generation of batteries is the understanding<br/>of Lithium based materials that are suitable for use in batteries. Researchers investigating<br/>battery materials often plot the output from their experiments as a cyclic<br/>voltammogram. A voltammogram is simply a plot of Current against Potential.<br/>In this thesis we investigate a range of empirical models for cyclic voltammograms<br/>with a Bayesian perspective, using data from experiments carried out in the School<br/>of Chemistry, University of Southampton. This work is motivated by the lack of well<br/>formulated mathematical models for cyclic voltammograms involving a Lithium-ion<br/>compound. By setting the models within a Bayesian framework, we are able to<br/>obtain posterior predictive distributions for characteristics of the voltammogram of<br/>interest to chemists.<br/><br/>Markov Chain Monte Carlo sampling methods are used to explore the posterior<br/>distribution of the model parameters and to estimate the posterior predictive distributions.<br/>We investigate four methods of modelling the experimental data: multiple<br/>regression models for summary statistics, autoregressive models, sinusoidal models<br/>and stochastic volatility models. The application of Bayesian model choice techniques<br/>showed that the sinusoidal model provided the best description of the data."]},{"key":"dc:format","label":"Dc Format","values":["text"]},{"key":"dc:title","label":"Title","values":["Empirical models for cyclic voltammograms"]}]}],"canonical_facts":{"dc:contributor.advisor":["Sahu, J.K."],"dc:creator":["Samuel, Jeffrey J."],"dc:date":["2010-08"],"dc:date.issued":["2010-08"],"dc:description.abstract":["Technological devices such as mobile phones and laptop computers have created an<br/>immense demand for efficient and long lasting power sources such as Lithium-ion<br/>batteries. Key to improving the current generation of batteries is the understanding<br/>of Lithium based materials that are suitable for use in batteries. Researchers investigating<br/>battery materials often plot the output from their experiments as a cyclic<br/>voltammogram. A voltammogram is simply a plot of Current against Potential.<br/>In this thesis we investigate a range of empirical models for cyclic voltammograms<br/>with a Bayesian perspective, using data from experiments carried out in the School<br/>of Chemistry, University of Southampton. This work is motivated by the lack of well<br/>formulated mathematical models for cyclic voltammograms involving a Lithium-ion<br/>compound. By setting the models within a Bayesian framework, we are able to<br/>obtain posterior predictive distributions for characteristics of the voltammogram of<br/>interest to chemists.<br/><br/>Markov Chain Monte Carlo sampling methods are used to explore the posterior<br/>distribution of the model parameters and to estimate the posterior predictive distributions.<br/>We investigate four methods of modelling the experimental data: multiple<br/>regression models for summary statistics, autoregressive models, sinusoidal models<br/>and stochastic volatility models. The application of Bayesian model choice techniques<br/>showed that the sinusoidal model provided the best description of the data."],"dc:format":["text"],"dc:identifier.uri":["https://eprints.soton.ac.uk/167559/1/Thesis_accepted.pdf"],"dc:publisher.department":["Mathematics (pre 2011 reorg)","School of Mathematics"],"dc:publisher.institution":["University of Southampton"],"dc:relation.isreferencedby":["https://eprints.soton.ac.uk/167559/"],"dc:title":["Empirical models for cyclic voltammograms"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["Ph.D."]},"updated_at":"2026-07-24T04:36:17Z"}