Parameterisation of voltammetry in a machine learning environment

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Important devices in modern society such as batteries, fuel cells and medical sensors exploit special properties of complex electrochemical reactions. The aim of this multidisciplinary project is to develop an integrated approach to intelligent collection and analysis of large electrochemical data sets in a machine-learning environment. As a result, it will become possible for the first time to globally model and quantitatively parameterise all aspects of the dynamic electrochemistry associated with exceptionally complex electrochemical reactions in a statistically significant framework. Problems to be addressed are of biological and chemical significance. An end product will be a commercially viable, user-friendly instrumentation package.