Parameterisation of voltammetry in a machine learning environment

现代社会中的重要设备，如电池、燃料电池和医学传感器，都利用了复杂的电化学反应的特殊性质。这一多学科项目的目的是开发一种在机器学习环境中智能收集和分析大型电化学数据集的综合方法。因此，在一个具有统计意义的框架内，首次有可能对与异常复杂的电化学反应有关的动态电化学反应的所有方面进行全球建模和量化参数。需要解决的问题具有生物和化学意义。最终产品将是一种商业上可行的、用户友好的仪表组件。

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.