Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry

Advanced data analysis tools such as mathematical optimisation, Bayesian inference and machine learning have the capability to revolutionise the field of quantitative voltammetry. Nowadays such approaches can be implemented routinely with widely available, user-friendly modern computing languages, algorithms and high speed computing to provide accurate and robust methods for quantitative comparison of experimental data with extensive simulated data sets derived from models proposed to describe complex electrochemical reactions. While the methodology is generic to all forms of dynamic electrochemistry, including the widely used direct current cyclic voltammetry, this review highlights advances achievable in the parameterisation of large amplitude alternating current voltammetry. One significant advantage this technique offers in terms of data analysis is that Fourier transformation provides access to the higher order harmonics that are almost devoid of background current. Perspectives on the technical advances needed to develop intelligent data analysis strategies and make them generally available to users of voltammetry are provided.

Automated summary

Advanced data analysis tools such as mathematical optimization, Bayesian inference and machine learning have the potential to revolutionize the field of quantitative voltammetry. These methods can now be implemented routinely with widely available computing languages and high-speed computing to provide accurate and robust quantitative comparison of experimental data. The developments in large amplitude alternating current voltammetry highlight the advantage of accessing higher-order harmonics in data analysis through Fourier transformation.