Issue 15, 2021

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

Abstract

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.

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

Article information

Article type
Feature Article
Submitted
17 Nov 2020
Accepted
21 Jan 2021
First published
21 Jan 2021

Chem. Commun., 2021,57, 1855-1870

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

L. Gundry, S. Guo, G. Kennedy, J. Keith, M. Robinson, D. Gavaghan, A. M. Bond and J. Zhang, Chem. Commun., 2021, 57, 1855 DOI: 10.1039/D0CC07549C

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements