Volume 233, 2022

Inclusion of multiple cycling of potential in the deep neural network classification of voltammetric reaction mechanisms

Abstract

The use of deep neural networks (DNNs) for the classification of electrochemical mechanisms using simulated voltammograms with one cycle of potential for training has previously been reported. In this paper, it is shown how valuable additional patterns for mechanism distinction become available when a new DNN is trained simultaneously on images obtained from three cycles of potential using tensor inputs. Significant improvements, relative to the single cycle training, in achieving the correct classification of E, EC1st and EC2nd mechanisms (E = electron transfer step and C1st and C2nd are first and second order follow up chemical reactions, respectively) are demonstrated with noisy simulated data for conditions where all mechanisms are close to chemically reversible and hence difficult to distinguish, even by an experienced electrochemist. Challenges anticipated in applying the new DNN to the classification of experimental data are highlighted. Directions for future development are also discussed.

Graphical abstract: Inclusion of multiple cycling of potential in the deep neural network classification of voltammetric reaction mechanisms

Associated articles

Article information

Article type
Paper
Submitted
12 अगस्त 2021
Accepted
05 अक्तूबर 2021
First published
06 अक्तूबर 2021

Faraday Discuss., 2022,233, 44-57

Inclusion of multiple cycling of potential in the deep neural network classification of voltammetric reaction mechanisms

L. Gundry, G. Kennedy, Alan M. Bond and J. Zhang, Faraday Discuss., 2022, 233, 44 DOI: 10.1039/D1FD00050K

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