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Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning.
- Source :
-
ACS measurement science au [ACS Meas Sci Au] 2022 Dec 21; Vol. 2 (6), pp. 595-604. Date of Electronic Publication: 2022 Aug 31. - Publication Year :
- 2022
-
Abstract
- For decades, employing cyclic voltammetry for mechanistic investigation has demanded manual inspection of voltammograms. Here, we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a probable electrochemical mechanism among five of the most common ones in homogeneous molecular electrochemistry. The reported algorithm will aid researchers' mechanistic analyses, utilize otherwise elusive features in voltammograms, and experimentally observe the gradual mechanism transitions encountered in electrochemistry. An automated voltammogram analysis will aid the analysis of complex electrochemical systems and promise autonomous high-throughput research in electrochemistry with minimal human interference.<br />Competing Interests: The authors declare the following competing financial interest(s): B.B., W.Z., Q.G., and C.L. have filed a provisional patent for the work reported here.<br /> (© 2022 The Authors. Published by American Chemical Society.)
Details
- Language :
- English
- ISSN :
- 2694-250X
- Volume :
- 2
- Issue :
- 6
- Database :
- MEDLINE
- Journal :
- ACS measurement science au
- Publication Type :
- Academic Journal
- Accession number :
- 36573074
- Full Text :
- https://doi.org/10.1021/acsmeasuresciau.2c00045