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Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning.

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.

SUBMITTER: Hoar BB

PROVIDER: S-EPMC9783079 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning.

Hoar Benjamin B BB Zhang Weitong W Xu Shuangning S Deeba Rana R Costentin Cyrille C Gu Quanquan Q Liu Chong C

ACS measurement science au 20220831 6

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 g ...[more]

PMID: 36573074

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