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Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation.

ABSTRACT: Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanism's presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention.

SUBMITTER: Sheng H

PROVIDER: S-EPMC10981680 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation.

Sheng Hongyuan H Sun Jingwen J Rodríguez Oliver O Hoar Benjamin B BB Zhang Weitong W Xiang Danlei D Tang Tianhua T Hazra Avijit A Min Daniel S DS Doyle Abigail G AG Sigman Matthew S MS Costentin Cyrille C Gu Quanquan Q Rodríguez-López Joaquín J Liu Chong C

Nature communications 20240330 1

Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a ...[more]

PMID: 38555303

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Dataset Information

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation.

Publications

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation.

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