Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation

Hongyuan Sheng (), Jingwen Sun, Oliver Rodríguez, Benjamin B. Hoar, Weitong Zhang, Danlei Xiang, Tianhua Tang, Avijit Hazra, Daniel S. Min, Abigail G. Doyle, Matthew S. Sigman, Cyrille Costentin, Quanquan Gu, Joaquín Rodríguez-López and Chong Liu ()
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Hongyuan Sheng: University of California, Los Angeles
Jingwen Sun: University of California, Los Angeles
Oliver Rodríguez: University of Illinois Urbana–Champaign
Benjamin B. Hoar: University of California, Los Angeles
Weitong Zhang: University of California, Los Angeles
Danlei Xiang: University of California, Los Angeles
Tianhua Tang: University of Utah
Avijit Hazra: University of Utah
Daniel S. Min: University of California, Los Angeles
Abigail G. Doyle: University of California, Los Angeles
Matthew S. Sigman: University of Utah
Cyrille Costentin: CNRS
Quanquan Gu: University of California, Los Angeles
Joaquín Rodríguez-López: University of Illinois Urbana–Champaign
Chong Liu: University of California, Los Angeles

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-47210-x

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