Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation

Sheng, Hongyuan; Sun, Jingwen; Rodríguez, Oliver; Hoar, Benjamin B.; Zhang, Weitong; Xiang, Danlei; Tang, Tianhua; Hazra, Avijit; Min, Daniel S.; Doyle, Abigail G.; Sigman, Matthew S.; Costentin, Cyrille; Gu, Quanquan; Rodríguez-López, Joaquín; Liu, Chong

doi:10.1038/s41467-024-47210-x

Title: Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation

Journal Article · 30 March 2024 · Nature Communications

DOI: https://doi.org/10.1038/s41467-024-47210-x · OSTI ID:2331328

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^[1]; Rodríguez, Oliver ^[2]; Hoar, Benjamin B. ^[1];

^[1]; Xiang, Danlei ^[1]; Tang, Tianhua ^[3];

^[3]; Min, Daniel S. ^[1];

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Univ. of California, Los Angeles, CA (United States)
Univ. of Illinois at Urbana-Champaign, IL (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)
Univ. of Utah, Salt Lake City, UT (United States)
Univ. of Grenoble Alpes, Grenoble (France); Centre National de la Recherche Scientifique (CNRS) (France)

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.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States). Joint Center for Energy Storage Research (JCESR)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 2331328

Alternate ID(s):: OSTI ID: 2470262

Journal Information:: Nature Communications, Vol. 15, Issue 1; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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