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Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation

Author

Listed:
  • Hongyuan Sheng

    (University of California, Los Angeles)

  • Jingwen Sun

    (University of California, Los Angeles)

  • Oliver Rodríguez

    (University of Illinois Urbana–Champaign
    University of Illinois Urbana–Champaign
    Argonne National Laboratory)

  • 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
    University of Illinois Urbana–Champaign
    Argonne National Laboratory)

  • Chong Liu

    (University of California, Los Angeles
    University of California, Los Angeles)

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.

Suggested Citation

  • 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 Costent, 2024. "Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47210-x
    DOI: 10.1038/s41467-024-47210-x
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    References listed on IDEAS

    as
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