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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.
Afiliación
  • Sheng H; Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA. hsheng7@g.ucla.edu.
  • Sun J; Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Rodríguez O; Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
  • Hoar BB; Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
  • Zhang W; Joint Center for Energy Storage Research (JCESR), Argonne National Laboratory, Lemont, IL, 60439, USA.
  • Xiang D; Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Tang T; Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Hazra A; Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Min DS; Department of Chemistry, University of Utah, Salt Lake City, UT, 84112, USA.
  • Doyle AG; Department of Chemistry, University of Utah, Salt Lake City, UT, 84112, USA.
  • Sigman MS; Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Costentin C; Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Gu Q; Department of Chemistry, University of Utah, Salt Lake City, UT, 84112, USA.
  • Rodríguez-López J; Université Grenoble Alpes, DCM, CNRS, 38000, Grenoble, France.
  • Liu C; Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Nat Commun ; 15(1): 2781, 2024 Mar 30.
Article en En | MEDLINE | ID: mdl-38555303
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.
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos