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Abstract Electrochemistry represents unique approaches for the promotion and mechanistic study of chemical reactions and has garnered increasing attention in different areas of chemistry. This expansion necessitates the enhancement of the traditional electrochemical cells that are intrinsically constrained by mass transport limitations. Herein, we present an approach for designing an electrochemical cell by limiting the reaction chamber to a thin layer of solution, comparable to the thickness of the diffusion layer. This thin layer electrode (TLE) provides a modular platform to bypass the constraints of traditional electrolysis cells and perform electrolysis reactions in the timescale of electroanalytical techniques. The utility of the TLE for electrosynthetic applications benchmarked using NHPI‐mediated electrochemical C−H functionalization. The application of microscale electrolysis for the study of drug metabolites was showcased by elucidating the oxidation pathways of the paracetamol drug. Moreover, hosting a microelectrode in the TLE, was shown to enable real‐time probing of the profiles of redox‐active components of these rapid electrosynthesis reactions.
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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 anECmechanism, an interfacial electron transfer (Estep) followed by a solution reaction (Cstep), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns theECmechanism’s presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of theCstep 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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- PAR ID:
- 10497808
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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