Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation
Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation
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DOI：
10.1038/s41467-024-47210-x
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发表时间：
2024-03-30
影响因子：
16.6
通讯作者：
Liu，Chong
中科院分区：
文献类型：
--
作者：
Sheng，Hongyuan;Sun，Jingwen;Liu，Chong
电化学研究通常需要严格的实验参数组合，而这些参数需要手动定位。自动化仪器和机器学习算法的最新进展开启了通过高通量、在线决策来加速研究电化学基础的可能性。在这里，我们报告了一个自主的电化学平台，它实现了一个自适应的、闭环的工作流，用于分子电化学的机械研究。作为概念验证，该平台自主地识别和研究了四苯基卟啉钴在有机卤化物亲电体库中的电化学机理，即界面电子转移(EStep)和随后的溶液反应(CSTEP)。普遍适用的工作流程准确地在负面对照和异常值中识别EC机制的存在，自适应地设计所需的实验条件，并定量提取跨越7个数量级的C步骤的动力学信息，从中获得对氧化加成途径的机械学见解。这项工作为自动驾驶电化学实验室中的自主机械发现提供了机会，而无需人工干预。
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.