计算机视觉支持高质量电化学实验

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Keiichi Okubo, Jaydeep Thik, Tomoya Yamaguchi and Chen Ling
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引用次数: 0

摘要

旋转盘电极(RDE)技术是研究电催化剂活性、稳定性和其他基本特性的重要工具。高质量的 RDE 实验需要在电极表面均匀涂覆催化剂层,这在很大程度上依赖于经验,目前缺乏必要的质量控制。除了基于专业知识的传统判断外,缺乏适当的评估方法来确保 RDE 实验的质量,这降低了效率,使数据解释变得复杂,并阻碍了未来 RDE 实验的自动化。在此,我们提出了一种简单、易于执行且非破坏性的方法,该方法结合了显微镜成像和基于人工智能的决策,用于评估制备电极的质量。我们开发了一种基于卷积神经网络的方法,利用制备电极的显微图像直接评估样品质量。在对用于氧还原反应的电极的研究中,该模型预测样品质量的准确率超过了 80%。我们的方法可以在实际的 RDE 测试之前去除劣质样品,从而确保高质量的电化学实验,并为实现高质量的自动化电化学实验铺平道路。这种方法适用于各种电化学系统,凸显了人工智能在自动化实验中的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Computer vision enabled high-quality electrochemical experimentation

Computer vision enabled high-quality electrochemical experimentation

The rotating disk electrode (RDE) technique is an essential tool for studying the activity, stability, and other fundamental properties of electrocatalysts. High-quality RDE experimentation requires evenly coating the catalyst layer on the electrode surface, which relies heavily on experience and currently lacks necessary quality control. The lack of an adequate evaluation method to ensure the quality of RDE experimentation, aside from conventional judgment based on expertise, reduces efficiency, complicates data interpretation, and hinders future automation of RDE experimentation. Here we propose a simple, easy-to-execute and non-destructive method that combines microscopy imaging and artificial intelligence-based decision-making to assess the quality of as-prepared electrodes. We develop a convolutional neural network-based method that uses microscopic images of as-prepared electrodes to directly evaluate the sample quality. In a study of electrodes used for the oxygen reduction reaction, the model achieved an accuracy of over 80% in predicting sample qualities. Our method enables the removal of low-quality samples prior to the actual RDE test, thereby ensuring high-quality electrochemical experimentation and paving the way towards high-quality automated electrochemical experimentation. This approach is applicable to various electrochemical systems and highlights the potential of artificial intelligence in automated experimentation.

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