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. We have developed a convolutional neural network-based method that utilizes microscopic images of prepared electrodes to directly assess sample quality. This method paves the way towards high-quality, automated electrochemical experimentation.