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Electrochemiluminescence (ECL)-based point-of-care testing (POCT) has the potential to facilitate the rapid identification of diseases, offering advantages such as high sensitivity, strong selectivity, and minimal background interference. However, as the throughput of these devices increases, the issues of increased energy consumption and cross-contamination of samples remain. In this study, a high-throughput ECL biosensor platform with the assistance of machine learning algorithms is developed by combining a microcolumn array electrode, a microelectrochemical workstation, and a smartphone with custom software.

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School of Materials Science and Engineering, Xiangtan University, Xiangtan 411105, China. Electronic address:

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