Fe-N-C single-atom nanozymes based sensor array for dual signal selective determination of antioxidants

Machine learning algorithms as a powerful tool can efficiently utilize and process large quantities of data generated by high-throughput experiments in various fields. In this work, we used a general ionic salt-assisted synthesis method to prepare oxidase-like Fe-N-C SANs. The possible reason for the excellent enzyme-mimicking activity and affinity of Fe-N-C SANs was further verified by density functional theory calculations. Due to the remarkable oxidase-mimicking activity, the prepared Fe-N-C SANs were used to detect ascorbic acid (AA) with a detection limit of 0.5 mu M. Based on the machine learning algorithms, we successfully distinguished six antioxidants (ascorbic acid, glutathione, L-cysteine, dithiothreitol, uric acid, and dopamine) with the same concentration by either one kind of Fe-N-C SANs or three kinds of different Fe-N-C SANs. The usefulness of the Fe-N-C SANs sensor arrays was further validated by the hierarchal cluster analysis, where they also can be correctly identified. More importantly, a SANs-based digital-image colorimetric sensor array has also been successfully constructed and thereby achieved visual and informative colorimetric analysis for practical samples out of the lab. This work not only provides a design synthesis method to prepare SANs but also combines machine learning algorithms with SANs sensors to identify analytes with similar properties, which can further expand to the detection of proteins and cells related to diseases in the future.

Automated summary

Machine learning algorithms combined with SANs sensors can efficiently process large quantities of data and accurately detect substances such as ascorbic acid. This method has broad application prospects and can be used for the detection of disease-related proteins and cells in the future.