Machine-Learning-Assisted Nanozyme-Based Sensor Arrays: Construction, Empowerment, and Applications

In the past decade, nanozymes have been attracting increasing interest in academia due to their stable performance, low cost, and easy modification. With the catalytic signal amplification feature, nanozymes not only find wide use in traditional "lock-and-key" single-target detection but hold great potential in high-throughput multiobjective analysis via fabricating sensor arrays. In particular, the rise of machine learning in recent years has greatly advanced the design, construction, signal processing, and utilization of sensor arrays. The constructive collaboration of nanozymes, sensor arrays, and machine learning is accelerating the development of biochemical sensors. To highlight the emerging field, in this minireview, we created a concise summary of machine-learning-assisted nanozyme-based sensor arrays. First, the construction of nanozyme-involved sensor arrays is introduced from several aspects, including nanozyme materials and activities, sensing variables, and signal outputs. Then, the roles of machine learning in signal treatment, information extraction, and outcome feedback are emphasized. Afterwards, typical applications of machine-learning-assisted nanozyme-involved sensor arrays in environmental detection, food analysis, and biomedical sensing are discussed. Finally, the promise of machine-learning-assisted nanozyme-based sensor arrays in biochemical sensing is highlighted, and some future trends are also pointed out to attract more interest and effort to promote the emerging field for better practical use.