Machine Learning‐Enabled Nanozyme Microsensors for Neurotransmitter Discrimination

ABSTRACT Discriminating structurally similar neurotransmitters in complex environments remains a major challenge for electrochemical sensing due to overlapping responses, cross‐reactivity, and interferences. Here, we report nanozyme‐engineered carbon fiber microsensors that generate catalytically differentiated electrochemical signatures, enabling neurotransmitter recognition when coupled with machine learning‐assisted signal analysis and feature engineering. We show that Au@CeO 2 nanozyme provides catalytic sites that modulate electron‐transfer kinetics at microelectrodes and enhance chemical selectivity toward dopamine, serotonin, L‐3,4‐dihydroxyphenylalanine, and ascorbic acid. By integrating feature extraction with multivariate learning, the platform achieves simultaneous identification and concentration prediction of multiple neurochemicals in mixtures. The Au@CeO 2 microsensors exhibit enhanced sensitivity and nanomolar detection limits for dopamine and serotonin, while enabling robust discrimination in complex mixtures. This work establishes a general framework for integrating functional nanozyme interfaces with electrochemistry and data‐driven analytics to enhance selectivity of sensing systems for neurochemical monitoring, bioelectronic interfaces, diagnostic and measurement technologies.