Bimetallic FeCu-N-C Single-Atom Nanozymes with Triple Enzymatic Activities Enabling a Colorimetric Sensor Array for Machine-Learning-Assisted Pesticide Discrimination.

Herein, by formamide self-condensation, an innovative Fe-Cu dual-atom nanozyme (FeCu-N-C) is presented, engineered with adjacent metal sites within a nitrogen-doped carbon scaffold, that exhibits synergistically enhanced peroxidase-, oxidase-, and laccase-like activities. This multienzymatic performance far exceeds that of single-atom Fe-N-C and Cu-N-C controls. Density functional theory (DFT) calculations reveal that the proximity of Cu sites induces an upshift in the Fe d-band center from -1.52 eV to -0.88 eV, optimizing substrate adsorption and providing a mechanistic rationale for the observed catalytic synergy. Leveraging this multienzymatic platform, we developed a single-material colorimetric sensor array capable of discriminating five distinct pesticides in complex matrices. These pesticides differentially modulate the triple-mimetic activities through compound-specific interaction mechanisms, as validated by DFT analysis. Integrated with an artificial neural network (ANN), the system achieved 100% identification accuracy. This work establishes a pioneering paradigm for the rational design of multienzyme mimics and underscores their potential for high-precision environmental monitoring.