Field-Deployable Risk Stratification of Pathogens via an AI-Integrated Nanozyme Sensor

Pathogen surveillance in complex environmental matrices requires analytical methods that are sensitive, robust, and suitable for field deployment. Here, we report a geometry-engineered trimetallic PdPtRu nanozyme-enabled multimodal lateral flow immunoassay (LFIA) for pathogen analysis. The spiky porous architecture, together with multimetallic synergy, promotes enhanced photothermal conversion and catalytic signal transduction through localized charge redistribution and structural effects. Finite element simulations reveal an enhancement of the local electric field and an increased power dissipation density, thereby improving photothermal conversion efficiency. The intensified local field induces strong interfacial polarization, resulting in a 5.43-fold increase in surface ·O₂^- flux and a reduced reaction energy barrier for peroxide activation. When integrated into LFIAs, this label improved the detection sensitivity for Salmonella typhimurium by 200-fold compared with conventional colloidal gold assays. From sample to answer, we further developed a smartphone application embedding a convolutional neural network and coupled it with a portable 3D-printed signal acquisition module to construct a self-contained, intelligent, and field-deployable detection system. This platform achieves risk-level stratification with high predictive performance (R² = 0.98, AUC = 0.99). This work establishes a scalable, modular foundation for early-warning surveillance systems, facilitating timely public health interventions.