Electron Transfer‐Tailored D‐Band Center to Boost Nanozyme Catalysis for Interpretable Machine Learning‐Empowered Intelligent Biosensing
Yuechun Li, Chunyan Ji, Zhaowen Cui, Jianxing Feng, Liang Zhang, Sha Liu, Wentao Zhang, Yanwei Ji, Yizhong Shen, Jianlong Wang
Abstract
Abstract The escalating global burden of infectious diseases demands biosensing technologies that transcend the complexity‐sensitivity‐accuracy trade‐off in real‐world applications. Herein, an interpretable machine learning‐empowered multimodal biosensor synergizing electron transfer‐enhanced nanozymes and aggregation‐induced emission luminogens (AIEgens) for ultrasensitive pathogen detection is presented. By engineering aminophenol formaldehyde resin nanobowls anchored with monodisperse Pt nanoparticles, interfacial electron transfer (N→Pt→O) induces an upshift of Pt d‐band center relative to the Fermi level, as validated by density functional theory. This electronic modulation optimizes H 2 O 2 adsorption energy, lowers the energy barrier of the rate‐determining step, and reduces activation energy, resulting in a 3.4‐fold enhancement in peroxidase‐like activity over conventional Pt nanozymes. Then, AIEgens are strategically integrated to generate cross‐validated anti‐interference signals, achieving a record‐low detection limit for Salmonella typhimurium , surpassing classical immunoassays in sensitivity and accuracy. A SHapley Additive exPlanations (SHAP)‐guided eXtreme Gradient Boosting (XGBoost) algorithm dynamically fuses multimodal signals, enhancing sensitivity by five fold over single‐mode detection and delivering 100% diagnostic accuracy for positive samples. SHAP further deciphers the synergetic mechanism, revealing concentration‐dependent signal contributions and validating decision logic. This work pioneers a nanozyme‐AI co‐design framework, bridging d‐band‐driven catalytic precision and machine learning‐powered signal intelligence to redefine biosensing paradigms for combating public health emergencies.