Electron Transfer‐Tailored D‐Band Center to Boost Nanozyme Catalysis for Interpretable Machine Learning‐Empowered Intelligent Biosensing

作者：Yuechun Li, Chunyan Ji, Zhaowen Cui, Jianxing Feng, Liang Zhang, Sha Liu, Wentao Zhang, Yanwei Ji, Yizhong Shen, Jianlong Wang · 发表于：Advanced Science · 年份：2025 · DOI：10.1002/advs.202505712 · 被引用次数：15 · 研究领域：Advanced Nanomaterials in Catalysis、Advanced biosensing and bioanalysis techniques、Biosensors and Analytical Detection

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 v...