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Herein, an interpretable machine learning\u2010empowered multimodal biosensor synergizing electron transfer\u2010enhanced nanozymes and aggregation\u2010induced emission luminogens (AIEgens) for ultrasensitive pathogen detection is presented. By engineering aminophenol formaldehyde resin nanobowls anchored with monodisperse Pt nanoparticles, interfacial electron transfer (N\u2192Pt\u2192O) induces an upshift of Pt d\u2010band center relative to the Fermi level, as validated by density functional theory. This electronic modulation optimizes H\n                    <jats:sub>2<\/jats:sub>\n                    O\n                    <jats:sub>2<\/jats:sub>\n                    adsorption energy, lowers the energy barrier of the rate\u2010determining step, and reduces activation energy, resulting in a 3.4\u2010fold enhancement in peroxidase\u2010like activity over conventional Pt nanozymes. Then, AIEgens are strategically integrated to generate cross\u2010validated anti\u2010interference signals, achieving a record\u2010low detection limit for\n                    <jats:italic>Salmonella typhimurium<\/jats:italic>\n                    , surpassing classical immunoassays in sensitivity and accuracy. A SHapley Additive exPlanations (SHAP)\u2010guided eXtreme Gradient Boosting (XGBoost) algorithm dynamically fuses multimodal signals, enhancing sensitivity by five fold over single\u2010mode detection and delivering 100% diagnostic accuracy for positive samples. SHAP further deciphers the synergetic mechanism, revealing concentration\u2010dependent signal contributions and validating decision logic. This work pioneers a nanozyme\u2010AI co\u2010design framework, bridging d\u2010band\u2010driven catalytic precision and machine learning\u2010powered signal intelligence to redefine biosensing paradigms for combating public health emergencies.\n                  <\/jats:p>","DOI":"10.1002\/advs.202505712","type":"journal-article","created":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T11:11:07Z","timestamp":1755947467000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Electron Transfer\u2010Tailored D\u2010Band Center to Boost Nanozyme Catalysis for Interpretable Machine Learning\u2010Empowered Intelligent Biosensing"],"prefix":"10.1002","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6550-6569","authenticated-orcid":false,"given":"Yuechun","family":"Li","sequence":"first","affiliation":[{"name":"College of Food Science and Engineering Northwest A&amp;F University  Yangling Shaanxi 712100 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenxin","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Food Science and Engineering Northwest A&amp;F University  Yangling Shaanxi 712100 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaowen","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Food Science and Engineering Northwest A&amp;F University  Yangling Shaanxi 712100 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxing","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Food Science and Engineering Northwest A&amp;F University  Yangling Shaanxi 712100 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Food Science and Engineering Northwest A&amp;F University  Yangling Shaanxi 712100 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sha","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Food &amp; Biological Engineering Hefei University of Technology  Hefei 230009 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wentao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Food Science and Engineering Northwest A&amp;F University  Yangling Shaanxi 712100 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanwei","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Food Science and Engineering Northwest A&amp;F University  Yangling Shaanxi 712100 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4463-3729","authenticated-orcid":false,"given":"Yizhong","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Food &amp; Biological Engineering Hefei University of Technology  Hefei 230009 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3529-542X","authenticated-orcid":false,"given":"Jianlong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Food Science and Engineering Northwest A&amp;F University  Yangling Shaanxi 712100 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,8,23]]},"reference":[{"key":"e_1_2_8_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodres.2018.09.014"},{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.analchem.3c05662"},{"key":"e_1_2_8_1_3","doi-asserted-by":"publisher","DOI":"10.1002\/chem.202201386"},{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tifs.2024.104486"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.analchem.2c04897"},{"key":"e_1_2_8_2_3","doi-asserted-by":"publisher","DOI":"10.1021\/acsami.2c11245"},{"key":"e_1_2_8_2_4","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.4c05899"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202300184"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/ange.202414625"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202300387"},{"key":"e_1_2_8_6_1","volume":"96","author":"Li Y.","year":"2024","journal-title":"Anal. 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