Mechanistic Design of Graphdiyne‐Based Multimodal Sensing Integrating Machine Learning and Photothermal Dynamics for Precision Recognition and On‐Demand Inactivation

ABSTRACT Rapid and accurate detection of pathogenic bacteria remains essential for infection control and timely treatment. Here, we report a graphdiyne (GDY)‐based self‐powered biosensing platform that integrates CRISPR/Cas12a molecular recognition with GDY/Au nanoparticle‐engineered bioelectrodes for multimodal detection and photothermal inactivation of Vibrio parahaemolyticus. The ultrathin GDY framework provides a high‐surface‐area scaffold for uniform Au nanoparticle dispersion, facilitating interfacial charge transfer and enhancing enzyme‐mediated redox kinetics at the bioanode. Target‐triggered CRISPR/Cas12a trans‐cleavage regulates the release of glucose oxidase from a hairpin probe, enabling a self‐powered electrochemical readout driven by glucose oxidation. In parallel, HRP‐catalyzed oxidation of TMB generates oxTMB with strong near‐infrared absorbance, providing complementary colorimetric and photothermal (808 nm) outputs and enabling in situ bacterial inactivation. The electrochemical, colorimetric, and thermal modes exhibit concentration‐dependent responses with limits of detection of 0.34, 0.41, and 0.78 CFU mL ⁻¹ , respectively. Integration of multimodal signals via machine learning further enables infection grading with an overall accuracy of 97.71%. This multimodal diagnostic‐therapeutic strategy demonstrates reliable performance in both in vitro and in vivo wound infection models, highlighting its potential for localized infection monitoring and point‐of‐care bacterial management. This study provides a proof‐of‐concept demonstration of an integrated self‐powered multimodal biosensing platform for simultaneous bacterial detection and inactivation.