Integrating AI-assisted SERS Biosensing and Photoactivated Antibacterial Therapy in Au@Cu _{2– <i>x</i>} Se for Combating Multidrug-Resistant Bacteria

The escalating global crisis of multidrug-resistant (MDR) bacteria demands innovative strategies that bypass conventional antibiotic limitations. This study introduced a multifunctional Au@Cu_2-<i>x</i>Se core-shell nanoplatform integrating artificial intelligence (AI)-assisted surface-enhanced Raman spectroscopy (SERS) biosensing with photoactivated antibacterial therapy for combating MDR pathogens. The Au@Cu_2-<i>x</i>Se nanoparticles exhibit exceptional photothermal conversion efficiency and robust photodynamic activity, generating cytotoxic reactive oxygen species (ROS) under light excitation. This dual photothermal-photodynamic mechanism enables rapid, broad-spectrum eradication of both Gram-positive and Gram-negative bacteria by disrupting cellular integrity and inducing oxidative damage, overcoming intrinsic resistance barriers. Complementing this therapeutic action, we engineered large-area, uniform nanobowl-array surface-enhanced Raman spectroscopy (SERS) substrates functionalized with Au@Cu_2-<i>x</i>Se hotspots. These substrates amplified bacterial Raman signals, enabling label-free detection of trace metabolites and biomarkers at single-cell resolution. To address spectral complexity, an AI driven workflow was developed: Raw spectra underwent rigorous preprocessing, followed by dimensionality reduction and classification via a tailored 1D convolutional neural network. The model achieved >95% accuracy in identifying six pathogenic species and >99% accuracy in Gram-type differentiation. This integrated platform bridges rapid, precise pathogen identification with targeted photomechanical sterilization, offering a promising "diagnose-and-treat" paradigm for point-of-care management of MDR infections.