Artificial intelligence-enabled flexible surface-enhanced Raman scattering substrate based on silver...

Selective detection of uric acid (UA), a key biomarker associated with cardiovascular diseases, gout and preeclampsia, is important for preventive healthcare and accurate diagnosis. Conventional analytical methods often suffer from low sensitivity, rely on complex and expensive instrumentation. Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful tool for non-invasive and highly sensitive detection of a wide range of molecules with their unique fingerprint information. Its high sensitivity arises from two cooperative mechanisms: electromagnetic enhancement, in which localized surface plasmon resonances in metallic nanostructures create intense near field "hotspots" that greatly amplify Raman scattering, and chemical enhancement, in which charge transfer interactions between the substrate and adsorbed molecules modify molecular polarizability and increase Raman cross sections. In this work, a flexible chitosan (CHT) film integrated with polypyrrole (PPy) nanowires and silver nanoparticles (Ag NPs) was fabricated using a simple drop and peel-off method. By tuning the Ag precursor concentration, optimized hotspot formation was achieved. The synergistic effects of plasmonic Ag NPs, conductive PPy and biocompatible CHT generated strong Raman enhancement, enabling ultrasensitive UA detection. The flexible substrate exhibited an enhancement factor of ∼10⁷, a lower limit of detection (LOD) of 10^-9 M, uniformity and reproducibility with the relative standard deviation (RSD) values of <10%. Mechanical stability was verified after 100 cycles of bending and torsion. This platform enabled reliable UA detection in serum, saliva, and urine. Furthermore, coupling with artificial intelligence (AI) algorithms achieved precise UA quantification with excellent accuracy. This study presents an innovative approach that integrates flexible SERS substrates with AI for next-generation and non-invasive diagnostics.