Machine Learning ‐Driven 3D Plasmonic Cavity‐in‐Cavity Surface‐Enhanced Raman Scattering Platform with Triple Synergistic Enhancement Toward Label‐Free Detection of Antibiotics in Milk

In this study, a machine learning-driven 3D plasmonic cavity-in-cavity (CIC) SERS platform is proposed for sensitive and quantitative detection of antibiotics. The platform is prepared by transferring truncated concave nanocubes (NCs) to an obconical-shaped template surface. Owing to the triple synergistic enhancement effect, the highly ordered 3D CIC arrays improve the simulated electromagnetic field intensity and experimental SERS activity, demonstrating a 33.1-fold enhancement compared to a typical system consisting of Au NCs deposited on a flat substrate. The integration of machine learning and Raman spectroscopy eliminates subjective judgments on the concentration of detectors using a single feature peak and achieves accurate identification. The machine learning-driven CIC SERS platform is capable of detecting ampicillin traces in milk with a detection limit of 0.1  ppm, facilitating quantitative analysis of different concentrations of ampicillin. Therefore, the proposed platform has potential applications in food safety monitoring, health care, and environmental sampling.
Source: Small - Category: Nanotechnology Authors: Tags: Research Article Source Type: research