Interfacial Regulation–Driven Dual‐Enrichment SERS Coupled With Deep Learning Enable Ultrasensitive and In Situ Identification of Microplastics in Natural Waters

ABSTRACT Accurate detection of trace‐level microplastics in natural waters remains challenging due to inefficient particle enrichment, unstable particle‐substrate interactions, inhomogeneous hotspot distributions, and spectral similarity among polymers. Here, we develop a membrane‐confined SERS platform integrated with machine‐learning‐assisted spectral analysis for sensitive and reproducible microplastic detection. In this platform, PSS‐Na‐modified Au@Ag nanocubes are employed as plasmonic building blocks to construct a charge‐regulated, vertically stacked hotspot architecture on a cellulose acetate membrane. By improving microplastic enrichment, hotspot accessibility, and interlayer electromagnetic coupling, the membrane‐guided configuration enables reproducible detection of polystyrene down to 50 ng mL ^{− 1} . It also delivers highly reproducible signals in microplastic spike samples prepared in six environmental water matrices without chemical pretreatment. To resolve spectral similarity among polymers, a multi‐polymer SERS library covering PS, PMMA, PVC, and PC was integrated with an attention‐based 1D‐CNN, enabling reliable polymer identification and semi‐quantitative analysis of binary and ternary mixtures (R ² > 0.83). This membrane‐confined plasmonic platform, together with data‐driven spectral analysis, provides a robust route for intelligent microplastic monitoring in complex aquatic environments.