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Interfacial Regulation–Driven Dual-Enrichment SERS Coupled With Deep Learning Enable Ultrasensitive and In Situ Identification of Microplastics in Natural Waters
Original title: Interfacial Regulation–Driven Dual‐Enrichment SERS Coupled With Deep Learning Enable Ultrasensitive and In Situ Identification of Microplastics in Natural Waters
Summary
Scientists have developed a new sensor technology that can detect tiny microplastic particles in real water samples—like rivers or tap water—at extremely low levels, without needing complex lab processing. By combining specially designed metal nanoparticles with AI-powered pattern recognition, the tool can also tell different types of plastic apart, even when they're mixed together. This matters because better detection tools are a key first step toward understanding how much plastic pollution is actually in our water supplies and food chain, which is important for assessing potential risks to human health.
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.