Small. 2026 Jun 19:e74231. doi: 10.1002/smll.74231. Online ahead of print.
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 (R2 > 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.
PMID:42318662 | DOI:10.1002/smll.74231