Reconfigurable Au Nanoparticle Monolayers on Regenerated Cellulose Hydrogels: Highly Sensitive SERS Detection of Polystyrene Micro/Nanoplastics With Interpretable Deep Learning

ABSTRACT Polystyrene micro‐ and nanoplastics (PS MNPs) are ubiquitous in aquatic and terrestrial environments; however, their trace‐level detection in complex matrices remains a considerable challenge. In this study, we report a regenerated cellulose (RC) hydrogel–based surface‐enhanced Raman scattering (SERS) platform that integrates a Marangoni‐transferred gold nanoparticle self‐assembled monolayer (Au‐SAM) for the sensitive detection of MNPs in complex environments. The Au‐SAM/RC substrate delivers a uniform, high‐throughput SERS response, exhibiting an analytical enhancement factor (AEF) of 1.4 × 10 7 and a detection limit of 10 −8 M for crystal violet. Notably, reswelling‐induced rearrangement of the Au‐SAM/RC substrate facilitates gold nanoparticle (AuNP) adsorption onto polystyrene nanoparticles (PSNPs), leading to the formation of dense hotspots, enabling the detection of PSNPs (80, 150, and 850 nm) at concentrations as low as 10 −3 mg/mL with high AEFs. The platform maintains reliable performance in various matrices, including reservoir water, milk, tap water, and simulated seawater. Integration with a Transformer‐based multi‐label classifier further enables accurate identification of multicomponent contaminants, while explainable artificial intelligence (XAI) analyses confirm that the predictions are grounded in chemically meaningful spectral features. Collectively, these results demonstrate that the Marangoni‐driven Au‐SAM/RC XAI–SERS strategy provides a robust, sensitive, and interpretable platform for PS MNP monitoring in complex environmental samples.