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In Situ Construction of Flexible Particle-in-Cavity Structured Film for Recyclable SERS Detection of Trace Multimicroplastics
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Researchers built a flexible SERS film with silver nanoparticles embedded in-situ within PDMS cavities, combining surface and volume hot spots to simultaneously detect polystyrene, polypropylene, and polyethylene microplastics in seawater and tap water at sub-microgram-per-milliliter limits, with the substrate remaining recyclable across five cleaning cycles and machine learning enabling quantification of multi-polymer mixtures.
Surface-enhanced Raman spectroscopy (SERS) is a promising method for identifying microplastics (MPs). Nonetheless, traditional solid SERS substrate-based detection often struggles with individual MPs, making it particularly difficult to detect multiple MPs simultaneously and in a recyclable manner. To achieve ultrasensitive, multiplex, and easily recyclable SERS detection of MPs in environmental water samples, an in situ construction method was developed to create a particle-in-cavity (PIC) structure. This involved the simultaneous formation of PDMS cavities and the in situ embedding of Ag NPs into these cavities. The PIC structure not only successfully combined "surface hot spots" and "volume hot spots" but also enhanced SERS uniformity and resistance to ultrasonication due to the in situ embedding strategy. The flexible PIC-structured film allowed for the simultaneous SERS detection of three MPs (polystyrene, polypropylene, and polyethylene) in both seawater and tap water, with detection limits (LODs) of 0.1, 0.5, and 0.5 μg/mL, respectively. Recyclable SERS detection of the MPs for five recycles was easily accomplished through ultrasonication cleaning with xylene. Utilizing the SERS spectra of the three MPs, machine learning algorithms enabled precise quantification of the MPs in environmental water. Visual identification was conducted using Raman mapping for the mixture of the three MPs. This detection method, which integrates the unique PIC structure and machine learning, paves the way for future advancements in ultrasensitive and easily recyclable SERS detection for environmental monitoring.
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