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Trapping tiny pollutants: SERS-driven strategies for microplastics and nanoplastics detection
Summary
This review explores how surface-enhanced Raman spectroscopy (SERS) is being developed as a highly sensitive tool for detecting and identifying micro- and nanoplastics in environmental and biological samples. Researchers highlight recent advances in sensor design, the integration of machine learning for improved accuracy, and the technique's potential for real-world monitoring. The study also identifies key challenges, including signal variability and the lack of standardized methods, that need to be resolved for broader adoption.
Microplastics and nanoplastics are almost everywhere in biological and environmental systems, posing serious risks to human health and ecology. However, due to their complex matrices, varied sizes, and morphologies, their detection and quantification remain challenging. Particularly, Raman and surface-enhanced Raman spectroscopy (SERS) hold great promise for the detection, characterization, and quantification of micro/nanoplastics. In this review, we introduce the Raman and SERS fundamental principles, instrumentation, and SERS substrate design strategies. Particularly, emphasis is placed on SERS-enabled ultrasensitive detection, integration with chemometrics and machine learning tools, culminating in the real-world applicability. Additionally, we elaborate on the current limitations, including signal variability, lack of standardization, and sample preparation challenges. Finally, future directions involving artificial intelligence (AI) integration, substrate engineering, and multi-modal analytical approaches are discussed.