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Machine Learning-Enhanced SERS Sensor Using Microgroove Structures for Enriching and Confining Nanoplastics in Localized 3D Hotspots
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Scientists developed a highly sensitive sensor that can detect tiny plastic particles (nanoplastics) in river water and fish, even at very low, trace amounts. By pairing a specially designed surface that traps these particles with AI that reads their unique light signatures, the tool could help track how much plastic pollution is ending up in our food and water supply—an important step for understanding potential risks to human health.
M for 4-MBA, with excellent spatial uniformity (RSD = 7.55%). Furthermore, the sensor successfully detected NPs of different sizes and types, including polystyrene (PS), poly(methyl methacrylate), and polyethylene terephthalate, with an LOD of 20 ng/mL for 100 nm PS. In practical analysis, the sensor achieved LODs of 360 ng/mL in river water and 2.94 μg/g in the fish matrix for PS. Additionally, ML-assisted surface-enhanced Raman spectroscopy (SERS) analysis using K-nearest neighbor, gradient boosting decision trees, convolutional neural networks (CNN), and Transformer models enabled precise classification; despite being trained on only 800 SERS spectra from deionized water, the CNN achieves 100% accuracy in river water and 99% accuracy in the fish matrix. The strategic harmonization of microgroove-induced confinement enrichment, precise localization, and 3D hotspots within microcavities, combined with small-data set machine learning, provides a field-ready solution for the detection of trace pollutants in real-world scenarios.
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AI summary Read the abstract
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