Reliable detection and identification of micro and nano plastics via convolutional neural network-driven SERS technology

Component identification for the SERS spectra of microplastics mixture with convolutional neural network

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Researchers developed a convolutional neural network that identified microplastic components in mixed surface-enhanced Raman spectroscopy samples with 99.54% accuracy, outperforming traditional methods without requiring spectral preprocessing.

Deep Learning Approaches for Microplastic Identification in Microscopic Water Samples

Modified hydrogel for high efficiency SERS detection and adsorption removal of microplastics

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Researchers developed a modified hydrogel material capable of both detecting and capturing microplastics using surface-enhanced Raman spectroscopy (SERS). The hydrogel's porous structure allows it to adsorb microplastic particles from water while SERS provides sensitive, rapid identification of plastic types. This dual-function approach offers a promising tool for environmental monitoring and microplastic removal from water systems.

Advanced Microplastic Identification in Marine Environments via Hybrid Deep Learning

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Researchers propose a hybrid deep learning architecture combining 3D convolutional neural networks and Vision Transformers applied to hyperspectral imagery to detect and classify microplastics in turbid marine environments, capturing both local spectral signatures and global contextual patterns that single-model approaches miss.

Laser induced fluorescence and machine learning: a novel approach to microplastic identification

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Researchers combined laser-induced fluorescence with machine learning to identify microplastics in water in real time, achieving 97.6% accuracy in distinguishing plastics from other organic matter and 88.3% accuracy in identifying specific plastic types — a promising step toward automated ocean monitoring.