Single shot multiplex SRS: from microplastic detection to stain free tissue segmentation

Raman Spectroscopy Based Approaches for Microplastics Investigations

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Researchers reviewed Raman spectroscopy-based approaches — including confocal Raman microscopy, surface-enhanced Raman scattering, and automated imaging — for identifying and characterizing microplastics in environmental samples, highlighting their superior chemical specificity compared to FTIR for sub-10-μm particles. Accurate chemical identification of microplastics at the nanoscale is foundational for both environmental monitoring and toxicological research, as particle composition (polymer type and additives) strongly influences biological harm.

Rapid and low-cost detection platform for micro- and nanoplastics

Techniques for Sampling, Detection, and Characterization of Microplastics in Water, Sediment, and Tissue Samples

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Researchers reviewed the sampling, detection, and characterization methods used to identify microplastics in water, sediment, and biological tissue samples. Standardized, reliable techniques are critical for generating comparable data across studies, which is necessary to accurately assess the true extent of microplastic contamination in ecosystems and living organisms.

Detection and characterization of microplastics and nanoplastics in biological samples

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Researchers reviewed current methods for detecting and characterizing microplastics and nanoplastics in biological samples, finding that most techniques are optimized for environmental media like water and perform poorly in complex biological matrices, and recommending improved workflows for digestion, separation, enrichment, and analysis of particles in organisms and human tissues.

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.