Machine learning-assisted etched silver SERS sensors for trace-level detection of micron-scale PET in water

SERS of Bottled Water: Automated PET Nanoplastics Detection and Quantification

A Novel Approach for Fast Microplastic Quantification in Sediments Using Machine Learning—Spectrometer Combinations

AI summary · Read the abstract

Researchers combined visible near-infrared and FTIR spectroscopy with machine learning models (SVR, PLSR, BPNN) to rapidly estimate concentrations of PE, PP, PS, and PVC microplastics in river and loess sediments without extraction or manual counting. Faster, non-destructive quantification methods are crucial for scaling up microplastic monitoring across diverse environmental matrices and enabling consistent cross-study comparisons of pollution levels.

Deep Learning Approaches for Microplastic Identification in Microscopic Water Samples

MicroDetect-Net (MDN): Leveraging Deep Learning to Detect Microplastics in Clam Blood, a Step Towards Human Blood Analysis

AI summary · Read the abstract

Researchers developed MicroDetect-Net, a deep learning model trained to detect microplastics in clam blood, demonstrating a pathway toward automated detection in human blood samples as plastic production exceeds 368 million tons annually and microplastics are found across all environmental and biological matrices.

Discrete sensors to detect microplastics in environment

AI summary · Read the abstract

This review surveys discrete sensor technologies — including optical, resonance, autonomous, and biosensors — developed for in situ detection of microplastics in environmental samples, highlighting the challenges posed by particle size, transparency, and varying surface structures. Rapid, field-deployable microplastic detection tools are essential for real-time environmental monitoring, which is a prerequisite for understanding pollution hotspots and assessing human exposure risks.