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Deep Learning-Powered Dark-Field Microscopy for Simultaneous Size and Concentration Analysis of Nanoplastics in Water
Summary
Researchers developed a convolutional neural network (CNN)-powered dark-field microscopy approach for simultaneously measuring the size and concentration of nanoplastics in water samples. The system achieved accurate size and count analysis for particles as small as tens of nanometers, demonstrating that AI-enhanced dark-field microscopy can overcome the detection limits of conventional light microscopy for nanoplastic quantification.
Nanoplastics have become a significant environmental and health concern due to their widespread presence. Accurately analyzing both size and concentration of nanoplastics is essential for assessing their environmental behavior and potential toxicity; however, this remains a significant challenge. In this study, we developed a novel approach of convolutional neural networks (CNNs) powered dark-field microscopy (DFM) to achieve concurrent size and concentration analysis of nanoplastics. DFM images of polystyrene nanoplastics (PSNPs) down to 150 nm were facilely acquired based on their scattering characteristics, which were subsequently extracted and studied by combining contour recognition algorithms with a streamlined VGGNet. The established approach achieves high accuracy (over 0.99 on test sets) and sensitivity (limit of detection: 1.7 ng mL-1) in identifying PSNPs ranging from 150 to 600 nm. Spiked recovery results yield 93.55-103.8% recovery rates across 200 to 400 nm PSNPs, demonstrating the ability of the developed method to simultaneously determine size and concentration of nanoplastics. Therefore, the proposed strategy can offer a reliable and visual alternative for nanoplastics analysis with potential applications in environmental and biological monitoring.