Machine learning-driven optical microfiltration device for improved nanoplastic sampling and detection in water systems
Document Type
Article
Date of Original Version
8-15-2025
Abstract
The rising presence of nanoplastics in water poses toxicity risks and long-term ecological and health impacts. Detecting nanoplastics remains challenging due to their small size, complex chemistry, and environmental interference. Traditional filtration combined with Raman spectroscopy is time-consuming, labor-intensive, and often lacks accuracy and sensitivity. This study presents an agarose-based microfiltration device integrated with machine learning–assisted Raman analysis for nanoplastic capture and identification. The 1 % agarose microfluidic channel features circular micropost arrays enabling dual filtration: nanoplastics diffuse into the porous matrix, while larger particles (>1000 nm) are blocked by the microposts. Unlike conventional systems, this design achieves both physical separation and preconcentration, enhancing nanoplastic detectability. Upon dehydration, the agarose forms a transparent film, significantly improving Raman compatibility by minimizing background interference. This transformation enables direct Raman analysis of retained nanoparticles with enhanced signal clarity and sensitivity. Using 100-nm polystyrene nanoparticles (PSNPs) as a model, we evaluated device performance in distilled water and seawater across concentrations (6.25–50 µg/mL) and flow rates (2.5–100 µL/min). Maximum capture efficiencies of 80 % (seawater) and 66 % (distilled water) were achieved at 2.5 µL/min. A convolutional neural network (CNN) further enhanced spectral analysis, reducing mapping time by 50 % and enabling PSNP detection in seawater at 6.25 µg/mL. This agarose-based system offers a scalable, cost-effective platform for nanoplastic sampling, demonstrating the potential of combining microfluidics with machine learning–assisted Raman spectroscopy to address critical environmental and public health challenges.
Publication Title, e.g., Journal
Journal of Hazardous Materials
Volume
494
Citation/Publisher Attribution
Gong, Liyuan, Bryan Varela, Erfan Eskandari, Juan Zubieta Lombana, Payel Biswas, Luyao Ma, Irene Andreu, and Yang Lin. "Machine learning-driven optical microfiltration device for improved nanoplastic sampling and detection in water systems." Journal of Hazardous Materials 494, (2025). doi: 10.1016/j.jhazmat.2025.138472.