A microfluidic approach for label-free identification of small-sized microplastics in seawater

Abstract

Marine microplastics are emerging as a growing environmental concern due to their potential harm to marine biota. The substantial variations in their physical and chemical properties pose a significant challenge when it comes to sampling and characterizing small-sized microplastics. In this study, we introduce a novel microfluidic approach that simplifies the trapping and identification process of microplastics in surface seawater, eliminating the need for labeling. We examine various models, including support vector machine, random forest, convolutional neural network (CNN), and residual neural network (ResNet34), to assess their performance in identifying 11 common plastics. Our findings reveal that the CNN method outperforms the other models, achieving an impressive accuracy of 93% and a mean area under the curve of 98 ± 0.02%. Furthermore, we demonstrate that miniaturized devices can effectively trap and identify microplastics smaller than 50 µm. Overall, this proposed approach facilitates efficient sampling and identification of small-sized microplastics, potentially contributing to crucial long-term monitoring and treatment efforts.

Simple microfluidic devices for in situ detection of water contamination: a state-of-art review

“…The microplastics are counted and detected with a fluorescence microscope, and the results are then processed with a software video (Figure 5B). In addition (Gong et al, 2023), developed a model that combined a microfluidic chip and machine learning to identify tinysized (smaller than 50 µm) microplastics in seawater. The chip made from PDMS was used to trap a tiny pristine microplastic particle to overcome the frequent overlapping of peaks encountered in direct analysis using Raman spectroscopy (Figure 5C).…”

“…Reproduced with permission from Elsevier. (C) A PDMS microfluidic device featuring sieve-like structures designed specifically for on-site, label-free identification of small-sized microplastics in seawater (Gong et al, 2023). Copyright 2023, with permission from Springer Nature.…”