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

SUBMITTER: Gong L 

PROVIDER: S-EPMC10329028 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature