Over time, the cumulative impact of microplastic pollution on marine biota has resulted in significant health threats, posing a serious risk to the entire ecosystem 2. ![]() Microplastic pollution has become a global concern, and it is estimated that there are approximately 24.4 trillion pieces of microplastics in the upper ocean, emphasizing the extensive presence of this pollutant in marine environments 1. ![]() Overall, this proposed approach facilitates efficient sampling and identification of small-sized microplastics, potentially contributing to crucial long-term monitoring and treatment efforts. Furthermore, we demonstrate that miniaturized devices can effectively trap and identify microplastics smaller than 50 µm. 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%. ![]() 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. 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. The substantial variations in their physical and chemical properties pose a significant challenge when it comes to sampling and characterizing small-sized microplastics. Marine microplastics are emerging as a growing environmental concern due to their potential harm to marine biota.
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