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Microplastics have emerged as ubiquitous contaminants, attracting increasing global attention. Recent evidence confirms the presence of microplastics in human blood, suggesting their potential to interact with cells and induce adverse physiological reactions in various organs as blood circulates. To quantify the distribution of microplastics and assess their potential effects on human health, the effective separation of microplastics from blood is crucial. However, current methods for separating microplastics from blood are limited in effectiveness and simplicity. This study proposes a microfluidic device that utilizes traveling surface acoustic waves to separate microplastics from blood. While traveling surface acoustic waves have been employed to separate various particles, a systematic study on the separation of microplastics from blood samples has not been previously reported. Specifically, the theoretical values of the acoustic radiation factor for various types of microplastics and blood cells were investigated. The significant differences in resonant frequencies indicated the feasibility of separating microplastics of different sizes and types from blood cells. Experimental validation was performed using a polydimethylsiloxane microfluidic device on a piezoelectric lithium niobate substrate. The device successfully separated 5- and 10-micrometer polystyrene microplastics from blood samples. The effects of power and flow rate on separation efficiency were also systematically investigated. This study provides a novel approach for the effective separation of microplastics from blood, contributing to the assessment of their distribution and potential health impacts. 

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.