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.

由于海洋微塑料对海洋生物群的潜在危害，它们正成为日益严重的环境问题。当对小尺寸微塑料进行采样和表征时，其物理和化学性质的巨大差异构成了重大挑战。在这项研究中，我们引入了一种新颖的微流体方法，该方法简化了表层海水中微塑料的捕获和识别过程，从而无需进行标记。我们检查了各种模型，包括支持向量机、随机森林、卷积神经网络 (CNN) 和残差神经网络 (ResNet34)，以评估它们在识别 11 种常见塑料方面的性能。我们的研究结果表明，CNN 方法优于其他模型，准确率高达 93%，平均曲线下面积为 98 ± 0.02%。此外，我们证明小型化设备可以有效捕获和识别小于 50 µm 的微塑料。总体而言，这种提出的方​​法有助于对小尺寸微塑料进行有效采样和识别，可能有助于关键的长期监测和处理工作。