Guselnikova Olga, Trelin Andrii, Kang Yunqing, Postnikov Pavel, Kobashi Makoto, Suzuki Asuka, Shrestha Lok Kumar, Henzie Joel, Yamauchi Yusuke
National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan.
Research School of Chemistry and Applied Biomedical Sciences, Tomsk Polytechnic University, Tomsk, Russian Federation.
Nat Commun. 2024 May 28;15(1):4351. doi: 10.1038/s41467-024-48148-w.
Low-cost detection systems are needed for the identification of microplastics (MPs) in environmental samples. However, their rapid identification is hindered by the need for complex isolation and pre-treatment methods. This study describes a comprehensive sensing platform to identify MPs in environmental samples without requiring independent separation or pre-treatment protocols. It leverages the physicochemical properties of macroporous-mesoporous silver (Ag) substrates templated with self-assembled polymeric micelles to concurrently separate and analyze multiple MP targets using surface-enhanced Raman spectroscopy (SERS). The hydrophobic layer on Ag aids in stabilizing the nanostructures in the environment and mitigates biofouling. To monitor complex samples with multiple MPs and to demultiplex numerous overlapping patterns, we develop a neural network (NN) algorithm called SpecATNet that employs a self-attention mechanism to resolve the complex dependencies and patterns in SERS data to identify six common types of MPs: polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon, and polyethylene terephthalate. SpecATNet uses multi-label classification to analyze multi-component mixtures even in the presence of various interference agents. The combination of macroporous-mesoporous Ag substrates and self-attention-based NN technology holds potential to enable field monitoring of MPs by generating rich datasets that machines can interpret and analyze.
环境样品中微塑料（MPs）的识别需要低成本检测系统。然而，复杂的分离和预处理方法阻碍了它们的快速识别。本研究描述了一种无需独立分离或预处理方案即可识别环境样品中MPs的综合传感平台。它利用以自组装聚合物胶束为模板的大孔-介孔银（Ag）基底的物理化学性质，通过表面增强拉曼光谱（SERS）同时分离和分析多个MP目标。Ag上的疏水层有助于稳定环境中的纳米结构并减轻生物污染。为了监测含有多种MPs的复杂样品并解复用众多重叠模式，我们开发了一种名为SpecATNet的神经网络（NN）算法，该算法采用自注意力机制来解析SERS数据中的复杂依赖性和模式，以识别六种常见类型的MPs：聚苯乙烯、聚乙烯、聚甲基丙烯酸甲酯、聚四氟乙烯、尼龙和聚对苯二甲酸乙二酯。SpecATNet使用多标签分类来分析多组分混合物，即使存在各种干扰剂。大孔-介孔Ag基底和基于自注意力的NN技术的结合有潜力通过生成机器可以解释和分析的丰富数据集来实现MPs的现场监测。
