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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.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: SERS, Microplastics, Plasmonics, Machine Learning, Self-attention-based neural networks

刊行年月日: 2024-05-28

出版者: Springer Science and Business Media LLC

掲載誌:

• Nature Communications (ISSN: 20411723) vol. 15 4351

研究助成金:

• MEXT | JST | Exploratory Research for Advanced Technology JPMJER2003
• MEXT | Japan Society for the Promotion of Science 20K05453 (Kakeni Grant-in-aid for Scientific Research (C))
• Korea Institute of Industrial Technology JE210028
• MEXT | Advanced Research Infrastructure for Materials and Nanotechnology in Japan (ARIM) JPMXP1224NM5002

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1038/s41467-024-48148-w

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更新時刻: 2024-08-20 12:30:25 +0900

MDRでの公開時刻: 2024-08-20 12:30:25 +0900