# Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams

https://mdr.nims.go.jp/datasets/396820f6-17d2-42a1-8d0c-995840d61dbf

## File

- [s41467-024-48148-w (1).pdf](https://mdr.nims.go.jp/filesets/e1178c27-ceeb-4f6c-bc4e-a1a0bbe69125/download) ([Detail](https://mdr.nims.go.jp/filesets/e1178c27-ceeb-4f6c-bc4e-a1a0bbe69125.md))

## Id

396820f6-17d2-42a1-8d0c-995840d61dbf

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-08-09T07:31:49.527920Z

## Updated at

2024-08-20T03:30:25.842472Z

## Published at

2024-08-20T03:30:25.912579Z

## Doi



## First published url

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

## Date published

2024-05-28

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Pretreatment-free SERS sensing of microplastics using a self-attention-based
    neural network on hierarchically porous Ag foams
  title_type: original
  lang: en

## Description

- description: '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.'
  description_type: abstract
  lang: en

## Creator

- name: Olga Guselnikova
  role: author
  orcid: https://orcid.org/0000-0002-2594-9605
- name: Andrii Trelin
  role: author
- name: Yunqing Kang
  role: author
  organization: National Institute for Materials Science
- name: Pavel Postnikov
  role: author
- name: Makoto Kobashi
  role: author
- name: Asuka Suzuki
  role: author
- name: Lok Kumar Shrestha
  role: author
  orcid: https://orcid.org/0000-0003-2680-6291
  organization: National Institute for Materials Science
- name: Joel Henzie
  role: author
  orcid: https://orcid.org/0000-0002-9190-2645
  organization: National Institute for Materials Science
- name: Yusuke Yamauchi
  role: author
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

## Managing organization



## Keyword

- subject: SERS
  schema: not_defined
- subject: Microplastics
  schema: not_defined
- subject: Plasmonics
  schema: not_defined
- subject: Machine Learning
  schema: not_defined
- subject: Self-attention-based neural networks
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Nature Communications
  issn: '20411723'
  volume: '15'
  article_number: '4351'

## Conference



## Related item



## Funding

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

## Instrument



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## Chemical composition



## Structure for specimen



## Structural feature for specimen



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## Process for specimen treatment



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## Fileset

- id: e1178c27-ceeb-4f6c-bc4e-a1a0bbe69125
  filename: s41467-024-48148-w (1).pdf
  content_type: application/pdf
  size: 3111844
  md5: 76ac2cb0627f983bd2906ca0a970b655

## Thumbnail

fileset_id: e1178c27-ceeb-4f6c-bc4e-a1a0bbe69125
filename: s41467-024-48148-w (1).pdf