Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22310%2F24%3A43929647" target="_blank" >RIV/60461373:22310/24:43929647 - isvavai.cz</a>
Result on the web
<a href="https://www.nature.com/articles/s41467-024-48148-w" target="_blank" >https://www.nature.com/articles/s41467-024-48148-w</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41467-024-48148-w" target="_blank" >10.1038/s41467-024-48148-w</a>
Alternative languages
Result language
angličtina
Original language name
Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams
Original language 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. Detection and identification of microplastics (MPs) in environmental samples is hampered by the need for isolation and pretreatment methods. Here, the authors combine porous Ag substrates with self-attention neural networks to directly identify six types of MPs in environmental samples.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20501 - Materials engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Nature Communications
ISSN
2041-1723
e-ISSN
2041-1723
Volume of the periodical
15
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
15
Pages from-to
"4351/1"-15
UT code for WoS article
001234660500004
EID of the result in the Scopus database
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