Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams; Nature Communications; Vol. 15
| Parent link: | Nature Communications.— .— New York: Springer Nature Vol. 15.— 2024.— Article number 4351, 15 p. |
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| Institution som forfatter: | |
| Andre forfattere: | , , , , , , , , |
| Summary: | Title screen 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. Текстовый файл |
| Sprog: | engelsk |
| Udgivet: |
2024
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| Fag: | |
| Online adgang: | https://doi.org/10.1038/s41467-024-48148-w |
| Format: | Electronisk Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=674366 |
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| 200 | 1 | |a Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams |f Olga Guselnikova, Andrii Trelin, Yunqing Kang [et al.] | |
| 203 | |a Текст |c электронный |b визуальный | ||
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| 300 | |a Title screen | ||
| 320 | |a References: 77 tit. | ||
| 330 | |a 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. | ||
| 336 | |a Текстовый файл | ||
| 461 | 1 | |t Nature Communications |c New York |n Springer Nature | |
| 463 | 1 | |t Vol. 15 |v Article number 4351, 15 p. |d 2024 | |
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| 701 | 1 | |a Trelin |b A. |g Andrey | |
| 701 | 0 | |a Kang Yunqing | |
| 701 | 1 | |a Postnikov |b P. S. |c organic chemist |c Associate Professor of Tomsk Polytechnic University, Candidate of chemical sciences |f 1984- |g Pavel Sergeevich |9 15465 | |
| 701 | 0 | |a Kobashi Makoto | |
| 701 | 0 | |a Suzuki Asuka | |
| 701 | 1 | |a Shrestha |b L. K. |g Lok Kumar | |
| 701 | 1 | |a Henzie Joel | |
| 701 | 1 | |a Yamauchi Yusuke | |
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