Detection of Polystyrene Microplastics up to the Single Nanoparticle Limit Using SERS and Advanced ANN Design (KANformer)
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22310%2F25%3A43931841" target="_blank" >RIV/60461373:22310/25:43931841 - isvavai.cz</a>
Result on the web
<a href="https://pubs.acs.org/doi/10.1021/acssensors.5c00846" target="_blank" >https://pubs.acs.org/doi/10.1021/acssensors.5c00846</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1021/acssensors.5c00846" target="_blank" >10.1021/acssensors.5c00846</a>
Alternative languages
Result language
angličtina
Original language name
Detection of Polystyrene Microplastics up to the Single Nanoparticle Limit Using SERS and Advanced ANN Design (KANformer)
Original language description
Due to uncontrolled release, gradual accumulation, low degradation rate, and potential negative impact on human health, microplastics (MPs) pose a serious environmental and healthcare risk. Thus, the spread of MPs should be at least carefully monitored to identify and eliminate their main sources, as well as to provide a suitable alarm in the case of MP concentration increase. Among various detection methods, surface-enhanced Raman spectroscopy (SERS) poses a unique detection limit and the ability to perform outdoor measurements without preliminary sample treatment. However, the utilization of SERS for MPs detection is significantly limited for a few reasons. First, the maximal SERS enhancement occurs in the so-called hot spots, where the MPs cannot penetrate due to their size. In addition, the natural environment can produce a significant spectral background, which blocks the microplastic characteristic signal. To overcome these limitations, we propose a new alternative route for introduction of MPs into the plasmonic hot spots, using in situ MP annealing and an advanced artificial neural network (ANN) design, the Kolmogorov-Arnold transformer (KANformer, KANF). Polystyrene (PS) MPs were used as a model compound. We have also demonstrated the potential versatility of our approach using different microplastics, such as polyethylene, polypropylene, and polyethylene terephthalate. The proposed approach allows us to detect the presence of PS up to the single nanoparticle limit (in the mL of analyzed solution) with a probability of above 95%, even under mixing with groundwater model matrices. © 2025 The Authors. Published by American Chemical Society.
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
10406 - Analytical chemistry
Result continuities
Project
<a href="/en/project/EH22_008%2F0004634" target="_blank" >EH22_008/0004634: Mechanical engineering of biological and bio-inspired systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ACS Sensors
ISSN
2379-3694
e-ISSN
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Volume of the periodical
10
Issue of the periodical within the volume
7
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
4983-4995
UT code for WoS article
001516860100001
EID of the result in the Scopus database
2-s2.0-105009001155