A surface enhanced Raman scattering-machine learning platform for monitoring anionic pollutants in water and human urine: Toward environmental and public health applications
Approved
Classifications
MinEdu publication type
A1 Journal article (peer-reviewed)
Definition
Article
Target group
Scientific
Peer reviewed
Peer-reviewed
Article type
Journal article
Host publication type
Journal
Publication channel information
Title of journal/series
Journal of environmental chemical engineering
ISSN (print)
2213-2929
ISSN (electronic)
2213-3437
ISSN (linking)
2213-2929
Publisher
Elsevier
Publication forum ID
80106
Publication forum level
1
Internationality
Yes
Detailed publication information
Publication year
2025
Bibliographical publication year
2025
Reporting year
2025
Journal/series volume number
13
Journal/series issue number
5
Article number
119037
DOI
10.1016/j.jece.2025.119037
Language of publication
English
Co-publication information
International co-publication
Yes
Co-publication with a company
No
Availability
Link to online publication
Classification and additional information
MinEdu field of science classification
217 Medical engineering
Keywords
Anion detection; Fluoride monitoring; Surface-enhanced Raman scattering (SERS); Machine learning; Water pollution
Funding information
Funding information in the publication
This work was supported by the National Key R&D Program of China (2022YFC2503000) , the National Natural Science Foundation for Youth (Nos. 82202648, 82404272) , the Introduce High-Level Talent Incentive Project (No. 0103-31021200052) , the HMU Marshal Initiative Funding Project (HMUMIF-21012) , the Open Fund Project of Key Laboratory of Forest Plant Ecology, Ministry of Education KLP2024B4.
Research data information
Research data information in the publication
Data will be made available on request.
Source database ID
WoS ID
WOS:001568649900005
Scopus ID
2-s2.0-105022628400