Chromium speciation monitoring platform for drinking water: machine learning-assisted dual-emission fluorescence sensor array

Simple item page
dc.contributor.authorZhu, Nuanfei
dc.contributor.authorTian, Yixing
dc.contributor.authorTao, Sinuo
dc.contributor.authorQiao, Ze
dc.contributor.authorYang, Zhugen
dc.contributor.authorHu, Ligang
dc.contributor.authorLiu, Jingfu
dc.contributor.authorZhang, Zhen
dc.date.accessioned2025-07-03T09:12:27Z
dc.date.available2025-07-03T09:12:27Z
dc.date.freetoread2025-07-03
dc.date.issued2025-06-16
dc.date.pubOnline2025-06-16
dc.description.abstractDifferent chromium (Cr) speciation in drinking water shows distinct risk levels to humans, failing to reflect real environmental impacts only by total Cr analysis. Integrated with machine learning, a novel fluorescence sensor array was developed for rapid identification and quantitative detection of Cr speciation without sample pretreatment other than filtration. This system prepared three-component fluorescence hybrid materials (MSN@Zr@Au and MSN@Zr@AgAu) with dual emission wavelengths. The sensing unit with a dual-mode algorithm was specific for Cr speciation and accurately identified chromium speciation among 11 coexisting cations. The algorithm of linear discriminant analysis (LDA) assisting hierarchical cluster analysis (HCA) provided higher selectivity for Cr speciation for real samples. Finally, this method showed good analytical performance ranging from 1 to 60 μM, exhibiting a low detection limit of 1.29 μM. This strategy shows excellent practicability for Cr speciation analysis in drinking and tap water, developing a practical monitoring platform for real water.
dc.description.journalNameEnvironmental Science & Technology Letters
dc.description.sponsorshipThis work was supported by the National Natural Science Foundation of China (Grants No. 22176075 and 21876067), the Natural Science Foundation of Jiangsu Province (No. BK20240887), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (24KJB610002), and the Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment.
dc.format.extentpp. xx-xx
dc.identifier.citationZhu N, Tian Y, Tao S, et al., (2025) Chromium speciation monitoring platform for drinking water: machine learning-assisted dual-emission fluorescence sensor array. Environmental Science & Technology Letters, Available online 16 June 2025en_UK
dc.identifier.eissn2328-8930
dc.identifier.elementsID673750
dc.identifier.issn2328-8930
dc.identifier.issueNoahead-of-print
dc.identifier.urihttps://doi.org/10.1021/acs.estlett.5c00506
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/24136
dc.identifier.volumeNoahead-of-print
dc.languageEnglish
dc.language.isoen
dc.publisherAmerican Chemical Society (ACS)en_UK
dc.publisher.urihttps://pubs.acs.org/doi/10.1021/acs.estlett.5c00506
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4004 Chemical Engineeringen_UK
dc.subject40 Engineeringen_UK
dc.subject41 Environmental Sciencesen_UK
dc.subject4105 Pollution and Contaminationen_UK
dc.subjectMachine Learning and Artificial Intelligenceen_UK
dc.subjectrapid detectionen_UK
dc.subjectfluorescence sensor arrayen_UK
dc.subjectchromium speciation analysisen_UK
dc.subjectenvironmental watersen_UK
dc.titleChromium speciation monitoring platform for drinking water: machine learning-assisted dual-emission fluorescence sensor arrayen_UK
dc.typeArticle
dcterms.dateAccepted2025-06-13

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Chromium_Speciation_Monitoring-2025.pdf
Size:
11.79 MB
Format:
Adobe Portable Document Format
Description:
Accepted version
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Plain Text
Description:
Download

Collections

Staff publications (ES)