Machine learning-driven sensor array based on luminescent metal–organic frameworks for simultaneous discrimination of multiple anions

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dc.contributor.authorWei, Dali
dc.contributor.authorXu, Cheng
dc.contributor.authorWang, Ying
dc.contributor.authorFeng, Weiwei
dc.contributor.authorDeng, Chunmeng
dc.contributor.authorWu, Xiangyang
dc.contributor.authorDeng, Yibin
dc.contributor.authorYang, Zhugen
dc.contributor.authorZhang, Zhen
dc.date.accessioned2025-05-07T14:38:20Z
dc.date.available2025-05-07T14:38:20Z
dc.date.freetoread2025-05-07
dc.date.issued2025-05-15
dc.date.pubOnline2025-04-19
dc.description.abstractDue to the high correlation of anions in waters to environmental quality and human health, thus there is urgent need for developing simple and effective sensors to discriminate multiple anions. Herein, a machine learning-assisted fluorescent sensor array based on two luminescent metal–organic frameworks (LMOFs, UiO-66-NH2 and UiO-66-OH) was developed for simultaneous discrimination of five anions (F−, PO43−, ClO44−, NO3−, and SO42−). Wherein, UiO-66-NH2 and UiO-66-OH were designed by anchoring 2,5-diaminoterephthalic acid and 2,5-dihydroxyterephthalic acid on UiO-66, respectively, which exhibited blue and green fluorescence emission, possessing good fluorescence property. Interestingly, the anions could effectively enhance the fluorescence intensity of UiO-66-NH2 and UiO-66-OH to generate diverse fluorescence responses and unique fingerprints, which could be utilized to develop a fluorescence sensor array for the rapid identification of five anions. Under the optimized conditions, the proposed sensor array showed good performance for identifying multiple anions and their mixtures with satisfactory sensitivity. More importantly, the integration of machine learning algorithm and sensor array has successfully achieved accurate identification and prediction of five anions in real water samples, affirming its practicability in actual samples. Our findings provided a promising tool for detecting multiple anions, and inspired potentials of the combination of sensor arrays and machine learning algorithm for pollution control in real waters.
dc.description.journalNameChemical Engineering Journal
dc.description.sponsorshipThis work was supported by the National Natural Science Foundation of China (Grants No. 22176075, 22406068), Natural Science Foundation of Jiangsu Province (BK20240884).
dc.identifier.citationWei D, Xu C, Wang Y, et al., (2025) Machine learning-driven sensor array based on luminescent metal–organic frameworks for simultaneous discrimination of multiple anions. Chemical Engineering Journal, Volume 512, May 2025, Article number 162796
dc.identifier.elementsID672909
dc.identifier.issn1385-8947
dc.identifier.urihttps://doi.org/10.1016/j.cej.2025.162796
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23865
dc.identifier.volumeNo512
dc.languageEnglish
dc.language.isoen
dc.publisherElsevier
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S1385894725036228
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4004 Chemical Engineering
dc.subject40 Engineering
dc.subject4016 Materials Engineering
dc.subject4011 Environmental Engineering
dc.subjectBioengineering
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectChemical Engineering
dc.subject4004 Chemical engineering
dc.subject4011 Environmental engineering
dc.subject4016 Materials engineering
dc.subjectAnions detection
dc.subjectSensor array
dc.subjectMetal–organic frameworks
dc.subjectEnvironmental monitoring
dc.titleMachine learning-driven sensor array based on luminescent metal–organic frameworks for simultaneous discrimination of multiple anions
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2025-04-17

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