Nuanfei Zhu, Yixing Tian, Sinuo Tao, Ze Qiao, Zhugen Yang, Ligang Hu, Jingfu Liu and Zhen Zhang*,
{"title":"饮用水铬形态监测平台：机器学习辅助双发射荧光传感器阵列","authors":"Nuanfei Zhu, Yixing Tian, Sinuo Tao, Ze Qiao, Zhugen Yang, Ligang Hu, Jingfu Liu and Zhen Zhang*, ","doi":"10.1021/acs.estlett.5c00506","DOIUrl":null,"url":null,"abstract":"<p >Different 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.</p>","PeriodicalId":37,"journal":{"name":"Environmental Science & Technology Letters Environ.","volume":"12 7","pages":"792–798"},"PeriodicalIF":8.8000,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Chromium Speciation Monitoring Platform for Drinking Water: Machine Learning-Assisted Dual-Emission Fluorescence Sensor Array\",\"authors\":\"Nuanfei Zhu, Yixing Tian, Sinuo Tao, Ze Qiao, Zhugen Yang, Ligang Hu, Jingfu Liu and Zhen Zhang*, \",\"doi\":\"10.1021/acs.estlett.5c00506\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >Different 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.</p>\",\"PeriodicalId\":37,\"journal\":{\"name\":\"Environmental Science & Technology Letters Environ.\",\"volume\":\"12 7\",\"pages\":\"792–798\"},\"PeriodicalIF\":8.8000,\"publicationDate\":\"2025-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Science & Technology Letters Environ.\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acs.estlett.5c00506\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ENVIRONMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Science & Technology Letters Environ.","FirstCategoryId":"1","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.estlett.5c00506","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
Different 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.
期刊介绍：
Environmental Science & Technology Letters serves as an international forum for brief communications on experimental or theoretical results of exceptional timeliness in all aspects of environmental science, both pure and applied. Published as soon as accepted, these communications are summarized in monthly issues. Additionally, the journal features short reviews on emerging topics in environmental science and technology.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
