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Analyzing the anodic stripping square wave voltammetry of heavy metal ions via machine learning: Information beyond a single voltammetric peak

文献类型：期刊论文

作者Ye, Jia-Jia1,2; Lin, Chu-Hong1; Huang, Xing-Jiu1
刊名JOURNAL OF ELECTROANALYTICAL CHEMISTRY
出版日期2020-09-01
卷号872
关键词Square wave voltammetry Heavy metal ion Machine learning Principal component analysis Classification
ISSN号1572-6657
DOI10.1016/j.jelechem.2020.113934
通讯作者Lin, Chu-Hong(chlin@iim.ac.cn) ; Huang, Xing-Jiu(xingjiuhuang@iim.ac.cn)
英文摘要The application of electrochemical analysis for the detection heavy metal ions (HMIs) faces tremendous challenges due to its poor reproducibility and selectivity. It is necessary to develop "fingerprint-type" electroanalytical approaches for the identification of the analyte. Herein, combining principal component analysis with support vector machine classifier, we classified successfully the electroanalytical signals of six heavy metals merely based on their voltammetric peak shapes, where the absolute values of the current and potential were not taken into consideration. The variation of the measurement parameter such as the scanning frequency did not affect much on the identification of the analyte. It was found that the addition of K+ and Cl- had little influence on the identification result; while altering the electrolyte concentration, it was difficult to identify accurately the analyte, showing that the influence of the electrolyte cannot be ignored even at high concentrations. The combination of electrochemistry and machine learning is expected to improve the selectivity for the detection of HMIs in complex water environments.
WOS关键词IDENTIFICATION ; CHEMOMETRICS ; INTERFERENCE
资助项目National Natural Science Foundation of China[21802145] ; National Natural Science Foundation of China[21735005] ; One Hundred Person Project ; CAS Interdisciplinary Innovation Team, Chinese Academy of Sciences, China
WOS研究方向Chemistry ; Electrochemistry
语种英语
WOS记录号WOS:000575156200018
出版者ELSEVIER SCIENCE SA
资助机构National Natural Science Foundation of China ; One Hundred Person Project ; CAS Interdisciplinary Innovation Team, Chinese Academy of Sciences, China
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/104422]  
专题中国科学院合肥物质科学研究院
通讯作者Lin, Chu-Hong; Huang, Xing-Jiu
作者单位1.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China
2.Univ Sci & Technol China, Dept Mat Sci & Engn, Hefei 230026, Peoples R China
推荐引用方式
GB/T 7714		 Ye, Jia-Jia,Lin, Chu-Hong,Huang, Xing-Jiu. Analyzing the anodic stripping square wave voltammetry of heavy metal ions via machine learning: Information beyond a single voltammetric peak[J]. JOURNAL OF ELECTROANALYTICAL CHEMISTRY,2020,872.
APA Ye, Jia-Jia,Lin, Chu-Hong,&Huang, Xing-Jiu.(2020).Analyzing the anodic stripping square wave voltammetry of heavy metal ions via machine learning: Information beyond a single voltammetric peak.JOURNAL OF ELECTROANALYTICAL CHEMISTRY,872.
MLA Ye, Jia-Jia,et al."Analyzing the anodic stripping square wave voltammetry of heavy metal ions via machine learning: Information beyond a single voltammetric peak".JOURNAL OF ELECTROANALYTICAL CHEMISTRY 872(2020).

入库方式： OAI收割

来源：合肥物质科学研究院

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