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

doi:10.1016/j.jelechem.2020.113934

	Analyzing the anodic stripping square wave voltammetry of heavy metal ions via machine learning: Information beyond a single voltammetric peak
	Ye, Jia-Jia 1,2; Lin, Chu-Hong 2; Huang, Xing-Jiu 2
刊名	JOURNAL OF ELECTROANALYTICAL CHEMISTRY
	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
DOI	10.1016/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.
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关键词	IDENTIFICATION ; CHEMOMETRICS ; INTERFERENCE
WOS研究方向	Chemistry ; Electrochemistry
语种	英语
出版者	ELSEVIER SCIENCE SA
WOS记录号	WOS:000575156200018
资助机构	National Natural Science Foundation of China ; One Hundred Person Project ; CAS Interdisciplinary Innovation Team, Chinese Academy of Sciences, China
内容类型	期刊论文
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.Univ Sci & Technol China, Dept Mat Sci & Engn, Hefei 230026, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, 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).