Machine Learning Algorithms for Intelligent Decision Recognition and Quantification of Cr(III) in Chromium Speciation-Reference-Cited by-同舟云学术

Machine Learning Algorithms for Intelligent Decision Recognition and Quantification of Cr(III) in Chromium Speciation

Published:2023-12-08 Issue:50 Volume:95 Page:18635-18643
ISSN:0003-2700
Container-title:Analytical Chemistry
language:en
Short-container-title:Anal. Chem.

Author:

Lu Yunfei¹²,Li Xin¹^ORCID,Yu Long¹^ORCID,Zhang Songlin¹,Wang Degui¹,Hao Xiangyang²,Sun Mingtai¹^ORCID,Wang Suhua¹^ORCID

Affiliation:

1. School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China

2. Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes, School of Material Sciences and Technology, China University of Geosciences, Beijing 100083, China

Funder

Basic and Applied Basic Research Foundation of Guangdong Province

National Natural Science Foundation of China

Guangdong University of Petrochemical Technology

Maoming Science and Technology Project

Publisher

American Chemical Society (ACS)

Subject

Analytical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.3c04878

Reference24 articles.

1. Speciation of Cr(III) and Cr(VI) in water after preconcentration of its 1,5-diphenylcarbazone complex on amberlite XAD-16 resin and determination by FAAS

2. Covalently dual functionalized graphene oxide-based multiplex electrochemical sensor for Hg(II) and Cr(VI) detection

3. Separation of microgram quantities of Cr(III) and Cr(VI) in aqueous solutions and determination by energy dispersive X-ray fluorescence spectrometry

4. Cr speciation in water samples by dispersive liquid–liquid microextraction combined with total reflection X-ray fluorescence spectrometry

5. Selective and sensitive detection of Cr(VI) pollution in waste water via polyaniline/sulfated zirconium dioxide/multi walled carbon nanotubes nanocomposite based electrochemical sensor

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