Photodegradation Kinetics and Deep Learning-Based Intelligent Colorimetric Method for Bioavailability-Based Dissolved Iron Speciation-Reference-Cited by-同舟云学术

Photodegradation Kinetics and Deep Learning-Based Intelligent Colorimetric Method for Bioavailability-Based Dissolved Iron Speciation

Published:2022-10-14 Issue:42 Volume:94 Page:14801-14809
ISSN:0003-2700
Container-title:Analytical Chemistry
language:en
Short-container-title:Anal. Chem.

Author:

Luo Jiayi¹,Huang Zhaojing¹,Li Shunxing¹²,Zheng Fengying¹²,Liu Fengjiao¹²,Huang Qianyan¹,Huang Xuguang¹²^ORCID,Xie Haijiao³

Affiliation:

1. College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China

2. Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 3630003, China

3. Hangzhou Yanqu Information Technology Company of Limited Liability, Hangzhou 310003, China

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Analytical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.2c04014

Reference34 articles.

1. Influence of pH and Dissolved Organic Matter on Iron Speciation and Apparent Iron Solubility in the Peruvian Shelf and Slope Region

2. Northern High-Latitude Organic Soils As a Vital Source of River-Borne Dissolved Iron to the Ocean

3. Influence of pH and Dissolved Organic Matter on Iron Speciation and Apparent Iron Solubility in the Peruvian Shelf and Slope Region

4. Natural organic matter controls metal speciation and toxicity for marine organisms: a review

5. SEAM is a spatial single nuclear metabolomics method for dissecting tissue microenvironment

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