Iterative Approach of Experiment–Machine Learning for Efficient Optimization of Environmental Catalysts: An Example of NO<sub><i>x</i></sub> Selective Reduction Catalysts-Reference-Cited by-同舟云学术

Iterative Approach of Experiment–Machine Learning for Efficient Optimization of Environmental Catalysts: An Example of NO_x Selective Reduction Catalysts

Published:2023-07-03 Issue:46 Volume:57 Page:18080-18090
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Chen Yulong¹,Feng Jia¹,Wang Xin¹,Zhang Cheng¹,Ke Dongfang¹,Zhu Huiyan¹,Wang Shuai¹^ORCID,Suo Hongri¹^ORCID,Liu Chongxuan¹^ORCID

Affiliation:

1. State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, People’s Republic of China

Funder

Guangdong Science and Technology Department

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.3c00293

Reference35 articles.

1. Unified Metallic Catalyst Aging Strategy and Implications for Water Treatment

2. Heterogeneous Photocatalytic Activation of Persulfate for the Removal of Organic Contaminants in Water: A Critical Review

3. Copper Doping Promotion on Ce/CAC-CNT Catalysts with High Sulfur Dioxide Tolerance for Low-Temperature NH3–SCR

4. Unsupervised discovery of solid-state lithium ion conductors

5. Machine learning in catalysis

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