| Machine Learning: New Ideas and Tools in Environmental Science and Engineering | |
Zhong, Shifa; Zhang, Kai ; Bagheri, Majid; Burken, Joel G.; Gu, April; Li, Baikun; Ma, Xingmao; Marrone, Babetta L.; Ren, Zhiyong Jason; Schrier, Joshua; Shi, Wei; Tan, Haoyue; Wang, Tianbao; Wang, Xu; Wong, Bryan M.; Xiao, Xusheng; Yu, Xiong; Zhu, Jun-Jie; Zhang, Huichun
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| 2021-12-05 | |
| Source Publication | ENVIRONMENTAL SCIENCE & TECHNOLOGY
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| ISSN | 2095-5138 |
| Volume | 8Issue:10Pages:- |
| Abstract | Zeolites, as efficient and stable catalysts, are widely used in the environmental catalysis field. Typically, Cu-SSZ-13 with small-pore structure shows excellent catalytic activity for selective catalytic reduction of NOx with ammonia (NH3-SCR) as well as high hydrothermal stability. This review summarizes major advances in Cu-SSZ-13 applied to the NH3-SCR reaction, including the state of copper species, standard and fast SCR reaction mechanism, hydrothermal deactivation mechanism, poisoning resistance and synthetic methodology. The review gives a valuable summary of new insights into the matching between SCR catalyst design principles and the characteristics of Cu2+-exchanged zeolitic catalysts, highlighting the significant opportunity presented by zeolite-based catalysts. Principles for designing zeolites with excellent NH3-SCR performance and hydrothermal stability are proposed. On the basis of these principles, more hydrothermally stable Cu-AEI and Cu-LTA zeolites are elaborated as well as other alternative zeolites applied to NH3-SCR. Finally, we call attention to the challenges facing Cu-based small-pore zeolites that still need to be addressed. |
| Department | 水污染控制实验室 |
| Keyword | environmental catalysis Cu-SSZ-13 NH3-SCR reaction mechanism small-pore zeolites |
| Document Type | 期刊论文 |
| Identifier | https://ir.rcees.ac.cn/handle/311016/46873 |
| Collection | 水污染控制实验室 |
| Affiliation | 1.Case Western Reserve Univ, Dept Civil & Environm Engn, Cleveland, OH 44106 USA 2.Missouri Univ Sci & Technol, Dept Civil Architectural & Environm Engn, Rolla, MO 65409 USA 3.Cornell Univ, Dept Civil & Environm Engn, Ithaca, NY 14850 USA 4.Univ Connecticut, Dept Civil & Environm Engn, Storrs, CT 06269 USA 5.Texas A&M Univ, Dept Civil & Environm Engn, College Stn, TX 77843 USA 6.[Marrone, Babetta L. 7.Los Alamos Natl Lab, Biosci Div, Los Alamos, NM 87545 USA 8.Princeton Univ, Dept Civil & Environm Engn, Princeton, NJ 08544 USA 9.Fordham Univ, Dept Chem, Bronx, NY 10458 USA |
| Recommended Citation GB/T 7714 | Zhong, Shifa,Zhang, Kai,Bagheri, Majid,et al. Machine Learning: New Ideas and Tools in Environmental Science and Engineering[J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY,2021,8(10):-. |
| APA | Zhong, Shifa.,Zhang, Kai.,Bagheri, Majid.,Burken, Joel G..,Gu, April.,...&Zhang, Huichun.(2021).Machine Learning: New Ideas and Tools in Environmental Science and Engineering.ENVIRONMENTAL SCIENCE & TECHNOLOGY,8(10),-. |
| MLA | Zhong, Shifa,et al."Machine Learning: New Ideas and Tools in Environmental Science and Engineering".ENVIRONMENTAL SCIENCE & TECHNOLOGY 8.10(2021):-. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| Machine Learning New（3640KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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