Mapping the Porous and Chemical Structure–Function Relationships of Trace CH<sub>3</sub>I Capture by Metal–Organic Frameworks using Machine Learning-Reference-Cited by-同舟云学术

Mapping the Porous and Chemical Structure–Function Relationships of Trace CH₃I Capture by Metal–Organic Frameworks using Machine Learning

Published:2022-10-05 Issue:41 Volume:14 Page:47209-47221
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Wu Xiaoyu¹²,Che Yu²^ORCID,Chen Linjiang³,Amigues Eric Jean¹,Wang Ruiyao¹,He Jinghui⁴,Dong Huilong⁵^ORCID,Ding Lifeng¹^ORCID

Affiliation:

1. Department of Chemistry, Xi’an Jiaotong-Liverpool University, Suzhou 215123, Jiangsu, P. R. China

2. Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool L69 7ZD, United Kingdom

3. School of Chemistry and School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom

4. College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou 215123, P. R. China

5. School of Materials Engineering, Changshu Institute of Technology, Changshu 215500, Jiangsu, P. R. China

Funder

Xi'an Jiaotong-Liverpool University

Publisher

American Chemical Society (ACS)

Subject

General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.2c10861

Reference63 articles.

1. Nuclear Energy - World Nuclear Association. Nuclear Power Today https://world-nuclear.org/information-library/current-and-future-generation/nuclear-power-in-the-world-today.aspx (accessed Dec 20, 2021).

2. Radioactive Iodine and Krypton Control for Nuclear Fuel Reprocessing Facilities

3. Metal-organic frameworks as a versatile platform for radionuclide management

4. Capture of harmful radioactive contaminants from off-gas stream using porous solid sorbents for clean environment – A review

5. Porous sorbents for the capture of radioactive iodine compounds: a review

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