Machine-Learning-Assisted High-Throughput computational screening of Metal–Organic framework membranes for hydrogen separation-Reference-Cited by-同舟云学术

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Machine-Learning-Assisted High-Throughput computational screening of Metal–Organic framework membranes for hydrogen separation

Published:2022-10 Issue: Volume:446 Page:136783
ISSN:1385-8947
Container-title:Chemical Engineering Journal
language:en
Short-container-title:Chemical Engineering Journal

Author:

Bai Xiangning,Shi Zenan,Xia Huan,Li Shuhua,Liu Zili,Liang Hong,Liu Zhiting,Wang Bangfen,Qiao Zhiwei

Funder

Pearl River S and T Nova Program of Guangzhou

Guangzhou Municipal Science and Technology Project

Guangdong Provincial Pearl River Talents Program

Natural Science Foundation of Guangdong Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,Environmental Chemistry,General Chemistry

Reference48 articles.

1. Hydrogen storage in metal-organic frameworks;Suh;Chem. Rev.,2012

2. Experimental analysis of membrane and pressure swing adsorption (PSA) for the hydrogen separation from natural gas;Liemberger;J. Cleaner Prod.,2017

3. Simulation studies of the characteristics of a cryogenic distillation column for hydrogen isotope separation;Bhattacharyya;Int. J. Hydrogen Energy,2016

4. Nanoporous film-mediated growth of ultrathin and continuous metal–organic framework membranes for high-performance hydrogen separation;Zhang;J. Mater. Chem. A,2017

5. High-throughput computational prediction of the cost of carbon capture using mixed matrix membranes;Budhathoki;Energy Environ. Sci.,2019

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