Prediction of CF4/N2 adsorption and separation performance in organic frameworks based on machine learning algorithms-Reference-Cited by-同舟云学术

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Prediction of CF4/N2 adsorption and separation performance in organic frameworks based on machine learning algorithms

Published:2023-08-16 Issue: Volume: Page:
ISSN:
Container-title:Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)
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Author:

Wang Hao,Peng Xuan

Publisher

SPIE

Reference25 articles.

1. Metal-organic frameworks

2. Covalent organic frameworks: a materials platform for structural and functional designs

3. Hydrogen Storage in Metal-Organic Frameworks

4. Opportunities and challenges of MOF-based membranes in gas separations

5. Rationally Fabricating Three-Dimensional Covalent Organic Frameworks for Propyne/Propylene Separation

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Simulation and Machine Learning-Integrated Investigation of Double-Linker MOFs for the Separation of CF₄ from CH₄ and N₂;Industrial & Engineering Chemistry Research;2024-07-03

2. High-Throughput Computation Evaluation of Metal–Organic Frameworks for Efficient Perfluorocarbons Recovery;The Journal of Physical Chemistry C;2024-01-04

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