Modeling solid solute solubility in supercritical carbon dioxide by machine learning algorithms using molecular sigma profiles-Reference-Cited by-同舟云学术

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Modeling solid solute solubility in supercritical carbon dioxide by machine learning algorithms using molecular sigma profiles

Published:2024-02 Issue: Volume:395 Page:123884
ISSN:0167-7322
Container-title:Journal of Molecular Liquids
language:en
Short-container-title:Journal of Molecular Liquids

Author:

Li Ji-En,Chien Szu-Chia^ORCID,Hsieh Chieh-Ming^ORCID

Funder

National Science and Technology Council

Publisher

Elsevier BV

Subject

Materials Chemistry,Physical and Theoretical Chemistry,Spectroscopy,Condensed Matter Physics,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Reference137 articles.

1. Supercritical fluid applications: Industrial developments and economic issues;Perrut;Ind. Eng. Chem. Res.,2000

2. Caffeine extraction rates from coffee beans with supercritical carbon dioxide;Peker;AIChE J.,1992

3. Supercritical fluid chromatography for the 21st century;Taylor;J. Supercrit. Fluids,2009

4. Past, present and future of supercritical fluid dyeing technology – an overview;Bach;Rev. Prog. Color.,2002

5. Particle design using supercritical fluids: Literature and patent survey;Jung;J. Supercrit. Fluids,2001

Cited by 1 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. Module-based machine learning models using sigma profiles of organic linkers to predict gaseous adsorption in metal-organic frameworks;Journal of the Taiwan Institute of Chemical Engineers;2024-12

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