Novel Strategy of Machine Learning for Predicting Henry’s Law Constants of CO2 in Ionic Liquids
Author:
Affiliation:
1. State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China
2. College of Chemistry and Materials, Taiyuan Normal University, Jinzhong, Shanxi 030619, China
Funder
National Natural Science Foundation of China
Publisher
American Chemical Society (ACS)
Subject
Renewable Energy, Sustainability and the Environment,General Chemical Engineering,Environmental Chemistry,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acssuschemeng.3c00874
Reference44 articles.
1. Prediction of CO2 solubility in ionic liquids using machine learning methods
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3. Efficient capture of CO 2 from flue gas at high temperature by tunable polyamine‐based hybrid ionic liquids
4. Thermodynamic interpretation and prediction of CO2 solubility in imidazolium ionic liquids based on regular solution theory
5. A new method for the determination of Henry's law constants (air-water-partition coefficients)
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