Predicting the solubility of CO2 and N2 in ionic liquids based on COSMO-RS and machine learning Article Swipe
YOU?
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· 2024
· Open Access
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· DOI: https://doi.org/10.3389/fchem.2024.1480468
As ionic liquids (ILs) continue to be prepared, there is a growing need to develop theoretical methods for predicting the properties of ILs, such as gas solubility. In this work, different strategies were employed to obtain the solubility of CO 2 and N 2 , where a conductor-like screening model for real solvents (COSMO-RS) was used as the basis. First, experimental data on the solubility of CO 2 and N 2 in ILs were collected. Then, the solubility of CO 2 and N 2 in ILs was predicted using COSMO-RS based on the structures of cations, anions, and gases. To further improve the performance of COSMO-RS, two options were used, i.e., the polynomial expression to correct the COSMO-RS results and the combination of COSMO-RS and machine learning algorithms (eXtreme Gradient Boosting, XGBoost) to develop a hybrid model. The results show that the COSMO-RS with correction can significantly improve the prediction of CO 2 solubility, and the corresponding average absolute relative deviation (AARD) is decreased from 43.4% to 11.9%. In contrast, such an option cannot improve that of the N 2 dataset. Instead, the results obtained from coupling machine learning algorithms with the COSMO-RS model agree well with the experimental results, with an AARD of 0.94% for the solubility of CO 2 and an average absolute deviation (AAD) of 0.15% for the solubility of N 2 .
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fchem.2024.1480468
- OA Status
- gold
- Cited By
- 5
- References
- 71
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403962506Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fchem.2024.1480468Digital Object Identifier
- Title
-
Predicting the solubility of CO2 and N2 in ionic liquids based on COSMO-RS and machine learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-31Full publication date if available
- Authors
-
Hongling Qin, Ke Wang, Xifei Ma, Fangfang Li, Yanrong Liu, Xiaoyan JiList of authors in order
- Landing page
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https://doi.org/10.3389/fchem.2024.1480468Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3389/fchem.2024.1480468Direct OA link when available
- Concepts
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COSMO-RS, Solubility, Ionic liquid, Chemistry, Thermodynamics, Ionic bonding, Physical chemistry, Materials science, Physics, Organic chemistry, Ion, CatalysisTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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