Enhancing aromatics extraction by double salt ionic liquids: rational screening-validation and mechanistic insights DOI Open Access
Kunchi Xie, Jiahui Chen,

Ruizhuan Wang

et al.

Authorea (Authorea), Journal Year: 2023, Volume and Issue: unknown

Published: July 16, 2023

Despite offering remarkable advantages as solvents, double salt ionic liquids (DSILs) have been scarcely studied for extractive dearomatization from hydrocarbons well many other applications, thus urging a theoretical guidance method. In this work, systematic framework combining the rational screening-validation and mechanistic analysis is proposed tailoring DSILs o-xylene/n-octane separation. From an initial pool of commercially available (ILs), key thermodynamic properties paired are predicted by COSMO-RS while their important physical estimated those corresponding parent ILs (retrieved experimental database or deep learning model). Promising tested liquid-liquid equilibrium experiments, wherein ion ratio-effect also evaluated. The mechanism underlying tunability DSIL disclosed means quantum chemistry calculation molecular dynamics simulation. This work can be valuable reference guiding design diverse applications.

Language: Английский

Developing a two-grade model for the thermal conductivity of ionic liquids and their mixtures DOI
Chengjie Wang, Xiaoyan Wei, Xin Jin

et al.

Chemical Engineering Science, Journal Year: 2024, Volume and Issue: 290, P. 119881 - 119881

Published: Feb. 13, 2024

Language: Английский

Citations

1
Graph transformer based transfer learning for aqueous pK prediction of organic small molecules DOI

Yuxin Qiu,

Jiahui Chen, Kunchi Xie

et al.

Chemical Engineering Science, Journal Year: 2024, Volume and Issue: 300, P. 120559 - 120559

Published: July 31, 2024

Language: Английский

Citations

1
Machine learning boosted eutectic solvent design for CO2 capture with experimental validation DOI Open Access
Xiaomin Liu, Jiahui Chen,

Yuxin Qiu

et al.

AIChE Journal, Journal Year: 2024, Volume and Issue: 71(2)

Published: Oct. 18, 2024

Abstract Although eutectic solvents (ESs) have garnered significant attention as promising for carbon dioxide (CO 2 ) capture, systematic studies on discovering novel ESs linking machine learning (ML) and experimental validation are scarce. For the reliable prediction of CO ‐in‐ES solubility, ensemble ML modeling based random forest extreme gradient boosting with inputs COSMO‐RS derived molecular descriptors is rigorously performed, which an extensive solubility database 2438 data points in 162 involving 106 ES systems collected. With best‐performing model obtained, solubilities 4735 combinations components first predicted estimating their potential capture. The top‐ranked candidate subsequently evaluated by examining environmental health safety properties individual assessing operating window solid–liquid equilibrium (SLE) prediction. Three most finally retained, thoroughly studied SLE absorption experiments.

Language: Английский

Citations

1
Carboxylic acid-based deep eutectic solvent for efficient desulfurization: Experimental and computational thermodynamics DOI
Wanxiang Zhang, X. Pan,

Zhengrun Chen

et al.

Fuel, Journal Year: 2024, Volume and Issue: 378, P. 132975 - 132975

Published: Sept. 1, 2024

Language: Английский

Citations

0
Enhancing aromatics extraction by double salt ionic liquids: rational screening-validation and mechanistic insights DOI Open Access
Kunchi Xie, Jiahui Chen,

Ruizhuan Wang

et al.

Authorea (Authorea), Journal Year: 2023, Volume and Issue: unknown

Published: July 16, 2023

Despite offering remarkable advantages as solvents, double salt ionic liquids (DSILs) have been scarcely studied for extractive dearomatization from hydrocarbons well many other applications, thus urging a theoretical guidance method. In this work, systematic framework combining the rational screening-validation and mechanistic analysis is proposed tailoring DSILs o-xylene/n-octane separation. From an initial pool of commercially available (ILs), key thermodynamic properties paired are predicted by COSMO-RS while their important physical estimated those corresponding parent ILs (retrieved experimental database or deep learning model). Promising tested liquid-liquid equilibrium experiments, wherein ion ratio-effect also evaluated. The mechanism underlying tunability DSIL disclosed means quantum chemistry calculation molecular dynamics simulation. This work can be valuable reference guiding design diverse applications.

Language: Английский

Citations

0