New molecular structure based models for estimation of the CO₂ solubility in different choline chloride-based deep...

Area of Science:

• Physical Chemistry
• Computational Chemistry
• Materials Science

Background:

• Deep eutectic solvents (DESs) are emerging as promising media for gas solubility applications.
• Choline chloride (ChCl) based DESs are widely studied due to their unique properties.
• Predicting carbon dioxide (CO2) solubility in these solvents is crucial for process design.

Purpose of the Study:

• To investigate CO2 solubility in various choline chloride-based deep eutectic solvents (DESs).
• To develop Quantitative Structure-Property Relationship (QSPR) models for predicting CO2 solubility.
• To identify key molecular descriptors influencing CO2 solubility in these DESs.

Main Methods:

• Utilized 12 datasets comprising 390 data points on CO2 solubility from existing literature.
• Developed predictive QSPR models incorporating pressure, temperature, and molecular descriptors.
• Employed the 'EEig02d' descriptor, related to molecular structure and dipole moments, for model development.

Main Results:

• Established predictive models for CO2 solubility in ChCl-based DESs at various temperatures and molar ratios.
• Identified the 'EEig02d' descriptor as a significant factor influencing CO2 solubility.
• Validated the models using additional external datasets with new conditions and structures.

Conclusions:

• QSPR models effectively predict CO2 solubility in choline chloride-based DESs.
• The 'EEig02d' molecular descriptor is a key predictor for CO2 solubility.
• This research provides valuable insights for designing DESs for CO2 capture applications.