Solvent system selection in counter-current chromatography using conductor-like screening model for real solvents.

Area of Science:

• Analytical Chemistry
• Computational Chemistry
• Separation Science

Background:

• Counter-current chromatography (CCC) relies on solute partitioning between two liquid phases for separation.
• Selecting optimal solvent systems for CCC typically requires extensive experimental screening.
• Minimizing experimental effort in solvent selection is crucial for efficient chromatographic method development.

Purpose of the Study:

• To reduce the experimental workload associated with selecting solvent systems for counter-current chromatography.
• To evaluate the efficacy of a computational approach for predicting solute partitioning behavior.
• To establish the utility of the conductor-like screening model for real solvents (COSMO-RS) in guiding solvent system selection for CCC.

Main Methods:

• Employed quantum chemical calculations integrated with statistical thermodynamics.
• Utilized the conductor-like screening model for real solvents (COSMO-RS) to predict partition coefficients.
• Validated predictions by comparing with experimental data for five model solute systems.

Main Results:

• COSMO-RS successfully predicted the partition coefficients of model solutes between liquid phases.
• A strong correlation was observed between predicted and experimental partitioning data.
• The computational method demonstrated significant potential for streamlining solvent system selection.

Conclusions:

• The conductor-like screening model for real solvents (COSMO-RS) is a viable computational tool for screening solvent systems in counter-current chromatography.
• This approach can substantially minimize the experimental effort required for optimizing CCC separations.
• The findings support the integration of predictive computational methods into chromatographic method development workflows.