Using nonrandom two-liquid model for solvent system selection in counter-current chromatography.

Area of Science:

• Chemical Separation
• Thermodynamics
• Chromatography

Background:

• Selecting appropriate solvent systems is crucial for effective counter-current chromatography (CCC).
• Traditional solvent system screening for CCC is often time-consuming and resource-intensive.
• Accurate prediction of solute partition coefficients is key to optimizing separation processes.

Purpose of the Study:

• To evaluate the Nonrandom Two-Liquid (NRTL) model for predicting partition coefficients in CCC.
• To demonstrate the utility of the NRTL model in rapidly screening solvent systems for CCC.
• To reduce experimental effort and cost in CCC solvent system selection.

Main Methods:

• Utilized the NRTL thermodynamic model to predict partition coefficients.
• Correlated measured partition coefficients in representative biphasic liquid systems.
• Predicted partition coefficients in novel two-phase liquid systems.
• Screened solvent systems based on predicted partition coefficients for CCC.

Main Results:

• The NRTL model provided satisfactory predictions for model solutes.
• Successfully correlated and predicted partition coefficients across different biphasic systems.
• Rapidly identified an effective solvent system (hexane/ethyl acetate/methanol/water, 1:4:1:4 v/v) for CCC.
• Achieved high-purity separation of two major compounds from Malus hupehensis leaves.

Conclusions:

• The NRTL model offers a practical strategy for estimating partition coefficients in CCC.
• This method significantly minimizes experimental time and costs associated with solvent system selection.
• The NRTL model facilitates efficient and targeted solvent system screening for CCC applications.