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Nonlinear liquid-liquid chromatography: Beyond a constant distribution coefficient.

Melanie Gerigk1, Gergő Dargó2, Árpád Könczöl2

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Summary
This summary is machine-generated.

This study examined liquid-liquid chromatography (LLC) in its nonlinear range for the first time. The findings show that a new model accurately predicts cannabidiol (CBD) elution profiles across various concentrations and unit sizes.

Keywords:
Cannabidiol (CBD)Centrifugal partition chromatographyCountercurrent chromatographyMathematical modelingNonlinear distribution equilibria

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Area of Science:

  • Analytical Chemistry
  • Separation Science

Background:

  • Liquid-liquid chromatography (LLC) separates compounds based on their distribution between two immiscible liquid phases.
  • Understanding nonlinear behavior is crucial for optimizing LLC processes, especially at higher concentrations.

Purpose of the Study:

  • To systematically investigate liquid-liquid chromatography (LLC) operation in the nonlinear range of the distribution isotherm.
  • To evaluate the influence of feed concentration on elution profiles using a model compound, cannabidiol (CBD).
  • To validate a predictive model for LLC performance across different scales.

Main Methods:

  • Systematic examination of LLC in the nonlinear range using three different unit sizes (20 mL to 2 L).
  • Pulse injections of cannabidiol (CBD) at concentrations from 1 to 300 mg/mL.
  • Simulation of elution profiles using the equilibrium-cell model and an anti-Langmuir-like equation for distribution equilibria.
  • Fitting distribution equilibria parameters to experimental data using peak fitting.

Main Results:

  • The study successfully examined nonlinear LLC behavior for the first time.
  • Cannabidiol (CBD) elution profiles were accurately predicted across a wide range of concentrations (1-300 mg/mL).
  • The validated model demonstrated good predictive performance for all tested LLC unit sizes.

Conclusions:

  • The equilibrium-cell model, coupled with an anti-Langmuir-like isotherm, effectively describes nonlinear LLC behavior.
  • This approach provides accurate predictions for cannabidiol (CBD) separation in LLC, even at high concentrations.
  • The findings are applicable to optimizing LLC processes across various scales and concentrations.