Predicting intraparticle diffusivity as function of stationary phase characteristics in preparative chromatography

Area of Science:

• Chemical Engineering
• Separation Science
• Materials Science

Background:

• Diffusion within porous materials is a critical factor in chromatographic separations.
• Accurate modeling of intraparticle diffusion is essential for optimizing processes in food and bio-processing.
• Understanding the relationship between molecular diffusion and stationary phase properties is key for process design.

Purpose of the Study:

• To establish a relationship between intraparticle diffusion and the properties of stationary phases and diffusing molecules.
• To evaluate the predictive capabilities of existing diffusion models.
• To develop an improved model for predicting intraparticle diffusion.

Main Methods:

• Determined intraparticle diffusivities for eight small molecules across nine stationary phases.
• Analyzed stationary phases for porosity, pore size distribution, and tortuosity.
• Compared experimental data with Mackie and Meares correlation and parallel pore model predictions.

Main Results:

• The parallel pore model offered insights but had limited predictive accuracy due to experimental parameter determination challenges.
• The Mackie and Meares model showed improved prediction accuracy when combined with the accessible pore volume fraction.
• Accessible pore volume fraction was successfully determined using inverse size exclusion chromatography.

Conclusions:

• The developed model, integrating accessible pore volume, enhances the prediction of intraparticle diffusion.
• Further research is needed to validate the model for wider accessible pore fractions and higher diffusion coefficients.
• The study provides a valuable database for advancing the understanding of diffusion in chromatographic materials.