Analysis of microstructure-effective diffusivity relationships for the interparticle pore space in physically...

Area of Science:

• Chemical Engineering
• Materials Science
• Physical Chemistry

Background:

• Accurate prediction of effective diffusion coefficients is crucial for optimizing chromatographic separations.
• Existing models often simplify the complex microstructure of packed beds, neglecting defects.
• Understanding the impact of microstructure on diffusion is essential for designing efficient chromatographic systems.

Purpose of the Study:

• To evaluate the effective diffusion coefficient in the interparticle pore space of reconstructed chromatographic packings.
• To assess an analytical formula using the three-point microstructural parameter for diffusivity estimation.
• To investigate the influence of packing defects on diffusion.

Main Methods:

• Physically reconstructed chromatographic packings of fully porous and core-shell particles were used.
• An analytical formula involving the three-point microstructural parameter was employed.
• Two-point and three-point correlation functions were calculated using sampling templates.
• Pore-scale simulations with a random-walk particle-tracking technique were performed for comparison.

Main Results:

• Diffusivities calculated by the analytical formula closely matched those from pore-scale simulations.
• Packing defects such as particle oligomers and debris significantly affected diffusivities.
• Non-monotonic behavior was observed in the relationship between the microstructural parameter and effective diffusion coefficient across different porosities.
• The three-point microstructural parameter effectively captured the morphological specificity of the packings.

Conclusions:

• The analytical formula provides accurate estimates of effective diffusion coefficients in packed beds.
• The three-point microstructural parameter is a key descriptor for predicting diffusion in complex packings.
• The numerical approach is applicable for evaluating diffusivities using geometrical information like bed porosity and the microstructural parameter.