Testing predictions of macroscopic binary diffusion coefficients using lattice models with site heterogeneity.

Area of Science:

• Materials Science
• Chemical Engineering
• Physical Chemistry

Background:

• Predicting mass transport of chemical mixtures in porous materials is vital for applications like adsorbents, membranes, and catalysts.
• Experimental assessment of mixture transport is complex, driving the need for theoretical models using single-component data.
• The Skoulidas, Sholl, and Krishna model offers a theoretical approach to predict mixture diffusion coefficients.

Purpose of the Study:

• To evaluate the accuracy of the Skoulidas, Sholl, and Krishna model for predicting mixture diffusion coefficients.
• To investigate the model's performance in porous materials with heterogeneous binding energy distributions.
• To compare model predictions with simulation results across various conditions.

Main Methods:

• Computed single-component and binary mixture diffusion coefficients using kinetic Monte Carlo simulations.
• Utilized a two-dimensional lattice model to represent porous materials.
• Examined a wide range of lattice occupancies and compositions.

Main Results:

• The Skoulidas, Sholl, and Krishna model demonstrated accuracy for homogeneous binding energy distributions.
• The model's accuracy significantly decreased for materials with strongly heterogeneous energy distributions.
• Simulation results highlighted the impact of binding energy heterogeneity on mixture transport prediction.

Conclusions:

• The theoretical model is reliable for homogeneous porous materials but requires refinement for heterogeneous systems.
• Binding energy heterogeneity is a critical factor influencing the predictive accuracy of mixture diffusion models.
• Further development of models is needed to account for complex energy landscapes in porous materials.