Estimation of diffusion anisotropy in microporous crystalline materials and optimization of crystal orientation in membranes.

The complex nature of the porous networks in microporous materials is primarily responsible for a high degree of intracrystalline diffusion anisotropy. Although this is a well-understood phenomenon, little attention has been paid in the literature with regards to classifying such anisotropy and elucidating its effect on the performance of membrane-based separation systems. In this paper, we develop a novel methodology to estimate full diffusion tensors based on the detailed description of the porous network geometry through our recent advances for the characterization of such networks. The proposed approach explicitly accounts for the tortuosity and complex connectivity of the porous framework, as well as for the variety of diffusion regimes that may be experienced by a guest molecule while it travels through the different localities of the crystal. Results on the diffusion of light gases in silicalite demonstrate good agreement with results from experiments and other computational techniques that have been reported in the literature. A comprehensive computational study involving 183 zeolite frameworks classifies these structures in terms of a number of anisotropy metrics. Finally, we utilize the computed diffusion tensors in a membrane optimization model that determines optimal crystal orientations. Application of the model in the context of separating carbon dioxide from nitrogen demonstrates that optimizing crystal orientation can offer significant benefit to membrane-based separation processes.