260544 Computational Characterization of MOF Pores and Determination of Molecular Selectivity
We previously reported on a computational approach for the systematic characterization of the three-dimensional porous networks of zeolites . Starting with only the crystallographic information for a structure, we automatically identify all void space that can potentially accommodate a guest molecule and classify it into a network of interconnected channels and cages. We have now extended our methodology to support the broader class of microporous materials known as metal-organic frameworks (MOFs).
Due to their large pore volumes and surface areas, MOFs are attractive materials for applications including carbon capture and hydrogen storage. The syntheses of hundreds of MOFs have been reported in the literature , and over a hundred thousand hypothetical MOFs have been generated . While MOFs have the potential to be systematically synthesized, the vast number of possibilities calls for computational methods for database screening, so that only the most promising structures for a desired application need to be studied experimentally.
We present the first computational method capable of automatically identifying the windows, channels, and cages of a MOF and describing their geometry and connectivity. In addition, we perform a variety of quantitative calculations including window sizes, pore size distribution, accessible volume and surface area, pore limiting diameter and largest cavity diameter, and coordination number. We have applied our method to a large selection of MOFs, including zeolitic imidazolate frameworks (ZIFs) and hypothetical MOFs.
We have also developed a procedure to screen databases of microporous materials and identify those most suitable for a desired molecular separation. The approach is an extension of our previous work on zeolites [4-6], where we calculate the selectivity of a microporous material for a separation by comparing the activation energy required for two different molecules to pass through the adsorbent's windows. We demonstrate our method by identifying MOFs with the greatest potential to separate carbon dioxide from nitrogen and hydrogen from methane.
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