The very large number of distinct structures that are known for metal-organic frameworks (MOFs) and zeolites presents both an opportunity and a challenge for identifying materials with useful properties for targeted separations. In this thesis we propose a three-stage computational methodology for addressing this issue and comprehensively screening all available nanoporous materials. We introduce efficient pore size calculations as a way of discarding large number of materials, which are unsuitable for a specific separation. Materials identified as having desired geometric characteristics can be further analyzed for their infinite dilution adsorption and diffusion properties by calculating the Henry's constants and activation energy barriers for diffusion. This enables us to calculate membrane selectivity in an unprecedented scale and use these values to generate a small set of materials for which the membrane selectivity can be calculated in detail and at finite loading using well-established computational tools. We display the results of using these methods for >500 MOFs and >160 silica zeolites for spherical adsorbates at first and for small linear molecules such as CO₂ later on. In addition we also demonstrate the size of the group of materials this procedure can be applied to, by performing these calculations, for simple adsorbate molecules, for an existing library of >250,000 hypothetical silica zeolites. Finally, efficient methods are introduced for assessing the role of framework flexibility on molecular diffusion in MOFs that do not require defining a classical forcefield for the MOF. These methods combine ab initio MD of the MOF with classical transition state theory and molecular dynamics simulations of the diffusing molecules. The effects of flexibility are shown to be large for CH₄, but not for CO₂ and other small spherical adsorbates, in ZIF-8.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/47669 |
| Date | 08 April 2013 |
| Creators | Haldoupis, Emmanuel |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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