The detrimental effects of rising CO₂ levels on the global climate have made carbon abatement technologies one of the most widely researched areas of recent times. In this thesis, we first present a techno-economic analysis of a novel approach to directly capture CO₂ from air (Air Capture) using highly selective adsorbents. Our process modeling calculations suggest that the monetary cost of Air Capture can be reduced significantly by identifying adsorbents that have high capacities and optimum heats of adsorption. The search for the best performing material is not limited to Air Capture, but is generally applicable for any adsorption-based separation. Recently, a new class of nanoporous materials, Metal-Organic Frameworks (MOFs), have been widely studied using both experimental and computational techniques. In this thesis, we use a combined quantum chemistry and classical simulations approach to predict macroscopic properties of MOFs. Specifically, we describe a systematic procedure for developing classical force fields that accurately represent hydrocarbon interactions with the MIL-series of MOFs using Density Functional Theory (DFT) calculations. We show that this force field development technique is easily extended for screening a large number of complex open metal site MOFs for various olefin/paraffin separations. Finally, we demonstrate the capability of DFT for predicting MOF topologies by studying the effect of ligand functionalization during CuBTC synthesis. This thesis highlights the versatility and opportunities of using multiscale modeling approach that combines process modeling, classical simulations and quantum chemistry calculations to study nanoporous materials for adsorptive separations.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/53053 |
| Date | 12 January 2015 |
| Creators | Kulkarni, Ambarish R. |
| Contributors | Sholl, David S., Jones, Christopher W. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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