This thesis explores the use of computational chemistry to aid in the design of metal-organic frameworks (MOFs) and other materials. A focus is placed on finding exceptional materials to be used for removing CO2 from fossil fuel burning power plants, with other avenues like vehicular methane storage and landfill gas separation being explored as well. These applications are under the umbrella of carbon capture and storage (CCS) which aims to reduce carbon emissions through selective sequestration. We utilize high-throughput screenings, as well as machine learning assisted discovery, to identify ideal candidate materials using a holistic approach instead of relying on conventional gas adsorption properties.
The development of ideal materials for CCS requires all aspects of a material to be considered, which can be time-consuming. A large portion of this work has been with high-throughput, or machine learning assisted discovery of ideal candidates for CCS applications. The chapters of this thesis are connected by the goal of finding ideal materials for CCS. They are primarily arranged in increasing complexity of how this research can be done, from using high-throughput screenings with more simple metrics, up to multi-scale machine learning optimization of pressure swing adsorption systems. The work is not presented chronologically, but in a way to tell the best story.
Work was done by first applying high-throughput computational screening on a set of experimentally realized MOFs for vehicular methane storage, post-combustion carbon capture, and landfill gas separation. Whenever possible, physically motivated figures of merits were used to give a better ranking and consideration of the materials. From this work, we were able to determine what the realistic limits might be for current MOFs. The work was continued by looking at carbon-based materials (primarily carbon nanoscrolls) for post-combustion carbon capture and vehicular methane storage. The carbon-based materials were found to outperform MOFs; however, further studies are needed to verify the results.
Next, we looked at ways to improve the high-throughput screening methodology. One problem area was in the charge calculation, which could lead to unrealistic gas adsorption results. Using the split-charge equilibration method, we developed a robust way to calculate the partial atomic charges that were more accurate than its quick calculation counterparts. This led to gas adsorption properties which more closely mimicked the results determined from time-consuming quantum mechanically derived charges.
Simplistic process optimization was then applied to nearly ~3500 experimental structures. To the best of our knowledge, this is the first time that any process optimization has been applied to more than 10s of materials for a study. The process optimization was done by evaluating the desorption at various pressures and choosing the value which gave the lowest energetic cost. It was found that a material synthesized by our collaborators, IISERP-MOF2, was the single best experimentally realized material for post-combustion carbon capture. What made this an interesting result is that by conventional metrics IISERP-MOF2 does not appear to be outstanding. Next, functionalized versions of MOFs were tested in a high-throughput manner, and some of those structures were found to outperform IISERP-MOF2.
Although high-throughput computational screenings can be used to determine high-performance materials, it would be impossible to test all functionalized versions of some MOFs, let alone all MOFs. Functionalized MOFs are noteworthy because MOFs are highly tuneable through functionalization and can be made into ideal materials for a given application. We developed a genetic algorithm which, given a base structure and a target parameter, would be able to find the ideal functionalization to optimize the parameter while testing only a small fraction of all structures. In some cases, the CO2 adsorption was found to more than quadruple when functionalized.
A better understanding of how materials perform in a PSA system was achieved by performing multi-scale optimizations. Experimentally realized MOFs were tested using atomistic simulations to derive gas adsorption properties. After passing through a few sensible filters, they were then screened using macro-scale pressure swing adsorption simulators, which model how gas separation may occur at a power plant. Using another genetic algorithm, the conditions that the pressure swing adsorption system runs at was optimized for over 200 materials. To the best of our knowledge, this is the highest amount of materials that have had been optimized for process conditions. IISERP-MOF2 was found to perform the best based on many relevant metrics, such as the energetic cost and how much CO2 was captured. It was also found that conventional metrics were unable to be used to predict a material’s pressure swing adsorption performance.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/39382 |
| Date | 08 July 2019 |
| Creators | Collins, Sean |
| Contributors | Woo, Tom |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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