Reducing anthropogenic carbon dioxide emissions from coal-fired power plants is an important step in mitigating climate change. To implement carbon dioxide capture technologies, materials capable of removing carbon dioxide efficiently are required. Currently, liquid amine technology is used for carbon dioxide capture. However, the mechanism for carbon dioxide removal in liquid amine requires extraordinary amounts of energy input. Alternatively, solid sorbents such as metal-organic frameworks (MOFs) show promising potentials as a type of material for carbon dioxide capture. Due their varying structural properties, MOFs can be configured for specific purposes. Certain MOFs carry a net charge on their frameworks, which may allow for increased interactions with carbon dioxide molecules. In this work, charged MOFs were studied for their potential in carbon dioxide capture. Due to the massive number of MOFs available, computational methods were employed for the study.
This project includes three major components: (1) the development of novel computational methods to simulate the gas adsorption properties in charged materials, (2) a diverse database of 47,244 hypothetical charged MOFs was constructed to represent the capabilities of charged MOFs, and (3) screening of high performing charged MOFs for carbon capture application by combining the previous two portions of the project. The methods developed in this work include fitting intermolecular interaction parameters to quantum mechanical calculations in periodic systems with net charges. No methods have been reported in literature for such parameter fittings, even in well studied materials such as zeolites. Therefore, the gas adsorption estimation method for charged materials developed in this work is proprietary. Also, databases of hypothetical MOFs with framework net charges have never been reported previously in literature.
By screening the charged MOFs in the database with the methods developed, gas adsorption capabilities were evaluated. The adsorption properties of a neutral group of hypothetical MOFs were also obtained for a baseline comparison. Between the two groups of MOFs, charged MOFs were found to outperform neutral MOFs in three key aspects. Firstly, charged MOFs were able to adsorb an average of three times as much carbon dioxide than the neutral group. Secondly, charged MOFs were capable of removing twice the amount of carbon dioxide per adsorption/desorption cycle than the neutral MOFs. Lastly, charged MOFs were able to selectively adsorb much more carbon dioxide over other gasses present in the carbon dioxide capture situations. Specific structural features that resulted in the selectiveness of adsorption in charged MOFs were identified. Also, positive correlations were found between the adsorption of carbon dioxide and the charge present in the MOFs.
As seen in the results, charges present in MOFs can greatly increase their ability to remove carbon dioxide. Charged MOFs in the hypothetical database not only outperformed neutral MOFs, certain top performers were also found to exceed the requirements for post-combustion carbon capture application. Therefore, charged MOFs were shown to be a possible material for future carbon dioxide capture. The proprietary methods developed in this work can not only be used to simulate gas adsorptions in charged MOFs, but also for other porous materials, regardless of net charges presented in their systems. Also, the database constructed in this work can be utilized in multiple ways. Aside from carbon dioxide capture capabilities, the charged MOFs in the database can be screened for other gas separations and catalysis via high throughput screening. The database and the computational methods developed in this work pave the way for discovering the capabilities of charged materials.
Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/35339 |
Date | January 2016 |
Creators | Lo, Jason Wai-Ho |
Contributors | Woo, Tommy K. |
Publisher | Université d'Ottawa / University of Ottawa |
Source Sets | Université d’Ottawa |
Language | English |
Detected Language | English |
Type | Thesis |
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