Computational investigations on the binding of methane and amino acid molecules with graphene, 2020
Lazare, Jovian
2020-12
2020-2029
Graphene is a strong, single atom-thick, lightweight, anti-corrosive, high surface area, low electrical noise, and high conductivity material. One part of this research evaluates graphene-based methane sensor and storage capabilities. Because methane is a greenhouse gas but also a potential source of energy, there is interest for both sensor and storage potential. Methane has a much higher global warming potential than carbon dioxide, the most abundant greenhouse gas. One of the reasons carbon dioxide gas is so abundant is because over time methane gas converts to carbon dioxide. It was determined that both sides of the graphene sheet can be utilized for methane storage. Results also showed simultaneous stabilizing forces between methane molecules while adsorbed on the surface of graphene. The knowledge from this study provides insights in effective designing and realization of graphene applications. Another part of this study assesses the possibility of graphene-based biomolecular sensors through analyzing binding affinity trends and atomic-scale structural features of complexes. Graphene-based biosensors can detect biomarkers for diagnosing diseases such as cancer and diabetes. The focus in this work is on the binding of acyclic α-amino acids adsorbed on graphene. It was found that distinct conformations and orientations determine the physiochemical interactions that take place at the surface of graphene and therefore determine the binding strength measured by energy, which is a key component for determining the sensor potential. It was also found that the size of graphene sheets impacted the thermal contributions to binding energy. Findings indicate that the conformations, orientations, and graphene nano-size are key to enhancing sensor performance by optimizing the binding energy. The distinct geometric features of amino acids and graphene size correlated with electronic properties. The high-quality data will improve force fields (for large molecule prediction models) and guide experiments.
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application/pdf
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Atlanta University and Clark Atlanta University Theses and Dissertations
dissertation
Doctor of Philosophy (PhD)
Clark Atlanta University
Department of Chemistry
Tandabany, Dinadayalane
Georgia--Atlanta
http://hdl.handle.net/20.500.12322/cau.td:2020_lazare_jovian
http://rightsstatements.org/vocab/InC-EDU/1.0/
