Fundamental studies of material surfaces are of continued interest to the development and improvement of many modern technologies, e.g. catalysis, energy efficient electronics, and high-capacity batteries etc. This dissertation targets two distinct sets of molecule-surface interactions relevant to the continued development of structure-property correlations using tools from Density Functional Theory with added verification from ultrahigh vacuum surface-science experiments. These include Haber-Bosch interactions at molybdenum-nitride surfaces and separation-dependent interactions between simple aromatics and Ru(0001) used to model a metal contact of Organic Electronic Devices (OEDs). In the first study, we focus on computational modelling of nitrogen fixation reactions on Mo- and N-terminated δ-MoN(0001). A comparative analysis to analogous predictions reported for Mo-terminated γ-Mo2N(111) sites demonstrates a near-total dependence on the atomic surface-structure with little to no impact from changes in sub-surface stoichiometry. Changing from Mo- to N-terminated surface drastically changes the reaction barriers such that the rate-limiting-step in the overall ammonia evolution reaction changes from NHx hydrogenation to N2 dissociative adsorption. In the second one, we explored the effect of changing metal-organic molecule separation on charge-transfer across the interface and the electronic properties of organic matter pertinent to OEDs. We studied various computational models of benzene and pyridine molecules held at fixed distances from Ru(0001) by introducing two-dimensional hexagonal SiO2 thin-films between molecules and the metal. Substantial metal-to-molecule charge-transfer is noted when molecules bind directly to the Ru interface, but virtually no interaction is noted when increasing metal-molecule separations up to ~12 Å. An analogous series of experiments investigating pyridine-Ru interactions introduced after exposing SiO2/Ru(0001) thin-films to varied doses of pyridine exhibits behavior similar to that predicted by theory.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2279 |
| Date | 01 January 2022 |
| Creators | Sajid, Muhammad |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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