Defect engineering for tuning electronic and optical properties of materials

Ward, Zachary, D
Thumbnail Image
Other Contributors
Meunier, Vincent
Rhone, Trevor
Shi, Sufei
Terrones, Humberto, M
Issue Date
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute (RPI), Troy, NY. Copyright of original work retained by author.
Full Citation
All pristine nanostructured materials are susceptible to disruptions in their chemical composition and expected crystal structure, where these disruptions are commonly referred to as defects. At first glance, defects may seem to be detrimental to the quality of a material, but they can actually improve the usability of the material and cause the material to perform even better for specific applications. In this thesis, density functional theory (DFT) calculations are applied to various defects across multiple nanostructured materials to identify the defects which are potentially responsible for observations found in experiments, and what makes those specific defects special and how those defects can optimize specific target applications. Defects in transition metal dichalcogenides (TMDs), hexagonal boron nitride (hBN), perovskites, and TMD alloys are investigated. For TMDs, an oxygen interstitial (Oins) defect in monolayer WSe2 is found to likely be responsible for single photon emission (SPE) and this Oins defect can assist in enhancing the adsorption strength for the sensing of ammonia (NH3) molecules. Additionally, transition metal defective MoS2 can assist in the sensing of neurotransmitter biomolecules. In hBN, the antisite defect (NBVN) is the most likely defect responsible for SPEs and its SPE qualities are enhanced in multilayer hBN when compared to monolayer hBN. The NBVN defect also assists in the migration of ions within an hBN anode/LiNiMnCoO2 cathode system. For perovskites, multiple dopants are introduced into the BaZrS3 perovskite to determine the best candidates to tune the bandgap into the Shockley-Queisser limit to potentially maximize photovoltaic efficiency. From the dopant dataset, a machine learning (ML) model is created which can accurately predict formation energies and bandgaps, and Ti and Ca substituting at the Zr and Ba sites, respectively, are deduced as the best dopants for potential photovoltaic devices. For alloys, multiple possible configurations are constructed in a WxMo1-xS2 alloy to determine the behavior of the electronic transitions when a single sulfur vacancy (VS) is introduced with models based on the geometry and chemical composition of the alloy being used to predict the energies for the A exciton and defect-mediated transitions. This thesis attempts to deduce the best defects in its respective nanomaterial to operate for its specific target application and answer why those defects are the best choice across all the aforementioned applications.
School of Science
Dept. of Physics, Applied Physics, and Astronomy
Rensselaer Polytechnic Institute, Troy, NY
Rensselaer Theses and Dissertations Online Collection
Restricted to current Rensselaer faculty, staff and students in accordance with the Rensselaer Standard license. Access inquiries may be directed to the Rensselaer Libraries.