Defects and alloying in semiconductors: computational studies of clusters and surfaces

Abstract

This thesis addresses two main systems that are important in the lore of energy efficient nanomaterials, titanium dioxide and group IV alloy nanoclusters. Titanium dioxide, widely used in heterogeneous catalysis, photocatalysis, solar cells, or gas sensors, has become the prototype material for studying the reactivity of metal-oxide surfaces. Defects such as oxygen vacancies are always present on rutile surfaces and, depending on their coverage and spatial distribution, can strongly influence the reactivity of the surface. The interactions between vacancies determine their spatial distribution on the surface. Highly reactive vacancy clusters or pairs have not been expected to form because of vacancy repulsions, but recent experiments do show the possibility of spontaneously formed oxygen vacancy pairs. In this thesis, The interaction between oxygen vacancies is studied, as well as their electronic properties and scanning tunneling microscopy signature. The second thrust of the thesis concerns group IV nanomaterials, which are important semiconductors for photovoltaic applications due to their relative abundance and nontoxicity. Alloy nanoclusters, specifically germanium-tin (GeSn) nanoclusters show great promise for achieving higher photoconversion efficiencies since the band gap can be tuned by adjusting the Ge/Sn ratio. To accurately model alloy nanoclusters one must first verify that the physical properties of the constituent nanoclusters are described correctly. As these nanoclusters are composed of thousands to hundreds of thousands of atoms, we employ molecular dynamics (MD) simulations which are capable of calculating properties for large systems in reasonable timeframes. In MD simulations, atomic interactions are described using empirical potentials. The well-known Tersoff potential has proven to describe well the properties of group IV elements up to Ge. As Sn is also a group IV element, we have extended this potential to include parameters for Sn by developing an algorithm that determines the parameters that best fit known experimental data. To supplement the study of the mechanical and thermal behavior of Ge and Sn nanoclusters, A rotationally invariant local order parameter capable of determining at what temperature nanoclusters undergo crystallization has also been developed. This local order parameter also has the ability to distinguish between different Bravais lattices.