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Thesis advisor: Dunwei Wang / Artificial photosynthesis is achieved by placing a semiconductor in water, where photoexcited charges generate a photovoltage at the surface of the semiconductor. However, solar to fuel efficiencies of earth abundant metal oxides and metal nitrides remain limited by their low photovoltages. Many different treatments have been used to improve the photovoltages of semiconductors, such as photocharging, surface regrowths, or the addition of heterogeneous catalysts. However, in these treatments, it remains unclear whether the enhanced photovoltage arises from improved kinetics or energetics. In many of the following studies, the surface kinetics of different semiconductors are measured in order to quantify how surface kinetics are related to the photovoltage of these materials. Different spectroscopic measurements are made along with detailed analysis of the Fermi level and quasi Fermi level in order to corroborate the kinetic data with energetic data. Together, this dissertation explores a multitude of methods and procedures that demonstrate how the photovoltage of semiconductors can be understood and manipulated for photoelectrochemial artificial photosynthesis. / Thesis (PhD) — Boston College, 2018. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Chemistry.

We quantify the chemical species present at and reactivity of the tetragonal (100) face of single-crystal methylammonium lead iodide, MAPbI3(100). MAPbI3 is an ABX3 perovskite, experiments utilized the orthogonal reactivity of the A+-site cation, the B2+-site cation, and the X–-site halide anion. Ambient-pressure exposure to BF3 solutions probe the reactivity of interfacial halides. Reactions with p-trifluoromethylanilinium chloride probe the exchange reactivity of the A+-site cation. The ligand 4,4’-bis(trifluoromethyl)-2,2’-bipyridine probe for interfacial B2+-site cations. Fluorine features in x-ray photoelectron spectroscopy (XPS) quantify reaction extents with each solution-phase species. XP spectra reveals adsorption of BF3 indicating surface-available halide anions on tetragonal MAPbI3(100) and preliminary examinations on the (112), (110), and thin-film surfaces. Temperature-programmed desorption (TPD) established a ~200 kJ mol–1 desorption activation energy from tetragonal MAPbI3(100). Adsorption of the fluorinated anilinium cation includes no concomitant adsorption of chlorine as revealed by the absence of Cl 2p features within the limits of XPS detection on the tetragonal (100) and (112) faces with no discernable exchange in preliminary experiments on tetragonal (110). Within detection limits, bipyridine ligand demonstrate no adsorption to tetragonal MAPbI3(100) or (112), while it does demonstrate significant adsorption on the (110) in preliminary experiments. We discuss the present results in the context of interfacial stability, passivation, and reactivity for perovskite-based energy conversion materials and some preliminary investigations into bilayer graphene-based dye sensitized photovoltaic materials.

Zinc oxide (ZnO) is a promising photoanode material which has been used in quantum dot-based depleted heterojunction solar cells. The specific influence of the defect chemistry of ZnO on its n-type conductivity remains a focus for research. This thesis presents results from a series of near-ambient pressure (NAP) XPS experiments (at The University of Manchester, UK), used to characterise surface adsorption of O2 and H2O on ZnO(10-10) surfaces in high pressure environments. Water dosing is shown to lead to surface hydroxylation and a change in the surface band bending consistent with an increase in the surface conductivity. Oxygen dosing is also observed to lead to the formation of surface species on the ZnO surface, revealing that ZnO is prone to hydroxylation even in oxygen-rich environments. The role of surface OH on influencing the transient surface photovoltage (SPV) of the ZnO(10-10) surface is probed through a series of time-resolved, pump-probe XPS experiments (at SOLEIL synchrotron, France). It is shown that increasing the degree of surface hydroxylation leads to a decrease in surface band bending, leading to longer-lived transient SPV. Other factors influencing the SPV dynamics are explored, such as the role of the oxygen vacancy concentration. The transient SPV decay lifetime is shown to increase with increasing oxygen vacancy concentration, consistent with the presence of persistent photoconductivity (PPC) in ZnO, mediated by oxygen vacancy-related hole traps. The influence of the concentration of thermally excited carriers in ZnO on the surface band bending is also described, showing that the equilibrium band bending and the surface photovoltage are both reduced at low temperature. It is shown that thermal excitation of carriers from the valence band of ZnO and from neutral oxygen vacancies have negligible influence on the magnitude of equilibrium band bending at the surface. The energy regime consistent with the observed temperature dependence is also consistent with a perturbed-host state 0.2 eV below the conduction band minimum. This meta-stable state is associated with doubly-ionised oxygen vacancies, that mediate the PPC in ZnO. However this does not rule out the contribution from other shallow donor levels such as those associated with hydrogen impurities. The influence of hydrogen on the SPV dynamics in ZnO is explored, through angle-resolved photoemission spectroscopy (ARPES) after implanting hydrogen atoms into the ZnO surface. H implantation is shown to lead to the formation of a 2D electron gas (2DEG) at the surface, consistent with an increase in conductivity at the surface large enough to change the nature of the space-charge region at the ZnO surface from depletion to accumulation.

Transition metals represent some of the first catalysts used in industrial processes and are still used today to produce many of the most needed chemicals. Adopting from ancient metallurgical techniques, it followed that the performance of these basic transition metals can be refined by adding multiple components. Since that time, improvements to these alloy catalysts has been mostly incremental due to the difficulty of producing new catalysts experimentally and a lack of fundamental understanding of the underlying physics. More recently, computational chemistry has proven itself an increasingly effective means for identifying these underlying physics. Through the use of d-band interactions of adsorbates with the surface, basic adsorption characteristics can be predicted across transition metals with limited initial information. However, although these models function well as high-level screening tools, much work is yet to be done before optimal catalysts can be comfortably designed from properties which experimentalists can directly control. This remains particularly challenging for alloy modeling, primarily due to the large number of possible atomic configurations, even for two metal systems. This work focuses on developing the methods for modeling optimal reaction properties at the surface of a transition metal alloy. Based on thermodynamic equilibrium between the surface, bulk, and gas reservoir, a model for the prediction of segregation under vacuum and adsorbate conditions can be predicted. Furthermore, by relating strain in the bulk lattice constant to the adsorption energies of varying local active sites, the optimal surface compositions can be related to bulk composition; a feature which can easily be selected for. Although useful for identifying trends across bulk composition space, these methods are limited to a small subset of active site configurations. To capture the complexity of more sophisticated processes, such as segregation, higher-timescale methods are required. Traditional computational tools are often too expensive to implement for these methods, and as such, they are usually completed with less-accurate potentials. In this work, we demonstrate that machine learning techniques have improved accuracy compared to physical potentials. We then go on to demonstrate how this improved accuracy can lead to experimentally accurate predictions of segregation.

Catalysts are essential for technological advances, because of their indispensable role in chemical and material manufacturing, energy conversion, and pollution control systems. Developing better catalysts is a highly desired goal that is impeded by the complexity of heterogeneous catalysts. This makes it extremely difficult to obtain information regarding active sites and reaction mechanisms, which is critical for improving catalyst design and performance. My research work has led to the understanding of how specific catalytic surface sites affect the performance of catalysts by constructing conceptually simpler planar model catalysts for kinetics and mechanism studies using model surface science tools and batch reaction testing. The work in this dissertation has demonstrated that planar model catalysts are versatile tools to probe reaction mechanisms on industrial catalysts. Supported gold nanoparticles have shown remarkable catalytic activity in a variety of reactions. However, many fundamental aspects of gold catalysts are still unclear, especially about the identity of active sites and oxidizing species. A Au(111) single crystal, the most stable and abundant facet on gold nanoparticles, is utilized to understand the reaction mechanisms of partial oxidation of 2-butanol and allyl alcohol. By controlling oxygen coverage on the surface, 100% selectivity to corresponding ketone and aldehyde, the desirable products, can be achieved. Two model catalysis systems, gold nanoclusters supported on a TiO₂(110) substrate and iron oxide dispersed on a Au(111) surface, were employed to understand the reaction pathways of CO oxidation and probe the role of the oxide/metal interface. The mechanistic and kinetic studies have shown that planar model catalysts are useful tools to probe reactions on industrial catalysts. The mechanistic understanding obtained from model catalyst studies can be used to create better catalysts. / text