The effects of the microstructure of metal films on electric component performance and longevity have become increasingly important with the recent advances in nanotechnology. Depending on the application of the metal films and interconnects, certain microscopic structures and properties are preferred over others. A common method to produce these films and interconnects is through electrodeposition. As with every process, the ability to control the end product requires a detailed understanding of the system and the effect of operating conditions on the resulting product. To address this problem, a three-dimensional on-lattice kinetic Monte Carlo (KMC) method is developed to conduct atomistic simulations of single crystal and polycrystalline metal electrodeposition. The method utilizes the semi-empirical multi-body embedded-atom method (EAM) potential that accounts for the cohesive forces in a metallic system. The resulting computational method, KMC-EAM, enables highly descriptive simulations of electrodeposition processes to be performed over experimentally relevant scales. In this work, kinetically controlled copper electrodeposition onto single crystal copper under galvanostatic direct-current conditions and polycrystalline copper under potentiostatic direct-current conditions is modelled using the aforementioned KMC method. Four types of surface processes are considered during electrodeposition: deposition, dissolution, surface diffusion and grain boundary diffusion. The equilibrium microstructures from single crystal experiments were validated using molecular dynamics (MD) simulations through the comparison of energy per atom and average coordination number. The growth mode observed is in agreement with experimental results for the same orientation of copper. MD simulation relaxes constraints and approximations resulting from the use of KMC. Results indicate that collective diffusion mechanisms are essential in order to accurately model the evolution of coating morphology during electrodeposition. In the polycrystalline simulations, the effect of surface energy is taken into account in the propensities of deposition and dissolution. Sub-surface grain volume measurements were obtained from simulation results and the grain volume evolution with time is in agreement with both qualitative observations based on the deposit morphology and surface energy calculations. Simulations of polycrystalline deposition agree with findings from experimental studies that the evolution of the root-mean-squared roughness of the deposit during the early stages of deposition follows a power law relationship with respect to time $\approx t^{n}$. Furthermore, the power law exponent on time is determined to be $n \approx 0.5$, also in agreement with the experimental values reported in the literature.

The self-assembly of proteins into filamentous structures underpins many aspects of biology, from dynamic cell scaffolding proteins such as actin, to the amyloid plaques responsible for a number of degenerative diseases. Typically, these self-assembly processes have been treated as nucleated, reversible polymerisation reactions, where dynamic fluctuations in a population of monomers eventually overcome an energy barrier, forming a stable aggregate that can then grow and shrink by the addition and loss of more protein from its ends. The nucleated, reversible polymerisation framework is very successful in describing a variety of protein systems such as the cell scaffolds actin and tubulin, and the aggregation of haemoglobin. Historically, amyloid fibrils were also thought to be described by this model, but measurements of their aggregation kinetics failed to match the model's predictions. Instead, recent work indicates that autocatalytic polymerisation - a process by which the number of growth competent species is increased through secondary nucleation, in proportion to the amount already present - is better at describing their formation. In this thesis, I will extend the predictions made in this mean-field, autocatalytic polymerisation model through use of kinetic Monte Carlo simulations. The ubiquitous sigmoid-like growth curve of amyloid fibril formation often possesses a notable quiescent lag phase which has been variously attributed to primary and secondary nucleation processes. Substantial variability in the length of this lag phase is often seen in replicate experimental growth curves, and naively may be attributed to fluctuations in one or both of these nucleation processes. By comparing analytic waiting-time distributions, to those produced by kinetic Monte Carlo simulation of the processes thought to be involved, I will demonstrate that this cannot be the case in sample volumes comparable with typical laboratory experiments. Experimentally, the length of the lag phase, or "lag time", is often found to scale with the total protein concentration, according to a power law with exponent γ. The models of nucleated polymerisation and autocatalytic polymerisation predict different values for this scaling exponent, and these are sometimes used to identify which of the models best describes a given protein system. I show that this approach is likely to result in a misidentification of the dominant mechanisms under conditions where the lag phase is dominated by a different process to the rest of the growth curve. Furthermore, I demonstrate that a change of the dominant mechanism associated with total protein concentration will produce "kinks" in the scaling of lag time with total protein concentration, and that these may be used to greater effect in identifying the dominant mechanisms from experimental kinetic data. Experimental data for bovine insulin aggregation, which is well described by the autocatalytic polymerisation model for low total protein concentrations, displays an intriguing departure from the predicted behaviour at higher protein concentrations. Additionally, the protein concentration at which the transition occurs, appears to be affected by the presence of salt. Coincident with this, an apparent change in the fibril structure indicates that different aggregation mechanisms may operate at different total protein concentrations. I demonstrate that a transition whereby the self-assembly mechanisms change once a critical concentration of fibrils or fibrillar protein is reached, can explain the observed behaviour and that this predicts a substantially higher abundance of shorter laments - which are thought to be pathogenic - at lower total protein concentrations than if self-assembly were consistently autocatalytic at all protein concentration. Amyloid-like loops have been observed in electron and atomic-force microscographs, together with non-looped fibrils, for a number of different proteins including ovalbumin. This implies that fibrils formed of these proteins are able to grow by fibrillar end-joining, and not only monomer addition as is more commonly assumed. I develop a simple analytic expression for polymerisation by monomer addition and fibrillar end-joining, (without autocatalysis) and show that this is not sufficient to explain the growth curves obtained experimentally for ovalbumin. I then demonstrate that the same data can be explained by combining fibrillar end-joining and fragmentation. Through the use of an analytic expression, I estimate the kinetic rates from the experimental growth curves and, via simulation, investigate the distribution of lament and loop lengths. Together, my findings demonstrate the relative importance of different molecular mechanisms in amyloid fibril formation, how these might be affected by various environmental parameters, and characteristic behaviour by which their involvement might be detected experimentally.

No description available.

Carbon nanotube bundles hold great promise for adsorption applications. However, most of the work done thus far has focused on the equilibrium properties of adsorption; the kinetics of adsorption is still not well understood. There also exist large discrepancies in the reported uptake of particles in the internal adsorption sites of carbon nanotube bundles. The purpose of this project was to elucidate the kinetics of adsorption in carbon nanotube bundles and to determine what kinetic factors, if any, may have caused the variations in experimental results. We studied the adsorption of particles in carbon nanotube bundles using analytical and computational techniques. By employing these separate but parallel methods, we were able to constantly compare and verify our results. We calculated and simulated the behavior of the system throughout its evolution and then analyzed our results to determine which system parameters had the greatest effect on the kinetics of adsorption. Our analytical and computational results showed good agreement with each other and with the experimental isotherm data provided by our collaborators. As a result of this project, we now know that the equilibration time of a system depends primarily on the binding energy of the adsorbates and the temperature. Specifically, the highest adsorption rates and shortest equilibration times are observed in systems with low binding energies and high temperatures. We also discovered that equilibration time for internal adsorption phases can be several orders of magnitude larger than those for external phases, which may have led to the disagreements in reported experimental results. Because of this work, we now better understand the process of equilibration.

The requirements for the next generation of nuclear reactors call for more radiation tolerant materials. One such material, nanostructured ferritic alloys (NFA) are a candidate material for use in cladding. The radiation tolerance of NFAs comes from the high number density of small oxide nanofeatures composed of Y, Ti, and O. These oxide nanofeatures or nano-oxides act as alternative nucleation sites for bubbles of transmutation He, thus preventing the accumulation of He atoms at the grain boundaries which would embrittle the metal. To further study the material, a mean-field rate theory model (MF-RTM) was created to simulate the radiation-induced segregation (RIS) of the alloy components Y, Ti, and O to the grain boundaries. Later, a kinetic Monte Carlo model (KMC) was made that replicated the results from the rate theory for the radiation induced segregation. Then the KMC model was modified to study the nano-oxide behavior in a range of different behaviors; the nano-oxide precipitation kinetics during heat treatment, resistance to dissolution under irradiation regimes similar to reactor conditions, and ability to trap He bubbles on the nano-oxide surfaces rather than the grain boundary. This KMC model is more complex than others as it includes 5 different atomic species (Fe, Y, Ti, O, and He) which migrate through three different mechanisms. Findings from the precipitation heat treatments were able to replicate the size, number density, and composition of nano-oxides from experiments and determined vacancy trapping at oxide interfaces was a significant for the NFA's coarsening resistance as opposed to interference from dislocations. In the irradiation simulations, the resistance of the nano-oxides to dissolution was confirmed and found the excess vacancy population plays an important role in healing the nano-oxides. He bubbles formed in the KMC simulations were found to preferentially form at the oxide interfaces, particularly the <111> interface, rather than the grain boundary and the characteristics of the He bubbles match expectations from literature. In the development of the KMC model, new insights into steady-state detection concepts were also found. A new type of steady-state detection (SSD) algorithm is described. Additionally, a method of forecasting the number of data points needed to make an accurate determination of steady-state, a 'predicting the pre-requisite to steady state detection' (ppSSD), is explored. / Doctor of Philosophy / Nuclear reactors need more radiation tolerant materials in the future, such as nanostructured ferritic alloys (NFA), used for nuclear fuel rod cladding, whose large amount of nanometer sized oxide particles contribute substantially to the radiation resistance of the metal overall. A mean-field rate theory method(MF-RTM) and a Kinetic Monte Carlo (KMC) computer model were made to study radiation induced segregation in the material. A more complex 5 element (Fe, Y, Ti, O, and He) KMC code was later developed to study the influence of the oxides at high temperatures and dose rates to gain insight into the causes the oxides remarkable thermal stability and resistance to irradiation. At all stages, the KMC model was able to replicate material behavior under high temperature heat treatment and irradiation. The model was used to simulate the formation of these oxides under different temperatures during their initial processing to gain more knowledge on how the oxide characteristics (size and number density) are influenced by temperature so we can tailor the processing method to achieve an ideal distribution of oxides in the material. Additionally, a mechanism for the oxides resistance to high temperature coarsening unrelated to the expected one caused by dislocations. The irradiation resistance of oxides to dissolution from irradiation was also investigated. While experimental measurements give a before and after picture of a material that underwent irradiation, the KMC can show the time evolution of the oxide size with increasing irradiation damage so the mechanisms behind the radiation resistance can be understood. The oxides remained stable at all temperatures and dose rates. Excess vacancies were found to play an important role in stabilizing the oxides against radiation damage. The KMC model also confirmed the ability of the oxides to trap transmutation He at the interfaces rather than the grain boundary and observed the process of He bubble nucleation. The He bubble form at the <111> oxide interface and they possess similar characteristics of He bubbles expected from literature. Additionally, a novel steady-state detection (SSD) algorithm was developed that can be used for long-term simulations and a method to determine how many data points the algorithm needs to accurately detect steady state is described here.

No description available.

Surface diffusion of group III and IV metals on Si(100) is studied. Three methods for obtaining diffusion barriers are presented and discrepancies in published results are discussed. Room temperature growth of Al on Si(100) is studied using STM, observing a monomodal scaled island size distribution function. A Kinetic Monte Carlo simulation model is employed to obtain bonding energies and diffusion barriers for Al/Si(100). The best agreement between the measured and simulated characteristics is found for strongly anisotropic diffusion with barriers 0.60 eV in the direction orthogonal to the Si dimer rows and 0.80 eV in the parallel direction. Modifications of the cooling system required for observing metal adatom diffusion on Si(100) using STM are described and the first low-temperature experiment is carried out.

A framework for constructing kinetic monte carlo (KMC) simulations of diffusive events on a lattice was developed. This code was then tested by running simulations of Fe adatom diffusion on graphene and graphene-boron nitride surfaces. The results from these simulations was then used to show that the modeled diffusion adheres to the laws of brownian motion and generates results similar to recent research findings.

Kinetic Monte Carlo(KMC) algorithm are used to simulate the evolution of Au cluster on the Au substrate . The morphology of the thin film and detailed diffusion mechanism can be demonstrated in this study. Molecular dynamics simulation (MD) is used to determine the energy barrier(activation energy) of an Au atom adsorbed on the clean surface of the substrate. Then, the thin cluster evolution process, surface reaction and surface diffusion of the Au atoms is modeled by KMC method. The morphologies of the clusters at different temperatures with different number of atoms are also investigated. The simulation results are compared with literature on experimental results to demonstrate the diffusion mechanism, which is difficultly observed in experiments. A three-dimensional KMC simulation model is used in this study. The results are compared with those from experiments in order to identity the reliability of this KMC model and to modify this model. Nudged Elastic band simulation is used to determine the adsorption energy barrier of an Au atom on a clean Au substrate surface. From the KMC result a surface diffusion of Au migration process is proposed. The effect of the substrate temperature, and the number of atoms duration on the morphology of the Au cluster is obtained. The simulation results show a pyramid structure is built and collapsed from the corner and edge atoms fellow suit and then the atoms of top layers do so as well. Then results indicate that it is possible to to produce nano-gold (metal)-pyramid from Au cluster s by 1, melting clusters in short period and then quenching them, or 2. Depositing Au atoms at lower temperature as well as 500 K with controlled rate.

In this thesis, we have studied the electronic and optical properties of solar energy m-terials. The studies are performed in the framework of density functional theory (DFT), GW, Bethe-Salpeter equation (BSE) approaches and Kinetic Monte Carlo (KMC). We present four sets of results. In the first part, we report our results on the band gap engineering issues for BiNbO4and NaTaO3, both of which are good photocatalysts. The band gap tuning is required for these materials in order to achieve the maximum solar to hydrogen conversion efficiency. The most common method for the band gap reduction is an introduction of foreign elements. The mono-doping in the system generates electrons or holes states near band edges, which reduce the efficiency of photocatalytic process. Co-doping with anion and cation or anion and anion can provide a clean band gap. We have shown that further band gap reduction can be achieved by double-hole mediated coupling between two anionic dopants. In the second part, the structure and optical properties of (CdSxSe1x)42nanoclusters have been studied. Within this study, the structures of the (CdS)42, (CdSe)42, Cd42Se32S10, Cd42Se22S20, and Cd42Se10S32 clusters have been determined using the simulated annealing method. Factors influencing the band gap value have been analyzed. We show that the gap is most significantly reduced when strongly under coordinated atoms are present on the surface of the nanoclusters. In addition, the band gap depends on the S concentration as well as on the distribution of the S and Se atoms in the clusters. We present the optical absorption spectra calculated with BSE and random phase approximation (RPA) methods based on the GW corrected quasiparticle energies. In the third part, we have employed the state-of-art computational methods to investigate the electronic structure and optical properties of TiO2high pressure polymorphs. GW and BSE methods have been used in these calculations. Our calculations suggest that the band gap of fluorite and pyrite phases have optimal values for the photocatalytic process of decomposing water in the visible light range. In the fourth part we have built a kinetic model of the first water monolayer growth on TiO2(110) using the kinetic Monte Carlo (KMC) method based on parameters describing water diffusion and dissociation obtained from first principle calculations. Our simulations reproduce the experimental trends and rationalize these observations in terms of a competition between different elementary processes. At high temperatures our simulation shows that the structure is well equilibrated, while at lower temperatures adsorbed water molecules are trapped in hydrogen-bonded chains around pairs of hydroxyl groups, causing the observed higher number of molecularly adsorbed species at lower temperature. / <p>QC 20140603</p>