The mol­ecular structure of ruthenocenecarbo­nitrile, [Ru([eta]5-C5H4C[triple bond]N)([eta]5-C5H5)], exhibits point group symmetry m, with the mirror plane bis­ecting the mol­ecule through the C[triple bond]N substituent. The RuII atom is slightly shifted from the [eta]5-C5H4 centroid towards the C[triple bond]N substituent. In the crystal, mol­ecules are arranged in columns parallel to [100]. One-dimensional inter­molecular [pi]-[pi] inter­actions [3.363 (3) Å] between the C[triple bond]N carbon atom and one carbon of the cyclo­penta­dienyl ring of the overlaying mol­ecule are present.

The principal aim of this thesis is to characterise quantitatively the influence of high temperature creep upon the structure of ß-NiAl and ß-NiAl(Fe) single crystals. A non-destructive procedure is established following the classic line of X-ray structure analysis, namely controlling the chemical composition with the electron probe microanalysis, determining the unit cell contents from the combined lattice parameter and mass density measurements, and refining the structure parameters according to the X-ray reflection intensity. Specifically, two special single crystal X-ray diffraction methods, namely the back reflection Kossel technique and the back reflection Laue method, are applied for the determination of lattice parameter and for the collection of intensity data. All experimental measurements can be performed in non-destructive manner, which allows a direct comparison to be made between the crystal structure determined prior to and after a creep test.

No description available.

Hafnium oxide is widely used for resistive switching devices, and recently it has been discovered that ferroelectricity can be established in (un-)doped hafnium oxide as well. Previous studies showed that both switching mechanisms are influenced by oxygen vacancies. For resistive switching, typically amorphous oxide layers with an asymmetric electrode configuration are used to create a gradient of oxygen vacancies. On the other hand, ferroelectric switching is performed by having symmetric electrodes and requires crystalline structures. The coexistence of both effects has recently been demonstrated. In this work, a detailed analysis of the reversible interplay of both switching mechanisms within a single capacitor cell is investigated. First, ferroelectric switching cycles were applied in order to drive the sample into the fatigued stage characterized by increased concentration of oxygen vacancies in the oxide layer. Afterwards, a forming step that is typical for the resistive switching devices was utilized to achieve a soft breakdown. In the next step, twofold alternation between the high and low resistance state is applied to demonstrate the resistive switching behavior of the device. Having the sample in the high resistance state with a ruptured filament, ferroelectric switching behavior is again shown within the same stack. Interestingly, the same endurance as before was observed without a hard breakdown of the device. Therefore, an effective sequence of ferroelectric—resistive—ferroelectric switching is realized. Additionally, the dependence of the forming, set, and reset voltage on the ferroelectric cycling stage (pristine, woken-up and fatigued) is analyzed giving insight into the physical device operation.

X-ray diffraction analysis (XRD) is an inexpensive method to quantify the relative proportions of mineral phases in a rock or soil sample. However, the analytical software available for XRD requires extensive user input to choose phases to include in the analysis. Consequently, analysis accuracy depends greatly on the experience of the analyst, especially as the number of phases in a sample increases (Raven & Self, 2017; Omotoso, 2006). The purpose of this project is to test whether incorporating machine learning methods into XRD software can improve the accuracy of analyses by assisting in the phase-picking process. In order to provide a large enough sample of X-ray diffraction (XRD) patterns and their known compositions to train the machine learning models, I created a dataset of 1.5 million calculated XRD patterns of realistic mineral mixtures. These synthetic XRD patterns were calculated using crystal structure files from the American Mineralogist Crystal Structure Database (AMCSD) with mineral occurrence data from the Mineral Evolution Database (MED) to mimic geologic knowledge used by expert analysts. Using this dataset, I trained and refined a variety of machine learning models to determine which model is most accurate in identifying the correct mineral phases.

The disproportionation of H₂O₂ to H₂O and molecular O₂ catalyzed by platinum nanocatalysts is technologically very important in several energy conversion technologies, such as steam propellant thrust applications and hydrogen fuel cells. However, the mechanism of H₂O₂ decomposition on platinum has been unresolved for more than 100 years and the kinetics of this reaction were poorly understood. Our goal was to quantify the effect of reaction conditions and catalyst properties on the decomposition of H₂O₂ by platinum nanocatalysts and determine the mechanism and rate-limiting step of the reaction. To this end, we have characterized two commercial platinum nanocatalysts, known as platinum black and platinum nanopowder, and studied the effect of different reaction conditions on their rates of H₂O₂ decomposition. These samples have different particle size and surface chemisorbed oxygen abundance, which were varied further by pretreating both samples at variable conditions. The rate of H₂O₂ decomposition was studied systematically as a function of H₂O₂ concentration, pH, temperature, particle size and surface chemisorbed oxygen abundance. The mechanism of H₂O₂ decomposition on platinum proceeds via two cyclic oxidation-reduction steps. Step 1 is the rate limiting step of the reaction. Step 1: Pt + H₂O₂ → H₂O + Pt(O). Step 2: Pt(O) + H₂O₂ → Pt + O₂ + H₂O. Overall: 2 H₂O₂ → O₂ + 2 H₂O. The decomposition of H₂O₂ on platinum follows 1st order kinetics in terms of H₂O₂ concentration. The effect of pH is small, yet statistically significant. The rate constant of step 2 is 13 times higher than that of step 1. Incorporation of chemisorbed oxygen at the nanocatalyst surface resulted in higher initial rate of H₂O₂ decomposition because more sites initiate their cyclic process in the faster step of the reaction. Particle size does not affect the kinetics of the reaction. This new molecular-scale understanding of the decomposition of H₂O₂ by platinum is expected to help advance many energy technologies that depend on the rate of H₂O₂ decomposition on nanocatalysts of platinum and other metals. / Ph. D. / Platinum nanomaterials are indispensable to catalyze a variety of industrial and technological processes ranging from catalytic conversion of carbon monoxide (CO), hydrocarbons, and nitrogen oxides (NO_x) in modern automobiles to energy production by hydrogen fuel cells and thrust generation in steam propellers. These technological innovations have a tremendous impact in modern society, including the areas of transportation, energy supply, soil and water quality, environmental remediation and global climate change. The decomposition of hydrogen peroxide (H₂O₂) to water (H₂O) and oxygen (O₂) on platinum nanomaterials is of particular importance because it affects the efficacy of many technological applications, such as hydrogen peroxide steam propellers and hydrogen fuel cells. However, the reaction pathway and kinetics of H₂O₂ decomposition on platinum were only partly understood. My goal was to understand how the reaction conditions and the nanocatalyst properties control the mechanism and kinetics of platinum-catalyzed hydrogen peroxide decomposition. To do that we characterized the atomic scale structural and chemical properties of two different platinum nanocatalysts, known as platinum black and platinum nanopowder and evaluated the effect of their properties in their catalytic activity. Our characterization studies were used to understand the reactivity of these two platinum nanocatalysts in the decomposition of H₂O₂, which we evaluated separately in laboratory studies. Establishing relationships between the catalyst properties and their activity, as we have done in this work for platinum nanocatalysts in the decomposition of hydrogen peroxide, has the potential to improve nanocatalyst design and performance for those applications.

<p> The mechanisms operating during the metal-assisted growth of III-V nanowires (NWs) by molecular beam epitaxy on (1 1 l)B substrates were investigated through a series of experiments aimed at determining the influence of growth conditions on the morphology and crystal structure. Using GaAs as the principal material system for these studies, it is shown that a good control of these two characteristics can be achieved via a tight control of the temperature, V /III flux ratio, and Ga flux. Low and intermediate growth temperatures of 400°C and 500°C resulted in a strongly tapered morphology, with stacking faults occurring at an average rate of 0.1 nm^(-1). NWs with uniform diameter and the occurrence of crystal defects reduced by more than an order of magnitude were achieved at 600°C, a V /III flux ratio of 2.3, and a Ga impingement rate on the surface of 0.07 nm/s, and suggest the axial growth is group V limited. Increasing the flux ratio favored uniform sidewall growth, thus making the process suitable for the fabrication of core-shell structures. Further observation of steps on the sidewall surface of strongly tapered NWs suggests that radial growth of the shell proceeds in a layer-by-layer fashion, with the edge progressing in a step-flow mode toward the tip. </p> <p> From the experimental considerations, an analytical description of the growth is proposed, based on a simple material conservation model. Direct impingement of growth species on the particle, coupled to their diffusion from the sidewall and the substrate surface, are considered in the derivation of expressions for the time evolution of both axial and radial growths. Factors that take into account the nonunity probability of inclusion of group III adatoms in the axially growing crystal are introduced. Moreover, a step-mediated growth is included to describe the axial evolution of the shell. </p> / Thesis / Doctor of Philosophy (PhD)

Electron Backscatter Diffraction (EBSD) is a well-developed technique used to perform quantitative microstructure analysis in the Scanning Electron Microscope (SEM); however, it has not been widely applied towards studying nanostructures. This work focuses on the use and limitations of EBSD in the characterization of Au nanoparticles on an MgAl2O4 substrate. Samples under investigation are prepared by depositing a thin film of Au on an MgAl2O4 substrate, and then finally heated in a furnace to induce dewetting and cluster formation. The challenges of obtaining crystallographic information from nanoparticles using EBSD are qualitatively and quantitatively described through an evaluation of the quality of the diffraction pattern at various locations of the primary electron beam on the nanoparticle. It is determined that for a high quality Electron Backscatter Diffraction Pattern (EBSP), the production of diffracted backscattered electrons travelling towards the detector must be high and the depth of the source point must be low. The top of the nanoparticle, where the local geometry of the system is similar to the geometry of a macroscopically flat sample, is found to produce diffraction patterns of the highest quality. On the other hand, reversed-contrast EBSPs are observed when the beam is positioned near the bottom of the nanoparticle. In addition, crystallographic information for each individual nanoparticle is gathered using EBSD. Each individual AuNP is observed to be single crystalline. Finally, the complete ensemble of crystalline orientations for individual nanoparticles is then compared to the global averaged crystallinity of the sample, as measured by X-ray diffraction. These results show that EBSD promises to be a powerful and robust technique in the characterization of nanoparticles. / Thesis / Master of Applied Science (MASc)

The irradiation of crystalline materials in environments such as nuclear reactors leads to the accumulation of micro and nano-scale defects with a negative impact on material properties such as strength, corrosion resistance, and dimensional stability. Point defects in the crystal lattice, the vacancy and self-interstitial, form the basis of this damage and are capable of migrating through the lattice to become part of defect clusters and sinks, or to annihilate themselves. Recently, attention has been given to HEAs for fusion and fission components, as some materials of this class have shown resilience to irradiation-induced damage. The ability to predict defect diffusion and accelerate simulations of defect behaviour in HEAs using ML techniques is consequently a subject that has gathered significant interest. The goal of this work was to produce an unsupervised neural network capable of learning the interatomic dynamics within a specific HEA system from MD data in order to create a KMC type predictor of defect diffusion paths for common point defects in crystal systems such as the vacancy and self-interstitial. Self-interstitial defect states were identified and purified from MD datasets using graph-isomorphism, and a proof-of-concept model for the HEA environment was used with several interaction setups to demonstrate the feasibility of training a GCN to predict vacancy defect transition rates in the HEA crystalline environment.

No description available.