This work is aimed at understanding the underlying processes of crystal growth in nanoporous materials at the molecular level utilising computational modelling. The coarse grain Monte Carlo program constructed over a number of works at the CNM has shown success in modelling cubic zeolite systems. The goal of this work is to adapt the program to deal with the complexities of a wide range of different crystal systems. There have been many studies of crystal growth and many problems solved. In zeolites, however, there are still a lot of questions to answer. Growth rates and activation energies for crystal growth processes in zeolites are some of the things that remain unsolved for zeolites. Coarse grain Monte Carlo modelling simplifies the problem and can provide an insight into the underlying processes that govern crystal growth. This study focused its energetics around the formation of stable closed cage surface structures deduced from careful study of the dissolution of zeolite L terraces. Two approaches from an energetic point of view were investigated during the course of this study. The first considered the energetics from an energy of attachment point of view whilst the second focused on the energy of destabilisation with respect to crystal bulk. In this study the crystal growth of the following systems were probed computationally: LTA, SOD, LTL, ERI, OFF. Both zeolite and MOF crystal systems were studied over the course of this work. The algorithm developed in study shows some potential in being able to give insight to experimental crystal growth chemists as to how changing the rates of growth of certain cage structures would affect the overall morphology of the crystal grown. They can then utilise their knowledge of how using certain cations or templates, for example, can alter the stabilisation of certain cage structures to in effect design crystals of desired properties.

The East African Community (EAC) suffers from a critical lack of cross-border railway networks that, if remedied, could improve regional connectivity and boost intraregional trade. The region would also become more investor friendly. Cross-border railway connectivity is particularly important owing to the challenging geographical location and small, uncompetitive and inefficient Partner States. The EAC Partner States have embarked on an ambitious programme to jointly revamp the region’s railways to address the transport deficits. Joint implementation of transport infrastructure projects offers economies of scale. However, joint efforts are constrained by inefficiencies of the region’s legal and institutional frameworks. The region is characterised by inefficient legal and institutional frameworks. This research argues that it is critical to first address the legal and institutional bottlenecks which will in turn constitute the backbone to support EAC’s efforts towards development and sustainable management of cross-border railways in the EAC. The research reviews effectiveness of the existing legal and institutional frameworks, identifies gaps and, using Southern African Development Community (SADC) as a benchmark proposes solutions for improvement. / Dissertation (LLM)--University of Pretoria, 2014. / gm2015 / Centre for Human Rights / LLM / Unrestricted

Este trabalho compara o desempenho e a estabilidade de dois arcabouços para o processamento de Big Data: Apache Spark e High Performance Analytics Toolkit (HPAT). A comparação foi realizada usando duas aplicações: soma dos elementos de um vetor unidimensional e o algoritmo de clusterização K-means. Os experimentos foram realizados em ambiente distribuído e com memória compartilhada com diferentes quantidades e configurações de máquinas virtuais. Analisando os resultados foi possível concluir que o HPAT tem um melhor desempenho em relação ao Apache Spark nos nossos casos de estudo. Também realizamos uma análise dos dois arcabouços com a presença de falhas. / This work compares the performance and stability of two Big Data processing tools: Apache Spark and High Performance Analytics Toolkit (HPAT). The comparison was performed using two applications: a unidimensional vector sum and the K-means clustering algorithm. The experiments were performed in distributed and shared memory environments with different numbers and configurations of virtual machines. By analyzing the results we are able to conclude that HPAT has performance improvements in relation to Apache Spark in our case studies. We also provide an analysis of both frameworks in the presence of failures.

<div>Data-processing programs are becoming increasingly important in the Big-data era. However, two notable problems of these programs may cause sub-optimal data- processing results. On one hand, these programs contain large number of floating-point computations. Due to the limited precision of floating-point representations, errors are introduced, propagated and accumulated in series of computations, making the computation results unreliable. We call this problem as floating-point instability. On the other hand, these programs are heavily parameterized. As no universal optimal parameter configuration exists for all possible inputs, the setting of program parameters should be carefully chosen and tuned for each input. Otherwise, the result would be sub-optimal. Manual tuning is infeasible because the number of parameters and the range of each parameter value may be big.</div><div><br></div><div>We try to address these two challenges in this dissertation. For floating-point instability problem, we develop a novel runtime technique to capture different output variations in the presence of instability. It features the idea of transforming every floating point value to a vector of multiple values $-$ the values added to create the vector are obtained by introducing artificial errors that are upper bounds of actual errors. The propagation of artificial errors models the propagation of actual errors. When values in vectors result in discrete execution differences (e.g., following different paths), the execution is forked to capture the resulting output variations.</div><div><br></div><div>For parameterized data-processing programs, we develop a white-box program tuning framework to tune the program parameter configuration for optimal data-processing result of each program input. </div><div>To further reduce the parameter configuration overhead, we propose the first general framework to inject artificial intelligence (AI) in the program, so the intelligent program is able to predict the parameter configuration for each incoming input directly. However, similar to many other ML/AI applications, the crucial challenge lies in feature selection, i.e., selection of the feature variables for predicting the target parameter specified by the users.</div><div>Thus, we propose a novel approach by combining program analysis and statistical analysis for better program feature variables selection which further helps better target parameter prediction and improves the result.</div>

Indigenous adoptee scholars across Turtle Island and beyond have done good work in coming to understand their identity through community connection, culture, education and practice. A plethora of research has guided young Indigenous interracial adoptees on their journey, yet there are few stories focused on the experiences of interracial Maya adoptees reconnecting to their culture in KKKanada. Currently there is limited research documenting Maya adoptees experiences of displacement and cultural reclamation in KKKanadian adoption studies. Research must make more space for these stories and the stories of local Indigenous communities supporting them. In this story (thesis), through engagement with current literature and ten research questions, I explored what it meant to live as an interracial adoptee in West Coast Indigenous communities. An Indigenous Youth Storywork methodology was applied to bring meaning to relationships I have with diverse Indigenous Old Ones, mentors and Knowledge Keepers and their influence on my journey as a Maya adoptee returning to my culture. My personal story was developed and analyzed using an Indigenous decolonial framework and Indigenous Arts-based methods. This storying journey sheds light on the intricate intersections of interracial adoption, specifically for Maya Indigenous Youth who currently live in KKKanada. The intention of this Youth Storywork research work is to create space for Indigenous, Interracial, Transracial and Maya adoptees in Child and Youth Care, Social Work and Counselling Psychology education, policy and practice. / Graduate / 2021-11-18

Gaining control over the assembly of crystalline solid-state materials has been significantly advanced through the field of reticular chemistry and metal organic frameworks (MOFs). MOFs have emerged as a unique modular class of porous materials amenable to a rational design with targeted properties for given applications. Several design approaches have been deployed to construct targeted functional MOFs, where desired structural and geometrical attributes are incorporated in preselected building units prior to the assembly process. This dissertation illustrates the merit of the molecular building block approach (MBB) for the rational construction and discovery of stable and highly porous MOFs, and their exploration as potential gas storage medium for sustainable and clean energy applications. Specifically, emphasis was placed on gaining insights into the structure-property relationships that impact the methane (CH4) storage in MOFs and its subsequent delivery. The foreseen gained understanding is essential for the design of new adsorbent materials or adjusting existing MOF platforms to encompass the desired features that subsequently afford meeting the challenging targets for methane storage in mobile and stationary applications.In this context, we report the successful use of the MBB approach for the design and deliberate construction of a series of novel isoreticular, highly porous and stable, aluminum based MOFs with the square-octahedral (soc) underlying net topology. From this platform, Al-soc-MOF-1, with more than 6000 m2/g apparent Langmuir specific surface area, exhibits outstanding gravimetric CH4 uptake (total and working capacities). It is shown experimentally, for the first time, that the Al-soc-MOF platform can address the U.S. Department of Energy (DOE) challenging gravimetric and volumetric targets for the CH4 working capacity for on-board CH4 storage. Furthermore, Al-soc-MOF-1 exhibits the highest total gravimetric and volumetric uptake for carbon dioxide and the utmost total and deliverable uptake for oxygen at relatively high pressures among all microporous MOFs. Additionally, the research studies presented in this dissertation highlight the latest discoveries on our continuous quest for highly-connected nets. Specifically, we report the discovery of two fascinating and highly-connected minimal edge-transitive nets in MOF chemistry, namely pek and aea topologies, via a systematic exploration of rare earth metal salts in combination with relatively less symmetrical 3-connected tricarboxylate ligands. Adsorption studies revealed that pek-MOF-1 offers excellent volumetric CO2 and CH4 uptakes at high pressures.

Synthesis of materials with certain properties for targeted applications is an ongoing challenge in materials science. One of the most interesting classes of solid-state materials that have been recently introduced with the potential to address this is metal-organic frameworks (MOFs). MOFs chemistry offers a higher degree of control over materials to be synthesized utilizing various new design strategies, such as the molecular building blocks (MBBs) and the supermolecular building layers (SBLs) approaches. Depending on using predetermined building blocks, these strategies permit the synthesis of MOFs with targeted topologies and enable fine tuning of their properties. This study examines a number of aspects of the design and synthesis of MOFs while exploring their possible utilization in two diverse fields related to energy and pharmaceutical applications. Concerning MOFs design and synthesis, the work presented here explores the rational design of various MOFs with predicted topologies and tunable cavities constructed by pillaring pre-targeted 2-periodic SBLs using the ligand-to-axial and six-connected axial-to-axial pillaring strategies. The effect of expanding the confined spaces in prepared MOFs or modifying their functionalities, while preserving the underlying network topology, was investigated. Additionally, The MBBs approach was employed to discover new modular polynuclear rare earth (RE)-MBBs in the presence of different angular polytopic ligands containing carboxylate and nitrogen moieties with the aid of a modulator. The goal was to assess the diverse possible coordination modes and construct highly-connected nets for utility in the design of new MOFs and enhance the predictability of structural outcomes. The effect of adjusting ligands’ length-to-width ratio on the prepared MOFs was also evaluated. As a result, the reaction conditions amenable for reliable formation of the unprecedented octadecanuclear, octanuclear and double tetranuclear RE-MBBs were isolated, and their corresponding MOFs were successfully synthesized and characterized. Regarding the applications of MOFs, gas sorption behavior of the novel prepared MOFs was studied to establish structure-property relationships that elucidate the effect of using different metals and/or ligands on tuning various properties of the prepared compounds. Furthermore, the magnetic properties of selected MOFs were investigated. Besides, as a proof-of-concept, known neutral and anionic MOFs were considered as potential drug delivery carriers.

The development and design of efficient catalysts are essential for catalytic energy technologies, accompanied with the fundamental understanding of structure-property relationships of these catalysts. Metal-organic frameworks (MOFs), as the new class of promising catalysts, have been intensively investigated primarily in their fundamental electrochemistry and the broad spectrum of catalytic applications due to their structural flexibility, tailorable crystalline, and multi-functionality. In this work, we combine experiments and mechanism investigation to gain a fundamental understanding of how the surface property and the structure of MOFs affect their catalytic performance. With the aim of material design for MOFs catalysts, we developed two novel superhydrophilic and aerophobic metal-organic frameworks (AlFFIVE-1-Ni MOFs and FeFFIVE-1-Ni MOFs) used as electrocatalysts for the first time during oxygen evolution reactions (OER). Under the facilitation of hydrophilicity and aerophobicity, developed FeFFIVE-1-Ni MOFs electrocatalysts deliver optimal OER performance, better than that of the state-of-art RuO2 and referred NiFe-BDC MOFs electrocatalysts. Most importantly, the practical strategy demonstrated that the hydrophilic and aerophobic structure of MOFs does indeed deliver the optimal electrocatalytic performance. With the aim of investigating the structural transformation process of metal-organic framework, we used a series of advanced characterization techniques to monitor the structure evolution and defects presence for post-heating treated UiO-66 MOFs. The structural and electronic features of UiO-66 MOFs were intensely studied in their hydroxylated, dehydroxylated, defected, and pyrolytic forms. Meanwhile, one concept about the framework situation, quasi-MOF (like a transition state, defined high activation along the structure evolution corresponding to the presence of many defects), was presented and demonstrated. Compared with pristine UiO-66 MOF, the Quasi-MOF with the presence of active defects showed enhanced catalytic activity on the Meerwein-Ponndorf-Verley reduction reaction, which offers an opportunity to understand the structure-property relationship along with the structure evolution process of UiO-66 MOFs.

Zeolitic imidazolate frameworks (ZIFs) is a new class of metal-organic frameworks (MOFs) with zeolite-like properties such as permanent porosity, uniform pore size, and exceptional thermal and chemical stability. Until recently, ZIF materials have been mostly synthesized by solvothermal method. In this thesis, further analysis to tune the size and morphology of ZIF-8 is done upon our group’s recent success in preparing ZIF-8 crystals in pure aqueous solutions. Compositional parameters (molar ratio of 2-methylimidazole/Zn2+, type of zinc salt reagents, reagent concentrations, addition of surfactants) as well as process parameters (temperature and time) were systematically investigated. Upon characterizations of as-synthesized samples by X-ray powder diffraction, thermal gravimetric analysis, N2 adsorption, and field-emission scanning electron microscope, the results show that the particle size and morphology of ZIF-8 crystals are extremely sensitive to the compotional parameters of reagent concentration and addition of surfactants. The particle size and morphology of hydrothermally synthesized ZIF-8 crystals can be finely tuned; with the size ranging from 90 nm to 4 μm and the shape from truncated cubic to rhombic dodecahedron.

Copper-based materials are one of the most promising catalysts for performing transformations of important organic compounds in both academic and industrial operations. However, it is challenging to consistently synthesize highly active and stable copper species as heterogeneous catalysts due to their relatively high surface energy. As a result, agglomeration usually occurs, which limits the catalytic activities of the copper species. The work presented in this dissertation shows different synthetic strategies for obtaining active and stable copper-based materials by modifying chemical/physical properties of copper nanoparticles (NPs). Emphasis is placed on discussing specific catalytic systems, including carbon-supported catalysts (monometallic and bimetallic copper-based heterogeneous catalysts) and titania-supported catalysts, and their advantages in terms of catalytic performance. In recent years, there has been increasing interest in using metal-organic frameworks (MOFs) as a sacrificial template to obtain carbon-supported NPs via a thermolysis process. The advantages of using MOFs to prepare carbon supported nanomaterials are a fine distribution of active particles on carbon matrix without post-synthesis treatments and corresponding increased catalytic activity and stability in many reaction conditions. To better understand the potential of this synthetic approach, MOF pyrolyzed products have been characterized. Then, they were applied as heterogeneous catalysts for several chemical reactions. In particular, the high energy copper-based MOF, CuNbO-1, was decomposed to obtain an amorphous copper species supported on carbon (a-Cu@C). This catalyst was found to be highly active for reduction, oxidation, and N-arylation reactions without further tuning or optimization. Higher catalyst turnover numbers for each of these transformations were obtained when comparing a-Cu@C activity to that of similar Cu-based materials. To improve catalyst performance, a secondary metal can be introduced to create synergistic effects with the parent copper species. In order to gain insights into the role of the second metal, a well-known Cu-MOF, HKUST-1, was doped with nickel, cobalt, and silver solutions, followed by a decomposition process with 2,4,6-trinitrotoluene (TNT) as additive. This additive was used to enhance the rapid thermolysis of the bimetallic MOFs. In these bimetallic systems, the addition of a second metal was found to help in dispersing both metals over the carbon composite support and in influencing the particle size and oxidation state of the metals. Catalytic performance showed that even <1% of a secondary metal increased the rate for nitrophenol reduction. Optimal catalytic performance was achieved using a Ni-CuO@C bimetallic catalyst. Another synthetic strategy for Cu-catalyst preparation involves using the deposition-precipitation method, in which a copper catalyst anchored on a titania support was synthesized at low weight % in order to obtain a single atom catalyst (1-Cu/TiO2). The higher copper loading catalyst, 5-Cu/TiO2, was synthesized as a benchmark catalyst for comparison. The copper structure in the synthesized catalysts was investigated by powder X-ray diffraction (PXRD), Raman, scanning transmission electron microscopy-energy dispersive X-ray spectroscopy (STEM-EDX), X-ray photoelectron spectroscopy (XPS), N2 physisorption and inductively coupled plasma mass spectrometry (ICP-MS) in order to characterize physical and chemical properties. STEM-EDX observations showed single atom copper species less than 0.75 nm in size, as well as nanoparticles with an average diameter of ~1.31 nm. This catalyst was highly active in the reduction of nitro-aromatic compounds with NaBH4 at room temperature. The small to atomic level sizes of the Cu species and multiple oxidation states of Ti species were found to play a crucial role in the catalytic activity.