Fast oxide-ion and proton conductors are the subject of considerable research due to their technological applications in sensors, ceramic membranes and solid oxide fuel cells (SOFCs). This thesis describes the use of computer modelling techniques to study point defects, dopants and clustering effects in fluorite-and perovskitetype ion conductors with potential SOFC applications. Bi2O3 related phases are being developed with the objective of high oxide-ion conductivities at lower operating temperatures than 1000°C, as in current generation SOFC electrolytes. Doped Bi2O3 phases have shown promise as materials capable of accomplishing this goal. First, the Y-doped phase, Bi3YO6, has been investigated including the ordering of intrinsic vacancies. The defect and dopant characteristics of Bi3YO6 have been examined and show that a highly mobile oxygen sub-lattice exists in this material. A preliminary structural modelling study of a new Re-doped Bi2O3 phase was also undertaken. A comprehensive investigation of the proton-conducting perovskites BaZrO3, BaPrO3 and BaThO3 is then presented. Our results suggest that intrinsic atomic disorder in BaZrO3 and BaThO3 is unlikely, but reduction of Pr4+ in BaPrO3 is favourable. The water incorporation energy is found to be less exothermic for BaZrO3 than for BaPrO3 and BaThO3, but in all cases the results suggest that the proton concentration would decrease with increasing temperature, in accord with experimental data. The high binding energies for all the dopant-OH pair clusters in BaPrO3 and BaThO3 suggest strong proton trapping effects. Finally, a study of multiferroic BiFeO3 is presented, in which the defect, dopant and migration properties of this highly topical phase are investigated. The reduction process involving the formation of oxygen vacancies and Fe2+ is the most favourable redox process. In addition, the results suggest that oxide-ion migration is anisotropic within this system.

Buried pipelines are vulnerable to the threat of corrosion. Hence, they are normally coated with a protective coating to isolate the metal substrate from the surrounding environment with the addition of CP current being applied to the pipeline surface to halt any corrosion activity that might be taking place. With time, this barrier will deteriorate which could potentially lead to corrosion of the pipe. The External Corrosion Direct Assessment (ECDA) methodology was developed with the intention of upholding the structural integrity of pipelines. Above ground indirect inspection techniques such as the DCVG which is an essential part of an ECDA, is commonly used to determine coating defect locations and measure the defect's severity. This is followed by excavation of the identified location for further examination on the extent of pipeline damage. Any coating or corrosion defect found at this stage is repaired and remediated. The location of such excavations is determined by the measurements obtained from the DCVG examination in the form of %IR and subjective inputs from experts which bases their justification on the environment and the physical characteristics of the pipeline. Whilst this seems to be a straight forward process, the factors that comes into play which gave rise to the initial %IR is not fully understood. The lack of understanding with the additional subjective inputs from the assessors has led to unnecessary excavations being conducted which has put tremendous financial strain on pipeline operators. Additionally, the threat of undiscovered defects due to the erroneous nature of the current method has the potential to severely compromise the pipeline's safe continual operation. Accurately predicting the coating defect size (TCDA) and interpretation of the indication signal (%IR) from an ECDA is important for pipeline operators to promote safety while keeping operating cost at a minimum. Furthermore, with better estimates, the uncertainty from the DCVG indication is reduced and the decisions made on the locations of excavation is better informed. However, ensuring the accuracy of these estimates does not come without challenges. These challenges include (1) the need of proper methods for large data analysis from indirect assessment and (2) uncertainty about the probability distribution of quantities. Standard mean regression models e.g. the OLS, were used but fail to take the skewness of the distributions involved into account. The aim of this thesis is thus, to come up with statistical models to better predict TCDA and to interpret the %IR from the indirect assessment of an ECDA more precisely. The pipeline data used for the analyses is based on a recent ECDA project conducted by TWI Ltd. for the Middle Eastern Oil Company (MEOC). To address the challenges highlighted above, Quantile Regression (QR) was used to comprehensively characterise the underlying distribution of the dependent variable. This can be effective for example, when determining the different effect of contributing variables towards different sizes of TCDA (different quantiles). Another useful advantage is that the technique is robust to outliers due to its reliance on absolute errors. With the traditional mean regression, the effect of contributing variables towards other quantiles of the dependent variable is ignored. Furthermore, the OLS involves the squaring of errors which makes it less robust to outliers. Other forms of QR such as the Bayesian Quantile Regression (BQR) which has the advantage of supplementing future inspection projects with prior data and the Logistic Quantile Regression (LQR) which ensures the prediction of the dependent variable is within its specified bounds was applied to the MEOC dataset. The novelty of research lies in the approaches (methods) taken by the author in producing the models highlighted above. The summary of such novelty includes: * The use of non-linear Quantile Regression (QR) with interacting variables for TCDA prediction. * The application of a regularisation procedure (LASSO) for the generalisation of the TCDA prediction model.* The usage of the Bayesian Quantile Regression (BQR) technique to estimate the %IR and TCDA. * The use of Logistic Regression as a guideline towards the probability of excavation * And finally, the use of Logistic Quantile Regression (LQR) in ensuring the predicted values are within bounds for the prediction of the %IR and POPD. Novel findings from this thesis includes: * Some degree of relationship between the DCVG technique (%IR readings) and corrosion dimension. The results of the relationship between TCDA and POPD highlights a negative trend which further supports the idea that %IR has some relation to corrosion. * Based on the findings from Chapter 4, 5 and 6 suggests that corrosion activity rate is more prominent than the growth of TCDA at its median depth. It is therefore suggested that for this set of pipelines (those belonging to MEOC) repair of coating defects should be done before the coating defect has reached its median size. To the best of the Author's knowledge, the process of employing such approaches has never been applied before towards any ECDA data. The findings from this thesis also shed some light into the stochastic nature of the evolution of corrosion pits. This was not known before and is only made possible by the usage of the approaches highlighted above. The resulting models are also of novelty since no previous model has ever been developed based on the said methods. The contribution to knowledge from this research is therefore the greater understanding of relationship between variables stated above (TCDA, %IR and POPD). With this new knowledge, one has the potential to better prioritise location of excavation and better interpret DCVG indications. With the availability of ECDA data, it is also possible to predict the magnitude of corrosion activity by using the models developed in this thesis. Furthermore, the knowledge gained here has the potential to translate into cost saving measures for pipeline operators while ensuring safety is properly addressed.

O presente trabalho apresenta uma alternativa ao processo de classificação do defeito da segregação central em amostras de aço, utilizando as imagens digitais que são geradas durante o ensaio de Baumann. O algoritmo proposto tem como objetivo agregar as técnicas de processamento digital de imagens e o conhecimento dos especialistas sobre o defeito da segregação central, visando a classificação do defeito de referência. O algoritmo implementado inclui a identificação e a segmentação da linha segregada por meio da aplicação da transformada de Hough e limiar adaptativo. Adicionalmente, o algoritmo apresenta uma proposta para o mapeamento dos atributos da segregação central nos diferentes graus de severidade do defeito, em função dos critérios de continuidade e intensidade. O mapeamento foi realizado por meio da análise das características individuais, como comprimento, largura e área, dos elementos segmentados que compõem a linha segregada. A avaliação do desempenho do algoritmo foi realizada em dois momentos específicos, de acordo com sua fase de implementação. Para a realização da avaliação, foram analisadas 255 imagens de amostras reais, oriundas de duas usinas siderúrgicas, distribuídas nos diferentes graus de severidade. Os resultados da primeira fase de implementação mostram que a identificação da linha segregada apresenta acurácia de 93%. As classificações oriundas do mapeamento realizado para as classes de criticidade do defeito, na segunda fase de implementação, apresentam acurácia de 92% para o critério de continuidade e 68% para o critério de intensidade. / This work presents an alternative to the classification process of centerline segregation in steel samples, using the digital images that are generated during the Baumann test. The proposed algorithm aims to aggregate the digital image processing techniques and experts knowledge on centerline segregation to classify the reference defect. The implemented algorithm includes the identification and segmentation of segregation line, applying the Hough transform and adaptive thresholding. Additionally, the algorithm presents a proposal for mapping the centerline segregation attributes on the different defect degrees of severity, according to the intensity and continuity criteria. The mapping was carried out by analyzing the individual characteristics such as length, width and area of the segmented elements that make up the segregation line. The evaluation of the algorithm performance was done in two specific moments, according to implementation phase. In carrying out this evaluation, 255 images of real samples from two steel plants were analyzed, distributed in different degrees of severity. The results of the first phase of implementation show that the identification of the segregation line has 93% accuracy. The classification results from the attributes mapping realized to the defect severity degrees in the second implementation phase, has accuracy of 92% for the continuity criteria and 68% for the intensity criteria.

abstract: Severe cases of congenital heart defect (CHD) require surgeries to fix the structural problem, in which artificial grafts are often used. Although outcome of surgeries has improved over the past decades, there remains to be patients who require re-operations due to graft-related complications and the growth of patients which results in a mismatch in size between the patient’s anatomy and the implanted graft. A graft in which cells of the patient could infiltrate, facilitating transformation of the graft to a native-like tissue, and allow the graft to grow with the patient heart would be ideal. Cardiac tissue engineering (CTE) technologies, including extracellular matrix (ECM)-based hydrogels has emerged as a promising approach for the repair of cardiac damage. However, most of the previous studies have mainly focused on treatments for ischemic heart disease and related heart failure in adults, therefore the potential of CTE for CHD treatment is underexplored. In this study, a hybrid hydrogel was developed by combining the ECM derived from cardiac tissue of pediatric CHD patients and gelatin methacrylate (GelMA). In addition, the influence of incorporating gold nanorods (GNRs) within the hybrid hydrogels was studied. The functionalities of the ECM-GelMA-GNR hydrogels as a CTE scaffold were assessed by culturing neonatal rat cardiomyocytes on the hydrogel. After 8 days of cell culture, highly organized sarcomeric alpha-actinin structures and connexin 43 expression were evident in ECM- and GNR-incorporated hydrogels compared to pristine GelMA hydrogel, indicating cell maturation and formation of cardiac tissue. The findings of this study indicate the promising potential of ECM-GelMA-GNR hybrid hydrogels as a CTE approach for CHD treatment. As another approach to improve CHD treatment, this study sought the possibility of performing a proteomic analysis on cardiac ECM of pediatric CHD patient tissue. As the ECM play important roles in regulating cell signaling, there is an increasing interest in studying the ECM proteome and the influences caused by diseases. Proteomics on ECM is challenging due to the insoluble nature of ECM proteins which makes protein extraction and digestion difficult. In this study, as a first step to perform proteomics, optimization on sample preparation procedure was attempted. / Dissertation/Thesis / Masters Thesis Biomedical Engineering 2018

The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections.

Replacing organic liquid electrolytes with nonflammable solid electrolytes can improve safety, offer higher volumetric and gravimetric energy densities, and lower the cost of lithium-ion batteries. However, today’s all-solid-state batteries suffer from low Li-ion conductivity in the electrolyte, slow Li-ion transport across the electrolyte/electrode interface, and slow solid-state Li-ion diffusion within the electrode. Defect chemistry is critical to understanding ionic conductivity and predicting the charge transport through heterogeneous solid interfaces. The goal of this dissertation is to analyze and improve solid state materials for energy storage applications by understanding their defect structure and transport properties. I have investigated defect chemistry of cubic Li7La3Zr2O12 (c-LLZO), one of the most promising candidate solid electrolytes for all-solid-state lithium batteries. By combining conductivity measurements with defect modeling, I constructed a defect diagram of c-LLZO featuring the intrinsic formation of lithium vacancy-hole pairs. The findings provided insights into tailoring single-phase mixed lithium-ion/electron conducting materials for emerging ionic devices, i.e., composite cathodes requiring both fast electronic and ionic paths in solid-state batteries. I suggested that oxygen vacancies could increase the Li-ion conductivity by reducing the amount of electron holes bound with lithium vacancies. Using a simpler but also attractive solid electrolyte Li2ZrO3 (LZO) as an example, I significantly improved Li-ion conductivity by creating extra oxygen vacancies via cation doping. In particular, Fe-doped LZO shows the highest Li-ion conductivity reported for the family of LZO compounds, reaching 3.3 mS/cm at 300 °C. This study brought attentions to the long-neglected oxygen vacancy defects in lithium-ion conductors and revealed their critical role in promoting Li-ion transport. More importantly, it established a novel defect engineering strategy for designing Li-oxide based solid electrolytes for all-solid-state batteries. I surface-modified LiNi0.6Co0.2Mn0.2O2 cathode material with a LZO coating prepared under dry air and oxygen, and systematically investigated the effect of coating atmosphere on their transport properties and electrochemical behaviors. The LZO coating prepared in oxygen is largely amorphous. It not only provided surface protection against the electrolyte corrosion but also enabled faster lithium-ion transport. Additionally, oxygen atmosphere facilitated Zr diffusion from the surface coating to the bulk of LiNi0.6Co0.2Mn0.2O2, which stabilized the crystal structure and enhanced lithium ion diffusion. Consequently, LiNi0.6Co0.2Mn0.2O2 cathodes coated with Li2ZrO3 in oxygen achieved a significant improvement in high-voltage cycling stability and high-rate performance.

This thesis devoted to the experimental studies of yellow and blue luminescence (YL and BL relatively) bands in Gallium Nitride samples doped with C and Si. The band BLC was at first observed in the steady-state photoluminescence spectrum under high excitation intensities and discerned from BL1 and BL2 bands appearing in the same region of the spectrum. Using the time-resolved photoluminescence spectrum, we were able to determine the shape of the BLC and its position at 2.87 eV. Internal quantum efficiency of the YL band was estimated to be 90\%. The hole capture coefficient of the BLC related state was determined as 7 10-10 cm3/s. Properties of BLC were investigated. The YL and BLC bands are attributed to electron transitions via the (0/-) and (+/0) transition levels of the CN defect.

One of the least controlled defects in Swiss cheese is development of splits. Split defect is characterized by fissures or cracks in the body of the cheese that can be as short as 1 cm in length or long enough to span a 90-kg block. This defect appears during refrigerated storage after the cheese is removed from the warm room. Swiss cheese with splits is downgraded because it is unsuitable for use on high-speed slicing equipment (up to 1,000 slices per minute). A 2x2x2 factorial experiment was used to determine the effect of different commercial Lactobacillus helveticus starters combined with commercial gas-forming strains of Propionibacterium freudenreichii ssp. shermanii on the occurrence of split defect in Swiss cheese. Two strains of L. helveticus recommended for Swiss cheese manufacture were used along with two strains of P. freudenreichii ssp. shermanii. The same strain of Streptococcus thermophilus was used in all treatments. To investigate the influence of seasonal variations in milk supply, eight vats were made in the summer and eight vats were made in the winter, each producing five 90-kg blocks of cheese. Each 90-kg block of cheese was cut into twenty-four 4-kg blocks, and each 4-kg block was graded based on the presence of splits. If splits were present, the cheese was downgraded from A to C grade. Only small variations were found in the composition of cheeses made during the same season. There were no correlations between cheese moisture, pH, fat, protein, calcium, lactose contents, D/L lactate ratio, or protein degradation that could be used to predict the amount of splits present after 90 d of storage. The extent of split formation was influenced by both the L. helveticus and P. freudenreichii ssp. shermanii cultures used. In this study, we were able to show a fivefold reduction in downgraded cheese through proper culture selection from 90% to 14% in the summer cheese. Even though less than 6% of the cheese split in the winter, the culture effect was nonetheless repeatable with a similar reduction through culture selection from 6% to 1% in winter cheese. Split formation also increases with storage time. If a cheese has a tendency to split, there will be a higher percentage of downgraded cheese the longer it is kept in storage.

A pink color defect is commonly observed in freshly cut surfaces of cooked turkey rolls and fades rapidly upon exposure to air. The non uniform pink color makes the product appear undercooked, and the product must be discounted. The oxidation-reduction potential of the meat is important in development of pink defect. A pink color similar to that of commercial product was observed when the cooked meat was treated with either sodium nitrite or sodium dithionite. The pink color in nitrite treated meat was due to nitroso pigment formation, but in samples treated with dithionite the pink color was due to formation of a hemochrome complex. Pink color was also observed in turkey rolls formulated with nicotinic acid, nicotinamide or sodium nitrite. Reflectance and absorbance spectrophotometric studies on commercial or laboratory prepared samples having pink defect showed that the responsible pigment was a reduced hemochrome rather than a nitroso pigment. The hemochrome is probably a nicotinamide-denatured globin complex with ferrous iron of the heme molecule. Oxidation-reduction potential measurement of meat systems showed that hemochrome formation is promoted by reducing conditions and prevented by oxidizing conditions. All constituents necessary for formation of pink defect are present in turkey meat, the variable most affecting its appearance being the redox potential of the meat.

The rise of organometal halide perovskite materials with extremely intriguing properties have opened a new horizon in the design of high speed and low price optoelectronic devices. The bandgap in the crystalline structure of these materials can be easily tuned for various applications and their dominant non-excitonic dynamics eliminate the requirement of a bulk or heterostructure for charge carrier separation. These unique properties increase the photo-sensitivity of perovskite-based optoelectronics and provide them with a low time constant, resulting in high precision fast devices. Realization of perovskite-based devices translates directly to inexpensive and simplified architectures of optoelectronic systems. In perovskite-based devices, costly silicon or wide bandgap semiconductor fabrication technology is largely replaced by solution processable methods. Their bandgap tunability allows the reduction of the required optical accessories and interconnects in optoelectronic components. For instance, a tuned perovskite-based detector can substitute a narrowband detecting system consisting of a conventional detector and its required optical accessories such as lenses and color filters. These properties of perovskite-based devices lead to the realization of inexpensive, low power and high-performance optoelectronic systems. In this work, the design of a narrowband, low noise, high performance and stable photodetector based on organic-inorganic hybrid perovskite structure is proposed. The full width at half maximum (FWHM) of the device would be in the nanometer range. The response of the device can be tuned using either different ratios of the lead salts or synthetic dyes (macromolecules) in the crystalline structure for color discrimination in machine vision and imaging applications. Non-excitonic photocarrier generation, tunability of the optical bandgap and low voltage requirements for charge carrier generation are the keys to the utility of this optoelectronic device. The goals of this project were to identify the required functional materials (lead salts and synthetic dyes based on their molecular structures) and optimize their performance; the study of their effect on the charge collection narrowing mechanism and bandwidth specifications defined for detectivity, linear dynamic range (LDR) and photoresponse speed. To achieve these goals, it was proposed to study the light detection properties as well as spectroscopic and semiconductor parameter characteristics of fabricated devices. The design considerations of such devices are versatile and may be modulated for different applications.