Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 10 of 203
Description
Fuel cells, particularly solid oxide fuel cells (SOFC), are important for the future of greener and more efficient energy sources. Although SOFCs have been in existence for over fifty years, they have not been deployed extensively because they need to be operated at a high temperature (∼1000 °C), are expensive, and have slow response to changes in energy demands. One important need for commercialization of SOFCs is a lowering of their operating temperature, which requires an electrolyte that can operate at lower temperatures. Doped ceria is one such candidate. For this dissertation work I have studied different types of doped ceria to understand the mechanism of oxygen vacancy diffusion through the bulk. Doped ceria is important because they have high ionic conductivities thus making them attractive candidates for the electrolytes of solid oxide fuel cells. In particular, I have studied how the ionic conductivities are improved in these doped materials by studying the oxygen-vacancy formations and migrations. In this dissertation I describe the application of density functional theory (DFT) and Kinetic Lattice Monte Carlo (KLMC) simulations to calculate the vacancy diffusion and ionic conductivities in doped ceria. The dopants used are praseodymium (Pr), gadolinium (Gd), and neodymium (Nd), all belonging to the lanthanide series. The activation energies for vacancy migration between different nearest neighbor (relative to the dopant) positions were calculated using the commercial DFT code VASP (Vienna Ab-initio Simulation Package). These activation energies were then used as inputs to the KLMC code that I co-developed. The KLMC code was run for different temperatures (673 K to 1073 K) and for different dopant concentrations (0 to 40%). These simulations have resulted in the prediction of dopant concentrations for maximum ionic conductivity at a given temperature.
ContributorsAnwar, Shahriar (Author) / Adams, James B (Thesis advisor) / Crozier, Peter (Committee member) / Krause, Stephen (Committee member) / Arizona State University (Publisher)
Created2011
Description
Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java code that runs within a JVM to interoperate with applications or libraries that are written in other languages and compiled to the host CPU ISA. JNI plays an important role in embedded system as it provides a mechanism to interact with libraries specific to the platform. This thesis addresses the overhead incurred in the JNI due to reflection and serialization when objects are accessed on android based mobile devices. It provides techniques to reduce this overhead. It also provides an API to access objects through its reference through pinning its memory location. The Android emulator was used to evaluate the performance of these techniques and we observed that there was 5 - 10 % performance gain in the new Java Native Interface.
ContributorsChandrian, Preetham (Author) / Lee, Yann-Hang (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2011
Description
As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily retrieving information from them given a user's information needs. Learning and using a structured query language (e.g., SQL and XQuery) is overwhelmingly burdensome for most users, as not only are these languages sophisticated, but the users need to know the data schema. Keyword search provides us with opportunities to conveniently access structured data and potentially significantly enhances the usability of structured data. However, processing keyword search on structured data is challenging due to various types of ambiguities such as structural ambiguity (keyword queries have no structure), keyword ambiguity (the keywords may not be accurate), user preference ambiguity (the user may have implicit preferences that are not indicated in the query), as well as the efficiency challenges due to large search space. This dissertation performs an expansive study on keyword search processing techniques as a gateway for users to access structured data and retrieve desired information. The key issues addressed include: (1) Resolving structural ambiguities in keyword queries by generating meaningful query results, which involves identifying relevant keyword matches, identifying return information, composing query results based on relevant matches and return information. (2) Resolving structural, keyword and user preference ambiguities through result analysis, including snippet generation, result differentiation, result clustering, result summarization/query expansion, etc. (3) Resolving the efficiency challenge in processing keyword search on structured data by utilizing and efficiently maintaining materialized views. These works deliver significant technical contributions towards building a full-fledged search engine for structured data.
ContributorsLiu, Ziyang (Author) / Chen, Yi (Thesis advisor) / Candan, Kasim S (Committee member) / Davulcu, Hasan (Committee member) / Jagadish, H V (Committee member) / Arizona State University (Publisher)
Created2011
Description
Amorphous oxide semiconductors are promising new materials for various optoelectronic applications. In this study, improved electrical and optical properties upon thermal and microwave processing of mixed-oxide semiconductors are reported. First, arsenic-doped silicon was used as a model system to understand susceptor-assisted microwave annealing. Mixed oxide semiconductor films of indium zinc oxide (IZO) and indium gallium zinc oxide (IGZO) were deposited by room-temperature RF sputtering on flexible polymer substrates. Thermal annealing in different environments - air, vacuum and oxygen was done. Electrical and optical characterization was carried out before and after annealing. The degree of reversal in the degradation in electrical properties of the thin films upon annealing in oxygen was assessed by subjecting samples to subsequent vacuum anneals. To further increase the conductivity of the IGZO films, Ag layers of various thicknesses were embedded between two IGZO layers. Optical performance of the multilayer structures was improved by susceptor-assisted microwave annealing and furnace-annealing in oxygen environment without compromising on their electrical conductivity. The post-processing of the films in different environments was used to develop an understanding of mechanisms of carrier generation, transport and optical absorption. This study establishes IGZO as a viable transparent conductor, which can be deposited at room-temperature and processed by thermal and microwave annealing to improve electrical and optical performance for applications in flexible electronics and optoelectronics.
ContributorsGadre, Mandar (Author) / Alford, Terry L. (Thesis advisor) / Schroder, Dieter (Committee member) / Krause, Stephen (Committee member) / Theodore, David (Committee member) / Arizona State University (Publisher)
Created2011
Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Many nanotechnology-related principles can be demonstrated in a way that is understandable for elementary school-aged children through at-home activity videos. As a part of a National Science Foundation funded grant, Dr. Qing Hua Wang’s research group at Arizona State University developed a nanotechnology-related activity website, Nano@Home, for students. In conjunction with ASU’s virtual Open Door 2021, this creative project aimed to create activity videos based on the Nano@Home website to make the activities more interactive for students.
ContributorsOliver, Ruth Kaylyn (Author) / Wang, Qing Hua (Thesis director) / Krause, Stephen (Committee member) / Materials Science and Engineering Program (Contributor, Contributor) / Watts College of Public Service & Community Solut (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Increasing demand for high strength powder metallurgy (PM) steels has resulted in the development of dual phase PM steels. In this work, the effects of thermal aging on the microstructure and mechanical behavior of dual phase precipitation hardened powder metallurgy (PM) stainless steels of varying ferrite-martensite content were examined. Quantitative analyses of the inherent porosity and phase fractions were conducted on the steels and no significant differences were noted with respect to aging temperature. Tensile strength, yield strength, and elongation to fracture all increased with increasing aging temperature reaching maxima at 538oC in most cases. Increased strength and decreased ductility were observed in steels of higher martensite content. Nanoindentation of the individual microconstituents was employed to obtain a fundamental understanding of the strengthening contributions. Both the ferrite and martensite hardness values increased with aging temperature and exhibited similar maxima to the bulk tensile properties. Due to the complex non-uniform stresses and strains associated with conventional nanoindentation, micropillar compression has become an attractive method to probe local mechanical behavior while limiting strain gradients and contributions from surrounding features. In this study, micropillars of ferrite and martensite were fabricated by focused ion beam (FIB) milling of dual phase precipitation hardened powder metallurgy (PM) stainless steels. Compression testing was conducted using a nanoindenter equipped with a flat punch indenter. The stress-strain curves of the individual microconstituents were calculated from the load-displacement curves less the extraneous displacements of the system. Using a rule of mixtures approach in conjunction with porosity corrections, the mechanical properties of ferrite and martensite were combined for comparison to tensile tests of the bulk material, and reasonable agreement was found for the ultimate tensile strength. Micropillar compression experiments of both as sintered and thermally aged material allowed for investigation of the effect of thermal aging.
ContributorsStewart, Jennifer (Author) / Chawla, Nikhilesh (Thesis advisor) / Jiang, Hanqing (Committee member) / Krause, Stephen (Committee member) / Arizona State University (Publisher)
Created2011
Description
This research work describes the design of a fault current limiter (FCL) using digital logic and a microcontroller based data acquisition system for an ultra fast pilot protection system. These systems have been designed according to the requirements of the Future Renewable Electric Energy Delivery and Management (FREEDM) system (or loop), a 1 MW green energy hub. The FREEDM loop merges advanced power electronics technology with information tech-nology to form an efficient power grid that can be integrated with the existing power system. With the addition of loads to the FREEDM system, the level of fault current rises because of increased energy flow to supply the loads, and this requires the design of a limiter which can limit this current to a level which the existing switchgear can interrupt. The FCL limits the fault current to around three times the rated current. Fast switching Insulated-gate bipolar transistor (IGBT) with its gate control logic implements a switching strategy which enables this operation. A complete simulation of the system was built on Simulink and it was verified that the FCL limits the fault current to 1000 A compared to more than 3000 A fault current in the non-existence of a FCL. This setting is made user-defined. In FREEDM system, there is a need to interrupt a fault faster or make intelligent deci-sions relating to fault events, to ensure maximum availability of power to the loads connected to the system. This necessitates fast acquisition of data which is performed by the designed data acquisition system. The microcontroller acquires the data from a current transformer (CT). Mea-surements are made at different points in the FREEDM system and merged together, to input it to the intelligent protection algorithm that has been developed by another student on the project. The algorithm will generate a tripping signal in the event of a fault. The developed hardware and the programmed software to accomplish data acquisition and transmission are presented here. The designed FCL ensures that the existing switchgear equipments need not be replaced thus aiding future power system expansion. The developed data acquisition system enables fast fault sensing in protection schemes improving its reliability.
ContributorsThirumalai, Arvind (Author) / Karady, George G. (Thesis advisor) / Vittal, Vijay (Committee member) / Hedman, Kory (Committee member) / Arizona State University (Publisher)
Created2011
Description
In recent years, the field of nanomedicine has progressed at an astonishing rate, particularly with respect to applications in cancer treatment and molecular imaging. Although organic systems have been the frontrunners, inorganic systems have also begun to show promise, especially those based upon silica and magnetic nanoparticles (NPs). Many of these systems are being designed for simultaneous therapeutic and diagnostic capabilities, thus coining the term, theranostics. A unique class of inorganic systems that shows great promise as theranostics is that of layered double hydroxides (LDH). By synthesis of a core/shell structures, e.g. a gold nanoparticle (NP) core and LDH shell, the multifunctional theranostic may be developed without a drastic increase in the structural complexity. To demonstrate initial proof-of-concept of a potential (inorganic) theranostic platform, a Au-core/LDH-shell nanovector has been synthesized and characterized. The LDH shell was heterogeneously nucleated and grown on the surface of silica coated gold NPs via a coprecipitation method. Polyethylene glycol (PEG) was introduced in the initial synthesis steps to improve crystallinity and colloidal stability. Additionally, during synthesis, fluorescein isothiocyanate (FITC) was intercalated into the interlayer spacing of the LDH. In contrast to the PEG stabilization, a post synthesis citric acid treatment was used as a method to control the size and short-term stability. The heterogeneous core-shell system was characterized with scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDX), dynamic light scattering (DLS), and powder x-ray diffraction (PXRD). A preliminary in vitro study carried out with the assistance of Dr. Kaushal Rege's group at Arizona State University was to demonstrate the endocytosis capability of homogeneously-grown LDH NPs. The DLS measurements of the core-shell NPs indicated an average particle size of 212nm. The PXRD analysis showed that PEG greatly improved the crystallinity of the system while simultaneously preventing aggregation of the NPs. The preliminary in vitro fluorescence microscopy revealed a moderate uptake of homogeneous LDH NPs into the cells.
ContributorsRearick, Colton (Author) / Dey, Sandwip K (Thesis advisor) / Krause, Stephen (Committee member) / Ramakrishna, B (Committee member) / Arizona State University (Publisher)
Created2011
Description
Most existing approaches to complex event processing over streaming data rely on the assumption that the matches to the queries are rare and that the goal of the system is to identify these few matches within the incoming deluge of data. In many applications, such as stock market analysis and user credit card purchase pattern monitoring, however the matches to the user queries are in fact plentiful and the system has to efficiently sift through these many matches to locate only the few most preferable matches. In this work, we propose a complex pattern ranking (CPR) framework for specifying top-k pattern queries over streaming data, present new algorithms to support top-k pattern queries in data streaming environments, and verify the effectiveness and efficiency of the proposed algorithms. The developed algorithms identify top-k matching results satisfying both patterns as well as additional criteria. To support real-time processing of the data streams, instead of computing top-k results from scratch for each time window, we maintain top-k results dynamically as new events come and old ones expire. We also develop new top-k join execution strategies that are able to adapt to the changing situations (e.g., sorted and random access costs, join rates) without having to assume a priori presence of data statistics. Experiments show significant improvements over existing approaches.
ContributorsWang, Xinxin (Author) / Candan, K. Selcuk (Thesis advisor) / Chen, Yi (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2011