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Description
Metal hydride materials have been intensively studied for hydrogen storage applications. In addition to potential hydrogen economy applications, metal hydrides offer a wide variety of other interesting properties. For example, hydrogen-dominant materials, which are hydrides with the highest hydrogen content for a particular metal/semimetal composition, are predicted to display high-temperature superconductivity. On the other side of the spectrum are hydrides with small amounts of hydrogen (0.1 - 1 at.%) that are investigated as viable magnetic, thermoelectric or semiconducting materials. Research of metal hydride materials is generally important to gain fundamental understanding of metal-hydrogen interactions in materials. Hydrogenation of Zintl phases, which are defined as compounds between an active metal (alkali, alkaline earth, rare earth) and a p-block metal/semimetal, were attempted by a hot sintering method utilizing an autoclave loaded with gaseous hydrogen (< 9 MPa). Hydride formation competes with oxidative decomposition of a Zintl phase. The oxidative decomposition, which leads to a mixture of binary active metal hydride and p-block element, was observed for investigated aluminum (Al) and gallium (Ga) containing Zintl phases. However, a new phase Li2Al was discovered when Zintl phase precursors were synthesized. Using the single crystal x-ray diffraction (SCXRD), the Li2Al was found to crystallize in an orthorhombic unit cell (Cmcm) with the lattice parameters a = 4.6404(8) Å, b = 9.719(2) Å, and c = 4.4764(8) Å. Increased demand for materials with improved properties necessitates the exploration of alternative synthesis methods. Conventional metal hydride synthesis methods, like ball-milling and autoclave technique, are not responding to the demands of finding new materials. A viable alternative synthesis method is the application of high pressure for the preparation of hydrogen-dominant materials. Extreme pressures in the gigapascal ranges can open access to new metal hydrides with novel structures and properties, because of the drastically increased chemical potential of hydrogen. Pressures up to 10 GPa can be easily achieved using the multi-anvil (MA) hydrogenations while maintaining sufficient sample volume for structure and property characterization. Gigapascal MA hydrogenations using ammonia borane (BH3NH3) as an internal hydrogen source were employed in the search for new hydrogen-dominant materials. Ammonia borane has high gravimetric volume of hydrogen, and additionally the thermally activated decomposition at high pressures lead to a complete hydrogen release at reasonably low temperature. These properties make ammonia borane a desired hydrogen source material. The missing member Li2PtH6 of the series of A2PtH6 compounds (A = Na to Cs) was accessed by employing MA technique. As the known heavier analogs, the Li2PtH6 also crystallizes in a cubic K2PtCl6-type structure with a cell edge length of 6.7681(3) Å. Further gigapascal hydrogenations afforded the compounds K2SiH6 and Rb2SiH6 which are isostructural to Li2PtH6. The cubic K2SiH6 and Rb2SiH6 are built from unique hypervalent SiH62- entities with the lattice parameters of 7.8425(9) and 8.1572(4) Å, respectively. Spectroscopic analysis of hexasilicides confirmed the presence of hypervalent bonding. The Si-H stretching frequencies at 1550 cm-1 appeared considerably decreased in comparison with a normal-valent (2e2c) Si-H stretching frequencies in SiH4 at around 2200 cm-1. However, the observed stretching modes in hypervalent hexasilicides were in a reasonable agreement with Ph3SiH2- (1520 cm-1) where the hydrogen has the axial (3e4c bonded) position in the trigoal bipyramidal environment.
ContributorsPuhakainen, Kati (Author) / Häussermann, Ulrich (Thesis advisor) / Seo, Dong (Thesis advisor) / Kouvetakis, John (Committee member) / Wolf, George (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Water resource management is becoming increasingly burdened by uncertain and fluctuating conditions resulting from climate change and population growth which place increased demands on already strained resources. Innovative water management schemes are necessary to address the reality of available water supplies. One such approach is the substitution of trade in virtual water for the use of local water supplies. This study provides a review of existing work in the use of virtual water and water footprint methods. Virtual water trade has been shown to be a successful method for addressing water scarcity and decreasing overall water consumption by shifting high water consumptive processes to wetter regions. These results however assume that all water resource supplies are equivalent regardless of physical location and they do not tie directly to economic markets. In this study we introduce a new mathematical framework, Embedded Resource Accounting (ERA), which is a synthesis of several different analytical methods presently used to quantify and describe human interactions with the economy and the natural environment. We define the specifics of the ERA framework in a generic context for the analysis of embedded resource trade in a way that links directly with the economics of that trade. Acknowledging the cyclical nature of water and the abundance of actual water resources on Earth, this study addresses fresh water availability within a given region. That is to say, the quantities of fresh water supplies annually available at acceptable quality for anthropogenic uses. The results of this research provide useful tools for water resource managers and policy makers to inform decision making on, (1) reallocation of local available fresh water resources, and (2) strategic supplementation of those resources with outside fresh water resources via the import of virtual water.
ContributorsAdams, Elizabeth Anne (Author) / Ruddell, Benjamin L (Thesis advisor) / Allenby, Braden R. (Thesis advisor) / Seager, Thomas P (Committee member) / Arizona State University (Publisher)
Created2013
Description
In a laboratory setting, the soil volume change behavior is best represented by using various testing standards on undisturbed or remolded samples. Whenever possible, it is most precise to use undisturbed samples to assess the volume change behavior but in the absence of undisturbed specimens, remodeled samples can be used. If that is the case, the soil is compacted to in-situ density and water content (or matric suction), which should best represent the expansive profile in question. It is standard practice to subject the specimen to a wetting process at a particular net normal stress. Even though currently accepted laboratory testing standard procedures provide insight on how the profile conditions changes with time, these procedures do not assess the long term effects on the soil due to climatic changes. In this experimental study, an assessment and quantification of the effect of multiple wetting/drying cycles on the volume change behavior of two different naturally occurring soils was performed. The changes in wetting and drying cycles were extreme when comparing the swings in matric suction. During the drying cycle, the expansive soil was subjected to extreme conditions, which decreased the moisture content less than the shrinkage limit. Nevertheless, both soils were remolded at five different compacted conditions and loaded to five different net normal stresses. Each sample was subjected to six wetting and drying cycles. During the assessment, it was evident from the results that the swell/collapse strain is highly non-linear at low stress levels. The strain-net normal stress relationship cannot be defined by one single function without transforming the data. Therefore, the dataset needs to be fitted to a bi-modal logarithmic function or to a logarithmic transformation of net normal stress in order to use a third order polynomial fit. It was also determined that the moisture content changes with time are best fit by non-linear functions. For the drying cycle, the radial strain was determined to have a constant rate of change with respect to the axial strain. However, for the wetting cycle, there was not enough radial strain data to develop correlations and therefore, an assumption was made based on 55 different test measurements/observations, for the wetting cycles. In general, it was observed that after each subsequent cycle, higher swelling was exhibited for lower net normal stress values; while higher collapse potential was observed for higher net normal stress values, once the net normal stress was less than/greater than a threshold net normal stress value. Furthermore, the swelling pressure underwent a reduction in all cases. Particularly, the Anthem soil exhibited a reduction in swelling pressure by at least 20 percent after the first wetting/drying cycle; while Colorado soil exhibited a reduction of 50 percent. After about the fourth cycle, the swelling pressure seemed to stabilized to an equilibrium value at which a reduction of 46 percent was observed for the Anthem soil and 68 percent reduction for the Colorado soil. The impact of the initial compacted conditions on heave characteristics was studied. Results indicated that materials compacted at higher densities exhibited greater swell potential. When comparing specimens compacted at the same density but at different moisture content (matric suction), it was observed that specimens compacted at higher suction would exhibit higher swelling potential, when subjected to the same net normal stress. The least amount of swelling strain was observed on specimens compacted at the lowest dry density and the lowest matric suction (higher water content). The results from the laboratory testing were used to develop ultimate heave profiles for both soils. This analysis showed that even though the swell pressure for each soil decreased with cycles, the amount of heave would increase or decrease depending upon the initial compaction condition. When the specimen was compacted at 110% of optimum moisture content and 90% of maximum dry density, it resulted in an ultimate heave reduction of 92 percent for Anthem and 685 percent for Colorado soil. On the other hand, when the soils were compacted at 90% optimum moisture content and 100% of the maximum dry density, Anthem specimens heave 78% more and Colorado specimens heave was reduced by 69%. Based on the results obtained, it is evident that the current methods to estimate heave and swelling pressure do not consider the effect of wetting/drying cycles; and seem to fail capturing the free swell potential of the soil. Recommendations for improvement current methods of practice are provided.
ContributorsRosenbalm, Daniel Curtis (Author) / Zapata, Claudia E (Thesis advisor) / Houston, Sandra L. (Committee member) / Kavazanjian, Edward (Committee member) / Witczak, Mathew W (Committee member) / Arizona State University (Publisher)
Created2013
Description
Statistics is taught at every level of education, yet teachers often have to assume their students have no knowledge of statistics and start from scratch each time they set out to teach statistics. The motivation for this experimental study comes from interest in exploring educational applications of augmented reality (AR) delivered via mobile technology that could potentially provide rich, contextualized learning for understanding concepts related to statistics education. This study examined the effects of AR experiences for learning basic statistical concepts. Using a 3 x 2 research design, this study compared learning gains of 252 undergraduate and graduate students from a pre- and posttest given before and after interacting with one of three types of augmented reality experiences, a high AR experience (interacting with three dimensional images coupled with movement through a physical space), a low AR experience (interacting with three dimensional images without movement), or no AR experience (two dimensional images without movement). Two levels of collaboration (pairs and no pairs) were also included. Additionally, student perceptions toward collaboration opportunities and engagement were compared across the six treatment conditions. Other demographic information collected included the students' previous statistics experience, as well as their comfort level in using mobile devices. The moderating variables included prior knowledge (high, average, and low) as measured by the student's pretest score. Taking into account prior knowledge, students with low prior knowledge assigned to either high or low AR experience had statistically significant higher learning gains than those assigned to a no AR experience. On the other hand, the results showed no statistical significance between students assigned to work individually versus in pairs. Students assigned to both high and low AR experience perceived a statistically significant higher level of engagement than their no AR counterparts. Students with low prior knowledge benefited the most from the high AR condition in learning gains. Overall, the AR application did well for providing a hands-on experience working with statistical data. Further research on AR and its relationship to spatial cognition, situated learning, high order skill development, performance support, and other classroom applications for learning is still needed.
ContributorsConley, Quincy (Author) / Atkinson, Robert K (Thesis advisor) / Nguyen, Frank (Committee member) / Nelson, Brian C (Committee member) / Arizona State University (Publisher)
Created2013
Description
The thesis studies new methods to fabricate optoelectronic Ge1-ySny/Si(100) alloys and investigate their photoluminescence (PL) properties for possible applications in Si-based photonics including IR lasers. The work initially investigated the origin of the difference between the PL spectrum of bulk Ge, dominated by indirect gap emission, and the PL spectrum of Ge-on-Si films, dominated by direct gap emission. It was found that the difference is due to the supression of self-absorption effects in Ge films, combined with a deviation from quasi-equilibrium conditions in the conduction band of undoped films. The latter is confirmed by a model suggesting that the deviation is caused by the shorter recombination lifetime in the films relative to bulk Ge. The knowledge acquired from this work was then utilized to study the PL properties of n-type Ge1-ySny/Si (y=0.004-0.04) samples grown via chemical vapor deposition of Ge2H6/SnD4/P(GeH3)3. It was found that the emission intensity (I) of these samples is at least 10x stronger than observed in un-doped counterparts and that the Idir/Iind ratio of direct over indirect gap emission increases for high-Sn contents due to the reduced gamma-L valley separation, as expected. Next the PL investigation was expanded to samples with y=0.05-0.09 grown via a new method using the more reactive Ge3H8 in place of Ge2H6. Optical quality, 1-um thick Ge1-ySny/Si(100) layers were produced using Ge3H10/SnD4 and found to exhibit strong, tunable PL near the threshold of the direct-indirect bandgap crossover. A byproduct of this study was the development of an enhanced process to produce Ge3H8, Ge4H10, and Ge5H12 analogs for application in ultra-low temperature deposition of Group-IV semiconductors. The thesis also studies synthesis routes of an entirely new class of semiconductor compounds and alloys described by Si5-2y(III-V)y (III=Al, V= As, P) comprising of specifically designed diamond-like structures based on a Si parent lattice incorporating isolated III-V units. The common theme of the two thesis topics is the development of new mono-crystalline materials on ubiquitous silicon platforms with the objective of enhancing the optoelectronic performance of Si and Ge semiconductors, potentially leading to the design of next generation optical devices including lasers, detectors and solar cells.
ContributorsGrzybowski, Gordon (Author) / Kouvetakis, John (Thesis advisor) / Chizmeshya, Andrew (Committee member) / Menéndez, Jose (Committee member) / Arizona State University (Publisher)
Created2013
Description
Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and proteins in biomedical literature is described. The first corpus for disease NER adequate for use as training data is introduced, and employed in a case study of disease NER. The first corpus locating adverse drug reactions (ADRs) in user posts to a health-related social website is also described, and a system to locate and identify ADRs in social media text is created and evaluated. The rich feature set approach to creating NER feature sets is argued to be subject to diminishing returns, implying that additional improvements may require more sophisticated methods for creating the feature set. This motivates the first application of multivariate feature selection with filters and false discovery rate analysis to biomedical NER, resulting in a feature set at least 3 orders of magnitude smaller than the set created by the rich feature set approach. Finally, two novel approaches to NER by modeling the semantics of token sequences are introduced. The first method focuses on the sequence content by using language models to determine whether a sequence resembles entries in a lexicon of entity names or text from an unlabeled corpus more closely. The second method models the distributional semantics of token sequences, determining the similarity between a potential mention and the token sequences from the training data by analyzing the contexts where each sequence appears in a large unlabeled corpus. The second method is shown to improve the performance of BANNER on multiple data sets.
ContributorsLeaman, James Robert (Author) / Gonzalez, Graciela (Thesis advisor) / Baral, Chitta (Thesis advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Nitrate is the most prevalent water pollutant limiting the use of groundwater as a potable water source. The overarching goal of this dissertation was to leverage advances in nanotechnology to improve nitrate photocatalysis and transition treatment to the full-scale. The research objectives were to (1) examine commercial and synthesized photocatalysts, (2) determine the effect of water quality parameters (e.g., pH), (3) conduct responsible engineering by ensuring detection methods were in place for novel materials, and (4) develop a conceptual framework for designing nitrate-specific photocatalysts. The key issues for implementing photocatalysis for nitrate drinking water treatment were efficient nitrate removal at neutral pH and by-product selectivity toward nitrogen gases, rather than by-products that pose a human health concern (e.g., nitrite). Photocatalytic nitrate reduction was found to follow a series of proton-coupled electron transfers. The nitrate reduction rate was limited by the electron-hole recombination rate, and the addition of an electron donor (e.g., formate) was necessary to reduce the recombination rate and achieve efficient nitrate removal. Nano-sized photocatalysts with high surface areas mitigated the negative effects of competing aqueous anions. The key water quality parameter impacting by-product selectivity was pH. For pH < 4, the by-product selectivity was mostly N-gas with some NH4+, but this shifted to NO2- above pH = 4, which suggests the need for proton localization to move beyond NO2-. Co-catalysts that form a Schottky barrier, allowing for localization of electrons, were best for nitrate reduction. Silver was optimal in heterogeneous systems because of its ability to improve nitrate reduction activity and N-gas by-product selectivity, and graphene was optimal in two-electrode systems because of its ability to shuttle electrons to the working electrode. "Environmentally responsible use of nanomaterials" is to ensure that detection methods are in place for the nanomaterials tested. While methods exist for the metals and metal oxides examined, there are currently none for carbon nanotubes (CNTs) and graphene. Acknowledging that risk assessment encompasses dose-response and exposure, new analytical methods were developed for extracting and detecting CNTs and graphene in complex organic environmental (e.g., urban air) and biological matrices (e.g. rat lungs).
ContributorsDoudrick, Kyle (Author) / Westerhoff, Paul (Thesis advisor) / Halden, Rolf (Committee member) / Hristovski, Kiril (Committee member) / Arizona State University (Publisher)
Created2013
Description
The overall objective of this project is to optimize the development of magnetic ferrite thin films targeted for enabling low-loss broadband communication devices, miniaturized low-microwave inductors and electromagnetic noise suppressors. The focus of this objective is to design and build a reactor and improve the spin-spray process. Each film is then characterized and optimized to have a high permeability and high frequency in the range of 500 MHz - 3 GHz. Films produced by the µ-droplet deposition regime yields a higher Snoek's product than the continuous liquid layer regime. The highest Snoek's product occurs when it is deposited at an oxidant pH of 8.28. The Ni-Zn-Co ferrite magnetic domains were imaged using the Lorentz TEM in which multi-grain domains are experimentally observed for the first time.
ContributorsRay, Nicole M (Author) / Petuskey, William T. (Thesis advisor) / Diaz, Rodolfo E. (Committee member) / Chamberlin, Ralph V. (Committee member) / Arizona State University (Publisher)
Created2013