Matching Items (222)
Description
This dissertation provides a fundamental understanding of the impact of bulk polymer properties on the nanometer length scale modulus. The elastic modulus of amorphous organic thin films is examined using a surface wrinkling technique. Potential correlations between thin film behavior and intrinsic properties such as flexibility and chain length are explored. Thermal properties, glass transition temperature (Tg) and the coefficient of thermal expansion, are examined along with the moduli of these thin films. It is found that the nanometer length scale behavior of flexible polymers correlates to its bulk Tg and not the polymers intrinsic size. It is also found that decreases in the modulus of ultrathin flexible films is not correlated with the observed Tg decrease in films of the same thickness. Techniques to circumvent reductions from bulk modulus were also demonstrated. However, as chain flexibility is reduced the modulus becomes thickness independent down to 10 nm. Similarly for this series minor reductions in Tg were obtained. To further understand the impact of the intrinsic size and processing conditions; this wrinkling instability was also utilized to determine the modulus of small organic electronic materials at various deposition conditions. Lastly, this wrinkling instability is exploited for development of poly furfuryl alcohol wrinkles. A two-step wrinkling process is developed via an acid catalyzed polymerization of a drop cast solution of furfuryl alcohol and photo acid generator. The ability to control the surface topology and tune the wrinkle wavelength with processing parameters such as substrate temperature and photo acid generator concentration is also demonstrated. Well-ordered linear, circular, and curvilinear patterns are also obtained by selective ultraviolet exposure and polymerization of the furfuryl alcohol film. As a carbon precursor a thorough understanding of this wrinkling instability can have applications in a wide variety of technologies.
ContributorsTorres, Jessica (Author) / Vogt, Bryan D (Thesis advisor) / Stafford, Christopher M (Committee member) / Richert, Ranko (Committee member) / Rege, Kaushal (Committee member) / Dai, Lenore (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
Current economic conditions necessitate the extension of service lives for a variety of aerospace systems. As a result, there is an increased need for structural health management (SHM) systems to increase safety, extend life, reduce maintenance costs, and minimize downtime, lowering life cycle costs for these aging systems. The implementation of such a system requires a collaborative research effort in a variety of areas such as novel sensing techniques, robust algorithms for damage interrogation, high fidelity probabilistic progressive damage models, and hybrid residual life estimation models. This dissertation focuses on the sensing and damage estimation aspects of this multidisciplinary topic for application in metallic and composite material systems. The primary means of interrogating a structure in this work is through the use of Lamb wave propagation which works well for the thin structures used in aerospace applications. Piezoelectric transducers (PZTs) were selected for this application since they can be used as both sensors and actuators of guided waves. Placement of these transducers is an important issue in wave based approaches as Lamb waves are sensitive to changes in material properties, geometry, and boundary conditions which may obscure the presence of damage if they are not taken into account during sensor placement. The placement scheme proposed in this dissertation arranges piezoelectric transducers in a pitch-catch mode so the entire structure can be covered using a minimum number of sensors. The stress distribution of the structure is also considered so PZTs are placed in regions where they do not fail before the host structure. In order to process the data from these transducers, advanced signal processing techniques are employed to detect the presence of damage in complex structures. To provide a better estimate of the damage for accurate life estimation, machine learning techniques are used to classify the type of damage in the structure. A data structure analysis approach is used to reduce the amount of data collected and increase computational efficiency. In the case of low velocity impact damage, fiber Bragg grating (FBG) sensors were used with a nonlinear regression tool to reconstruct the loading at the impact site.
ContributorsCoelho, Clyde (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Wu, Tong (Committee member) / Das, Santanu (Committee member) / Rajadas, John (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Ordered mesoporous materials have tunable pore sizes between 2 and 50 nm and are characterized by ordered pore structures and high surface areas (~1000 m2/g). This makes them particularly favorable for a number of membrane applications such as protein separation, polymer extrusion, nanowire fabrication and membrane reactors. These membranes can be fabricated as top-layers on macroporous supports or as embedded membranes in a dense matrix. The first part of the work deals with the hydrothermal synthesis and water-vapor/oxygen separation properties of supported MCM-48 and a new Al-MCM-48 type membrane for potential use in air conditioning systems. Knudsen-type permeation is observed in these membranes. The combined effect of capillary condensation and the aluminosilicate matrix resulted in the highest separation factor (142) in Al-MCM-48 membranes, with a water vapor permeance of 6×10-8mol/m2Pas. The second part focuses on synthesis of embedded mesoporous silica membranes with helically ordered pores by a novel Counter Diffusion Self-Assembly (CDSA) method. This method is an extension of the interfacial synthesis method for fiber synthesis using tetrabutylorthosilicate (TBOS) and cetyltrimethylammonium bromide (CTAB) as the silica source and surfactant respectively. The initial part of this study determined the effect of TBOS height and humidity on fiber formation. From this study, the range of TBOS heights for best microscopic and macroscopic ordering were established. Next, the CDSA method was used to successfully synthesize membranes, which were characterized to have good support plugging and an ordered pore structure. Factors that influence membrane synthesis and plug microstructure were determined. SEM studies revealed the presence of gaps between the plugs and support pores, which occur due to shrinking of the plug on drying. Development of a novel liquid deposition method to seal these defects constituted the last part of this work. Post sealing, excess silica was removed by etching with hydrofluoric acid. Membrane quality was evaluated at each step using SEM and gas permeation measurements. After surfactant removal by liquid extraction, the membranes exhibited an O2 permeance of 1.65x10-6mol/m2.Pa.s and He/O2 selectivity of 3.30. The successful synthesis of this membrane is an exciting new development in the area of ordered mesoporous membrane technology.
ContributorsSeshadri, Shriya (Author) / Lin, Jerry Y. S. (Thesis advisor) / Dai, Lenore (Committee member) / Rege, Kaushal (Committee member) / Smith, David J. (Committee member) / Vogt, Bryan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Temporary bonding-debonding of flexible plastic substrates to rigid carriers may facilitate effective substrate handling by automated tools for manufacture of flexible microelectronics. The primary challenges in implementing practical temporary bond-debond technology originate from the stress that is developed during high temperature processing predominately through thermal-mechanical property mismatches between carrier, adhesive and substrate. These stresses are relaxed through bowing of the bonded system (substrate-adhesive-carrier), which causes wafer handling problems, or through delamination of substrate from rigid carrier. Another challenge inherent to flexible plastic substrates and linked to stress is their dimensional instability, which may manifest itself in irreversible deformation upon heating and cooling cycles. Dimensional stability is critical to ensure precise registration of different layers during photolithography. The global objective of this work is to determine comprehensive experimental characterization and develop underlying fundamental engineering concept that could enable widespread adoption and scale-up of temporary bonding processing protocols for flexible microelectronics manufacturing. A series of carriers with different coefficient of thermal expansion (CTE), modulus and thickness were investigated to correlate the thermo-mechanical properties of carrier with deformation behavior of bonded systems. The observed magnitude of system bow scaled with properties of carriers according to well-established Stoney's equation. In addition, rheology of adhesive impacted the deformation of bonded system. In particular, distortion-bowing behavior correlated directly with the relative loss factor of adhesive and flexible plastic substrate. Higher loss factor of adhesive compared to that of substrate allowed the stress to be relaxed with less bow, but led to significantly greater dimensional distortion. Conversely, lower loss factor of adhesive allowed less distortion but led to larger wafer bow. A finite element model using ANSYS was developed to predict the trend in bow-distortion of bonded systems as a function of the viscoelastic properties of adhesive. Inclusion of the viscoelasticity of flexible plastic substrate itself was critical to achieving good agreement between simulation and experiment. Simulation results showed that there is a limited range within which tuning the rheology of adhesive can control the stress-distortion. Therefore, this model can aid in design of new adhesive formulations compatible with different processing requirements of various flexible microelectronics applications.
ContributorsHaq, Jesmin (Author) / Raupp, Gregory B (Thesis advisor) / Vogt, Bryan D (Thesis advisor) / Dai, Lenore (Committee member) / Loy, Douglas (Committee member) / Li, Jian (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
Mesoporous materials that possess large surface area, tunable pore size, and ordered structures are attractive features for many applications such as adsorption, protein separation, enzyme encapsulation and drug delivery as these materials can be tailored to host different guest molecules. Films provide a model system to understand how the pore orientation impacts the potential for loading and release of selectively sized molecules. This research work aims to develop structure-property relationships to understand how pore size, geometry, and surface hydrophobicity influence the loading and release of drug molecules. In this study, the pore size is systematically varied by incorporating pore-swelling agent of polystyrene oligomers (hPS) to soft templated mesoporous carbon films fabricated by cooperative assembly of poly(styrene-block-ethylene oxide) (SEO) with phenolic resin. To examine the impact of morphology, different compositions of amphiphilic triblock copolymer templates, poly(ethylene oxide)-block-poly(propylene oxide)-block-poly(ethylene oxide) (PEO-PPO-PEO), are used to form two-dimensional hexagonal and cubic mesostructures. Lastly, the carbonization temperature provides a handle to tune the hydrophobicity of the film. These mesoporous films are then utilized to understand the uptake and release of a model drug Mitoxantrone dihydrochloride from nanostructured materials. The largest pore size (6nm) mesoporous carbon based on SEO exhibits the largest uptake (3.5μg/cm2); this is attributed to presence of larger internal volume compared to the other two films. In terms of release, a controlled response is observed for all films with the highest release for the 2nm cubic film (1.45 μg/cm2) after 15 days, but this is only 56 % of the drug loaded. Additionally, the surface hydrophobicity impacts the fraction of drug release with a decrease from 78% to 43%, as the films become more hydrophobic when carbonized at higher temperatures. This work provides a model system to understand how pore morphology, size and chemistry influence the drug loading and release for potential implant applications.
ContributorsLabiano, Alpha (Author) / Vogt, Bryan (Thesis advisor) / Rege, Kaushal (Committee member) / Dai, Lenore (Committee member) / Potta, Thrimoorthy (Committee member) / Arizona State University (Publisher)
Created2011
Description
Biological membranes are critical to cell sustainability by selectively permeating polar molecules into the intracellular space and providing protection to the interior organelles. Biomimetic membranes (model cell membranes) are often used to fundamentally study the lipid bilayer backbone structure of the biological membrane. Lipid bilayer membranes are often supported using inorganic materials in an effort to improve membrane stability and for application to novel biosensing platforms. Published literature has shown that a variety of dense inorganic materials with various surface properties have been investigated for the study of biomimetic membranes. However, literature does not adequately address the effect of porous materials or supports with varying macroscopic geometries on lipid bilayer membrane behavior. The objective of this dissertation is to present a fundamental study on the synthesis of lipid bilayer membranes supported by novel inorganic supports in an effort to expand the number of available supports for biosensing technology. There are two fundamental areas covered including: (1) synthesis of lipid bilayer membranes on porous inorganic materials and (2) synthesis and characterization of cylindrically supported lipid bilayer membranes. The lipid bilayer membrane formation behavior on various porous supports was studied via direct mass adsorption using a quartz crystal microbalance. Experimental results demonstrate significantly different membrane formation behaviors on the porous inorganic supports. A lipid bilayer membrane structure was formed only on SiO2 based surfaces (dense SiO2 and silicalite, basic conditions) and gamma-alumina (acidic conditions). Vesicle monolayer adsorption was observed on gamma-alumina (basic conditions), and yttria stabilized zirconia (YSZ) of varying roughness. Parameters such as buffer pH, surface chemistry and surface roughness were found to have a significant impact on the vesicle adsorption kinetics. Experimental and modeling work was conducted to study formation and characterization of cylindrically supported lipid bilayer membranes. A novel sensing technique (long-period fiber grating refractometry) was utilized to measure the formation mechanism of lipid bilayer membranes on an optical fiber. It was found that the membrane formation kinetics on the fiber was similar to its planar SiO2 counterpart. Fluorescence measurements verified membrane transport behavior and found that characterization artifacts affected the measured transport behavior.
ContributorsEggen, Carrie (Author) / Lin, Jerry Y.S. (Thesis advisor) / Dai, Lenore (Committee member) / Rege, Kaushal (Committee member) / Thornton, Trevor (Committee member) / Vogt, Bryan (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