ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 10 of 158
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
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
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
With the rapid growth of mobile computing and sensor technology, it is now possible to access data from a variety of sources. A big challenge lies in linking sensor based data with social and cognitive variables in humans in real world context. This dissertation explores the relationship between creativity in teamwork, and team members' movement and face-to-face interaction strength in the wild. Using sociometric badges (wearable sensors), electronic Experience Sampling Methods (ESM), the KEYS team creativity assessment instrument, and qualitative methods, three research studies were conducted in academic and industry R&D; labs. Sociometric badges captured movement of team members and face-to-face interaction between team members. KEYS scale was implemented using ESM for self-rated creativity and expert-coded creativity assessment. Activities (movement and face-to-face interaction) and creativity of one five member and two seven member teams were tracked for twenty five days, eleven days, and fifteen days respectively. Day wise values of movement and face-to-face interaction for participants were mean split categorized as creative and non-creative using self- rated creativity measure and expert-coded creativity measure. Paired-samples t-tests [t(36) = 3.132, p < 0.005; t(23) = 6.49 , p < 0.001] confirmed that average daily movement energy during creative days (M = 1.31, SD = 0.04; M = 1.37, SD = 0.07) was significantly greater than the average daily movement of non-creative days (M = 1.29, SD = 0.03; M = 1.24, SD = 0.09). The eta squared statistic (0.21; 0.36) indicated a large effect size. A paired-samples t-test also confirmed that face-to-face interaction tie strength of team members during creative days (M = 2.69, SD = 4.01) is significantly greater [t(41) = 2.36, p < 0.01] than the average face-to-face interaction tie strength of team members for non-creative days (M = 0.9, SD = 2.1). The eta squared statistic (0.11) indicated a large effect size. The combined approach of principal component analysis (PCA) and linear discriminant analysis (LDA) conducted on movement and face-to-face interaction data predicted creativity with 87.5% and 91% accuracy respectively. This work advances creativity research and provides a foundation for sensor based real-time creativity support tools for teams.
ContributorsTripathi, Priyamvada (Author) / Burleson, Winslow (Thesis advisor) / Liu, Huan (Committee member) / VanLehn, Kurt (Committee member) / Pentland, Alex (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis deals with the analysis of interpersonal communication dynamics in online social networks and social media. Our central hypothesis is that communication dynamics between individuals manifest themselves via three key aspects: the information that is the content of communication, the social engagement i.e. the sociological framework emergent of the communication process, and the channel i.e. the media via which communication takes place. Communication dynamics have been of interest to researchers from multi-faceted domains over the past several decades. However, today we are faced with several modern capabilities encompassing a host of social media websites. These sites feature variegated interactional affordances, ranging from blogging, micro-blogging, sharing media elements as well as a rich set of social actions such as tagging, voting, commenting and so on. Consequently, these communication tools have begun to redefine the ways in which we exchange information, our modes of social engagement, and mechanisms of how the media characteristics impact our interactional behavior. The outcomes of this research are manifold. We present our contributions in three parts, corresponding to the three key organizing ideas. First, we have observed that user context is key to characterizing communication between a pair of individuals. However interestingly, the probability of future communication seems to be more sensitive to the context compared to the delay, which appears to be rather habitual. Further, we observe that diffusion of social actions in a network can be indicative of future information cascades; that might be attributed to social influence or homophily depending on the nature of the social action. Second, we have observed that different modes of social engagement lead to evolution of groups that have considerable predictive capability in characterizing external-world temporal occurrences, such as stock market dynamics as well as collective political sentiments. Finally, characterization of communication on rich media sites have shown that conversations that are deemed "interesting" appear to have consequential impact on the properties of the social network they are associated with: in terms of degree of participation of the individuals in future conversations, thematic diffusion as well as emergent cohesiveness in activity among the concerned participants in the network. Based on all these outcomes, we believe that this research can make significant contribution into a better understanding of how we communicate online and how it is redefining our collective sociological behavior.
ContributorsDe Choudhury, Munmun (Author) / Sundaram, Hari (Thesis advisor) / Candan, K. Selcuk (Committee member) / Liu, Huan (Committee member) / Watts, Duncan J. (Committee member) / Seligmann, Doree D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms.
ContributorsSun, Liang (Author) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Liu, Huan (Committee member) / Mittelmann, Hans D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
As the information available to lay users through autonomous data sources continues to increase, mediators become important to ensure that the wealth of information available is tapped effectively. A key challenge that these information mediators need to handle is the varying levels of incompleteness in the underlying databases in terms of missing attribute values. Existing approaches such as Query Processing over Incomplete Autonomous Databases (QPIAD) aim to mine and use Approximate Functional Dependencies (AFDs) to predict and retrieve relevant incomplete tuples. These approaches make independence assumptions about missing values--which critically hobbles their performance when there are tuples containing missing values for multiple correlated attributes. In this thesis, I present a principled probabilis- tic alternative that views an incomplete tuple as defining a distribution over the complete tuples that it stands for. I learn this distribution in terms of Bayes networks. My approach involves min- ing/"learning" Bayes networks from a sample of the database, and using it do both imputation (predict a missing value) and query rewriting (retrieve relevant results with incompleteness on the query-constrained attributes, when the data sources are autonomous). I present empirical studies to demonstrate that (i) at higher levels of incompleteness, when multiple attribute values are missing, Bayes networks do provide a significantly higher classification accuracy and (ii) the relevant possible answers retrieved by the queries reformulated using Bayes networks provide higher precision and recall than AFDs while keeping query processing costs manageable.
ContributorsRaghunathan, Rohit (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2011