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- All Subjects: Computer Science
- Creators: Arizona State University
Description
Emergent environmental issues, ever-shrinking petroleum reserves, and rising fossil fuel costs continue to spur interest in the development of sustainable biofuels from renewable feed-stocks. Meanwhile, however, the development and viability of biofuel fermentations remain limited by numerous factors such as feedback inhibition and inefficient and generally energy intensive product recovery processes. To circumvent both feedback inhibition and recovery issues, researchers have turned their attention to incorporating energy efficient separation techniques such as adsorption in in situ product recovery (ISPR) approaches. This thesis focused on the characterization of two novel adsorbents for the recovery of alcohol biofuels from model aqueous solutions. First, a hydrophobic silica aerogel was evaluated as a biofuel adsorbent through characterization of equilibrium behavior for conventional second generation biofuels (e.g., ethanol and n-butanol). Longer chain and accordingly more hydrophobic alcohols (i.e., n-butanol and 2-pentanol) were more effectively adsorbed than shorter chain alcohols (i.e., ethanol and i-propanol), suggesting a mechanism of hydrophobic adsorption. Still, the adsorbed alcohol capacity at biologically relevant conditions were low relative to other `model' biofuel adsorbents as a result of poor interfacial contact between the aqueous and sorbent. However, sorbent wettability and adsorption is greatly enhanced at high concentrations of alcohol in the aqueous. Consequently, the sorbent exhibits Type IV adsorption isotherms for all biofuels studied, which results from significant multilayer adsorption at elevated alcohol concentrations in the aqueous. Additionally, sorbent wettability significantly affects the dynamic binding efficiency within a packed adsorption column. Second, mesoporous carbons were evaluated as biofuel adsorbents through characterization of equilibrium and kinetic behavior. Variations in synthetic conditions enabled tuning of specific surface area and pore morphology of adsorbents. The adsorbed alcohol capacity increased with elevated specific surface area of the adsorbents. While their adsorption capacity is comparable to polymeric adsorbents of similar surface area, pore morphology and structure of mesoporous carbons greatly influenced adsorption rates. Multiple cycles of adsorbent regeneration rendered no impact on adsorption equilibrium or kinetics. The high chemical and thermal stability of mesoporous carbons provide potential significant advantages over other commonly examined biofuel adsorbents. Correspondingly, mesoporous carbons should be further studied for biofuel ISPR applications.
ContributorsLevario, Thomas (Author) / Nielsen, David R (Thesis advisor) / Vogt, Bryan D (Committee member) / Lind, Mary L (Committee member) / Arizona State University (Publisher)
Created2011
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
Emission of CO2 into the atmosphere has become an increasingly concerning issue as we progress into the 21st century Flue gas from coal-burning power plants accounts for 40% of all carbon dioxide emissions. The key to successful separation and sequestration is to separate CO2 directly from flue gas (10-15% CO2, 70% N2), which can range from a few hundred to as high as 1000°C. Conventional microporous membranes (carbons/silicas/zeolites) are capable of separating CO2 from N2 at low temperatures, but cannot achieve separation above 200°C. To overcome the limitations of microporous membranes, a novel ceramic-carbonate dual-phase membrane for high temperature CO2 separation was proposed. The membrane was synthesized from porous La0.6Sr0.4Co0.8Fe0.2O3-d (LSCF) supports and infiltrated with molten carbonate (Li2CO3/Na2CO3/K2CO3). The CO2 permeation mechanism involves a reaction between CO2 (gas phase) and O= (solid phase) to form CO3=, which is then transported through the molten carbonate (liquid phase) to achieve separation. The effects of membrane thickness, temperature and CO2 partial pressure were studied. Decreasing thickness from 3.0 to 0.375 mm led to higher fluxes at 900°C, ranging from 0.186 to 0.322 mL.min-1.cm-2 respectively. CO2 flux increased with temperature from 700 to 900°C. Activation energy for permeation was similar to that for oxygen ion conduction in LSCF. For partial pressures above 0.05 atm, the membrane exhibited a nearly constant flux. From these observations, it was determined that oxygen ion conductivity limits CO2 permeation and that the equilibrium oxygen vacancy concentration in LSCF is dependent on the partial pressure of CO2 in the gas phase. Finally, the dual-phase membrane was used as a membrane reactor. Separation at high temperatures can produce warm, highly concentrated streams of CO2 that could be used as a chemical feedstock for the synthesis of syngas (H2 + CO). Towards this, three different membrane reactor configurations were examined: 1) blank system, 2) LSCF catalyst and 3) 10% Ni/y-alumina catalyst. Performance increased in the order of blank system < LSCF catalyst < Ni/y-alumina catalyst. Favorable conditions for syngas production were high temperature (850°C), low sweep gas flow rate (10 mL.min-1) and high methane concentration (50%) using the Ni/y-alumina catalyst.
ContributorsAnderson, Matthew Brandon (Author) / Lin, Jerry (Thesis advisor) / Alford, Terry (Committee member) / Rege, Kaushal (Committee member) / Anderson, James (Committee member) / Rivera, Daniel (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
Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
ContributorsPeng, Bo (Author) / Qian, Gang (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis describes a synthetic task environment, CyberCog, created for the purposes of 1) understanding and measuring individual and team situation awareness in the context of a cyber security defense task and 2) providing a context for evaluating algorithms, visualizations, and other interventions that are intended to improve cyber situation awareness. CyberCog provides an interactive environment for conducting human-in-loop experiments in which the participants of the experiment perform the tasks of a cyber security defense analyst in response to a cyber-attack scenario. CyberCog generates the necessary performance measures and interaction logs needed for measuring individual and team cyber situation awareness. Moreover, the CyberCog environment provides good experimental control for conducting effective situation awareness studies while retaining realism in the scenario and in the tasks performed.
ContributorsRajivan, Prashanth (Author) / Femiani, John (Thesis advisor) / Cooke, Nancy J. (Thesis advisor) / Lindquist, Timothy (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2011
Description
Alzheimer's Disease (AD) is a debilitating neurodegenerative disease. The disease leads to dementia and loss of cognitive functions and affects about 4.5 million people in the United States. It is the 7th leading cause of death and is a huge financial burden on the healthcare industry. There are no means of diagnosing the disease before neurodegeneration is significant and sadly there is no cure that controls its progression. The protein beta-amyloid or Aâ plays an important role in the progression of the disease. It is formed from the cleavage of the Amyloid Precursor Protein by two enzymes - â and ã-secretases and is found in the plaques that are deposits found in Alzheimer brains. This work describes the generation of therapeutics based on inhibition of the cleavage by â-secretase. Using in-vitro recombinant antibody display libraries to screen for single chain variable fragment (scFv) antibodies; this work describes the isolation and characterization of scFv that target the â-secretase cleavage site on APP. This approach is especially relevant since non-specific inhibition of the enzyme may have undesirable effects since the enzyme has been shown to have other important substrates. The scFv iBSEC1 successfully recognized APP, reduced â-secretase cleavage of APP and reduced Aâ levels in a cell model of Alzheimer's Disease. This work then describes the first application of bispecific antibody therapeutics to Alzheimer's Disease. iBSEC1 scFv was combined with a proteolytic scFv that enhances the "good" pathway (á-secretase cleavage) that results in alternative cleavage of APP to generate the bispecific tandem scFv - DIA10D. DIA10D reduced APP cleavage by â-secretase and steered it towards the "good" pathway thus increasing the generation of the fragment sAPPá which is neuroprotective. Finally, treatment with iBSEC1 is evaluated for reduced oxidative stress, which is observed in cells over expressing APP when they are exposed to stress. Recombinant antibody based therapeutics like scFv have several advantages since they retain the high specificity of the antibodies but are safer since they lack the constant region and are smaller, potentially facilitating easier delivery to the brain
ContributorsBoddapati, Shanta (Author) / Sierks, Michael (Thesis advisor) / Arizona State University (Publisher)
Created2011
Description
With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications of compressive sensing and sparse representation with regards to image enhancement, restoration and classication. The first application deals with image Super-Resolution through compressive sensing based sparse representation. A novel framework is developed for understanding and analyzing some of the implications of compressive sensing in reconstruction and recovery of an image through raw-sampled and trained dictionaries. Properties of the projection operator and the dictionary are examined and the corresponding results presented. In the second application a novel technique for representing image classes uniquely in a high-dimensional space for image classification is presented. In this method, design and implementation strategy of the image classification system through unique affine sparse codes is presented, which leads to state of the art results. This further leads to analysis of some of the properties attributed to these unique sparse codes. In addition to obtaining these codes, a strong classier is designed and implemented to boost the results obtained. Evaluation with publicly available datasets shows that the proposed method outperforms other state of the art results in image classication. The final part of the thesis deals with image denoising with a novel approach towards obtaining high quality denoised image patches using only a single image. A new technique is proposed to obtain highly correlated image patches through sparse representation, which are then subjected to matrix completion to obtain high quality image patches. Experiments suggest that there may exist a structure within a noisy image which can be exploited for denoising through a low-rank constraint.
ContributorsKulkarni, Naveen (Author) / Li, Baoxin (Thesis advisor) / Ye, Jieping (Committee member) / Sen, Arunabha (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