Matching Items (214)
Description
With the increasing focus on developing environmentally benign electronic packages, lead-free solder alloys have received a great deal of attention. Mishandling of packages, during manufacture, assembly, or by the user may cause failure of solder joint. A fundamental understanding of the behavior of lead-free solders under mechanical shock conditions is lacking. Reliable experimental and numerical analysis of lead-free solder joints in the intermediate strain rate regime need to be investigated. This dissertation mainly focuses on exploring the mechanical shock behavior of lead-free tin-rich solder alloys via multiscale modeling and numerical simulations. First, the macroscopic stress/strain behaviors of three bulk lead-free tin-rich solders were tested over a range of strain rates from 0.001/s to 30/s. Finite element analysis was conducted to determine appropriate specimen geometry that could reach a homogeneous stress/strain field and a relatively high strain rate. A novel self-consistent true stress correction method is developed to compensate the inaccuracy caused by the triaxial stress state at the post-necking stage. Then the material property of micron-scale intermetallic was examined by micro-compression test. The accuracy of this measure is systematically validated by finite element analysis, and empirical adjustments are provided. Moreover, the interfacial property of the solder/intermetallic interface is investigated, and a continuum traction-separation law of this interface is developed from an atomistic-based cohesive element method. The macroscopic stress/strain relation and microstructural properties are combined together to form a multiscale material behavior via a stochastic approach for both solder and intermetallic. As a result, solder is modeled by porous plasticity with random voids, and intermetallic is characterized as brittle material with random vulnerable region. Thereafter, the porous plasticity fracture of the solders and the brittle fracture of the intermetallics are coupled together in one finite element model. Finally, this study yields a multiscale model to understand and predict the mechanical shock behavior of lead-free tin-rich solder joints. Different fracture patterns are observed for various strain rates and/or intermetallic thicknesses. The predictions have a good agreement with the theory and experiments.
ContributorsFei, Huiyang (Author) / Jiang, Hanqing (Thesis advisor) / Chawla, Nikhilesh (Thesis advisor) / Tasooji, Amaneh (Committee member) / Mobasher, Barzin (Committee member) / Rajan, Subramaniam D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
The high strength to weight ratio of woven fabric offers a cost effective solution to be used in a containment system for aircraft propulsion engines. Currently, Kevlar is the only Federal Aviation Administration (FAA) approved fabric for usage in systems intended to mitigate fan blade-out events. This research builds on an earlier constitutive model of Kevlar 49 fabric developed at Arizona State University (ASU) with the addition of new and improved modeling details. Latest stress strain experiments provided new and valuable data used to modify the material model post peak behavior. These changes reveal an overall improvement of the Finite Element (FE) model's ability to predict experimental results. First, the steel projectile is modeled using Johnson-Cook material model and provides a more realistic behavior in the FE ballistic models. This is particularly noticeable when comparing FE models with laboratory tests where large deformations in projectiles are observed. Second, follow-up analysis of the results obtained through the new picture frame tests conducted at ASU provides new values for the shear moduli and corresponding strains. The new approach for analysis of data from picture frame tests combines digital image analysis and a two-level factorial optimization formulation. Finally, an additional improvement in the material model for Kevlar involves checking the convergence at variation of mesh density of fabrics. The study performed and described herein shows the converging trend, therefore validating the FE model.
ContributorsMorea, Mihai I (Author) / Rajan, Subramaniam D. (Thesis advisor) / Arizona State University (Publisher)
Created2011
Description
In the 1970s James Watson recognized the inability of conventional DNA replication machinery to replicate the extreme termini of chromosomes known as telomeres. This inability is due to the requirement of a building block primer and was termed the end replication problem. Telomerase is nature's answer to the end replication problem. Telomerase is a ribonucleoprotein which extends telomeres through reverse transcriptase activity by reiteratively copying a short intrinsic RNA sequence to generate 3' telomeric extensions. Telomeres protect chromosomes from erosion of coding genes during replication, as well as differentiate native chromosome ends from double stranded breaks. However, controlled erosion of telomeres functions as a naturally occurring molecular clock limiting the replicative capacity of cells. Telomerase is over activated in many cancers, while inactivation leads to multiple lifespan limiting human diseases. In order to further study the interaction between telomerase RNA (TR) and telomerase reverse transcriptase protein (TERT), vertebrate TERT fragments were screened for solubility and purity following bacterial expression. Soluble fragments of medaka TERT including the RNA binding domain (TRBD) were identified. Recombinant medaka TRBD binds specifically to telomerase RNA CR4/CR5 region. Ribonucleotide and amino acid pairs in close proximity within the medaka telomerase RNA-protein complex were identified using photo-activated cross-linking in conjunction with mass spectrometry. The identified cross-linking amino acids were mapped on known crystal structures of TERTs to reveal the RNA interaction interface of TRBD. The identification of this RNA TERT interaction interface furthers the understanding of the telomerase complex at a molecular level and could be used for the targeted interruption of the telomerase complex as a potential cancer treatment.
ContributorsBley, Christopher James (Author) / Chen, Julian (Thesis advisor) / Allen, James (Committee member) / Ghirlanda, Giovanna (Committee member) / Arizona State University (Publisher)
Created2011
Description
The use of enzyme-catalyst interfaces is underexplored in the field of biocatalysis, particularly in studies on enabling novel reactivity of enzymes. For this thesis, the HaloTag® protein tagging platform was proposed as a bioconjugation method for a pinacol coupling reaction using lipases, as a model for novel reactivities proceeding via ketyl radical intermediates and hydrogen-bonding-facilitated redox attenuation. After an initial lipase screening of 9 lipases, one lipase (Candida rugosa) was found to perform the pinacol coupling of p-anisaldehyde under standard conditions (fluorescein and 530nm light, 3% yield). Based on a retrosynthetic analysis for the photocatalyst-incorporated HaloTag® linker, the intermediates haloamine 1 and aldehyde 6 were synthesized. Further experiments are underway or planned to complete linker synthesis and conduct pinacol coupling experiments with a bioconjugated system. This project underscores the promising biocatalytic promiscuity of lipases for performing reactions proceeding through ketyl radical intermediates, as well as the underdeveloped potential of incorporating bioengineering principles like bioconjugation into biocatalysis to overcome kinetic barriers to electron transfer and optimize biocatalytic reactions.
ContributorsMcrae, Kenna Christine (Author) / Biegasiewicz, Kyle (Thesis director) / Ghirlanda, Giovanna (Committee member) / Moore, Ana (Committee member) / Department of Physics (Contributor) / School of Human Evolution & Social Change (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Telomerase ribonucleoprotein is a unique reverse transcriptase that adds telomeric DNA repeats to chromosome ends. Telomerase RNA (TER) is extremely divergent in size, sequence and has to date only been identified in vertebrate, yeast, ciliate and plant species. Herein, the identification and characterization of TERs from an evolutionarily distinct group, filamentous fungi, is presented. Based on phylogenetic analysis of 69 TER sequences and mutagenesis analysis of in vitro reconstituted Neurospora telomerase, we discovered a conserved functional core in filamentous fungal TERs sharing homologous structural features with vertebrate TERs. This core contains the template-pseudoknot and P6/P6.1 domains, essential for enzymatic activity, which retain function in trans. The in vitro reconstituted Neurospora telomerase is highly processive, synthesizing canonical TTAGGG repeats. Similar to Schizosaccharomycetes pombe, filamentous fungal TERs utilize the spliceosomal splicing machinery for 3' processing. Neurospora telomerase, while associating with the Est1 protein in vivo, does not bind homologous Ku or Sm proteins found in both budding and fission yeast telomerase holoenzyme, suggesting a unique biogenesis pathway. The development of Neurospora as a model organism to study telomeres and telomerase may shed light upon the evolution of the canonical TTAGGG telomeric repeat and telomerase processivity within fungal species.
ContributorsQi, Xiaodong (Author) / Chen, Julian (Thesis advisor) / Ghirlanda, Giovanna (Committee member) / Chaput, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
Telomerase is a specialized enzyme that adds telomeric DNA repeats to the chromosome ends to counterbalance the progressive telomere shortening over cell divisions. It has two essential core components, a catalytic telomerase reverse transcriptase protein (TERT), and a telomerase RNA (TR). TERT synthesizes telomeric DNA by reverse transcribing a short template sequence in TR. Unlike TERT, TR is extremely divergent in size, sequence and structure and has only been identified in three evolutionarily distant groups. The lack of knowledge on TR from important model organisms has been a roadblock for vigorous studies on telomerase regulation. To address this issue, a novel in vitro system combining deep-sequencing and bioinformatics search was developed to discover TR from new phylogenetic groups. The system has been validated by the successful identification of TR from echinoderm purple sea urchin Strongylocentrotus purpuratus. The sea urchin TR (spTR) is the first invertebrate TR that has been identified and can serve as a model for understanding how the vertebrate TR evolved with vertebrate-specific traits. By using phylogenetic comparative analysis, the secondary structure of spTR was determined. The spTR secondary structure reveals unique sea urchin specific structure elements as well as homologous structural features shared by TR from other organisms. This study enhanced the understanding of telomerase mechanism and the evolution of telomerase RNP. The system that was used to identity telomerase RNA can be employed for the discovery of other TR as well as the discovery of novel RNA from other RNP complex.
ContributorsLi, Yang (Author) / Chen, Julian Jl (Thesis advisor) / Yan, Hao (Committee member) / Ghirlanda, Giovanna (Committee member) / Arizona State University (Publisher)
Created2011
Description
Natural products that target the DNA of cancer cells have been an important source of knowledge and understanding in the development of anticancer chemotherapeutic agents. Bleomycin (BLM) exemplifies this class of DNA damaging agent. The ability of BLM to chelate metal ions and effect oxidative damage of the deoxyribose sugar moiety of DNA has been studied extensively for four decades. Here, the study of BLM A5 was conducted using a previously isolated library of hairpin DNAs found to bind strongly to metal free BLM. The ability of BLM to effect single-stranded was then extensively characterized on both the 3′ and 5′-arms of the hairpin DNAs. The strongly bound DNAs were found to be efficient substrates for Fe·BLM A5-mediated cleavage. Surprisingly, the most prevalent site of damage by BLM was found to be a 5′-AT-3′ dinucleotide sequence. This dinucleotide sequence and others generally not cleaved by BLM when examined using arbitrarily chosen DNA substrate were found in examining the library of ten hairpin DNAs. In total, 111 sites of DNA damage were found to be produced by exposure of the hairpin DNA library to Fe·BLM A5. Also, an assay was developed with which to test the propensity of the hairpin DNAs to undergo double stranded DNA damage. Adapting methods previously described by the Povirk laboratory, one hairpin was characterized using this method. The results were in accordance with those previously reported.
ContributorsSegerman, Zachary (Author) / Hecht, Sidney M. (Thesis advisor) / Levitus, Marcia (Committee member) / Ghirlanda, Giovanna (Committee member) / Arizona State University (Publisher)
Created2011
Description
Early-age cracks in fresh concrete occur mainly due to high rate of surface evaporation and restraint offered by the contracting solid phase. Available test methods that simulate severe drying conditions, however, were not originally designed to focus on evaporation and transport characteristics of the liquid-gas phases in a hydrating cementitious microstructure. Therefore, these tests lack accurate measurement of the drying rate and data interpretation based on the principles of transport properties is limited. A vacuum-based test method capable of simulating early-age cracks in 2-D cement paste is developed which continuously monitors the weight loss and changes to the surface characteristics. 2-D crack evolution is documented using time-lapse photography. Effects of sample size, w/c ratio, initial curing and fiber content are studied. In the subsequent analysis, the cement paste phase is considered as a porous medium and moisture transport is described based on surface mass transfer and internal moisture transport characteristics. Results indicate that drying occurs in two stages: constant drying rate period (stage I), followed by a falling drying rate period (stage II). Vapor diffusion in stage I and unsaturated flow within porous medium in stage II determine the overall rate of evaporation. The mass loss results are analyzed using diffusion-based models. Results show that moisture diffusivity in stage I is higher than its value in stage II by more than one order of magnitude. The drying model is used in conjunction with a shrinkage model to predict the development of capillary pressures. Similar approach is implemented in drying restrained ring specimens to predict 1-D crack width development. An analytical approach relates diffusion, shrinkage, creep, tensile and fracture properties to interpret the experimental data. Evaporation potential is introduced based on the boundary layer concept, mass transfer, and a driving force consisting of the concentration gradient. Effect of wind velocity is reflected on Reynolds number which affects the boundary layer on sample surface. This parameter along with Schmidt and Sherwood numbers are used for prediction of mass transfer coefficient. Concentration gradient is shown to be a strong function of temperature and relative humidity and used to predict the evaporation potential. Results of modeling efforts are compared with a variety of test results reported in the literature. Diffusivity data and results of 1-D and 2-D image analyses indicate significant effects of fibers on controlling early-age cracks. Presented models are capable of predicting evaporation rates and moisture flow through hydrating cement-based materials during early-age drying and shrinkage conditions.
ContributorsBakhshi, Mehdi (Author) / Mobasher, Barzin (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Zapata, Claudia E. (Committee member) / Arizona State University (Publisher)
Created2011
Description
The properties of materials depend heavily on the spatial distribution and connectivity of their constituent parts. This applies equally to materials such as diamond and glasses as it does to biomolecules that are the product of billions of years of evolution. In science, insight is often gained through simple models with characteristics that are the result of the few features that have purposely been retained. Common to all research within in this thesis is the use of network-based models to describe the properties of materials. This work begins with the description of a technique for decoupling boundary effects from intrinsic properties of nanomaterials that maps the atomic distribution of nanomaterials of diverse shape and size but common atomic geometry onto a universal curve. This is followed by an investigation of correlated density fluctuations in the large length scale limit in amorphous materials through the analysis of large continuous random network models. The difficulty of estimating this limit from finite models is overcome by the development of a technique that uses the variance in the number of atoms in finite subregions to perform the extrapolation to large length scales. The technique is applied to models of amorphous silicon and vitreous silica and compared with results from recent experiments. The latter part this work applies network-based models to biological systems. The first application models force-induced protein unfolding as crack propagation on a constraint network consisting of interactions such as hydrogen bonds that cross-link and stabilize a folded polypeptide chain. Unfolding pathways generated by the model are compared with molecular dynamics simulation and experiment for a diverse set of proteins, demonstrating that the model is able to capture not only native state behavior but also partially unfolded intermediates far from the native state. This study concludes with the extension of the latter model in the development of an efficient algorithm for predicting protein structure through the flexible fitting of atomic models to low-resolution cryo-electron microscopy data. By optimizing the fit to synthetic data through directed sampling and context-dependent constraint removal, predictions are made with accuracies within the expected variability of the native state.
ContributorsDe Graff, Adam (Author) / Thorpe, Michael F. (Thesis advisor) / Ghirlanda, Giovanna (Committee member) / Matyushov, Dmitry (Committee member) / Ozkan, Sefika B. (Committee member) / Treacy, Michael M. J. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011