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- Language: English
- Creators: Ira A. Fulton Schools of Engineering
Description
Chemical and mineralogical data from Mars shows that the surface has been chemically weathered on local to regional scales. Chemical trends and the types of chemical weathering products present on the surface and their abundances can elucidate information about past aqueous processes. Thermal-infrared (TIR) data and their respective models are essential for interpreting Martian mineralogy and geologic history. However, previous studies have shown that chemical weathering and the precipitation of fine-grained secondary silicates can adversely affect the accuracy of TIR spectral models. Furthermore, spectral libraries used to identify minerals on the Martian surface lack some important weathering products, including poorly-crystalline aluminosilicates like allophane, thus eliminating their identification in TIR spectral models. It is essential to accurately interpret TIR spectral data from chemically weathered surfaces to understand the evolution of aqueous processes on Mars. Laboratory experiments were performed to improve interpretations of TIR data from weathered surfaces. To test the accuracy of deriving chemistry of weathered rocks from TIR spectroscopy, chemistry was derived from TIR models of weathered basalts from Baynton, Australia and compared to actual weathering rind chemistry. To determine how specific secondary silicates affect the TIR spectroscopy of weathered basalts, mixtures of basaltic minerals and small amounts of secondary silicates were modeled. Poorly-crystalline aluminosilicates were synthesized and their TIR spectra were added to spectral libraries. Regional Thermal Emission Spectrometer (TES) data were modeled using libraries containing these poorly-crystalline aluminosilicates to test for their presence on the Mars. Chemistry derived from models of weathered Baynton basalts is not accurate, but broad chemical weathering trends can be interpreted from the data. TIR models of mineral mixtures show that small amounts of crystalline and amorphous silicate weathering products (2.5-5 wt.%) can be detected in TIR models and can adversely affect modeled plagioclase abundances. Poorly-crystalline aluminosilicates are identified in Northern Acidalia, Solis Planum, and Meridiani. Previous studies have suggested that acid sulfate weathering was the dominant surface alteration process for the past 3.5 billion years; however, the identification of allophane indicates that alteration at near-neutral pH occurred on regional scales and that acid sulfate weathering is not the only weathering process on Mars.
ContributorsRampe, Elizabeth Barger (Author) / Sharp, Thomas G (Thesis advisor) / Christensen, Phillip (Committee member) / Hervig, Richard (Committee member) / Shock, Everett (Committee member) / Williams, Lynda (Committee member) / Arizona State University (Publisher)
Created2011
Description
Applications of non-traditional stable isotope variations are moving beyond geosciences to biomedicine, made possible by advances in multiple collector inductively coupled plasma mass spectrometry (MC-ICP-MS) technology. Mass-dependent isotope variation can provide information about the sources of elements and the chemical reactions that they undergo. Iron and calcium isotope systematics in biomedicine are relatively unexplored but have great potential scientific interest due to their essential nature in metabolism. Iron, a crucial element in biology, fractionates during biochemically relevant reactions. To test the extent of this fractionation in an important reaction process, equilibrium iron isotope fractionation during organic ligand exchange was determined. The results show that iron fractionates during organic ligand exchange, and that isotope enrichment increases as a function of the difference in binding constants between ligands. Additionally, to create a mass balance model for iron in a whole organism, iron isotope compositions in a whole mouse and in individual mouse organs were measured. The results indicate that fractionation occurs during transfer between individual organs, and that the whole organism was isotopically light compared with food. These two experiments advance our ability to interpret stable iron isotopes in biomedicine. Previous research demonstrated that calcium isotope variations in urine can be used as an indicator of changes in net bone mineral balance. In order to measure calcium isotopes by MC-ICP-MS, a chemical purification method was developed to quantitatively separate calcium from other elements in a biological matrix. Subsequently, this method was used to evaluate if calcium isotopes respond when organisms are subjected to conditions known to induce bone loss: 1) Rhesus monkeys were given an estrogen-suppressing drug; 2) Human patients underwent extended bed rest. In both studies, there were rapid, detectable changes in calcium isotope compositions from baseline - verifying that calcium isotopes can be used to rapidly detect changes in bone mineral balance. By characterizing iron isotope fractionation in biologically relevant processes and by demonstrating that calcium isotopes vary rapidly in response to bone loss, this thesis represents an important step in utilizing these isotope systems as a diagnostic and mechanistic tool to study the metabolism of these elements in vivo.
ContributorsMorgan, Jennifer Lynn Louden (Author) / Anbar, Ariel D. (Thesis advisor) / Wasylenki, Laura E. (Committee member) / Jones, Anne K. (Committee member) / Shock, Everett (Committee member) / Arizona State University (Publisher)
Created2011
Description
A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. Compared to the front-end process that is dominated by re-entrant product flows, the back-end process is linear and therefore more suitable for scheduling. However, the production scheduling of the back-end process is still very difficult due to the wide product mix, large number of parallel machines, product family related setups, machine-product qualification, and weekly demand consisting of thousands of lots. In this research, a novel mixed-integer-linear-programming (MILP) model is proposed for the batch production scheduling of a semiconductor back-end facility. In the MILP formulation, the manufacturing process is modeled as a flexible flow line with bottleneck stages, unrelated parallel machines, product family related sequence-independent setups, and product-machine qualification considerations. However, this MILP formulation is difficult to solve for real size problem instances. In a semiconductor back-end facility, production scheduling usually needs to be done every day while considering updated demand forecast for a medium term planning horizon. Due to the limitation on the solvable size of the MILP model, a deterministic scheduling system (DSS), consisting of an optimizer and a scheduler, is proposed to provide sub-optimal solutions in a short time for real size problem instances. The optimizer generates a tentative production plan. Then the scheduler sequences each lot on each individual machine according to the tentative production plan and scheduling rules. Customized factory rules and additional resource constraints are included in the DSS, such as preventive maintenance schedule, setup crew availability, and carrier limitations. Small problem instances are randomly generated to compare the performances of the MILP model and the deterministic scheduling system. Then experimental design is applied to understand the behavior of the DSS and identify the best configuration of the DSS under different demand scenarios. Product-machine qualification decisions have long-term and significant impact on production scheduling. A robust product-machine qualification matrix is critical for meeting demand when demand quantity or mix varies. In the second part of this research, a stochastic mixed integer programming model is proposed to balance the tradeoff between current machine qualification costs and future backorder costs with uncertain demand. The L-shaped method and acceleration techniques are proposed to solve the stochastic model. Computational results are provided to compare the performance of different solution methods.
ContributorsFu, Mengying (Author) / Askin, Ronald G. (Thesis advisor) / Zhang, Muhong (Thesis advisor) / Fowler, John W (Committee member) / Pan, Rong (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
In this study, the influence of fluid mixing on temperature and geochemistry of hot spring fluids is investigated. Yellowstone National Park (YNP) is home to a diverse range of hot springs with varying temperature and chemistry. The mixing zone of interest in this paper, located in Geyser Creek, YNP, has been a point of interest since at least the 1960’s (Raymahashay, 1968). Two springs, one basic (~pH 7) and one acidic (~pH 3) mix together down an outflow channel. There are visual bands of different photosynthetic pigments which suggests the creation of temperature and chemical gradients due to the fluids mixing. In this study, to determine if fluid mixing is driving these changes of temperature and chemistry in the system, a model that factors in evaporation and cooling was developed and compared to measured temperature and chemical data collected downstream. Comparison of the modeled temperature and chemistry to the measured values at the downstream mixture shows that many of the ions, such as Cl⁻, F⁻, and Li⁺, behave conservatively with respect to mixing. This indicates that the influence of mixing accounts for a large proportion of variation in the chemical composition of the system. However, there are some chemical constituents like CH₄, H₂, and NO₃⁻, that were not conserved, and the concentrations were either depleted or increased in the downstream mixture. Some of these constituents are known to be used by microorganisms. The development of this mixing model can be used as a tool for predicting biological activity as well as building the framework for future geochemical and computational models that can be used to understand the energy availability and the microbial communities that are present.
ContributorsOrrill, Brianna Isabel (Author) / Shock, Everett (Thesis director) / Howells, Alta (Committee member) / School of Life Sciences (Contributor) / School of Earth and Space Exploration (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
A new challenge on the horizon is to utilize the large amounts of protein found in the atmosphere to identify different organisms from which the protein originated. Included here is work investigating the presence of identifiable patterns of different proteins collected from the air and biological samples for the purposes of remote identification. Protein patterns were generated using high performance liquid chromatography (HPLC). Patterns created could identify high-traffic and low-traffic indoor spaces. Samples were collected from the air using air pumps to draw air through a filter paper trapping particulates, including large amounts of shed protein matter. In complimentary research aerosolized biological samples were collected from various ecosystems throughout Ecuador to explore the relationship between environmental setting and aerosolized protein concentrations. In order to further enhance protein separation and produce more detailed patterns for the identification of individual organisms of interest; a novel separation device was constructed and characterized. The separation device incorporates a longitudinal gradient as well as insulating dielectrophoretic features within a single channel. This design allows for the production of stronger local field gradients along a global gradient allowing particles to enter, initially transported through the channel by electrophoresis and electroosmosis, and to be isolated according to their characteristic physical properties, including charge, polarizability, deformability, surface charge mobility, dielectric features, and local capacitance. Thus, different types of particles are simultaneously separated at different points along the channel distance given small variations of properties. The device has shown the ability to separate analytes over a large dynamic range of size, from 20 nm to 1 μm, roughly the size of proteins to the size of cells. In the study of different sized sulfate capped polystyrene particles were shown to be selectively captured as well as concentrating particles from 103 to 106 times. Qualitative capture and manipulation of β-amyloid fibrils were also shown. The results demonstrate the selective focusing ability of the technique; and it may form the foundation for a versatile tool for separating complex mixtures. Combined this work shows promise for future identification of individual organisms from aerosolized protein as well as for applications in biomedical research.
ContributorsStaton, Sarah J. R (Author) / Hayes, Mark A. (Committee member) / Anbar, Ariel D (Committee member) / Shock, Everett (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2011
Description
This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree models. Associative classifiers can achieve high accuracy, but the combination of many rules is difficult to interpret. Rule condition subset selection (RCSS) methods for associative classification are considered. RCSS aims to prune the rule conditions into a subset via feature selection. The subset then can be summarized into rule-based classifiers. Experiments show that classifiers after RCSS can substantially improve the classification interpretability without loss of accuracy. An ensemble feature selection method is proposed to learn Markov blankets for either discrete or continuous networks (without linear, Gaussian assumptions). The method is compared to a Bayesian local structure learning algorithm and to alternative feature selection methods in the causal structure learning problem. Feature selection is also used to enhance the interpretability of time series classification. Existing time series classification algorithms (such as nearest-neighbor with dynamic time warping measures) are accurate but difficult to interpret. This research leverages the time-ordering of the data to extract features, and generates an effective and efficient classifier referred to as a time series forest (TSF). The computational complexity of TSF is only linear in the length of time series, and interpretable features can be extracted. These features can be further reduced, and summarized for even better interpretability. Lastly, two variable importance measures are proposed to reduce the feature selection bias in tree-based ensemble models. It is well known that bias can occur when predictor attributes have different numbers of values. Two methods are proposed to solve the bias problem. One uses an out-of-bag sampling method called OOBForest, and the other, based on the new concept of a partial permutation test, is called a pForest. Experimental results show the existing methods are not always reliable for multi-valued predictors, while the proposed methods have advantages.
ContributorsDeng, Houtao (Author) / Runger, George C. (Thesis advisor) / Lohr, Sharon L (Committee member) / Pan, Rong (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2011
Description
Hydropower generation is one of the clean renewable energies which has received great attention in the power industry. Hydropower has been the leading source of renewable energy. It provides more than 86% of all electricity generated by renewable sources worldwide. Generally, the life span of a hydropower plant is considered as 30 to 50 years. Power plants over 30 years old usually conduct a feasibility study of rehabilitation on their entire facilities including infrastructure. By age 35, the forced outage rate increases by 10 percentage points compared to the previous year. Much longer outages occur in power plants older than 20 years. Consequently, the forced outage rate increases exponentially due to these longer outages. Although these long forced outages are not frequent, their impact is immense. If reasonable timing of rehabilitation is missed, an abrupt long-term outage could occur and additional unnecessary repairs and inefficiencies would follow. On the contrary, too early replacement might cause the waste of revenue. The hydropower plants of Korea Water Resources Corporation (hereafter K-water) are utilized for this study. Twenty-four K-water generators comprise the population for quantifying the reliability of each equipment. A facility in a hydropower plant is a repairable system because most failures can be fixed without replacing the entire facility. The fault data of each power plant are collected, within which only forced outage faults are considered as raw data for reliability analyses. The mean cumulative repair functions (MCF) of each facility are determined with the failure data tables, using Nelson's graph method. The power law model, a popular model for a repairable system, can also be obtained to represent representative equipment and system availability. The criterion-based analysis of HydroAmp is used to provide more accurate reliability of each power plant. Two case studies are presented to enhance the understanding of the availability of each power plant and represent economic evaluations for modernization. Also, equipment in a hydropower plant is categorized into two groups based on their reliability for determining modernization timing and their suitable replacement periods are obtained using simulation.
ContributorsKwon, Ogeuk (Author) / Holbert, Keith E. (Thesis advisor) / Heydt, Gerald T (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2011
Description
ABSTRACT Group III-nitride semiconductor materials have been commercially used in fabrication of light-emitting diodes (LEDs) and laser diodes (LDs) covering the spectral range from UV to visible and infrared, and exhibit unique properties suitable for modern optoelectronic applications. Great advances have recently happened in the research and development in high-power and high-efficiency blue-green-white LEDs, blue LDs and other optoelectronic applications. However, there are still many unsolved challenges with these materials. In this dissertation, several issues concerning structural, electronic and optical properties of III-nitrides have been investigated using a combination of transmission electron microscopy (TEM), electron holography (EH) and cathodoluminescence (CL) techniques. First, a trend of indium chemical inhomogeneity has been found as the indium composition increases for the InGaN epitaxial layers grown by hydride vapor phase epitaxy. Second, different mechanisms contributing to the strain relaxation have been studied for non-polar InGaN epitaxial layers grown on zinc oxide (ZnO) substrate. Third, various structural morphologies of non-polar InGaN epitaxial layers grown on free-standing GaN substrate have been investigated. Fourth, the effect of the growth temperature on the performance of GaN lattice-matched InAlN electron blocking layers has been studied. Finally, the electronic and optical properties of GaN nanowires containing a AlN/GaN superlattice structure have been investigated showing relatively small internal electric field and superlattice- and defect-related emissions along the nanowires.
ContributorsSun, Kewei (Author) / Ponce, Fernando (Thesis advisor) / Smith, David (Committee member) / Treacy, Michael (Committee member) / Drucker, Jeffery (Committee member) / Schmidt, Kevin (Committee member) / Arizona State University (Publisher)
Created2011
Description
Nowadays product reliability becomes the top concern of the manufacturers and customers always prefer the products with good performances under long period. In order to estimate the lifetime of the product, accelerated life testing (ALT) is introduced because most of the products can last years even decades. Much research has been done in the ALT area and optimal design for ALT is a major topic. This dissertation consists of three main studies. First, a methodology of finding optimal design for ALT with right censoring and interval censoring have been developed and it employs the proportional hazard (PH) model and generalized linear model (GLM) to simplify the computational process. A sensitivity study is also given to show the effects brought by parameters to the designs. Second, an extended version of I-optimal design for ALT is discussed and then a dual-objective design criterion is defined and showed with several examples. Also in order to evaluate different candidate designs, several graphical tools are developed. Finally, when there are more than one models available, different model checking designs are discussed.
ContributorsYang, Tao (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Borror, Connie (Committee member) / Rigdon, Steve (Committee member) / Arizona State University (Publisher)
Created2013
Description
Stable isotopes were measured in the groundwaters of the Salt River Valley basin in central Arizona to explore the utility of stable isotopes for sourcing recharge waters and engineering better well designs. Delta values for the sampled groundwaters range from -7.6‰ to -10‰ in 18O and -60‰ to -91‰ in D and display displacements off the global meteoric water line indicative of surficial evaporation during river transport into the area. Groundwater in the basin is all derived from top-down river recharge; there is no evidence of ancient playa waters even in the playa deposits. The Salt and Verde Rivers are the dominant source of groundwater for the East Salt River valley- the Agua Fria River also contributes significantly to the West Salt River Valley. Groundwater isotopic compositions are generally more depleted in 18O and D with depth, indicating past recharge in cooler climates, and vary within subsurface aquifer layers as sampled during well drilling. When isotopic data were evaluated together with geologic and chemical analyses and compared with data from the final well production water it was often possible to identify: 1) which horizons are the primary producers of groundwater flow and how that might change with time, 2) the chemical exchange of cations and anions via water-rock interaction during top-down mixing of recharge water with older waters, 3) how much well production might be lost if arsenic-contributing horizons were sealed off, and 4) the extent to which replacement wells tap different subsurface water sources. In addition to identifying sources of recharge, stable isotopes offer a new and powerful approach for engineering better and more productive water wells.
ContributorsBond, Angela Nicole (Author) / Knauth, Paul (Thesis advisor) / Hartnett, Hilairy (Committee member) / Shock, Everett (Committee member) / Arizona State University (Publisher)
Created2010