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- Creators: Arizona State University
- Creators: Hindemith, Paul, 1895-1963
Description
The cell is a dense environment composes of proteins, nucleic acids, as well as other small molecules, which are constantly bombarding each other and interacting. These interactions and the diffusive motions are driven by internal thermal fluctuations. Upon collision, molecules can interact and form complexes. It is of interest to learn kinetic parameters such as reaction rates of one molecule converting to different species or two molecules colliding and form a new species as well as to learn diffusion coefficients.
Several experimental measurements can probe diffusion coefficients at the single-molecule and bulk level. The target of this thesis is on single-molecule methods, which can assess diffusion coefficients at the individual molecular level. For instance, super resolution methods like stochastic optical reconstruction microscopy (STORM) and photo activated localization microscopy (PALM), have a high spatial resolution with the cost of lower temporal resolution. Also, there is a different group of methods, such as MINFLUX, multi-detector tracking, which can track a single molecule with high spatio-temporal resolution. The problem with these methods is that they are only applicable to very diluted samples since they need to ensure existence of a single molecule in the region of interest (ROI).
In this thesis, the goal is to have the best of both worlds by achieving high spatio-temporal resolutions without being limited to a few molecules. To do so, one needs to refocus on fluorescence correlation spectroscopy (FCS) as a method that applies to both in vivo and in vitro systems with a high temporal resolution and relies on multiple molecules traversing a confocal volume for an extended period of time. The difficulty here is that the interpretation of the signal leads to different estimates for the kinetic parameters such as diffusion coefficients based on a different number of molecules we consider in the model. It is for this reason that the focus of this thesis is now on using Bayesian nonparametrics (BNPs) as a way to solve this model selection problem and extract kinetic parameters such as diffusion coefficients at the single-molecule level from a few photons, and thus with the highest temporal resolution as possible.
Several experimental measurements can probe diffusion coefficients at the single-molecule and bulk level. The target of this thesis is on single-molecule methods, which can assess diffusion coefficients at the individual molecular level. For instance, super resolution methods like stochastic optical reconstruction microscopy (STORM) and photo activated localization microscopy (PALM), have a high spatial resolution with the cost of lower temporal resolution. Also, there is a different group of methods, such as MINFLUX, multi-detector tracking, which can track a single molecule with high spatio-temporal resolution. The problem with these methods is that they are only applicable to very diluted samples since they need to ensure existence of a single molecule in the region of interest (ROI).
In this thesis, the goal is to have the best of both worlds by achieving high spatio-temporal resolutions without being limited to a few molecules. To do so, one needs to refocus on fluorescence correlation spectroscopy (FCS) as a method that applies to both in vivo and in vitro systems with a high temporal resolution and relies on multiple molecules traversing a confocal volume for an extended period of time. The difficulty here is that the interpretation of the signal leads to different estimates for the kinetic parameters such as diffusion coefficients based on a different number of molecules we consider in the model. It is for this reason that the focus of this thesis is now on using Bayesian nonparametrics (BNPs) as a way to solve this model selection problem and extract kinetic parameters such as diffusion coefficients at the single-molecule level from a few photons, and thus with the highest temporal resolution as possible.
ContributorsJazani, Sina (Author) / Presse, Steve (Thesis advisor) / Matyushov, Dmitry (Committee member) / Levitus, Marcia (Committee member) / Fricks, John (Committee member) / Arizona State University (Publisher)
Created2020
Description
Abnormally low or high blood iron levels are common health conditions worldwide and can seriously affect an individual’s overall well-being. A low-cost point-of-care technology that measures blood iron markers with a goal of both preventing and treating iron-related disorders represents a significant advancement in medical care delivery systems. Methods: A novel assay equipped with an accurate, storable, and robust dry sensor strip, as well as a smartphone mount and (iPhone) app is used to measure total iron in human serum. The sensor strip has a vertical flow design and is based on an optimized chemical reaction. The reaction strips iron ions from blood-transport proteins, reduces Fe(III) to Fe(II), and chelates Fe(II) with ferene, with the change indicated by a blue color on the strip. The smartphone mount is robust and controls the light source of the color reading App, which is calibrated to obtain output iron concentration results. The real serum samples are then used to assess iron concentrations from the new assay and validated through intra-laboratory and inter-laboratory experiments. The intra-laboratory validation uses an optimized iron detection assay with multi-well plate spectrophotometry. The inter-laboratory validation method is performed in a commercial testing facility (LabCorp). Results: The novel assay with the dry sensor strip and smartphone mount, and App is seen to be sensitive to iron detection with a dynamic range of 50 - 300 µg/dL, sensitivity of 0.00049 µg/dL, coefficient of variation (CV) of 10.5%, and an estimated detection limit of ~15 µg/dL These analytical specifications are useful for predicting iron deficiency and overloads. The optimized reference method has a sensitivity of 0.00093 µg/dL and CV of 2.2%. The correlation of serum iron concentrations (N=20) between the optimized reference method and the novel assay renders a slope of 0.95, and a regression coefficient of 0.98, suggesting that the new assay is accurate. Lastly, a spectrophotometric study of the iron detection reaction kinetics is seen to reveal the reaction order for iron and chelating agent. Conclusion: The new assay is able to provide accurate results in intra- and inter- laboratory validations and has promising features of both mobility and low-cost.
ContributorsSerhan, Michael (Author) / Forzani, Erica (Thesis advisor) / Raupp, Gregory (Committee member) / Acharya, Abhinav (Committee member) / Hu, Tony (Committee member) / Smith, Barbara (Committee member) / Arizona State University (Publisher)
Created2020
Description
This thesis aims to design of wings for a laminate biped robot for providing locomotion stabilization during jump gliding. The wings are designed to collapse down during the jumping phase to maximize jump height and deployed back for gliding phase using anisotropic buckling in tape spring hinges. The project aims to develop a reliable dynamics model which can be utilized for design and evaluation of optimized systems for jump-gliding. The aerodynamic simulations are run on a vortex-lattice code which provides numeric simulations of the defined geometric bodies. The aerodynamic simulations assist in improving the design parameters such as planform, camber and twist to achieve the best possible Coefficient of Lift for maximizing glide distance. The aerodynamic simulation output is then plugged into a dynamics model built in Python, which is validated and correlated with experimental testing of a key wing designs. The experimental results are then utilized to improve the dynamics model and obtain better designs for improved performance. The simulation model informs the aerodynamic design of wings for sustaining glide for the biped platform and maximizing glide length to increase range.
ContributorsGadekar, Vipul (Author) / Takahashi, Timothy (Thesis advisor) / Aukes, Daniel (Thesis advisor) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2020
Description
In this dissertation two research questions in the field of applied experimental design were explored. First, methods for augmenting the three-level screening designs called Definitive Screening Designs (DSDs) were investigated. Second, schemes for strategic subdata selection for nonparametric predictive modeling with big data were developed.
Under sparsity, the structure of DSDs can allow for the screening and optimization of a system in one step, but in non-sparse situations estimation of second-order models requires augmentation of the DSD. In this work, augmentation strategies for DSDs were considered, given the assumption that the correct form of the model for the response of interest is quadratic. Series of augmented designs were constructed and explored, and power calculations, model-robustness criteria, model-discrimination criteria, and simulation study results were used to identify the number of augmented runs necessary for (1) effectively identifying active model effects, and (2) precisely predicting a response of interest. When the goal is identification of active effects, it is shown that supersaturated designs are sufficient; when the goal is prediction, it is shown that little is gained by augmenting beyond the design that is saturated for the full quadratic model. Surprisingly, augmentation strategies based on the I-optimality criterion do not lead to better predictions than strategies based on the D-optimality criterion.
Computational limitations can render standard statistical methods infeasible in the face of massive datasets, necessitating subsampling strategies. In the big data context, the primary objective is often prediction but the correct form of the model for the response of interest is likely unknown. Here, two new methods of subdata selection were proposed. The first is based on clustering, the second is based on space-filling designs, and both are free from model assumptions. The performance of the proposed methods was explored visually via low-dimensional simulated examples; via real data applications; and via large simulation studies. In all cases the proposed methods were compared to existing, widely used subdata selection methods. The conditions under which the proposed methods provide advantages over standard subdata selection strategies were identified.
Under sparsity, the structure of DSDs can allow for the screening and optimization of a system in one step, but in non-sparse situations estimation of second-order models requires augmentation of the DSD. In this work, augmentation strategies for DSDs were considered, given the assumption that the correct form of the model for the response of interest is quadratic. Series of augmented designs were constructed and explored, and power calculations, model-robustness criteria, model-discrimination criteria, and simulation study results were used to identify the number of augmented runs necessary for (1) effectively identifying active model effects, and (2) precisely predicting a response of interest. When the goal is identification of active effects, it is shown that supersaturated designs are sufficient; when the goal is prediction, it is shown that little is gained by augmenting beyond the design that is saturated for the full quadratic model. Surprisingly, augmentation strategies based on the I-optimality criterion do not lead to better predictions than strategies based on the D-optimality criterion.
Computational limitations can render standard statistical methods infeasible in the face of massive datasets, necessitating subsampling strategies. In the big data context, the primary objective is often prediction but the correct form of the model for the response of interest is likely unknown. Here, two new methods of subdata selection were proposed. The first is based on clustering, the second is based on space-filling designs, and both are free from model assumptions. The performance of the proposed methods was explored visually via low-dimensional simulated examples; via real data applications; and via large simulation studies. In all cases the proposed methods were compared to existing, widely used subdata selection methods. The conditions under which the proposed methods provide advantages over standard subdata selection strategies were identified.
ContributorsNachtsheim, Abigael (Author) / Stufken, John (Thesis advisor) / Fricks, John (Committee member) / Kao, Ming-Hung (Committee member) / Montgomery, Douglas C. (Committee member) / Reiser, Mark R. (Committee member) / Arizona State University (Publisher)
Created2020
Description
A key open problem within galaxy evolution is to understand the evolution of galaxies towards quiescence. This work investigates the suppression of star-formation through shocks and turbulence at low-redshift, and at higher-redshifts, this work investigates the use of features within quiescent galaxy spectra to redshift estimation, and passive evolution of aging stellar populations to understand their star-formation histories.
At low-$z$, this work focuses on the analysis of optical integral field spectroscopy data of a nearby ($z\sim0.0145$) unusual merging system, called the Taffy system because of radio emission that stretches between the two galaxies. This system, although a recent major-merger of gas-rich spirals, exhibits an atypically low star-formation rate and infrared luminosity. Strong evidence of shock heating as a mechanism for these atypical properties is presented. This result (in conjunction with many others) from the nearby Universe provides evidence for shocks and turbulence, perhaps due to mergers, as an effective feedback mechanism for the suppression of star-formation.
At intermediate and higher-$z$, this work focuses on the analysis of Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) G800L grism spectroscopy and photometry of galaxies with a discernible 4000\AA\ break. The usefulness of 4000\AA/Balmer breaks as redshift indicators by comparing photometric, grism, and spectrophotometric redshifts (SPZs) to ground-based spectroscopic redshifts, is quantified. A spectral energy distribution (SED) fitting pipeline that is optimized for combined HST grism and photometric data, developed for this project, is presented. This pipeline is a template-fitting based routine which accounts for correlated data between neighboring points within grism spectra via the covariance matrix formalism, and also accounts for galaxy morphology along the dispersion direction. Evidence is provided showing that SPZs typically improve the accuracy of photometric redshifts by $\sim$17--60\%. For future space-based observatories like the Nancy Grace Roman Space Telescope (formerly the Wide Field InfraRed Survey Telescope, i.e., WFIRST) and Euclid, this work predicts $\sim$700--4400 galaxies\,degree$^{-2}$, within $1.6 \lesssim z \lesssim 3.4$, for galaxies with 4000\AA\ breaks and continuum-based redshifts accurate to $\lesssim$2\%.
This work also investigates the star-formation histories of massive galaxies ($\mathrm{M_s \geq 10^{10.5}\, M_\odot}$). This is done through the analysis of the strength of the Magnesium absorption feature, Mgb, at $\sim$5175\AA. This analysis is carried out on stacks of HST ACS G800L grism data, stacked for galaxies binned on a color vs stellar mass plane.
At low-$z$, this work focuses on the analysis of optical integral field spectroscopy data of a nearby ($z\sim0.0145$) unusual merging system, called the Taffy system because of radio emission that stretches between the two galaxies. This system, although a recent major-merger of gas-rich spirals, exhibits an atypically low star-formation rate and infrared luminosity. Strong evidence of shock heating as a mechanism for these atypical properties is presented. This result (in conjunction with many others) from the nearby Universe provides evidence for shocks and turbulence, perhaps due to mergers, as an effective feedback mechanism for the suppression of star-formation.
At intermediate and higher-$z$, this work focuses on the analysis of Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) G800L grism spectroscopy and photometry of galaxies with a discernible 4000\AA\ break. The usefulness of 4000\AA/Balmer breaks as redshift indicators by comparing photometric, grism, and spectrophotometric redshifts (SPZs) to ground-based spectroscopic redshifts, is quantified. A spectral energy distribution (SED) fitting pipeline that is optimized for combined HST grism and photometric data, developed for this project, is presented. This pipeline is a template-fitting based routine which accounts for correlated data between neighboring points within grism spectra via the covariance matrix formalism, and also accounts for galaxy morphology along the dispersion direction. Evidence is provided showing that SPZs typically improve the accuracy of photometric redshifts by $\sim$17--60\%. For future space-based observatories like the Nancy Grace Roman Space Telescope (formerly the Wide Field InfraRed Survey Telescope, i.e., WFIRST) and Euclid, this work predicts $\sim$700--4400 galaxies\,degree$^{-2}$, within $1.6 \lesssim z \lesssim 3.4$, for galaxies with 4000\AA\ breaks and continuum-based redshifts accurate to $\lesssim$2\%.
This work also investigates the star-formation histories of massive galaxies ($\mathrm{M_s \geq 10^{10.5}\, M_\odot}$). This is done through the analysis of the strength of the Magnesium absorption feature, Mgb, at $\sim$5175\AA. This analysis is carried out on stacks of HST ACS G800L grism data, stacked for galaxies binned on a color vs stellar mass plane.
ContributorsJoshi, Bhavin (Author) / Windhorst, Rogier (Thesis advisor) / Jansen, Rolf (Committee member) / Appleton, Philip (Committee member) / Scannapieco, Evan (Committee member) / Borthakur, Sanchayeeta (Committee member) / Arizona State University (Publisher)
Created2020
Description
This dissertation focuses on the structural and optical properties of III-V semiconductor materials. Transmission electron microscopy and atomic force microscopy are used to study at the nanometer scale, the structural properties of defects, interfaces, and surfaces. A correlation with optical properties has been performed using cathodoluminescence.
The dissertation consists of four parts. The first part focuses on InAs quantum dots (QDs) embedded in a GaInP matrix for applications into intermediate band solar cells. The CuPt ordering of the group-III elements in Ga0.5In0.5P has been found to vary during growth of InAs QDs capped with GaAs. The degree of ordering depends on the deposition time of the QDs and on the thickness of the capping layer. The results indicate that disordered GaInP occurs in the presence of excess indium at the growth front.
The second part focuses on the effects of low-angle off-axis GaN substrate orientation and growth rates on the surface morphology of Mg-doped GaN epilayers. Mg doping produces periodic steps and a tendency to cover pinholes associated with threading dislocations. With increasing miscut angle, the steps are observed to increase in height from single to double basal planes, with the coexistence of surfaces with different inclinations. The structural properties are correlated with the electronic properties of GaN epilayers, indicating step bunching reduces the p-type doping efficiency. It is also found that the slower growth rates can enhance step-flow growth and suppress step bunching.
The third part focuses on the effects of inductively-coupled plasma etching on GaN epilayers. The results show that ion energy rather than ion density plays the key role in the etching process, in terms of structural and optical properties of the GaN films. Cathodoluminescence depth-profiling indicates that the band-edge emission of etched GaN is significantly quenched.
The fourth part focuses on growth of Mg-doped GaN on trench patterns. Anisotropic growth and nonuniform acceptor incorporation in p-GaN films have been observed. The results indicate that growth along the sidewall has a faster growth rate and therefore a lower acceptor incorporation efficiency, compared to the region grown on the basal plane.
The dissertation consists of four parts. The first part focuses on InAs quantum dots (QDs) embedded in a GaInP matrix for applications into intermediate band solar cells. The CuPt ordering of the group-III elements in Ga0.5In0.5P has been found to vary during growth of InAs QDs capped with GaAs. The degree of ordering depends on the deposition time of the QDs and on the thickness of the capping layer. The results indicate that disordered GaInP occurs in the presence of excess indium at the growth front.
The second part focuses on the effects of low-angle off-axis GaN substrate orientation and growth rates on the surface morphology of Mg-doped GaN epilayers. Mg doping produces periodic steps and a tendency to cover pinholes associated with threading dislocations. With increasing miscut angle, the steps are observed to increase in height from single to double basal planes, with the coexistence of surfaces with different inclinations. The structural properties are correlated with the electronic properties of GaN epilayers, indicating step bunching reduces the p-type doping efficiency. It is also found that the slower growth rates can enhance step-flow growth and suppress step bunching.
The third part focuses on the effects of inductively-coupled plasma etching on GaN epilayers. The results show that ion energy rather than ion density plays the key role in the etching process, in terms of structural and optical properties of the GaN films. Cathodoluminescence depth-profiling indicates that the band-edge emission of etched GaN is significantly quenched.
The fourth part focuses on growth of Mg-doped GaN on trench patterns. Anisotropic growth and nonuniform acceptor incorporation in p-GaN films have been observed. The results indicate that growth along the sidewall has a faster growth rate and therefore a lower acceptor incorporation efficiency, compared to the region grown on the basal plane.
ContributorsSU, PO-YI (Author) / Ponce, Fernando A. (Thesis advisor) / Smith, David J. (Committee member) / Crozier, Peter A. (Committee member) / Zhao, Yuji (Committee member) / Arizona State University (Publisher)
Created2020
Description
A framework to obtain the failure surface of a unidirectional composite which can be used as an input for Generalized Tabulated Failure Criterion in MAT_213 – an orthotropic elasto-plastic material model implemented in LS-DYNA, a commercial finite element program, is discussed in this research. A finite element model consisting of the fiber and the matrix is generated using the Virtual Testing Software System (VTSS) developed at Arizona State University (ASU). The framework is illustrated using the T800-F3900 unidirectional composite material manufactured by Toray Composites. The T800S fiber is modeled using MAT_213. The F3900 matrix phase is modeled using MAT_187-SAMP1. The response of the virtual tests in 1-direction tension, 1-direction compression, 2-direction tension, 2-direction compression and 2-1 plane shear are verified against the results obtained from experiments performed under quasi-static and room temperature conditions (QS-RT). Finally, a roadmap to generate the failure surface using virtual test is proposed.
ContributorsParakhiya, Yatin (Author) / Rajan, Subramaniam D. (Thesis advisor) / Mobahser, Barzin (Committee member) / Hoover, Christian (Committee member) / Arizona State University (Publisher)
Created2020
Description
Do response advertisements influence individuals’ evaluations of political candidates and vote preferences? This dissertation explores the impact of response advertisements on citizens’ expressed vote preferences and favorability towards political candidates. This project utilized an original focus group to determine citizens feelings regarding American political campaigns more generally and attitudes towards negative campaigning more specifically, including how candidates should respond when attacked. Additionally, an experiment was conducted to determine which type of response advertisements influences citizen attitudes most.
ContributorsDempsey, Matthew (Author) / Fridkin, Kim (Thesis advisor) / Kenney, Patrick (Committee member) / Ramirez, Mark D. (Committee member) / Arizona State University (Publisher)
Created2020
Description
The United States Supreme Court’s 1993 Daubert v. Merrell Dow Pharmaceuticals case established criteria for admitting scientific evidence in federal courts. It holds that scientific evidence must be valid, reliable, and relevant, and judges are required to be “gatekeepers” of evidence by screening out evidence that has not been empirically tested or vetted through the academic community. Yet, little is known about whether psychological assessment tools are subjected to scrutiny through the standards courts are supposed to apply. In three different studies, from the perspectives of judges, attorneys, and forensic mental health experts, the authors investigate whether psychological assessment evidence is being challenged. Information was collected on participants’ experiences with challenges to psychological assessments. Judges and lawyers completed a series of experimental case vignettes to assess their decision-making about legal admissibility of different qualities of psychological assessments. It was hypothesized they would not distinguish between low- and high-quality psychological assessments in admissibility. Bayesian model selection methods did not support the null hypothesis, however. It was found attorneys differentiate between the conditions. The rates in which legal professionals and forensic mental health evaluators experienced challenges were also higher than was expected. These positive findings show there is some degree of gatekeeping psychological assessment evidence in the courts.
ContributorsNeu Line, Emily C (Author) / Neal, Tess M.S. (Thesis advisor) / Horne, Zachary (Committee member) / Saks, Michael (Committee member) / Arizona State University (Publisher)
Created2020
Description
My dissertation research broadly focuses on the geochemical and physical exchange of materials between the Earth’s crust and mantle at convergent margins, and how this drives the compositional diversity observed on the Earth’s surface. I combine traditional petrologic and geochemical studies of natural and experimental high-pressure mafic rocks, with thermodynamic modeling of high-pressure aqueous fluids and mafic-ultramafic lithologies allowing for more complete understanding of fluid-melt-rock interactions. The results of the research that follows has important implications for: the role of lower crustal foundering in the geochemical origin and evolution of the modern continental crust (Chapter 2; Guild et al., under review), metasomatic processes involving aqueous metal-carbon complexes in high pressure-temperature subduction zone fluids (Chapter 3; Guild & Shock, 2020), natural hydrous mineral stability at the slab-mantle interface (Chapter 4; Guild, et al., in preparation) and water-undersaturated melting in the sub-arc (Chapter 5; Guild & Till, in preparation).
ContributorsGuild, Meghan Rose (Author) / Till, Christy B. (Thesis advisor) / Shock, Everett L (Committee member) / Hervig, Richard L (Committee member) / Hartnett, Hilairy (Committee member) / Clarke, Amanda (Committee member) / Arizona State University (Publisher)
Created2020