Matching Items (209)
Description
Functional materials can be characterized as materials that have tunable properties and are attractive solutions to the improvement and optimization of processes that require specific physiochemical characteristics. Through tailoring and altering these materials, their characteristics can be fine-tuned for specific applications. Computational modeling proves to be a crucial methodology in the design and optimization of such materials. This dissertation encompasses the utilization of molecular dynamics simulations and quantum calculations in two fields of functional materials: electrolytes and semiconductors. Molecular dynamics (MD) simulations were performed on ionic liquid-based electrolyte systems to identify molecular interactions, structural changes, and transport properties that are often reflected in experimental results. The simulations aid in the development process of the electrolyte systems in terms of concentrations of the constituents and can be invoked as a complementary or predictive tool to laboratory experiments. The theme of this study stretches further to include computational studies of the reactivity of atomic layer deposition (ALD) precursors. Selected aminosilane-based precursors were chosen to undergo density functional theory (DFT) calculations to determine surface reactivity and viability in an industrial setting. The calculations were expanded to include the testing of a semi-empirical tight binding program to predict growth per cycle and precursor reactivity with a high surface coverage model. Overall, the implementation of computational methodologies and techniques within these applications improves materials design and process efficiency while streamlining the development of new functional materials.
ContributorsGliege, Marisa Elise (Author) / Dai, Lenore (Thesis advisor) / Derecskei-Kovacs, Agnes (Thesis advisor) / Muhich, Christopher (Committee member) / Emady, Heather (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
Asymmetry of bilateral mammographic tissue density and patterns is a potentially strong indicator of having or developing breast abnormalities or early cancers. The purpose of this study is to design and test the global asymmetry features from bilateral mammograms to predict the near-term risk of women developing detectable high risk breast lesions or cancer in the next sequential screening mammography examination. The image dataset includes mammograms acquired from 90 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including image preprocessing, suspicious region segmentation, image feature extraction, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under curve (AUC) is 0.754 ± 0.024 when applying the new computerized aided diagnosis (CAD) scheme to our testing dataset. The positive predictive value and the negative predictive value were 0.58 and 0.80, respectively.
ContributorsSun, Wenqing (Author) / Zheng, Bin (Author) / Lure, Fleming (Author) / Wu, Teresa (Author) / Zhang, Jianying (Author) / Wang, Benjamin Y. (Author) / Saltzstein, Edward C. (Author) / Qian, Wei (Author) / Ira A. Fulton Schools of Engineering (Contributor)
Created2014-07-01
Description
This thesis aims to investigate the impacts of foreign banks’ management model on their degree of localization and operating efficiency. I decompose their management model into five major factors, including two formative factors and three reflective factors. The two formative factors are (1) strategic orientation and (2) target customers, and the three reflective factors are (1) top management team composition, (2) organizational structure, and (3) managerial authority and incentives. I propose that the formative factors influence foreign banks’ degree of localization, as demonstrated by the reflective factors, which subsequently influence foreign banks’ operating efficiency in China.
To test the above proposition, I conduct the empirical analysis in three steps. In the first step, I investigate foreign banks’ management model by surveying 13 major foreign banks locally incorporated in Mainland China. The results suggest that these 13 foreign banks can be categorized into three distinct groups based on their management model: intergrators, customer-followers, and parent-followers. The results also indicate that intergrators have the highest level of localization while parent-followers have the lowest level of localization.
In the second step, I conduct DEA (Data Envelope Analysis) and CAMEL (Capital Adequacy, Asset Quality, Management, Earnings, Liquidity Analysis) to assess the operating efficiency of these 13 foreign banks. The assessment is conducted in two ways: 1) the inter-group comparison between foreign banks and local Chinese banks; 2) the intra-group comparison between the three distinct groups of foreign banks identified in the first step. The results indicates that the principal factor driving the operating efficiency of both local Chinese banks and foreign banks is the comprehensive technical efficiency, which includes both the quality of management and the quality of technical elements. I also find the uptrend of technical efficiency of the integrators is more stable than that of the other two groups of foreign banks.
Finally, I integrate the results from step one and step two to assess the relevance between foreign banks’ localization level and operating efficiency. I find that foreign banks that score higher in localization tend to have a higher level of operating efficiency. Although this finding is not conclusive about the causal relationship between localization and operating efficiency, it nevertheless suggests that the management model of the higher performing integrators can serve as references for the other foreign banks attempting to enhance their localization and operating efficiency. I also discuss the future trends of development in the banking industry in China and what foreign banks can learn from local Chinese banks to improve their market positions.
To test the above proposition, I conduct the empirical analysis in three steps. In the first step, I investigate foreign banks’ management model by surveying 13 major foreign banks locally incorporated in Mainland China. The results suggest that these 13 foreign banks can be categorized into three distinct groups based on their management model: intergrators, customer-followers, and parent-followers. The results also indicate that intergrators have the highest level of localization while parent-followers have the lowest level of localization.
In the second step, I conduct DEA (Data Envelope Analysis) and CAMEL (Capital Adequacy, Asset Quality, Management, Earnings, Liquidity Analysis) to assess the operating efficiency of these 13 foreign banks. The assessment is conducted in two ways: 1) the inter-group comparison between foreign banks and local Chinese banks; 2) the intra-group comparison between the three distinct groups of foreign banks identified in the first step. The results indicates that the principal factor driving the operating efficiency of both local Chinese banks and foreign banks is the comprehensive technical efficiency, which includes both the quality of management and the quality of technical elements. I also find the uptrend of technical efficiency of the integrators is more stable than that of the other two groups of foreign banks.
Finally, I integrate the results from step one and step two to assess the relevance between foreign banks’ localization level and operating efficiency. I find that foreign banks that score higher in localization tend to have a higher level of operating efficiency. Although this finding is not conclusive about the causal relationship between localization and operating efficiency, it nevertheless suggests that the management model of the higher performing integrators can serve as references for the other foreign banks attempting to enhance their localization and operating efficiency. I also discuss the future trends of development in the banking industry in China and what foreign banks can learn from local Chinese banks to improve their market positions.
ContributorsSun, Minjie (Author) / Shen, Wei (Thesis advisor) / Qian, Jun (Thesis advisor) / Pei, Ker-Wei (Committee member) / Arizona State University (Publisher)
Created2016
Description
Buildings consume nearly 50% of the total energy in the United States, which drives the need to develop high-fidelity models for building energy systems. Extensive methods and techniques have been developed, studied, and applied to building energy simulation and forecasting, while most of work have focused on developing dedicated modeling approach for generic buildings. In this study, an integrated computationally efficient and high-fidelity building energy modeling framework is proposed, with the concentration on developing a generalized modeling approach for various types of buildings. First, a number of data-driven simulation models are reviewed and assessed on various types of computationally expensive simulation problems. Motivated by the conclusion that no model outperforms others if amortized over diverse problems, a meta-learning based recommendation system for data-driven simulation modeling is proposed. To test the feasibility of the proposed framework on the building energy system, an extended application of the recommendation system for short-term building energy forecasting is deployed on various buildings. Finally, Kalman filter-based data fusion technique is incorporated into the building recommendation system for on-line energy forecasting. Data fusion enables model calibration to update the state estimation in real-time, which filters out the noise and renders more accurate energy forecast. The framework is composed of two modules: off-line model recommendation module and on-line model calibration module. Specifically, the off-line model recommendation module includes 6 widely used data-driven simulation models, which are ranked by meta-learning recommendation system for off-line energy modeling on a given building scenario. Only a selective set of building physical and operational characteristic features is needed to complete the recommendation task. The on-line calibration module effectively addresses system uncertainties, where data fusion on off-line model is applied based on system identification and Kalman filtering methods. The developed data-driven modeling framework is validated on various genres of buildings, and the experimental results demonstrate desired performance on building energy forecasting in terms of accuracy and computational efficiency. The framework could be easily implemented into building energy model predictive control (MPC), demand response (DR) analysis and real-time operation decision support systems.
ContributorsCui, Can (Author) / Wu, Teresa (Thesis advisor) / Weir, Jeffery D. (Thesis advisor) / Li, Jing (Committee member) / Fowler, John (Committee member) / Hu, Mengqi (Committee member) / Arizona State University (Publisher)
Created2016
Description
The current study combines field study, survey study, and public financial reports, and conducts an in-depths comprehensive study of the cost of the global tire industry. By comparing the price and the total cost structure of standardized tire products, we investigate Chinese tire industry’s global competitiveness, especially in light of China’s fast increasing labor cost. By constructing a comprehensive cost index (CCI), this dissertation estimates the evolution and forecasts the trend of global tire industry’s cost structure. Based on our empirical analysis, we provide various recommendations for Chinese tire manufacturers, other manufacturing industries, and foreign trade policy makers.
ContributorsZhang, Ning (Author) / Zhu, Ning (Thesis advisor) / Shen, Wei (Thesis advisor) / Chen, Hong (Committee member) / Arizona State University (Publisher)
Created2015
Description
Background: Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM.
Methods: We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set.
Results: We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients).
Conclusion: Multi-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.
ContributorsHu, Leland S. (Author) / Ning, Shuluo (Author) / Eschbacher, Jennifer M. (Author) / Gaw, Nathan (Author) / Dueck, Amylou C. (Author) / Smith, Kris A. (Author) / Nakaji, Peter (Author) / Plasencia, Jonathan (Author) / Ranjbar, Sara (Author) / Price, Stephen J. (Author) / Tran, Nhan (Author) / Loftus, Joseph (Author) / Jenkins, Robert (Author) / O'Neill, Brian P. (Author) / Elmquist, William (Author) / Baxter, Leslie C. (Author) / Gao, Fei (Author) / Frakes, David (Author) / Karis, John P. (Author) / Zwart, Christine (Author) / Swanson, Kristin R. (Author) / Sarkaria, Jann (Author) / Wu, Teresa (Author) / Mitchell, J. Ross (Author) / Li, Jing (Author) / Ira A. Fulton Schools of Engineering (Contributor)
Created2015-11-24
Description
The problem of catastrophic damage purveys in any material application, and minimizing its occurrence is paramount for general health and safety. We have successfully synthesized, characterized, and applied dimeric 9-anthracene carboxylic acid (Di-AC)-based mechanophores particles to form stress sensing epoxy matrix composites. As Di-AC had never been previously applied as a mechanophore and thermosets are rarely studied in mechanochemistry, this created an alternative avenue for study in the field. Under an applied stress, the cyclooctane-rings in the Di-AC particles reverted back to their fluorescent anthracene form, which linearly enhanced the overall fluorescence of the composite in response to the applied strain. The fluorescent signal further allowed for stress sensing in the elastic region of the stress\u2014strain curve, which is considered to be a form of damage precursor detection. Overall, the incorporation of Di-AC to the epoxy matrix added much desired stress sensing and damage precursor detection capabilities with good retention of the material properties.
ContributorsWickham, Jason Alexander (Co-author) / Nofen, Elizabeth (Co-author, Committee member) / Koo, Bonsung (Co-author) / Chattopadhyay, Aditi (Co-author) / Dai, Lenore (Co-author, Thesis director) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Poly(ionic liquid)s (PILs) with an intrinsically conducting pyrrole polymer (ICP) backbone were synthesized and utilized as novel dispersants of carbon nanotubes (CNTs) in various polar and nonpolar solvents. This is due to their highly tunable nature, in which the anions can be easily exchanged to form PILs of varying polarity but with the same polycation. These CNT dispersions were exceedingly stable over many months, and with the addition of hexane, Pickering emulsions with the PIL-stabilized CNTs at the droplet interfaces were formed. Depending on the hydrophobicity of the PIL, hexane-in-water and hexane-in-acetonitrile emulsions were formed, the latter marking the first non-aqueous stabilized-CNT emulsions and corresponding CNT-in-acetonitrile dispersion, further advancing the processability of CNTs. The PIL-stabilized CNT Pickering emulsion droplets generated hollow conductive particles by subsequent drying of the emulsions. With the emulsion templating, the hollow shells can be used as a payload carrier, depending on the solubility of the payload in the droplet phase of the emulsion. This was demonstrated with silicon nanoparticles, which have limited solubility in aqueous environments, but great scientific interest due to their potential electrochemical applications. Overall, this work explored a new class of efficient PIL-ICP hybrid stabilizers with tunable hydrophobicity, offering extended stability of carbon nanotube dispersions with novel applications in hollow particle formation via Pickering emulsion templating and in placing payloads into the shells.
ContributorsHom, Conrad Oliver (Co-author) / Chatterjee, Prithwish (Co-author) / Nofen, Elizabeth (Co-author, Committee member) / Xu, Wenwen (Co-author) / Jiang, Hanqing (Co-author) / Dai, Lenore (Co-author, Thesis director) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
In order to better understand the physical properties of polyethylene, an extremely common plastic used mostly in packaging, many scientists and engineers use olecular dynamics. To reduce the computational expense associated with traditional atomistic molecular dynamics, coarse-grained molecular dynamics is often used. Coarse-grained molecular dynamics groups multiple atoms into single beads, reducing the number of degrees of freedom in a system and eliminating smaller atoms with faster kinematics. However, even coarse-grained methods have their limitations, one of which is timestep duration, which is limited by the maximum vibrational frequency in the coarse-grained system. To study this limitation, a coarse-grained model of polyethylene was created such that every C 2 H 4 unit was replaced with a bead. Coarse-grained potentials for bond-stretching, bond-bending, and non-bonded interaction were generated using the iterative Boltzmann inversion method, which matches coarse-grained distribution functions to atomistic distribution functions. After the creation of the model, the coarse-grained potentials were rescaled by a constant so that they were less stiff, decreasing the maximum vibrational frequency of the system. It is found that by diminishing the bond-stretching potential to 6.25% of its original value, the maximum stable timestep can be increased 85% over that of the unmodified potential functions. The results of this work suggest that it may be possible to simulate lengthy processes, such as the crystallization of polyethylene, in less time with adjusted coarse-grained potentials. Additionally, the large discrepancies in the speed of bond-stretching, bond-bending, and non- bonded interaction dynamics suggest that a multi-timestep method may be worth investigating in future work.
ContributorsWiles, Christian Scott (Author) / Oswald, Jay (Thesis director) / Dai, Lenore (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12