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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
Electrostatic Discharge (ESD) is a unique issue in the electronics industry that can cause failures of electrical components and complete electronic systems. There is an entire industry that is focused on developing ESD compliant tooling using traditional manufacturing methods. This research work evaluates the feasibility to fabricate a PEEK-Carbon Nanotube composite filament for Fused Filament Fabrication (FFF) Additive Manufacturing that is ESD compliant. In addition, it demonstrates that the FFF process can be used to print tools with the required accuracy, ESD compliance and mechanical properties necessary for the electronics industry at a low rate production level. Current Additive Manufacturing technology can print high temperature polymers, such as PEEK, with the required mechanical properties but they are not ESD compliant and require post processing to create a product that is. There has been some research conducted using mixed multi-wall and single wall carbon nanotubes in a PEEK polymers, which improves mechanical properties while reducing bulk resistance to the levels required to be ESD compliant. This previous research has been used to develop a PEEK-CNT polymer matrix for the Fused Filament Fabrication additive manufacturing process
ContributorsChurchwell, Raymond L (Author) / Sugar, Thomas (Thesis advisor) / Rogers, Bradley (Committee member) / Morrell, Darryl (Committee member) / Arizona State University (Publisher)
Created2020
Description
This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations completed for power customers and for utilities identify how disruptions are occurring in conventional energy business models. Analyses completed for Chicago, Seattle, and Phoenix demonstrate site-specific and generalizable findings. Results indicate that net metering had a significant effect on the optimal amount of solar photovoltaics (PV) for households to install and how utilities could recover lost revenue through increasing energy rates or monthly fees. System-wide ramp rate requirements also increased as solar PV penetration increased. These issues are resolved using a generalizable, scalable transactive energy framework for microgrids to enable coordination and automation of DERs and microgrids to ensure cost effective use of energy for all stakeholders. This technique is demonstrated on a 3-node and 9-node network of microgrid nodes with various amounts of load, solar, and storage. Results found that enabling trading could achieve cost savings for all individual nodes and for the network up to 5.4%. Trading behaviors are expressed using an exponential valuation curve that quantifies the reputation of trading partners using historical interactions between nodes for compatibility, familiarity, and acceptance of trades. The same 9-node network configuration is used with varying levels of connectivity, resulting in up to 71% cost savings for individual nodes and up to 13% cost savings for the network as a whole. The effect of a trading fee is also explored to understand how electricity utilities may gain revenue from electricity traded directly between customers. If a utility imposed a trading fee to recoup lost revenue then trading is financially infeasible for agents, but could be feasible if only trying to recoup cost of distribution charges. These scientific findings conclude with a brief discussion of physical deployment opportunities.
ContributorsJanko, Samantha Ariel (Author) / Johnson, Nathan (Thesis advisor) / Zhang, Wenlong (Committee member) / Herche, Wesley (Committee member) / Arizona State University (Publisher)
Created2019
Description
A literature search revealed that previous research on the Attentional Blink (AB) has not examined the role of salience in AB results. I examined how salience affects the AB through multiple forms and degrees of salience in target 1 (T1) and target 2 (T2) stimuli. When examining increased size as a form of salience, results showed a more salient T2 increased recall, attenuating the AB. A more salient T1 did not differ from the control, suggesting the salience (increased size) of T2 is an important factor in the AB, while salience (increased size) of T1 does not affect the AB. Additionally, the differences in target size (50% or 100% larger) were not significantly different, showing size differences at these intervals do not affect AB results. To further explore the lack of difference in results when T1 is larger in size, I examined dynamic stimuli used as T1. T1 stimuli were presented as looming or receding. When T1 was presented as looming or receding, the AB was attenuated (T2 recall at lag 2 was significantly greater). Additionally, T2 recall was significantly worse at lags three and four (showing a larger decrease directly following the attenuated AB). When comparing looming and receding against each other, at lag 2 (when recall accuracy at its lowest) looming increased recall significantly more than receding stimuli. This is expected to be due to the immediate attentional needs related to looming stimuli. Overall, the results showed T2 salience in the form of size significantly increases recall accuracy while T1 size salience does not affect the AB results. With that, dynamic T1 stimuli increase recall accuracy at early lags (lag 2) while it decreases recall accuracy at later lags (lags 3 and 4). This result is found when the stimuli are presented at a larger size (stimuli appearing closer), suggesting the more eminent need for attention results in greater effects on the AB.
ContributorsLafko, Stacie (Author) / Becker, Vaughn (Thesis advisor) / Branaghan, Russell (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2019
Description
Robotic assisted devices in gait rehabilitation have not seen penetration into clinical settings proportionate to the developments in this field. A possible reason for this is due to the development and evaluation of these devices from a predominantly engineering perspective. One way to mitigate this effect is to further include the principles of neurophysiology into the development of these systems. To further include these principles, this research proposes a method for grounded evaluation of three machine learning algorithms to gain insight on what modeling approaches are able to both replicate therapist assistance and emulate therapist strategies. The algorithms evaluated in this paper include ordinary least squares regression (OLS), gaussian process regression (GPR) and inverse reinforcement learning (IRL). The results show that grounded evaluation is able to provide evidence to support the algorithms at a higher resolution. Also, it was observed that GPR is likely the most accurate algorithm to replicate therapist assistance and to emulate therapist adaptation strategies.
ContributorsSmith, Mason Owen (Author) / Zhang, Wenlong (Thesis advisor) / Ben Amor, Hani (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2021
Description
This dissertation studies the methods to enhance the performance of foldable robots manufactured by laminated techniques. This class of robots are unique in their manufacturing process, which involves cutting and staking up thin layers of different materials with various stiffness. While inheriting the advantages of soft robots -- low weight, affordable manufacturing cost and a fast prototyping process -- a wider range of actuators is available to these mechanisms, while modeling their behavior requires less computational cost.The fundamental question this dissertation strives to answer is how to decode and leverage the effect of material stiffness in these robots. These robots' stiffness is relatively limited due to their slender design, specifically at larger scales. While compliant robots may have inherent advantages such as being safer to work around, this low rigidity makes modeling more complex. This complexity is mostly contained in material deformation since the conventional actuators such as servo motors can be easily leveraged in these robots. As a result, when introduced to real-world environments, efficient modeling and control of these robots are more achievable than conventional soft robots.
Various approaches have been taken to design, model, and control a variety of laminate robot platforms by investigating the effect of material deformation in prototypes while they interact with their working environments. The results obtained show that data-driven approaches such as experimental identification and machine learning techniques are more reliable in modeling and control of these mechanisms. Also, machine learning techniques for training robots in non-ideal experimental setups that encounter the uncertainties of real-world environments can be leveraged to find effective gaits with high performance. Our studies on the effect of stiffness of thin, curved sheets of materials has evolved into introducing a new class of soft elements which we call Soft, Curved, Reconfigurable, Anisotropic Mechanisms (SCRAMs). Like bio-mechanical systems, SCRAMs are capable of re-configuring the stiffness of curved surfaces to enhance their performance and adaptability. Finally, the findings of this thesis show promising opportunities for foldable robots to become an alternative for conventional soft robots since they still offer similar advantages in a fraction of computational expense.
ContributorsSharifzadeh, Mohammad (Author) / Aukes, Daniel (Thesis advisor) / Sugar, Thomas (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
Corrosion is one of the key failure modes for stainless steel (SS) piping assets handling water resources managed by utility companies. During downtime, the costs start to incur as the field engineer procures its replacement parts. The parts may or may not be in stock depending on how old, complex, and common the part model is. As a result, water utility companies and its resilience to operate amid part failure are a strong function of the supply chain for replacement piping. Metal additive manufacturing (AM) has been widely recognized for its ability to (a) deliver small production scales, (b) address complex part geometries, (c) offer large elemental metal and alloy selections, (d) provide superior material properties. The key motive is to harvest the short lead time of metal AM to explore its use for replacement parts for legacy piping assets in utility-scale water management facilities. In this paper, the goal was to demonstrate 3D printing of stainless steel (SS) 316L parts using selective laser melting (SLM) technology. The corrosion resistance of 3D printed SS 316L was investigated using (a) Chronoamperometry (b) Cyclic Potentiodynamic Polarization (CPP) and Electrochemical Impedance Spectroscopy (EIS) and its improved resistance from wrought (conventional) part was also studied. Then the weldability of 3D printed SS 316L to wrought SS 316L was illustrated and finally, the mechanical strength of the weld and the effect of corrosion on weld strength was investigated using uniaxial tensile testing. The results show that 3D printed part compared to the wrought part has a) lower mass loss before and after corrosion, (b) higher pitting potential, and (c) higher charge transfer resistance. The tensile testing of welded dog bone specimens indicates that the 3D printed parts despite being less ductile were observed to have higher weld strength compared to the wrought part. On this basis, metal AM holds great value to be explored further for replacement piping parts owing to their better corrosion resistance and mechanical performance.
ContributorsSampath, Venkata Krishnan (Author) / Azeredo, Bruno (Thesis advisor) / Torres, Cesar (Committee member) / Mu, Bin (Committee member) / Arizona State University (Publisher)
Created2021
Description
Image segmentation is an important and challenging area of research in computer vision with various applications in medical imaging. Image segmentation refers to the process of partitioning an image into meaningful parts having similar attributes. Traditional manual segmentation approaches rely on human expertise to outline object boundaries in images which is a tedious and expensive process. In recent years, Deep Convolutional Neural Networks have demonstrated excellent performance in tasks such as detection, localization, recognition and segmentation of objects. However, these models require a large set of labeled training data which is difficult to obtain for medical images. To solve this problem, interactive segmentation techniques can be used to serve as a trade-off between fully automated and manual approaches. This allows a human expert in the loop as a form of guidance and refinement together with deep neural networks. This thesis proposes an interactive training strategy for segmentation, where a robot-user is utilized during training to mimic an actual annotator and provide corrections to the predicted masks by drawing scribbles. These scribbles are then used as supervisory signals and fed to the network; which interactively refines the segmentation map through several iterations of training. Further, the conducted experiments using various heuristic click strategies demonstrate that user interaction in the form of curves inside the organ of interest achieve optimal editing performance. Moreover, by using the popular image segmentation architectures based on U-Net as base models, segmentation performance is further improved; signifying that the accuracy gain of the interactive correction conform to the accuracy of the initial segmentation map.
ContributorsGoyal, Diksha (Author) / Liang, Jianming Dr. (Thesis advisor) / Wang, Yalin Dr. (Committee member) / Demakethepalli Venkateswara, Hemanth Kumar Dr. (Committee member) / Arizona State University (Publisher)
Created2021
Description
Granular materials demonstrate complexity in many physical attributes with various shapes and sizes, varying from several centimeters down to less than a few microns. Some materials are highly cohesive, while others are free-flowing. Despite such complexity in their physical properties, they are extremely important in industries dealing with bulk materials. Through this research, the factors affecting flowability of particulate solids and their interaction with projectiles were explored. In Part I, a novel set of characterization tools to relate various granular material properties to their flow behavior in confined and unconfined environments was investigated. Through this work, a thorough characterization study to examine the effects of particle size, particle size distribution, and moisture on bulk powder flowability were proposed. Additionally, a mathematical model to predict the flow function coefficient (FFC) was developed, based on the surface mean diameter and moisture level, which can serve as a flowability descriptor.
Part II of this research focuses on the impact dynamics of low velocity projectiles on granular media. Interaction of granular media with external foreign bodies occurs in everyday events like a human footprint on the beach. Several studies involving numerical and experimental methods have focused on the study of impact dynamics in both dry and wet granular media. However, most of the studies involving impact dynamics considered spherical projectiles under different conditions, while practical models should involve more complex, realistic shapes. Different impacting geometries with conserved density, volume, and velocity on a granular bed may experience contrasting drag forces upon penetration. This is due to the difference in the surface areas coming into contact with the granular media. In this study, a set of non-spherical geometries comprising cuboids, cylinders, hexagonal prisms and triangular prisms with constant density, volume, and impact velocities, were released onto a loosely packed, non-cohesive, dry granular bed. From these experimental results, a model to determine the penetration depth of projectiles upon impact was developed and how it is influenced by the release height and surface area of the projectiles in contact with the granular media was studied.
ContributorsVajrala, Spandana (Author) / Emady, Heather N (Thesis advisor) / Marvi, Hamidreza (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2021