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- All Subjects: Materials Science
- Creators: Jiao, Yang
- Creators: Rajagopalan, Jagannathan
Description
Intelligent engineering designs require an accurate understanding of material behavior, since any uncertainties or gaps in knowledge must be counterbalanced with heightened factors of safety, leading to overdesign. Therefore, building better structures and pushing the performance of new components requires an improved understanding of the thermomechanical response of advanced materials under service conditions. This dissertation provides fundamental investigations of several advanced materials: thermoset polymers, a common matrix material for fiber-reinforced composites and nanocomposites; aluminum alloy 7075-T6 (AA7075-T6), a high-performance aerospace material; and ceramic matrix composites (CMCs), an advanced composite for extreme-temperature applications. To understand matrix interactions with various interfaces and nanoinclusions at their fundamental scale, the properties of thermoset polymers are studied at the atomistic scale. An improved proximity-based molecular dynamics (MD) technique for modeling the crosslinking of thermoset polymers is carefully established, enabling realistic curing simulations through its ability to dynamically and probabilistically perform complex topology transformations. The proximity-based MD curing methodology is then used to explore damage initiation and the local anisotropic evolution of mechanical properties in thermoset polymers under uniaxial tension with an emphasis on changes in stiffness through a series of tensile loading, unloading, and reloading experiments. Aluminum alloys in aerospace applications often require a fatigue life of over 109 cycles, which is well over the number of cycles that can be practically tested using conventional fatigue testing equipment. In order to study these high-life regimes, a detailed ultrasonic cycle fatigue study is presented for AA7075-T6 under fully reversed tension-compression loading. The geometric sensitivity, frequency effects, size effects, surface roughness effects, and the corresponding failure mechanisms for ultrasonic fatigue across different fatigue regimes are investigated. Finally, because CMCs are utilized in extreme environments, oxidation plays an important role in their degradation. A multiphysics modeling methodology is thus developed to address the complex coupling between oxidation, mechanical stress, and oxygen diffusion in heterogeneous carbon fiber-reinforced CMC microstructures.
ContributorsSchichtel, Jacob (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Ghoshal, Anindya (Committee member) / Huang, Huei-Ping (Committee member) / Jiao, Yang (Committee member) / Oswald, Jay (Committee member) / Arizona State University (Publisher)
Created2022
Description
High-entropy alloys (HEAs) is a new class of materials which have been studied heavily due to their special mechanical properties. HEAs refers to alloys with multiple equimolar or nearly equimolar elements. HEAs show exceptional and attractive properties currently absent from conventional alloys, which make them the center of intense investigation. HEAs obtain their properties from four core effects that they exhibit and most of the work on them have been dedicated to study their mechanical properties. In contrast, little or no research have gone into studying the functional or even thermal properties of HEAs. Some HEAs have also shown exceptional or very high melting points. According to the definition of HEAs, Si-Ge-Sn alloys with equal or comparable concentrations of the three group IV elements belong to the category of HEAs. Thus, the equimolar components of Si-Ge-Sn alloys probably allow their atomic structures to display the same fundamental effects of metallic HEAs. The experimental fabrication of such alloys has been proven to be very difficult, which is mainly due to differences between the properties of their constituent elements, as indicated from their binary phase diagrams. However, previous computational studies have shown that SiGeSn HEAs have some very interesting properties, such as high electrical conductivity, low thermal conductivity and semiconducting properties. In this work, going for a complete characterization of the SiGeSn HEA properties, the melting point of this alloy is studied using classical molecular dynamics (MD) simulations and density functional theory (DFT) calculations. The aim is to investigate the effects of high Sn content in this alloy on the melting point compared with the traditional SiGe alloys. Classical MD simulations results strongly indicates that none of the available empirical potentials is able to predict accurate or reasonable melting points for SiGeSn HEAs and most of its subsystems. DFT calculations results show that SiGeSn HEA have a melting point which represent the mean value of its constituent elements and that no special deviations are found. This work contributes to the study of SiGeSn HEA properties, which can serve as guidance before the successful experimental fabrication of this alloy.
ContributorsAlqaisi, Ahmad Madhat Odeh (Author) / Hong, Qi-Jun (Thesis advisor) / Zhuang, Houlong (Thesis advisor) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2023
Description
Electromigration, the net atomic diffusion associated with the momentum transfer from electrons moving through a material, is a major cause of device and component failure in microelectronics. The deleterious effects from electromigration rise with increased current density, a parameter that will only continue to increase as our electronic devices get smaller and more compact. Understanding the dynamic diffusional pathways and mechanisms of these electromigration-induced and propagated defects can further our attempts at mitigating these failure modes. This dissertation provides insight into the relationships between these defects and parameters of electric field strength, grain boundary misorientation, grain size, void size, eigenstrain, varied atomic mobilities, and microstructure.First, an existing phase-field model was modified to investigate the various defect modes associated with electromigration in an equiaxed non-columnar microstructure. Of specific interest was the effect of grain boundary misalignment with respect to current flow and the mechanisms responsible for the changes in defect kinetics. Grain size, magnitude of externally applied electric field, and the utilization of locally distinct atomic mobilities were other parameters investigated. Networks of randomly distributed grains, a common microstructure of interconnects, were simulated in both 2- and 3-dimensions displaying the effects of 3-D capillarity on diffusional dynamics.
Also, a numerical model was developed to study the effect of electromigration on void migration and coalescence. Void migration rates were found to be slowed from compressive forces and the nature of the deformation concurrent with migration was examined through the lens of chemical potential. Void migration was also validated with previously reported theoretical explanations. Void coalescence and void budding were investigated and found to be dependent on the magnitude of interfacial energy and electric field strength.
A grasp on the mechanistic pathways of electromigration-induced defect evolution is imperative to the development of reliable electronics, especially as electronic devices continue to miniaturize. This dissertation displays a working understanding of the mechanistic pathways interconnects can fail due to electromigration, as well as provide direction for future research and understanding.
ContributorsFarmer, William McHann (Author) / Ankit, Kumar (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Jiao, Yang (Committee member) / McCue, Ian (Committee member) / Arizona State University (Publisher)
Created2022
Description
Microstructure refinement and alloy additions are considered potential routes to increase high temperature performance of existing metallic superalloys used under extreme conditions. Nanocrystalline (NC) Cu-10at%Ta exhibits such improvements over microstructurally unstable NC metals, leading to enhanced creep behavior compared to its coarse-grained (CG) counterparts. However, the low melting point of Cu compared to other FCC metals, e.g., Ni, might lead to an early onset of diffusional creep mechanisms. Thus, this research seeks to study the thermo-mechanical behavior and stability of hierarchical (prepared using arc-melting) and NC (prepared by collaborators through powder pressing and annealing) Ni-Y-Zr alloys where Zr is expected to provide solid solution and grain boundary strengthening in hierarchical and NC alloys, respectively, while Ni-Y and Ni-Zr intermetallic precipitates (IMCs) would provide kinetic stability. Hierarchical alloys had microstructures stable up to 1100 °C with ultrafine eutectic of ~300 nm, dendritic arm spacing of ~10 μm, and grain size ~1-2 mm. Room temperature hardness tests along with uniaxial compression performed at 25 and 600 °C revealed that microhardness and yield strength of hierarchical alloys with small amounts of Y (0.5-1wt%) and Zr (1.5-3 wt%) were comparable to Ni-superalloys, due to the hierarchical microstructure and potential presence of nanoscale IMCs. In contrast, NC alloys of the same composition were found to be twice as hard as the hierarchical alloys. Creep tests at 0.5 homologous temperature showed active Coble creep mechanisms in hierarchical alloys at low stresses with creep rates slower than Fe-based superalloys and dislocation creep mechanisms at higher stresses. Creep in NC alloys at lower stresses was only 20 times faster than hierarchical alloys, with the difference in grain size ranging from 10^3 to 10^6 times at the same temperature. These NC alloys showed enhanced creep properties over other NC metals and are expected to have rates equal to or improved over the CG hierarchical alloys with ECAP processing techniques. Lastly, the in-situ wide-angle x-ray scattering (WAXS) measurements during quasi-static and creep tests implied stresses being carried mostly by the matrix before yielding and in the primary creep stage, respectively, while relaxation was observed in Ni5Zr for both hierarchical and NC alloys. Beyond yielding and in the secondary creep stage, lattice strains reached a steady state, thereby, an equilibrium between plastic strain rates was achieved across different phases, so that deformation reaches a saturation state where strain hardening effects are compensated by recovery mechanisms.
ContributorsSharma, Shruti (Author) / Peralta, Pedro (Thesis advisor) / Alford, Terry (Committee member) / Jiao, Yang (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2022
Description
With the abundance of increasingly large datasets, the ability to predict the phase of high-entropy alloys (HEAs) based solely on elemental composition could become a reliable tool for the discovery of new HEAs. However, as the amount of data expands so does the computational time and resources required to train predictive classical machine learning models. Quantum computers, which use quantum bits (qubits), could be the solution to overcoming these demands. Their ability to use quantum superposition and interference to perform calculations could be the key to handling large amounts of data. In this work, a hybrid quantum-classical machine learning algorithm is implemented on both quantum simulators and quantum processors to perform the supervised machine learning task. Their feasibility as a future tool for HEA discovery is evaluated based on the algorithm’s performance. An artificial neural network (ANN), run by classical computers, is also trained on the same data for performance comparison. The accuracy of the quantum-classical model was found to be comparable to the accuracy achieved by the classical ANN with a slight decrease in accuracy when ran on quantum hardware due to qubit susceptibility to decoherence. Future developments in the applied quantum machine learning method are discussed.
ContributorsBrown, Payden Lance (Author) / Zhuang, Houlong (Thesis advisor) / Ankit, Kumar (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2022
Description
Achieving a viable process for advanced manufacturing of ceramics and metal-ceramic composites is a sought-after goal in a wide range of fields including electronics and sensors for harsh environments, microelectromechanical devices, energy storage materials, and structural materials, among others. In this dissertation, the processing, and manufacturing of ceramics and ceramic composites are addressed, specifically, a process for three-dimensional (3D) printing of polymer-derived ceramics (PDC), and a process for low-cost manufacturing as well as healing of metal-ceramic composites is demonstrated.Three-dimensional printing of ceramics is enabled by dispensing the preceramic polymer at the tip of a moving nozzle into a gel that can reversibly switch between fluid and solid states, and subsequently thermally cross-linking the entire printed part “at once” while still inside the same gel was demonstrated. The solid gel converts to fluid at the tip of the moving nozzle, allowing the polymer solution to be dispensed and quickly returns to a solid state to maintain the geometry of the printed polymer both during printing and the subsequent high-temperature (160 °C) cross-linking. After retrieving the cross-linked part from the gel, the green body is converted to ceramic by high-temperature pyrolysis. This scalable process opens new opportunities for low-cost and high-speed production of complex three-dimensional ceramic parts and will be widely used for high-temperature and corrosive environment applications, including electronics and sensors, microelectromechanical systems, energy, and structural applications.
Metal-ceramic composites are technologically significant as structural and functional materials and are among the most expensive materials to manufacture and repair. Hence, technologies for self-healing metal-ceramic composites are important. Here, a concept to fabricate and heal co-continuous metal-ceramic composites at room temperature were demonstrated. The composites were fabricated by infiltration of metal (here Copper) into a porous alumina preform (fabricated by freeze-casting) through electroplating; a low-temperature and low-cost process for the fabrication of such composites. Additionally, the same electroplating process was demonstrated for healing damages such as grooves and cracks in the original composite, such that the healed composite recovered its strength by more than 80%. Such technology may be expanded toward fully autonomous self-healing structures.
ContributorsMahmoudi, Mohammadreza (Author) / Minary-Jolandan, Majid (Thesis advisor) / Rajagopalan, Jagannathan (Committee member) / Cramer, Corson (Committee member) / Kang, Wonmo (Committee member) / Bhate, Dhruv (Committee member) / Arizona State University (Publisher)
Created2022
Description
Phase-field (PF) models are one of the most powerful tools to simulate microstructural evolution in metallic materials, polymers, and ceramics. However, existing PF approaches rely on rigorous mathematical model development, sophisticated numerical schemes, and high-performance computing for accuracy. Although recently developed surrogate microstructure models employ deep-learning techniques and reconstruction of microstructures from lower-dimensional data, their accuracy is fairly limited as spatio-temporal information is lost in the pursuit of dimensional reduction. Given these limitations, a novel data-driven emulator (DDE) for extrapolation prediction of microstructural evolution is presented, which combines an image-based convolutional and recurrent neural network (CRNN) with tensor decomposition, while leveraging previously obtained PF datasets for training. To assess the robustness of DDE, the emulation sequence and the scaling behavior with phase-field simulations for several noisy initial states are compared. In conclusion, the effectiveness of the microstructure emulation technique is explored in the context of accelerating runtime, along with an emphasis on its trade-off with accuracy.Meanwhile, an interpolation DDE has also been tested, which is based on obtaining
a low-dimensional representation of the microstructures via tensor decomposition and subsequently predicting the microstructure evolution in the low-dimensional space using Gaussian process regression (GPR). Once the microstructure predictions are obtained in the low-dimensional space, a hybrid input-output phase retrieval algorithm will be employed to reconstruct the microstructures. As proof of concept, the results on microstructure prediction for spinodal decomposition are presented, although the method itself is agnostic of the material parameters. Results show that GPR-based DDE model are able to predict microstructure evolution sequences that closely resemble the true microstructures (average normalized mean square of 6.78 × 10−7) at time scales half of that employed in obtaining training data. This data-driven
microstructure emulator opens new avenues to predict the microstructural evolution by leveraging phase-field simulations and physical experimentation where the time resolution is often quite large due to limited resources and physical constraints, such as the phase coarsening experiments previously performed in microgravity.
Future work will also be discussed and demonstrate the intended utilization of these two approaches for 3D microstructure prediction through their combined application.
ContributorsWu, Peichen (Author) / Ankit, Kumar (Thesis advisor) / Iquebal, Ashif (Committee member) / Jiao, Yang (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2024
Description
Layered chalcogenides are a diverse class of crystalline materials that consist of covalently bound building blocks held together by van der Waals forces, including the transition metal dichalcogenides (TMDCs) and the pnictogen chalcogenides (PCs) among all. These materials, in particular, MoS2 which is the most widely studied TMDC material, have attracted significant attention in recent years due to their unique physical, electronic, optical, and chemical properties that depend on the number of layers. Due to their high aspect ratios and extreme thinness, 2D materials are sensitive to modifications via chemistry on their surfaces. For instance, covalent functionalization can be used to robustly modify the electronic properties of 2D materials, and can also be used to attach other materials or structures. Metal adsorption on the surfaces of 2D materials can also tune their electronic structures, and can be used as a strategy for removing metal contaminants from water. Thus, there are many opportunities for studying the fundamental surface interactions of 2D materials and in particular the TMDCs and PCs.
The work reported in this dissertation represents detailed fundamental studies of the covalent functionalization and metal adsorption behavior of layered chalcogenides, which are two significant aspects of the surface interactions of 2D materials. First, we demonstrate that both the Freundlich and Temkin isotherm models, and the pseudo-second-order reaction kinetics model are good descriptors of the reaction due to the energetically inhomogeneous surface MoS2 and the indirect adsorbate-adsorbate interactions from previously attached nitrophenyl (NP) groups. Second, the covalent functionalization using aryl diazonium salts is extended to nanosheets of other representative TMDC materials MoSe2, WS2, and WSe2, and of the representative PC materials Bi2S3 and Sb2S3, demonstrated using atomic force microscopy (AFM) imaging and Fourier transform infrared spectroscopy (FTIR). Finally, using AFM and X-ray photoelectron spectroscopy (XPS), it is shown that Pb, Cd Zn and Co form nanoclusters on the MoS2 surface without affecting the structure of the MoS2 itself. The metals can also be thermally desorbed from MoS2, thus suggesting a potential application as a reusable water purification technology.
The work reported in this dissertation represents detailed fundamental studies of the covalent functionalization and metal adsorption behavior of layered chalcogenides, which are two significant aspects of the surface interactions of 2D materials. First, we demonstrate that both the Freundlich and Temkin isotherm models, and the pseudo-second-order reaction kinetics model are good descriptors of the reaction due to the energetically inhomogeneous surface MoS2 and the indirect adsorbate-adsorbate interactions from previously attached nitrophenyl (NP) groups. Second, the covalent functionalization using aryl diazonium salts is extended to nanosheets of other representative TMDC materials MoSe2, WS2, and WSe2, and of the representative PC materials Bi2S3 and Sb2S3, demonstrated using atomic force microscopy (AFM) imaging and Fourier transform infrared spectroscopy (FTIR). Finally, using AFM and X-ray photoelectron spectroscopy (XPS), it is shown that Pb, Cd Zn and Co form nanoclusters on the MoS2 surface without affecting the structure of the MoS2 itself. The metals can also be thermally desorbed from MoS2, thus suggesting a potential application as a reusable water purification technology.
ContributorsLi, Duo, Ph.D (Author) / Wang, Qing Hua (Thesis advisor) / Green, Alexander A. (Committee member) / Chan, Candace K. (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2019
Description
Niobium is the primary material for fabricating superconducting radio-frequency (SRF) cavities. However, presence of impurities and defects degrade the superconducting behavior of niobium twofold, first by nucleating non-superconducting phases and second by increasing the residual surface resistance of cavities. In particular, niobium absorbs hydrogen during cavity fabrication and promotes precipitation of non-superconducting niobium hydride phases. Additionally, magnetic flux trapping at defects leads to a normal conducting (non-superconducting) core which increases surface resistance and negatively affects niobium performance for superconducting applications. However, undelaying mechanisms related to hydride formation and dissolution along with defect interaction with magnetic fields is still unclear. Therefore, this dissertation aims to investigate the role of defects and impurities on functional properties of niobium for SRF cavities using first-principles methods.
Here, density functional theory calculations revealed that nitrogen addition suppressed hydrogen absorption interstitially and at grain boundaries, and it also decreased the energetic stability of niobium hydride precipitates present in niobium. Further, hydrogen segregation at the screw dislocation was observed to transform the dislocation core structure and increase the barrier for screw dislocation motion. Valence charge transfer calculations displayed a strong tendency of nitrogen to accumulate charge around itself, thereby decreasing the strength of covalent bonds between niobium and hydrogen leading to a very unstable state for interstitial hydrogen and hydrides. Thus, presence of nitrogen during processing plays a critical role in controlling hydride precipitation and subsequent SRF properties.
First-principles methods were further implemented to gain a theoretical perspective about the experimental observations that lattice defects are effective at trapping magnetic flux in high-purity superconducting niobium. Full-potential linear augmented plane-wave methods were used to analyze the effects of magnetic field on the superconducting state surrounding these defects. A considerable amount of trapped flux was obtained at the dislocation core and grain boundaries which can be attributed to significantly different electronic structure of defects as compared to bulk niobium. Electron redistribution at defects enhances non-paramagnetic effects that perturb superconductivity, resulting in local conditions suitable for flux trapping. Therefore, controlling accumulation or depletion of charge at the defects could mitigate these tendencies and aid in improving superconductive behavior of niobium.
Here, density functional theory calculations revealed that nitrogen addition suppressed hydrogen absorption interstitially and at grain boundaries, and it also decreased the energetic stability of niobium hydride precipitates present in niobium. Further, hydrogen segregation at the screw dislocation was observed to transform the dislocation core structure and increase the barrier for screw dislocation motion. Valence charge transfer calculations displayed a strong tendency of nitrogen to accumulate charge around itself, thereby decreasing the strength of covalent bonds between niobium and hydrogen leading to a very unstable state for interstitial hydrogen and hydrides. Thus, presence of nitrogen during processing plays a critical role in controlling hydride precipitation and subsequent SRF properties.
First-principles methods were further implemented to gain a theoretical perspective about the experimental observations that lattice defects are effective at trapping magnetic flux in high-purity superconducting niobium. Full-potential linear augmented plane-wave methods were used to analyze the effects of magnetic field on the superconducting state surrounding these defects. A considerable amount of trapped flux was obtained at the dislocation core and grain boundaries which can be attributed to significantly different electronic structure of defects as compared to bulk niobium. Electron redistribution at defects enhances non-paramagnetic effects that perturb superconductivity, resulting in local conditions suitable for flux trapping. Therefore, controlling accumulation or depletion of charge at the defects could mitigate these tendencies and aid in improving superconductive behavior of niobium.
ContributorsGarg, Pulkit (Author) / Solanki, Kiran N (Thesis advisor) / Jiao, Yang (Committee member) / Oswald, Jay (Committee member) / Arizona State University (Publisher)
Created2019
Description
Nanomaterials that exhibit enzyme-like catalytic activity or nanozymes have many advantages compared to biological enzymes such as low cost of production and high stability. There is a substantial interest in studying two-dimensional materials due to their exceptional properties. Hafnium diboride is a type of two-dimensional material and belongs to the metal diborides family made of hexagonal layers of boron atoms separated by metal layers. In this work, the peroxidase-like activity of hafnium diboride nanoflakes dispersed in the block copolymer F77 was discovered for the first time. The kinetics, mechanisms and catalytic performance towards the oxidation of the chromogenic substrate 3,3,5,5-tetramethylbenzidine (TMB) in the presence of hydrogen peroxide are presented in this work. Kinetic parameters were determined by steady-state kinetics and a comparison with other nanozymes is given. Results show that the HfB2/F77 nanozyme possesses a unique combination of unusual high affinity towards hydrogen peroxide and high activity per cost. These findings are important for applications that involve reactions with hydrogen peroxide.
ContributorsMatar Abed, Mahmoud (Author) / Wang, Qing Hua (Thesis advisor) / Green, Alexander (Thesis advisor) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2019