Matching Items (16)
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- All Subjects: Materials Science
- Genre: Masters Thesis
- Creators: Jiao, Yang
- Creators: Chan, Candace
- Status: Published
Description
Topological insulators with conducting surface states yet insulating bulk states have generated a lot of interest amongst the physics community due to their varied characteristics and possible applications. Doped topological insulators have presented newer physical states of matter where topological order co&ndashexists; with other physical properties (like magnetic order). The electronic states of these materials are very intriguing and pose problems and the possible solutions to understanding their unique behaviors. In this work, we use Electron Energy Loss Spectroscopy (EELS) – an analytical TEM tool to study both core&ndashlevel; and valence&ndashlevel; excitations in Bi2Se3 and Cu(doped)Bi2Se3 topological insulators. We use this technique to retrieve information on the valence, bonding nature, co-ordination and lattice site occupancy of the undoped and the doped systems. Using the reference materials Cu(I)Se and Cu(II)Se we try to compare and understand the nature of doping that copper assumes in the lattice. And lastly we utilize the state of the art monochromated Nion UltraSTEM 100 to study electronic/vibrational excitations at a record energy resolution from sub-nm regions in the sample.
ContributorsSubramanian, Ganesh (Author) / Spence, John (Thesis advisor) / Jiang, Nan (Committee member) / Chen, Tingyong (Committee member) / Chan, Candace (Committee member) / Arizona State University (Publisher)
Created2013
Description
Metal Organic Frameworks(MOFs) have been used in various applications, including
sensors. The unique crystalline structure of MOFs in addition to controllability of
their pore size and their intake selectivity makes them a promising method of detection.
Detection of metal ions in water using a binary mixture of luminescent MOFs
has been reported. 3 MOFs(ZrPDA, UiO-66 and UiO-66-NH2) as detectors and 4
metal ions(Pb2+, Ni2+, Ba2+ and Cu2+) as the target species were chosen based on
cost, water stability, application and end goals.
It is possible to detect metal ions such as Pb2+ at concentrations at low as 0.005
molar using MOFs. Also, based on the luminescence responses, a method of distinguishing
between similar metal ions has been proposed. It is shown that using a
mixture of MOFs with dierent reaction to metal ions can lead to unique and specic
3D luminescence maps, which can be used to identify the present metal ions in water
and their amount.
In addition to the response of a single MOF to addition of a single metal ion,
luminescence response of ZrPDA + UiO-66 mixture to increasing concentration of
each of 4 metal ions was studied, and summarized. A new peak is observed in the
mixture, that did not exist before, and it is proposed that this peak requires metal
ions to activate
sensors. The unique crystalline structure of MOFs in addition to controllability of
their pore size and their intake selectivity makes them a promising method of detection.
Detection of metal ions in water using a binary mixture of luminescent MOFs
has been reported. 3 MOFs(ZrPDA, UiO-66 and UiO-66-NH2) as detectors and 4
metal ions(Pb2+, Ni2+, Ba2+ and Cu2+) as the target species were chosen based on
cost, water stability, application and end goals.
It is possible to detect metal ions such as Pb2+ at concentrations at low as 0.005
molar using MOFs. Also, based on the luminescence responses, a method of distinguishing
between similar metal ions has been proposed. It is shown that using a
mixture of MOFs with dierent reaction to metal ions can lead to unique and specic
3D luminescence maps, which can be used to identify the present metal ions in water
and their amount.
In addition to the response of a single MOF to addition of a single metal ion,
luminescence response of ZrPDA + UiO-66 mixture to increasing concentration of
each of 4 metal ions was studied, and summarized. A new peak is observed in the
mixture, that did not exist before, and it is proposed that this peak requires metal
ions to activate
ContributorsSirous, Peyman (Author) / Mu, Bin (Thesis advisor) / Alford, Terry (Thesis advisor) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2018
Description
Fatigue is a degradation process of materials that would lead to failure when materials are subjected to cyclic loadings. During past centuries, various of approaches have been proposed and utilized to help researchers understand the underlying theories of fatigue behavior of materials, as well as design engineering structures so that catastrophic disasters that arise from fatigue failure could be avoided. The stress-life approach is the most classical way that academia applies to analyze fatigue data, which correlates the fatigue lifetime with stress amplitudes during cyclic loadings. Fracture mechanics approach is another well-established way, by which people regard the cyclic stress intensity factor as the driving force during fatigue crack nucleation and propagation, and numerous models (such as the well-known Paris’ law) are developed by researchers.
The significant drawback of currently widely-used fatigue analysis approaches, nevertheless, is that they are all cycle-based, limiting researchers from digging into sub-cycle regime and acquiring real-time fatigue behavior data. The missing of such data further impedes academia from validating hypotheses that are related to real-time observations of fatigue crack nucleation and growth, thus the existence of various phenomena, such as crack closure, remains controversial.
In this thesis, both classical stress-life approach and fracture-mechanics-based approach are utilized to study the fatigue behavior of alloys. Distinctive material characterization instruments are harnessed to help collect and interpret key data during fatigue crack growth. Specifically, an investigation on the sub-cycle fatigue crack growth behavior is enabled by in-situ SEM mechanical testing, and a non-uniform growth mechanism within one loading cycle is confirmed by direct observation as well as image interpretation. Predictions based on proposed experimental procedure and observations show good match with cycle-based data from references, which indicates the credibility of proposed methodology and model, as well as their capability of being applied to a wide range of materials.
The significant drawback of currently widely-used fatigue analysis approaches, nevertheless, is that they are all cycle-based, limiting researchers from digging into sub-cycle regime and acquiring real-time fatigue behavior data. The missing of such data further impedes academia from validating hypotheses that are related to real-time observations of fatigue crack nucleation and growth, thus the existence of various phenomena, such as crack closure, remains controversial.
In this thesis, both classical stress-life approach and fracture-mechanics-based approach are utilized to study the fatigue behavior of alloys. Distinctive material characterization instruments are harnessed to help collect and interpret key data during fatigue crack growth. Specifically, an investigation on the sub-cycle fatigue crack growth behavior is enabled by in-situ SEM mechanical testing, and a non-uniform growth mechanism within one loading cycle is confirmed by direct observation as well as image interpretation. Predictions based on proposed experimental procedure and observations show good match with cycle-based data from references, which indicates the credibility of proposed methodology and model, as well as their capability of being applied to a wide range of materials.
ContributorsLiu, Siying (Author) / Liu, Yongming (Thesis advisor) / Jiao, Yang (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2018
Description
In this research work, the process optimization of silver iodide-silver meta phosphate ionic glass molding for solid state super ionic stamping was performed. Solid state super ionic stamping is a process of all solid ambient condition electrochemical nano patterning technique. In solid state super ionic stamping, anodic dissolution on a solid electrolyte –metal interface and subsequent charge-mass transport in the solid electrolyte is used for obtaining nanometer features on the metallic surface. The solid electrolyte referred to as the stamp is pre-patterned with features to be obtained on the metallic surface. This research developed the process for obtaining stamp with specific dimensions by making use of compression molding. The compression molding process was optimized by varying the five process parameters-temperature, pressure, holding time, pressing time and cooling time. The objective of the process optimization was to obtain best geometrical features for the stamp including flatness and surface roughness and by optimizing the compression molding process, stamp with minimum flatness and surface roughness was obtained. After the experimental optimization of the process was completed, statistical analysis was performed to understand the relative significance of the process parameters and the interaction of the process parameters on the flatness and surface roughness values of the molded stamp. Structural characterization was performed to obtain the variation of average domain size of ionic glass particles within the molded glass disk by varying the process parameters of holding time, pressing time and cooling time.
ContributorsPanikkar, Gautam (Author) / Hsu, Keng H (Thesis advisor) / Chan, Candace (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2015
Description
Mechanical behavior of metallic thin films at room temperature (RT) is relatively well characterized. However, measuring the high temperature mechanical properties of thin films poses several challenges. These include ensuring uniformity in sample temperature and minimizing temporal fluctuations due to ambient heat loss, in addition to difficulties involved in mechanical testing of microscale samples. To address these issues, we designed and analyzed a MEMS-based high temperature tensile testing stage made from single crystal silicon. The freestanding thin film specimens were co-fabricated with the stage to ensure uniaxial loading. Multi-physics simulations of Joule heating, incorporating both radiation and convection heat transfer, were carried out using COMSOL to map the temperature distribution across the stage and the specimen. The simulations were validated using temperature measurements from a thermoreflectance microscope.
ContributorsEswarappa Prameela, Suhas (Author) / Rajagopalan, Jagannathan (Thesis advisor) / Wang, Liping (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2016
Description
Electromigration (EM) has been a serious reliability concern in microelectronics packaging for close to half a century now. Whenever the challenges of EM are overcome newer complications arise such as the demand for better performance due to increased miniaturization of semiconductor devices or the problems faced due to undesirable properties of lead-free solders. The motivation for the work is that there exists no fully computational modeling study on EM damage in lead-free solders (and also in lead-based solders). Modeling techniques such as one developed here can give new insights on effects of different grain features and offer high flexibility in varying parameters and study the corresponding effects. In this work, a new computational approach has been developed to study void nucleation and initial void growth in solders due to metal atom diffusion. It involves the creation of a 3D stochastic mesoscale model of the microstructure of a polycrystalline Tin structure. The next step was to identify regions of current crowding or ‘hot-spots’. This was done through solving a finite difference scheme on top of the 3D structure. The nucleation of voids due to atomic diffusion from the regions of current crowding was modeled by diffusion from the identified hot-spot through a rejection free kinetic Monte-Carlo scheme. This resulted in the net movement of atoms from the cathode to the anode. The above steps of identifying the hotspot and diffusing the atoms at the hot-spot were repeated and this lead to the initial growth of the void. This procedure was studied varying different grain parameters. In the future, the goal is to explore the effect of more grain parameters and consider other mechanisms of failure such as the formation of intermetallic compounds due to interstitial diffusion and dissolution of underbump metallurgy.
ContributorsKarunakaran, Deepak (Thesis advisor) / Jiao, Yang (Committee member) / Chawla, Nikhilesh (Committee member) / Rajagopalan, Jagannathan (Committee member) / Arizona State University (Publisher)
Created2016
Description
Lithium nickel manganese cobalt oxides (NMCs) are layered oxide cathode materials which are becoming increasingly popular as the demand for lithium-ion batteries increases. Lithium-ion batteries are used to power modern vehicles and for other battery applications. To better understand the structure and energetics of NMCs, various molar ratios of these compounds were synthesized via a sol-gel method and characterized with powder X-ray diffraction profile fitting. Lattice constants for the nickel, manganese, and cobalt solid solutions were determined. High temperature oxide melt solution calorimetry was used to determine the enthalpies of formation and mixing. All but Li2MnO3 had the same space group as LiCoO2 (R-3m). The lattice constants approximately followed a linear fit with cobalt mole fraction (R2average= 0.973) for the cobalt series. As the molar ratio of cobalt increased the lattice constants decreased. The nickel series was less linear (R2average=0.733) and had an opposite lattice constant trend to cobalt. The manganese series possessed a roughly linear trend when excluding the outlier Li2MnO3 (R2average=0.282). The formation enthalpy of the cobalt series becomes more negative as more cobalt is added. A second order polynomial fit could be used to model the enthalpies of mixing for the series. NMC2.5,2.5,5 exhibited the most stable energetics. A third order polynomial fit could be used to model the enthalpy of mixing for the nickel and manganese series with NMC811 and NMC181 exhibiting the most stable energetics.
ContributorsKanitz, William James (Author) / Navrotsky, Alexandra (Thesis advisor) / Chan, Candace (Committee member) / Xu, Hongwu (Committee member) / Arizona State University (Publisher)
Created2023
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
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
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