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Alloying in semiconductors has enabled many civilian technologies in optoelectronic, photonic fields and more. While the phenomenon of alloying is well established in traditional bulk semiconductors, owing to vastly available ternary phase diagrams, the ability to alloy in 2D systems are less clear. Recently anisotropic materials such as ReS2 and

Alloying in semiconductors has enabled many civilian technologies in optoelectronic, photonic fields and more. While the phenomenon of alloying is well established in traditional bulk semiconductors, owing to vastly available ternary phase diagrams, the ability to alloy in 2D systems are less clear. Recently anisotropic materials such as ReS2 and TiS3 have been extensively studied due to their direct-gap semiconductor and high mobility behaviors. This work is a report on alloys of ReS2 & ReSe2 and TiS3 &TiSe3.

Alloying selenium into ReS2 in the creation of ReS2xSe2-x, tunes the band gap and changes its vibrational spectrum. Depositing this alloy using bottom up approach has resulted in the loss of crystallinity. This loss of crystallinity was evidenced by grain boundaries and point defect shown by TEM images.

Also, in the creation of TiS3xSe3-x, by alloying Se into TiS3, a fixed ratio of 8% selenium deposit into TiS3 host matrix is observed. This is despite the vastly differing precursor amounts and growth temperatures, as evinced by detailed TEM, EDAX, TEM diffraction, and Raman spectroscopy measurements. This unusual behavior contrasts with other well-known layered material systems such as MoSSe, WMoS2 where continuous alloying can be attained. Cluster expansion theory calculations suggest that only limited composition (x) can be achieved. Considering the fact that TiSe3 vdW crystals have not been synthesized in the past, these alloying rejections can be attributed to energetic instability in the ternary phase diagrams estimated by calculations performed. Overall findings highlight potential means and challenges in achieving stable alloying in promising direct gap and high carrier mobility TiS3 materials.
ContributorsAgarwal, Ashutosh (Author) / Tongay, Sefaattin (Thesis advisor) / Green, Matthew (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Recently, two-dimensional (2D) materials have emerged as a new class of materials with highly attractive electronic, optical, magnetic, and thermal properties. However, there exists a sub-category of 2D layers wherein constituent metal atoms are arranged in a way that they form weakly coupled chains confined in the 2D landscape. These

Recently, two-dimensional (2D) materials have emerged as a new class of materials with highly attractive electronic, optical, magnetic, and thermal properties. However, there exists a sub-category of 2D layers wherein constituent metal atoms are arranged in a way that they form weakly coupled chains confined in the 2D landscape. These weakly coupled chains extend along particular lattice directions and host highly attractive properties including high thermal conduction pathways, high-mobility carriers, and polarized excitons. In a sense, these materials offer a bridge between traditional one-dimensional (1D) materials (nanowires and nanotubes) and 2D layered systems. Therefore, they are often referred as pseudo-1D materials, and are anticipated to impact photonics and optoelectronics fields.

This dissertation focuses on the novel growth routes and fundamental investigation of the physical properties of pseudo-1D materials. Example systems are based on transition metal chalcogenide such as rhenium disulfide (ReS2), titanium trisulfide (TiS3), tantalum trisulfide (TaS3), and titanium-niobium trisulfide (Nb(1-x)TixS3) ternary alloys. Advanced growth, spectroscopy, and microscopy techniques with density functional theory (DFT) calculations have offered the opportunity to understand the properties of these materials both experimentally and theoretically. The first controllable growth of ReS2 flakes with well-defined domain architectures has been established by a state-of-art chemical vapor deposition (CVD) method. High-resolution electron microscopy has offered the very first investigation into the structural pseudo-1D nature of these materials at an atomic level such as the chain-like features, grain boundaries, and local defects.

Pressure-dependent Raman spectroscopy and DFT calculations have investigated the origin of the Raman vibrational modes in TiS3 and TaS3, and discovered the unusual pressure response and its effect on Raman anisotropy. Interestingly, the structural and vibrational anisotropy can be retained in the Nb(1-x)TixS3 alloy system with the presence of phase transition at a nominal Ti alloying limit. Results have offered valuable experimental and theoretical insights into the growth routes as well as the structural, optical, and vibrational properties of typical pseudo-1D layered systems. The overall findings hope to shield lights to the understanding of this entire class of materials and benefit the design of 2D electronics and optoelectronics.
ContributorsWu, Kedi (Author) / Tongay, Sefaattin (Thesis advisor) / Zhuang, Houlong (Committee member) / Green, Matthew (Committee member) / Arizona State University (Publisher)
Created2018
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Description

Lithium ion batteries are quintessential components of modern life. They are used to power smart devices — phones, tablets, laptops, and are rapidly becoming major elements in the automotive industry. Demand projections for lithium are skyrocketing with production struggling to keep up pace. This drive is due mostly to the

Lithium ion batteries are quintessential components of modern life. They are used to power smart devices — phones, tablets, laptops, and are rapidly becoming major elements in the automotive industry. Demand projections for lithium are skyrocketing with production struggling to keep up pace. This drive is due mostly to the rapid adoption of electric vehicles; sales of electric vehicles in 2020 are more than double what they were only a year prior. With such staggering growth it is important to understand how lithium is sourced and what that means for the environment. Will production even be capable of meeting the demand as more industries make use of this valuable element? How will the environmental impact of lithium affect growth? This thesis attempts to answer these questions as the world looks to a decade of rapid growth for lithium ion batteries.

ContributorsMelton, John (Author) / Brian, Jennifer (Thesis director) / Karwat, Darshawn (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many

High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.

ContributorsDaly, John H (Author) / Ren, Yi (Thesis director) / Zhuang, Houlong (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description
National Airspace Systems (NAS) are complex cyber-physical systems that require swift air traffic management (ATM) to ensure flight safety and efficiency. With the surging demand for air travel and the increasing intricacy of aviation systems, the need for advanced technologies to support air traffic management and air traffic control (ATC)

National Airspace Systems (NAS) are complex cyber-physical systems that require swift air traffic management (ATM) to ensure flight safety and efficiency. With the surging demand for air travel and the increasing intricacy of aviation systems, the need for advanced technologies to support air traffic management and air traffic control (ATC) service has become more crucial than ever. Data-driven models or artificial intelligence (AI) have been conceptually investigated by various parties and shown immense potential, especially when provided with a vast volume of real-world data. These data include traffic information, weather contours, operational reports, terrain information, flight procedures, and aviation regulations. Data-driven models learn from historical experiences and observations and provide expeditious recommendations and decision support for various operation tasks, directly contributing to the digital transformation in aviation. This dissertation reports several research studies covering different aspects of air traffic management and ATC service utilizing data-driven modeling, which are validated using real-world big data (flight tracks, flight events, convective weather, workload probes). These studies encompass a range of topics, including trajectory recommendations, weather studies, landing operations, and aviation human factors. Specifically, the topics explored are (i) trajectory recommendations under weather conditions, which examine the impact of convective weather on last on-file flight plans and provide calibrated trajectories based on convective weather; (ii) multi-aircraft trajectory predictions, which study the intention of multiple mid-air aircraft in the near-terminal airspace and provide trajectory predictions; (iii) flight scheduling operations, which involve probabilistic machine learning-enhanced optimization algorithms for robust and efficient aircraft landing sequencing; (iv) aviation human factors, which predict air traffic controller workload level from flight traffic data with conformalized graph neural network. The uncertainties associated with these studies are given special attention and addressed through Bayesian/probabilistic machine learning. Finally, discussions on high-level AI-enabled ATM research directions are provided, hoping to extend the proposed studies in the future. This dissertation demonstrates that data-driven modeling has great potential for aviation digital twins, revolutionizing the aviation decision-making process and enhancing the safety and efficiency of ATM. Moreover, these research directions are not merely add-ons to existing aviation practices but also contribute to the future of transportation, particularly in the development of autonomous systems.
ContributorsPang, Yutian (Author) / Liu, Yongming (Thesis advisor) / Yan, Hao (Committee member) / Zhuang, Houlong (Committee member) / Marvi, Hamid (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2023
Description
Current Li-ion battery technologies are limited by the low capacities of theelectrode materials and require developments to meet stringent performance demands for future energy storage devices. Electrode materials that alloy with Li, such as Si, are one of the most promising alternatives for Li-ion battery anodes due to their high capacities. Tetrel (Si,

Current Li-ion battery technologies are limited by the low capacities of theelectrode materials and require developments to meet stringent performance demands for future energy storage devices. Electrode materials that alloy with Li, such as Si, are one of the most promising alternatives for Li-ion battery anodes due to their high capacities. Tetrel (Si, Ge, Sn) clathrates are a class of host-guest crystalline structures in which Tetrel elements form a cage framework and encapsulate metal guest atoms. These structures can form with defects such as framework/guest atom substitutions and vacancies which result in a wide design space for tuning materials properties. The goal of this work is to establish structure property relationships within the context of Li-ion battery anode applications. The type I Ba 8 Al y Ge 46-y clathrates are investigated for their electrochemical reactions with Li and show high capacities indicative of alloying reactions. DFT calculations show that Li insertion into the framework vacancies is favorable, but the migration barriers are too high for room temperature diffusion. Then, guest free type I clathrates are investigated for their Li and Na migration barriers. The results show that Li migration in the clathrate frameworks have low energy barriers (0.1- 0.4 eV) which suggest the possibility for room temperature diffusion. Then, the guest free, type II Si clathrate (Na 1 Si 136 ) is synthesized and reversible Li insertion into the type II Si clathrate structure is demonstrated. Based on the reasonable capacity (230 mAh/g), low reaction voltage (0.30 V) and low volume expansion (0.21 %), the Si clathrate could be a promising insertion anode for Li-ion batteries. Next, synchrotron X-ray measurements and pair distribution function (PDF) analysis are used to investigate the lithiation pathways of Ba 8 Ge 43 , Ba 8 Al 16 Ge 30 , Ba 8 Ga 15 Sn 31 and Na 0.3 Si 136 . The results show that the Ba-clathrates undergo amorphous phase transformations which is distinct from their elemental analogues (Ge, Sn) which feature crystalline lithiation pathways. Based on the high capacities and solid-solution reaction mechanism, guest-filled clathrates could be promising precursors to form alloying anodes with novel electrochemical properties. Finally, several high temperature (300-550 °C) electrochemical synthesis methods for Na-Si and Na-Ge clathrates are demonstrated in a cell using a Na β’’-alumina solid electrolyte.
ContributorsDopilka, Andrew (Author) / Chan, Candace K (Thesis advisor) / Zhuang, Houlong (Committee member) / Peng, Xihong (Committee member) / Sieradzki, Karl (Committee member) / Arizona State University (Publisher)
Created2021
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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

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