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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.
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.
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.
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.