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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.
In this thesis, a novel series of grafted siloxanes have been explored for their probable application in the healthcare industry. The siloxanes are grafted with poly(ethylene glycol) (PEG) and quaternary ammonium salt (QUAT). The effects of varying 1) molar ratios of QUAT to PEG and 2) PEG chain length on contact angle, surface tension, critical micelle concentration (CMC), and micelle assembly properties were studied. In contact angle experiments, the hydrophilicity of grafted siloxanes increased by grafting PEG and QUAT. The amphiphilicity increases and CMC decreases as the PEG chain length shortens. Adding QUAT also reduces CMC. These trends were observed in surface tension and Isothermal Titration Calorimetry experiments. A change in self-assembly behaviour was also observed in Dynamic Light Scattering experiments upon increasing the PEG chain length and its ratio relative to the quaternary ammonium in the siloxane polymer.
These polymers have also been studied for their probable application as a sensitive 1H NMR spectroscopy indicator of tissue oxygenation (pO2) based on spectroscopic spin-lattice relaxometry. The proton imaging of siloxanes to map tissue oxygenation levels (PISTOL) technique is used to map T1 of siloxane polymer, which is correlated to dynamic changes in tissue pO2 at various locations by a linear relationship between pO2 and 1/T1. The T1-weighted echo spin signals were observed in an initial study of siloxanes using the PISTOL technique.
The change in the ratio of QUAT to PEG and the varying chain length of PEG have a significant effect on the physical property characteristics of siloxane graft copolymers. The conclusions and observations of the present work serve as a benchmark study for further development of adaptive polymers and for the creation of integrated “nanoscale” probes for PISTOL oximetry and drug delivery.
In this dissertation, vapor phase transport is used to synthesize layer structured gallium chalcogenide nanomaterials with highly controlled structure, morphology and properties, with particular emphasis on GaSe, GaTe and GaSeTe alloys. Multiple routes are used to manipulate the physical properties of these materials including strain engineering, defect engineering and phase engineering. First, 2D GaSe with controlled morphologies is synthesized on Si(111) substrates and the bandgap is significantly reduced from 2 eV to 1.7 eV due to lateral tensile strain. By applying vertical compressive strain using a diamond anvil cell, the band gap can be further reduced to 1.4 eV. Next, pseudo-1D GaTe nanomaterials with a monoclinic structure are synthesized on various substrates. The product exhibits highly anisotropic atomic structure and properties characterized by high-resolution transmission electron microscopy and angle resolved Raman and photoluminescence (PL) spectroscopy. Multiple sharp PL emissions below the bandgap are found due to defects localized at the edges and grain boundaries. Finally, layer structured GaSe1-xTex alloys across the full composition range are synthesized on GaAs(111) substrates. Results show that GaAs(111) substrate plays an essential role in stabilizing the metastable single-phase alloys within the miscibility gaps. A hexagonal to monoclinic phase crossover is observed as the Te content increases. The phase crossover features coexistence of both phases and isotropic to anisotropic structural transition.
Overall, this work provides insights into the controlled synthesis of gallium chalcogenides and opens up new opportunities towards optoelectronic applications that require tunable material properties.
Despite the advances in carbon-based technology, researchers have been limited to sp3 and sp2 hybridizations. While sp3 and sp2 hybridizations of carbon are well established and understood, the simplest sp1 hybridized carbon allotrope, carbyne, has been impossible to synthesize and remains elusive. This dissertation presents recent results in characterizing a new sp1 carbon material produced from using pulsed laser ablation in liquid (PLAL) to ablate a gold surface that is immersed in a carbon rich liquid. The PLAL technique provides access to extremely non-thermal environmental conditions where unexplored chemical reactions occur and can be explored to access the production of new materials. A combination of experimental and theoretical results suggests gold clusters can act as stabilizing agents as they react and adsorb onto the surface of one dimensional carbon chains to form a new class of materials termed “pseudocarbynes”. Data from several characterization techniques, including Raman spectroscopy, UV/VIS spectroscopy, and transmission electron microscopy (TEM), provide evidence for the existence of pseudocarbyne. This completely new material may possess outstanding properties, a trend seen among carbon allotropes, that can further scientific advancements.
In this work, this novel mechanotaxis mechanism is investigated, i.e., the role of the ECM mediated active cellular force propagation in coordinating collective cell migration via computational modeling and simulations. The work mainly includes two components: (i) microstructure and micromechanics modeling of cellularized ECM (collagen) networks and (ii) modeling collective cell migration and self-organization in 3D ECM. For ECM modeling, a procedure for generating realizations of highly heterogeneous 3D collagen networks with prescribed microstructural statistics via stochastic optimization is devised. Analysis shows that oriented fibers can significantly enhance long-range force transmission in the network. For modeling collective migratory behaviors of the cells, a minimal active-particle-on-network (APN) model is developed, in which reveals a dynamic transition in the system as the particle number density ρ increases beyond a critical value ρc, from an absorbing state in which the particles segregate into small isolated stationary clusters, to a dynamic state in which the majority of the particles join in a single large cluster undergone constant dynamic reorganization. The results, which are consistent with independent experimental results, suggest a robust mechanism based on ECM-mediated mechanical coupling for collective cell behaviors in 3D ECM.
For the future plan, further substantiate the minimal cell migration model by incorporating more detailed cell-ECM interactions and relevant sub-cellular mechanisms is needed, as well as further investigation of the effects of fiber alignment, ECM mechanical properties and externally applied mechanical cues on collective migration dynamics.
quickly follows the initial transient flow regime in the constant-rate production of
a closed boundary hydrocarbon reservoir. The characterization of the PSS flow
regime is of importance in describing the reservoir pressure distribution as well as the
productivity index (PI) of the flow regime. The PI describes the production potential
of the well and is often used in fracture optimization and production-rate decline
analysis. In 2016, Chen determined the exact analytical solution for PSS flow of a
fully penetrated vertically fractured well with finite fracture conductivity for reservoirs
of elliptical shape. The present work aimed to expand Chen’s exact analytical solution
to commonly encountered reservoirs geometries including rectangular, rhomboid,
and triangular by introducing respective shape factors generated from extensive
computational modeling studies based on an identical drainage area assumption. The
aforementioned shape factors were generated and characterized as functions for use
in spreadsheet calculations as well as graphical format for simplistic in-field look-up
use. Demonstrative use of the shape factors for over 20 additional simulations showed
high fidelity of the shape factor to accurately predict (mean average percentage error
remained under 1.5 %) the true PSS constant by modulating Chen’s solution for
elliptical reservoirs. The methodology of the shape factor generation lays the ground
work for more extensive and specific shape factors to be generated for cases such as
non-concentric wells and other geometries not studied.
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UV, the samples were exposed to sunlight for up to 210 days and analyzed under Raman spectroscopy. Overall the physical and chemical changes with the polymers are evident and makes a way for the wastewater treatment plant to take necessary steps to capture the microplastics to avoid the release of any kind of degraded microplastics that could affect marine life and the environment.
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.