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- Genre: Doctoral Dissertation
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 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.
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
Description
Layer structured two dimensional (2D) semiconductors have gained much interest due to their intriguing optical and electronic properties induced by the unique van der Waals bonding between layers. The extraordinary success for graphene and transition metal dichalcogenides (TMDCs) has triggered a constant search for novel 2D semiconductors beyond them. Gallium chalcogenides, belonging to the group III-VI compounds, are a new class of 2D semiconductors that carry a variety of interesting properties including wide spectrum coverage of their bandgaps and thus are promising candidates for next generation electronic and optoelectronic devices. Pushing these materials toward applications requires more controllable synthesis methods and facile routes for engineering their properties on demand.
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.
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.
ContributorsCai, Hui, Ph.D (Author) / Tongay, Sefaattin (Thesis advisor) / Dwyer, Christian (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2018
Description
Collective cell migration in the 3D fibrous extracellular matrix (ECM) is crucial to many physiological and pathological processes such as tissue regeneration, immune response and cancer progression. A migrating cell also generates active pulling forces, which are transmitted to the ECM fibers via focal adhesion complexes. Such active forces consistently remodel the local ECM (e.g., by re-orienting the collagen fibers, forming fiber bundles and increasing the local stiffness of ECM), leading to a dynamically evolving force network in the system that in turn regulates the collective migration of cells.
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.
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.
ContributorsNan, Hanqing (Author) / Jiao, Yang (Thesis advisor) / Alford, Terry (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2019
Description
Advanced material systems refer to materials that are comprised of multiple traditional constituents but complex microstructure morphologies, which lead to their superior properties over conventional materials. This dissertation is motivated by the grand challenge in accelerating the design of advanced material systems through systematic optimization with respect to material microstructures or processing settings. While optimization techniques have mature applications to a large range of engineering systems, their application to material design meets unique challenges due to the high dimensionality of microstructures and the high costs in computing process-structure-property (PSP) mappings. The key to addressing these challenges is the learning of material representations and predictive PSP mappings while managing a small data acquisition budget. This dissertation thus focuses on developing learning mechanisms that leverage context-specific meta-data and physics-based theories. Two research tasks will be conducted: In the first, we develop a statistical generative model that learns to characterize high-dimensional microstructure samples using low-dimensional features. We improve the data efficiency of a variational autoencoder by introducing a morphology loss to the training. We demonstrate that the resultant microstructure generator is morphology-aware when trained on a small set of material samples, and can effectively constrain the microstructure space during material design. In the second task, we investigate an active learning mechanism where new samples are acquired based on their violation to a theory-driven constraint on the physics-based model. We demonstrate using a topology optimization case that while data acquisition through the physics-based model is often expensive (e.g., obtaining microstructures through simulation or optimization processes), the evaluation of the constraint can be far more affordable (e.g., checking whether a solution is optimal or equilibrium). We show that this theory-driven learning algorithm can lead to much improved learning efficiency and generalization performance when such constraints can be derived. The outcomes of this research is a better understanding of how physics knowledge about material systems can be integrated into machine learning frameworks, in order to achieve more cost-effective and reliable learning of material representations and predictive models, which are essential to accelerate computational material design.
ContributorsCang, Ruijin (Author) / Ren, Yi (Thesis advisor) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2018
Description
Past experiments have revealed several unusual properties about interstitial hydrogen atoms in niobium. Absorption isotherms showed that niobium absorbs a large amount of hydrogen without changing its crystal structure. These isotherms also revealed that the interactions between hydrogen atoms in niobium are a combination of long-range attraction and short-range repulsion and exhibit many-body characteristics. Other experiments reported the facile thermal diffusion of hydrogen and deuterium in niobium. Contrary to the classical theory of diffusion, these experiments revealed a break in the activation energy of hydrogen diffusion at low temperatures, but no such break was reported for deuterium. Finally, experiments report a phenomenon called electromigration, where hydrogen atoms inside niobium respond to weak electric fields as if they had a positive effective charge. These experimental results date back to when tools like density functional theory (DFT) and modern high-performance computing abilities did not exist. Therefore, the current understanding of these properties is primarily based on inferences from experimental results. Understanding these properties at a deeper level, besides being scientifically important, can profoundly affect various applications involving hydrogen separation and transport. The high-level goal of this work is to use first-principles methods to explain the discussed properties of interstitial hydrogen in niobium. DFT calculations were used to study hydrogen atoms' site preference in niobium and its effect on the cell shape and volume of the host cell. The nature and origin of the interactions between hydrogen atoms were studied through interaction energy, structural, partial charge, and electronic densities of state analysis. A phenomenological model with fewer parameters than traditional models was developed and fit to the experimental absorption data. Thermodynamic quantities such as the enthalpy and entropy of hydrogen dissolution in niobium were derived from this model. The enthalpy of hydrogen dissolution in niobium was also calculated using DFT by sampling different geometric configurations and performing an ensemble-based averaging. Further work is required to explain the observed isotope effects for hydrogen diffusion in niobium and the electromigration phenomena. Applications of the niobium-hydrogen system require studying hydrogen's behavior on niobium's surface.
ContributorsRamcahandran, Arvind (Author) / Lackner, Klaus S. (Thesis advisor) / Zhuang, Houlong (Thesis advisor) / Muhich, Christopher (Committee member) / Singh, Arunima (Committee member) / Arizona State University (Publisher)
Created2021
Description
Stiffness and flexibility are essential in many fields, including robotics, aerospace, bioengineering, etc. In recent years, origami-based mechanical metamaterials were designed for better mechanical properties including tunable stiffness and tunable collapsibility. However, in existing studies, the tunable stiffness is only with limited range and limited controllability. To overcome these challenges, two objectives were proposed and achieved in this dissertation: first, to design mechanical metamaterials with metamaterials with selective stiffness and collapsibility; second, to design mechanical metamaterials with in-situ tunable stiffness among positive, zero, and negative.In the first part, triangulated cylinder origami was employed to build deployable mechanical metamaterials through folding and unfolding along the crease lines. These deployable structures are flexible in the deploy direction so that it can be easily collapsed along the same way as it was deployed. An origami-inspired mechanical metamaterial was designed for on-demand deployability and selective collapsibility: autonomous deployability from the collapsed state and selective collapsibility along two different paths, with low stiffness for one path and substantially high stiffness for another path. The created mechanical metamaterial yields unprecedented load bearing capability in the deploy direction while possessing great deployability and collapsibility. The principle in this prospectus can be utilized to design and create versatile origami-inspired mechanical metamaterials that can find many applications.
In the second part, curved origami patterns were designed to accomplish in situ stiffness manipulation covering positive, zero, and negative stiffness by activating predefined creases on one curved origami pattern. This elegant design enables in situ stiffness switching in lightweight and space-saving applications, as demonstrated through three robotic-related components. Under a uniform load, the curved origami can provide universal gripping, controlled force transmissibility, and multistage stiffness response. This work illustrates an unexplored and unprecedented capability of curved origami, which opens new applications in robotics for this particular family of origami patterns.
ContributorsZhai, Zirui (Author) / Nian, Qiong (Thesis advisor) / Zhuang, Houlong (Committee member) / Huang, Huei-Ping (Committee member) / Zhang, Wenlong (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2021
Description
Disordered many-body systems are ubiquitous in condensed matter physics, materials science and biological systems. Examples include amorphous and glassy states of matter, granular materials, and tissues composed of packings of cells in the extra-cellular matrix (ECM). Understanding the collective emergent properties in these systems is crucial to improving the capability for controlling, engineering and optimizing their behaviors, yet it is extremely challenging due to their complexity and disordered nature. The main theme of the thesis is to address this challenge by characterizing and understanding a variety of disordered many-body systems via unique statistical geometrical and topological tools and the state-of-the-art simulation methods. Two major topics of the thesis are modeling ECM-mediated multicellular dynamics and understanding hyperuniformity in 2D material systems. Collective migration is an important mode of cell movement for several biological processes, and it has been the focus of a large number of studies over the past decades. Hyperuniform (HU) state is a critical state in a many-particle system, an exotic property of condensed matter discovered recently. The main focus of this thesis is to study the mechanisms underlying collective cell migration behaviors by developing theoretical/phenomenological models that capture the features of ECM-mediated mechanical communications in vitro and investigate general conditions that can be imposed on hyperuniformity-preserving and hyperuniformity-generating operations, as well as to understand how various novel transport physical properties arise from the unique hyperuniform long-range correlations.
ContributorsZheng, Yu (Author) / Jiao, Yang (Thesis advisor) / Zhuang, Houlong (Committee member) / Beckstein, Oliver (Committee member) / Ros, Robert (Committee member) / Arizona State University (Publisher)
Created2022
Description
Laser powder bed fusion (LPBF) additive manufacturing (AM) has received widespread attention due to its ability to produce parts with complicated design and better surface finish compared to other additive techniques. LPBF uses a laser heat source to melt layers of powder particles and manufactures a part based on the CAD design. This process can benefit significantly through computational modeling. The objective of this thesis was to understand the thermal transport, and fluid flow phenomena of the process, and to optimize the main process parameters such as laser power and scan speed through a combination of computational, experimental, and statistical analysis. A multi-physics model was built using to model temperature profile, bead geometry and elemental evaporation in powder bed process using a non-gaussian interaction between laser heat source and metallic powder. Owing to the scarcity of thermo-physical properties of metallic powders in literature, thermal conductivity, diffusivity, and heat capacity was experimentally tested up to a temperature of 1400 degrees C. The values were used in the computational model, which improved the results significantly. The computational work was also used to assess the impact of fluid flow around melt pool. Dimensional analysis was conducted to determine heat transport mode at various laser power/scan speed combinations. Convective heat flow proved to be the dominant form of heat transfer at higher energy input due to violent flow of the fluid around the molten region, which can also create keyhole effect. The last part of the thesis focused on gaining useful information about several features of the bead area such as contact angle, porosity, voids and melt pool that were obtained using several combinations of laser power and scan speed. These features were quantified using process learning, which was then used to conduct a full factorial design that allows to estimate the effect of the process parameters on the output features. Both single and multi-response analysis are applied to analyze the output response. It was observed that laser power has more influential effect on all the features. Multi response analysis showed 150 W laser power and 200 mm/s produced bead with best possible features.
ContributorsAhsan, Faiyaz (Author) / Ladani, Leila (Thesis advisor) / Razmi, Jafar (Committee member) / Kwon, Beomjin (Committee member) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
Magnetic liquids called ferrofluids have been used in applications ranging from audio speaker cooling and rotary pressure seals to retinal detachment surgery and implantable artificial glaucoma valves. Recently, ferrofluids have been investigated as a material for use in magnetically controllable liquid droplet robotics. Liquid droplet robotics is an emerging technology that aims to apply control theory to manipulate fluid droplets as robotic agents to perform a wide range of tasks. Furthermore, magnetically controlled micro-robotics is another popular area of study where manipulating a magnetic field allows for the control of magnetized micro-robots. Both of these emerging fields have potential for impact toward medical applications: liquid characteristics such as being able to dissolve various compounds, be injected via a needle, and the potential for the human body to automatically filter and remove a liquid droplet robot, make liquid droplet robots advantageous for medical applications; while the ability to remotely control the torques and forces on an untethered microrobot via modulating the magnetic field and gradient is also highly advantageous. The research described in this dissertation explores applications and methods for the electromagnetic control of ferrofluid droplet robots. First, basic electrical components built from fluidic channels containing ferrofluid are made remotely tunable via the placement of ferrofluid within the channel. Second, a ferrofluid droplet is shown to be fully controllable in position, stretch direction, and stretch length in two dimensions using proportional-integral-derivative (PID) controllers. Third, control of a ferrofluid’s position, stretch direction, and stretch length is extended to three dimensions, and control gains are optimized via a Bayesian optimization process to achieve higher accuracy. Finally, magnetic control of both single and multiple ferrofluid droplets in two dimensions is investigated via a visual model predictive control approach based on machine learning. These achievements take both liquid droplet robotics and magnetic micro-robotics fields several steps closer toward real-world medical applications such as embedded soft electronic health monitors, liquid-droplet-robot-based drug delivery, and automated magnetically actuated surgeries.
ContributorsAhmed, Reza James (Author) / Marvi, Hamidreza (Thesis advisor) / Espanol, Malena (Committee member) / Rajagopalan, Jagannathan (Committee member) / Zhuang, Houlong (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2022
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) 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