Mixed-ionic electronic conducting (MIEC) oxides have drawn much attention from researchers because of their potential in high temperature separation processes. Among many materials available, perovskite type and fluorite type oxides are the most studied for their excellent oxygen ion transport property. These oxides not only can be oxygen adsorbent or O2-permeable membranes themselves, but also can be incorporated with molten carbonate to form dual-phase membranes for CO2 separation.

Oxygen sorption/desorption properties of perovskite oxides with and without oxygen vacancy were investigated first by thermogravimetric analysis (TGA) and fixed-bed experiments. The oxide with unique disorder-order phase transition during desorption exhibited an enhanced oxygen desorption rate during the TGA measurement but not in fixed-bed demonstrations. The difference in oxygen desorption rate is due to much higher oxygen partial pressure surrounding the sorbent during the fixed-bed oxygen desorption process, as revealed by X-ray diffraction (XRD) patterns of rapidly quenched samples.

Research on using perovskite oxides as CO2-permeable dual-phase membranes was subsequently conducted. Two CO2-resistant MIEC perovskite ceramics, Pr0.6Sr0.4Co0.2Fe0.8 O3-δ (PSCF) and SrFe0.9Ta0.1O3-δ (SFT) were chosen as support materials for membrane synthesis. PSCF-molten carbonate (MC) and SFT-MC membranes were prepared for CO2-O2 counter-permeation. The geometric factors for the carbonate phase and ceramic phase were used to calculate the effective carbonate and oxygen ionic conductivity in the carbonate and ceramic phase. When tested in CO2-O2 counter-permeation set-up, CO2 flux showed negligible change, but O2 flux decreased by 10-32% compared with single-component permeation. With CO2 counter-permeation, the total oxygen permeation flux is higher than that without counter-permeation.

A new concept of CO2-permselective membrane reactor for hydrogen production via steam reforming of methane (SRM) was demonstrated. The results of SRM in the membrane reactor confirm that in-situ CO2 removal effectively promotes water-gas shift conversion and thus enhances hydrogen yield. A modeling study was also conducted to assess the performance of the membrane reactor in high-pressure feed/vacuum sweep conditions, which were not carried out due to limitations in current membrane testing set-up. When 5 atm feed pressure and 10-3 atm sweep pressure were applied, the membrane reactor can produce over 99% hydrogen stream in simulation.

Aluminum alloys are commonly used for engineering applications due to their high strength to weight ratio, low weight, and low cost. Pitting corrosion, accelerated by saltwater environments, leads to fatigue cracks and stress corrosion cracking during service. Two-dimensional (2D) characterization methods are typically used to identify and characterize corrosion; however, these methods are destructive and do not enable an efficient means of quantifying mechanisms of pit initiation and growth. In this study, lab-scale x-ray microtomography was used to non-destructively observe, quantify, and understand pit growth in three dimensions over a 20-day corrosion period in the AA7075-T651 alloy. The XRT process, capable of imaging sample volumes with a resolution near one micrometer, was found to be an ideal tool for large-volume pit examination. Pit depths were quantified over time using renderings of sample volumes, leading to an understanding of how inclusion particles, oxide breakdown, and corrosion mechanisms impact the growth and morphology of pits. This process, when carried out on samples produced with two different rolling directions and rolling extents, yielded novel insights into the long-term macroscopic corrosion behaviors impacted by alloy production and design. Key among these were the determinations that the alloy’s rolling direction produces a significant difference in the average growth rate of pits and that the corrosion product layer loses its passivating effect as a result of cyclic immersion. In addition, a new mechanism of pitting corrosion is proposed which is focused on the pseudo-random spatial distribution of iron-rich inclusion particles in the alloy matrix, which produces a random distribution of pit depths based on the occurrence of co-operative corrosion near inclusion clusters.

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.

Niobium is the primary material for fabricating superconducting radio-frequency (SRF) cavities. However, presence of impurities and defects degrade the superconducting behavior of niobium twofold, first by nucleating non-superconducting phases and second by increasing the residual surface resistance of cavities. In particular, niobium absorbs hydrogen during cavity fabrication and promotes precipitation of non-superconducting niobium hydride phases. Additionally, magnetic flux trapping at defects leads to a normal conducting (non-superconducting) core which increases surface resistance and negatively affects niobium performance for superconducting applications. However, undelaying mechanisms related to hydride formation and dissolution along with defect interaction with magnetic fields is still unclear. Therefore, this dissertation aims to investigate the role of defects and impurities on functional properties of niobium for SRF cavities using first-principles methods.

Here, density functional theory calculations revealed that nitrogen addition suppressed hydrogen absorption interstitially and at grain boundaries, and it also decreased the energetic stability of niobium hydride precipitates present in niobium. Further, hydrogen segregation at the screw dislocation was observed to transform the dislocation core structure and increase the barrier for screw dislocation motion. Valence charge transfer calculations displayed a strong tendency of nitrogen to accumulate charge around itself, thereby decreasing the strength of covalent bonds between niobium and hydrogen leading to a very unstable state for interstitial hydrogen and hydrides. Thus, presence of nitrogen during processing plays a critical role in controlling hydride precipitation and subsequent SRF properties.

First-principles methods were further implemented to gain a theoretical perspective about the experimental observations that lattice defects are effective at trapping magnetic flux in high-purity superconducting niobium. Full-potential linear augmented plane-wave methods were used to analyze the effects of magnetic field on the superconducting state surrounding these defects. A considerable amount of trapped flux was obtained at the dislocation core and grain boundaries which can be attributed to significantly different electronic structure of defects as compared to bulk niobium. Electron redistribution at defects enhances non-paramagnetic effects that perturb superconductivity, resulting in local conditions suitable for flux trapping. Therefore, controlling accumulation or depletion of charge at the defects could mitigate these tendencies and aid in improving superconductive behavior of niobium.

Layered chalcogenides are a diverse class of crystalline materials that consist of covalently bound building blocks held together by van der Waals forces, including the transition metal dichalcogenides (TMDCs) and the pnictogen chalcogenides (PCs) among all. These materials, in particular, MoS2 which is the most widely studied TMDC material, have attracted significant attention in recent years due to their unique physical, electronic, optical, and chemical properties that depend on the number of layers. Due to their high aspect ratios and extreme thinness, 2D materials are sensitive to modifications via chemistry on their surfaces. For instance, covalent functionalization can be used to robustly modify the electronic properties of 2D materials, and can also be used to attach other materials or structures. Metal adsorption on the surfaces of 2D materials can also tune their electronic structures, and can be used as a strategy for removing metal contaminants from water. Thus, there are many opportunities for studying the fundamental surface interactions of 2D materials and in particular the TMDCs and PCs.

The work reported in this dissertation represents detailed fundamental studies of the covalent functionalization and metal adsorption behavior of layered chalcogenides, which are two significant aspects of the surface interactions of 2D materials. First, we demonstrate that both the Freundlich and Temkin isotherm models, and the pseudo-second-order reaction kinetics model are good descriptors of the reaction due to the energetically inhomogeneous surface MoS2 and the indirect adsorbate-adsorbate interactions from previously attached nitrophenyl (NP) groups. Second, the covalent functionalization using aryl diazonium salts is extended to nanosheets of other representative TMDC materials MoSe2, WS2, and WSe2, and of the representative PC materials Bi2S3 and Sb2S3, demonstrated using atomic force microscopy (AFM) imaging and Fourier transform infrared spectroscopy (FTIR). Finally, using AFM and X-ray photoelectron spectroscopy (XPS), it is shown that Pb, Cd Zn and Co form nanoclusters on the MoS2 surface without affecting the structure of the MoS2 itself. The metals can also be thermally desorbed from MoS2, thus suggesting a potential application as a reusable water purification technology.

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.

With the growth of global population, the demand for sustainable infrastructure is significantly increasing. Substructures with appropriate materials are required to be built in or above soil that can support the massive volume of construction demand. However, increased structural requirements often require ground improvement to increase the soil capacity. Moreover, certain soils are prone to liquefaction during an earthquake, which results in significant structural damage and loss of lives. While various soil treatment methods have been developed in the past to improve the soil’s load carrying ability, most of these traditional treatment methods have been found either hazardous and may cause irreversible damage to natural environment, or too disruptive to use beneath or adjacent to existing structures. Thus, alternative techniques are required to provide a more natural and sustainable solution. Biomediated methods of strengthening soil through mineral precipitation, in particular through microbially induced carbonate precipitation (MICP), have recently emerged as a promising means of soil improvement. In MICP, the precipitation of carbonate (usually in the form of calcium carbonate) is mediated by microorganisms and the process is referred to as biomineralization. The precipitated carbonate coats soil particles, precipitates in the voids, and bridges between soil particles, thereby improving the mechanical properties (e.g., strength, stiffness, and dilatancy). Although it has been reported that the soil’s mechanical properties can be extensively enhanced through MICP, the micro-scale mechanisms that influence the macro-scale constitutive response remain to be clearly explained.

The utilization of alternative techniques such as MICP requires an in-depth understanding of the particle-scale contact mechanisms and the ability to predict the improvement in soil properties resulting from calcite precipitation. For this purpose, the discrete element method (DEM), which is extensively used to investigate granular materials, is adopted in this dissertation. Three-dimensional discrete element method (DEM) based numerical models are developed to simulate the response of bio-cemented sand under static and dynamic loading conditions and the micro-scale mechanisms of MICP are numerically investigated. Special focus is paid to the understanding of the particle scale mechanisms that are dominant in the common laboratory scale experiments including undrained and drained triaxial compression when calcite bridges are present in the soil, that enhances its load capacity. The mechanisms behind improvement of liquefaction resistance in cemented sands are also elucidated through the use of DEM. The thesis thus aims to provide the fundamental link that is important in ensuring proper material design for granular materials to enhance their mechanical performance.

Nanolaminate materials are layered composites with layer thickness ≤ 100 nm. They exhibit unique properties due to their small length scale, the presence of a high number of interfaces and the effect of imposed constraint. This thesis focuses on the mechanical behavior of Al/SiC nanolaminates. The high strength of ceramics combined with the ductility of Al makes this combination desirable. Al/SiC nanolaminates were synthesized through magnetron sputtering and have an overall thickness of ~ 20 μm which limits the characterization techniques to microscale testing methods. A large amount of work has already been done towards evaluating their mechanical properties under indentation loading and micropillar compression. The effects of temperature, orientation and layer thickness have been well established. Al/SiC nanolaminates exhibited a flaw dependent deformation, anisotropy with respect to loading direction and strengthening due to imposed constraint. However, the mechanical behavior of nanolaminates under tension and fatigue loading has not yet been studied which is critical for obtaining a complete understanding of their deformation behavior. This thesis fills this gap and presents experiments which were conducted to gain an insight into the behavior of nanolaminates under tensile and cyclic loading. The effect of layer thickness, tension-compression asymmetry and effect of a wavy microstructure on mechanical response have been presented. Further, results on in situ micropillar compression using lab-based X-ray microscope through novel experimental design are also presented. This was the first time when a resolution of 50 nms was achieved during in situ micropillar compression in a lab-based setup. Pores present in the microstructure were characterized in 3D and sites of damage initiation were correlated with the channel of pores present in the microstructure.

The understanding of these deformation mechanisms paved way for the development of co-sputtered Al/SiC composites. For these composites, Al and SiC were sputtered together in a layer. The effect of change in the atomic fraction of SiC on the microstructure and mechanical properties were evaluated. Extensive microstructural characterization was performed at the nanoscale level and Al nanocrystalline aggregates were observed dispersed in an amorphous matrix. The modulus and hardness of co- sputtered composites were much higher than their traditional counterparts owing to denser atomic packing and the absence of synthesis induced defects such as pores and columnar boundaries.

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.

Pipeline infrastructure forms a vital aspect of the United States economy and standard of living. A majority of the current pipeline systems were installed in the early 1900’s and often lack a reliable database reporting the mechanical properties, and information about manufacturing and installation, thereby raising a concern for their safety and integrity. Testing for the aging pipe strength and toughness estimation without interrupting the transmission and operations thus becomes important. The state-of-the-art techniques tend to focus on the single modality deterministic estimation of pipe strength and do not account for inhomogeneity and uncertainties, many others appear to rely on destructive means. These gaps provide an impetus for novel methods to better characterize the pipe material properties. The focus of this study is the design of a Bayesian Network information fusion model for the prediction of accurate probabilistic pipe strength and consequently the maximum allowable operating pressure. A multimodal diagnosis is performed by assessing the mechanical property variation within the pipe in terms of material property measurements, such as microstructure, composition, hardness and other mechanical properties through experimental analysis, which are then integrated with the Bayesian network model that uses a Markov chain Monte Carlo (MCMC) algorithm. Prototype testing is carried out for model verification, validation and demonstration and data training of the model is employed to obtain a more accurate measure of the probabilistic pipe strength. With a view of providing a holistic measure of material performance in service, the fatigue properties of the pipe steel are investigated. The variation in the fatigue crack growth rate (da/dN) along the direction of the pipe wall thickness is studied in relation to the microstructure and the material constants for the crack growth have been reported. A combination of imaging and composition analysis is incorporated to study the fracture surface of the fatigue specimen. Finally, some well-known statistical inference models are employed for prediction of manufacturing process parameters for steel pipelines. The adaptability of the small datasets for the accuracy of the prediction outcomes is discussed and the models are compared for their performance.