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Description
Membrane-based technology for gas separations is currently at an emerging stage of advancement and adoption for environmental and industrial applications due to its substantial advantages like lower energy and operating costs over the conventional gas separation technologies. Unfortunately, the available polymeric (or organic) membranes suffer a trade-off between permeance and selectivity. Mixed matrix membranes (MMMs) containing two-dimensional (2D) metal-organic frameworks (MOFs) as fillers are a highly sought approach to redress this trade-off given their enhanced gas permeabilities and selectivities compared to the pure polymeric membrane. These MMMs are increasingly gaining attention by researchers due to their unique properties and wide small- and large-scale gas separation applications. However, straightforward and scalable methods for the synthesis of MOFs nanosheets have thus far been persistently elusive. This study reports the single-phase preparation, and characterization of MMMs with 2D MOFs nanosheets as fillers. The prepared MOF and the polymer matrix form the ‘dense’ MMMs which exhibit increased gas diffusion resistance, and thus improved separation abilities. The single-phase approach was more successful than the bi-phase at synthesizing the MOFs. The influence of sonication power and time on the characteristics and performance of the membranes are examined and discussed. Increasing the sonication power from 50% to 100% reduces the pore size. Additionally, the ultimate effect on the selectivity and permeance of the MMMs with different single gases is reported. Analysis of results with various gas mixers indicates further performance improvements in these MMMs could be achieved by increasing sonication time and tuning suitable membrane thicknesses. Reported results reveal that MMMs are excellent candidates for next-generation gas mixture separations, with potential applications in CO2 capture and storage, hydrogen recovery, alkene recovery from alkanes, and natural gas purification.
ContributorsNkuutu, John (Author) / Mu, Bin (Thesis director) / Shan, Bohan (Committee member) / Chemical Engineering Program (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
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.
ContributorsSinclair, Daniel Ritchie (Author) / Chawla, Nikhilesh (Thesis director) / Jiao, Yang (Committee member) / Bale, Hrishikesh (Committee member) / School of International Letters and Cultures (Contributor) / Materials Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Separation of carbon dioxide and methane for the upgrade of natural gas through use of pressure swing adsorption could potentially save large amounts of energy from the current, costly process of cryogenic distillation and provides greater cost effectiveness for carbon dioxide capture, and provide larger product flowrates than membrane permeation separation. The purpose of this study is to analyze the effects of varying initial conditions of a MatLab simulation, courtesy of Mai Xu, a graduate student at ASU, designed to use Langmuir isotherms, mass transfer equations, and adsorbent and gas properties to simulate a pressure swing adsorption process with a mixture of methane and carbon dioxide gas feed. The effects that will be varied are the adsorption/desorption time, pressurization/depressurization time, adsorption feed composition, desorption purge composition, adsorption pressure, desorption pressure, adsorption flow rate, and desorption flow rate. The study found that the trends in methane purity and production generally follow the trends predicted by literature and relevant equations, with pressure boundaries being the largest impacting factor. In addition there was a markedly inverse correlation between purity of methane product and the productivity of the system. This trend was only violated in one instance, at very low vacuum pressure during desorption, which could indicate an area that requires further study. Overall, the main areas of improvement in pressure swing adsorption for this system would be improving the selectivity of adsorption of carbon dioxide over methane, which requires improvement and change of the adsorbent, and more extreme vacuum pressures during desorption, both of which will increase methane yield and reduce operating costs.
ContributorsCook, Alexander Charles (Author) / Deng, Shuguang (Thesis director) / Mu, Bin (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Metal-organic frameworks (MOFs) are a new set of porous materials comprised of metals or metal clusters bonded together in a coordination system by organic linkers. They are becoming popular for gas separations due to their abilities to be tailored toward specific applications. Zirconium MOFs in particular are known for their high stability under standard temperature and pressure due to the strength of the Zirconium-Oxygen coordination bond. However, the acid modulator needed to ensure long range order of the product also prevents complete linker deprotonation. This leads to a powder product that cannot easily be incorporated into continuous MOF membranes. This study therefore implemented a new bi-phase synthesis technique with a deprotonating agent to achieve intergrowth in UiO-66 membranes. Crystal intergrowth will allow for effective gas separations and future permeation testing. During experimentation, successful intergrown UiO-66 membranes were synthesized and characterized. The degree of intergrowth and crystal orientations varied with changing deprotonating agent concentration, modulator concentration, and ligand:modulator ratios. Further studies will focus on achieving the same results on porous substrates.
ContributorsClose, Emily Charlotte (Author) / Mu, Bin (Thesis director) / Shan, Bohan (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Polymer-nanoparticle composites (PNCs) show improved chemical and physical properties compared to pure polymers. However, nanoparticles dispersed in a polymer matrix tend to aggregate due to strong interparticle interactions. Electrospun nanofibers impregnated with nanoparticles have shown improved dispersion of nanoparticles. Currently, there are few models for quantifying dispersion in a PNC, and none for electrospun PNC fibers. A simulation model was developed to quantify the effects of nanoparticle volume loading and fiber to particle diameter ratios on the dispersion in a nanofiber. The dispersion was characterized using the interparticle distance along the fiber. Distributions of the interparticle distance were fit to Weibull distributions and a two-parameter empirical equation for the mean and standard deviation was found. A dispersion factor was defined to quantify the dispersion along the polymer fiber. This model serves as a standard for comparison for future experimental studies through its comparability with microscopy techniques, and as way to quantify and predict dispersion in polymer-nanoparticle electrospinning systems with a single performance metric.
ContributorsBalzer, Christopher James (Author) / Mu, Bin (Thesis director) / Armstrong, Mitchell (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Solid oxide fuel cells have become a promising candidate in the development of high-density clean energy sources for the rapidly increasing demands in energy and global sustainability. In order to understand more about solid oxide fuel cells, the important step is to understand how to model heterogeneous materials. Heterogeneous materials are abundant in nature and also created in various processes. The diverse properties exhibited by these materials result from their complex microstructures, which also make it hard to model the material. Microstructure modeling and reconstruction on a meso-scale level is needed in order to produce heterogeneous models without having to shave and image every slice of the physical material, which is a destructive and irreversible process. Yeong and Torquato [1] introduced a stochastic optimization technique that enables the generation of a model of the material with the use of correlation functions. Spatial correlation functions of each of the various phases within the heterogeneous structure are collected from a two-dimensional micrograph representing a slice of a solid oxide fuel cell through computational means. The assumption is that two-dimensional images contain key structural information representative of the associated full three-dimensional microstructure. The collected spatial correlation functions, a combination of one-point and two-point correlation functions are then outputted and are representative of the material. In the reconstruction process, the characteristic two-point correlation functions is then inputted through a series of computational modeling codes and software to generate a three-dimensional visual model that is statistically similar to that of the original two-dimensional micrograph. Furthermore, parameters of temperature cooling stages and number of pixel exchanges per temperature stage are utilized and altered accordingly to observe which parameters has a higher impact on the reconstruction results. Stochastic optimization techniques to produce three-dimensional visual models from two-dimensional micrographs are therefore a statistically reliable method to understanding heterogeneous materials.
ContributorsPhan, Richard Dylan (Author) / Jiao, Yang (Thesis director) / Ren, Yi (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Widespread knowledge of fracture mechanics is mostly based on previous models that generalize crack growth in materials over several loading cycles. The objective of this project is to characterize crack growth that occurs in titanium alloys, specifically Grade 5 Ti-6Al-4V, at the sub-cycle scale, or within a single loading cycle. Using scanning electron microscopy (SEM), imaging analysis is performed to observe crack behavior at ten loading steps throughout the loading and unloading paths. Analysis involves measuring the incremental crack growth and crack tip opening displacement (CTOD) of specimens at loading ratios of 0.1, 0.3, and 0.5. This report defines the relationship between crack growth and the stress intensity factor, K, of the specimens, as well as the relationship between the R-ratio and stress opening level. The crack closure phenomena and effect of microcracks are discussed as they influence the crack growth behavior. This method has previously been used to characterize crack growth in Al 7075-T6. The results for Ti-6Al-4V are compared to these previous findings in order to strengthen conclusions about crack growth behavior.
ContributorsNazareno, Alyssa Noelle (Author) / Liu, Yongming (Thesis director) / Jiao, Yang (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Electromigration, the net atomic diffusion associated with the momentum transfer from electrons moving through a material, is a major cause of device and component failure in microelectronics. The deleterious effects from electromigration rise with increased current density, a parameter that will only continue to increase as our electronic devices get smaller and more compact. Understanding the dynamic diffusional pathways and mechanisms of these electromigration-induced and propagated defects can further our attempts at mitigating these failure modes. This dissertation provides insight into the relationships between these defects and parameters of electric field strength, grain boundary misorientation, grain size, void size, eigenstrain, varied atomic mobilities, and microstructure.First, an existing phase-field model was modified to investigate the various defect modes associated with electromigration in an equiaxed non-columnar microstructure. Of specific interest was the effect of grain boundary misalignment with respect to current flow and the mechanisms responsible for the changes in defect kinetics. Grain size, magnitude of externally applied electric field, and the utilization of locally distinct atomic mobilities were other parameters investigated. Networks of randomly distributed grains, a common microstructure of interconnects, were simulated in both 2- and 3-dimensions displaying the effects of 3-D capillarity on diffusional dynamics.
Also, a numerical model was developed to study the effect of electromigration on void migration and coalescence. Void migration rates were found to be slowed from compressive forces and the nature of the deformation concurrent with migration was examined through the lens of chemical potential. Void migration was also validated with previously reported theoretical explanations. Void coalescence and void budding were investigated and found to be dependent on the magnitude of interfacial energy and electric field strength.
A grasp on the mechanistic pathways of electromigration-induced defect evolution is imperative to the development of reliable electronics, especially as electronic devices continue to miniaturize. This dissertation displays a working understanding of the mechanistic pathways interconnects can fail due to electromigration, as well as provide direction for future research and understanding.
ContributorsFarmer, William McHann (Author) / Ankit, Kumar (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Jiao, Yang (Committee member) / McCue, Ian (Committee member) / Arizona State University (Publisher)
Created2022
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
The increasing availability of data and advances in computation have spurred the development of data-driven approaches for modeling complex dynamical systems. These approaches are based on the idea that the underlying structure of a complex system can be discovered from data using mathematical and computational techniques. They also show promise for addressing the challenges of modeling high-dimensional, nonlinear systems with limited data. In this research expository, the state of the art in data-driven approaches for modeling complex dynamical systems is surveyed in a systemic way. First the general formulation of data-driven modeling of dynamical systems is discussed. Then several representative methods in feature engineering and system identification/prediction are reviewed, including recent advances and key challenges.
ContributorsShi, Wenlong (Author) / Ren, Yi (Thesis advisor) / Hong, Qijun (Committee member) / Jiao, Yang (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022