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Description
Membrane-based gas separation is promising for efficient propylene/propane (C3H6/C3H8) separation with low energy consumption and minimum environment impact. Two microporous inorganic membrane candidates, MFI-type zeolite membrane and carbon molecular sieve membrane (CMS) have demonstrated excellent thermal and chemical stability. Application of these membranes into C3H6/C3H8 separation has not been well investigated. This dissertation presents fundamental studies on membrane synthesis, characterization and C3H6/C3H8 separation properties of MFI zeolite membrane and CMS membrane.
MFI zeolite membranes were synthesized on α-alumina supports by secondary growth method. Novel positron annihilation spectroscopy (PAS) techniques were used to non-destructively characterize the pore structure of these membranes. PAS reveals a bimodal pore structure consisting of intracrystalline zeolitic micropores of ~0.6 nm in diameter and irregular intercrystalline micropores of 1.4 to 1.8 nm in size for the membranes. The template-free synthesized membrane exhibited a high permeance but a low selectivity in C3H6/C3H8 mixture separation.
CMS membranes were synthesized by coating/pyrolysis method on mesoporous γ-alumina support. Such supports allow coating of thin, high-quality polymer films and subsequent CMS membranes with no infiltration into support pores. The CMS membranes show strong molecular sieving effect, offering a high C3H6/C3H8 mixture selectivity of ~30. Reduction in membrane thickness from 500 nm to 300 nm causes an increase in C3H8 permeance and He/N2 selectivity, but a decrease in the permeance of He, N2 and C3H6 and C3H6/C3H8 selectivity. This can be explained by the thickness dependent chain mobility of the polymer film resulting in final carbon membrane of reduced pore size with different effects on transport of gas of different sizes, including possible closure of C3H6-accessible micropores.
CMS membranes demonstrate excellent C3H6/C3H8 separation performance over a wide range of feed pressure, composition and operation temperature. No plasticization was observed at a feed pressure up to 100 psi. The permeation and separation is mainly controlled by diffusion instead of adsorption. CMS membrane experienced a decline in permeance, and an increase in selectivity over time under on-stream C3H6/C3H8 separation. This aging behavior is due to the reduction in effective pore size and porosity caused by oxygen chemisorption and physical aging of the membrane structure.
MFI zeolite membranes were synthesized on α-alumina supports by secondary growth method. Novel positron annihilation spectroscopy (PAS) techniques were used to non-destructively characterize the pore structure of these membranes. PAS reveals a bimodal pore structure consisting of intracrystalline zeolitic micropores of ~0.6 nm in diameter and irregular intercrystalline micropores of 1.4 to 1.8 nm in size for the membranes. The template-free synthesized membrane exhibited a high permeance but a low selectivity in C3H6/C3H8 mixture separation.
CMS membranes were synthesized by coating/pyrolysis method on mesoporous γ-alumina support. Such supports allow coating of thin, high-quality polymer films and subsequent CMS membranes with no infiltration into support pores. The CMS membranes show strong molecular sieving effect, offering a high C3H6/C3H8 mixture selectivity of ~30. Reduction in membrane thickness from 500 nm to 300 nm causes an increase in C3H8 permeance and He/N2 selectivity, but a decrease in the permeance of He, N2 and C3H6 and C3H6/C3H8 selectivity. This can be explained by the thickness dependent chain mobility of the polymer film resulting in final carbon membrane of reduced pore size with different effects on transport of gas of different sizes, including possible closure of C3H6-accessible micropores.
CMS membranes demonstrate excellent C3H6/C3H8 separation performance over a wide range of feed pressure, composition and operation temperature. No plasticization was observed at a feed pressure up to 100 psi. The permeation and separation is mainly controlled by diffusion instead of adsorption. CMS membrane experienced a decline in permeance, and an increase in selectivity over time under on-stream C3H6/C3H8 separation. This aging behavior is due to the reduction in effective pore size and porosity caused by oxygen chemisorption and physical aging of the membrane structure.
ContributorsMa, Xiaoli (Author) / Lin, Jerry (Thesis advisor) / Alford, Terry (Committee member) / Chan, Candace (Committee member) / Arizona State University (Publisher)
Created2015
Description
Emission of CO2 into the atmosphere has become an increasingly concerning issue as we progress into the 21st century Flue gas from coal-burning power plants accounts for 40% of all carbon dioxide emissions. The key to successful separation and sequestration is to separate CO2 directly from flue gas (10-15% CO2, 70% N2), which can range from a few hundred to as high as 1000°C. Conventional microporous membranes (carbons/silicas/zeolites) are capable of separating CO2 from N2 at low temperatures, but cannot achieve separation above 200°C. To overcome the limitations of microporous membranes, a novel ceramic-carbonate dual-phase membrane for high temperature CO2 separation was proposed. The membrane was synthesized from porous La0.6Sr0.4Co0.8Fe0.2O3-d (LSCF) supports and infiltrated with molten carbonate (Li2CO3/Na2CO3/K2CO3). The CO2 permeation mechanism involves a reaction between CO2 (gas phase) and O= (solid phase) to form CO3=, which is then transported through the molten carbonate (liquid phase) to achieve separation. The effects of membrane thickness, temperature and CO2 partial pressure were studied. Decreasing thickness from 3.0 to 0.375 mm led to higher fluxes at 900°C, ranging from 0.186 to 0.322 mL.min-1.cm-2 respectively. CO2 flux increased with temperature from 700 to 900°C. Activation energy for permeation was similar to that for oxygen ion conduction in LSCF. For partial pressures above 0.05 atm, the membrane exhibited a nearly constant flux. From these observations, it was determined that oxygen ion conductivity limits CO2 permeation and that the equilibrium oxygen vacancy concentration in LSCF is dependent on the partial pressure of CO2 in the gas phase. Finally, the dual-phase membrane was used as a membrane reactor. Separation at high temperatures can produce warm, highly concentrated streams of CO2 that could be used as a chemical feedstock for the synthesis of syngas (H2 + CO). Towards this, three different membrane reactor configurations were examined: 1) blank system, 2) LSCF catalyst and 3) 10% Ni/y-alumina catalyst. Performance increased in the order of blank system < LSCF catalyst < Ni/y-alumina catalyst. Favorable conditions for syngas production were high temperature (850°C), low sweep gas flow rate (10 mL.min-1) and high methane concentration (50%) using the Ni/y-alumina catalyst.
ContributorsAnderson, Matthew Brandon (Author) / Lin, Jerry (Thesis advisor) / Alford, Terry (Committee member) / Rege, Kaushal (Committee member) / Anderson, James (Committee member) / Rivera, Daniel (Committee member) / Arizona State University (Publisher)
Created2011
Description
This work demonstrates a capable reverse pulse deposition methodology to influence gap fill behavior inside microvia along with a uniform deposit in the fine line patterned regions for substrate packaging applications. Interconnect circuitry in IC substrate packages comprises of stacked microvia that varies in depth from 20µm to 100µm with an aspect ratio of 0.5 to 1.5 and fine line patterns defined by photolithography. Photolithography defined pattern regions incorporate a wide variety of feature sizes including large circular pad structures with diameter of 20µm - 200µm, fine traces with varying widths of 3µm - 30µm and additional planar regions to define a IC substrate package. Electrodeposition of copper is performed to establish the desired circuit. Electrodeposition of copper in IC substrate applications holds certain unique challenges in that they require a low cost manufacturing process that enables a void-free gap fill inside the microvia along with uniform deposition of copper on exposed patterned regions. Deposition time scales to establish the desired metal thickness for such packages could range from several minutes to few hours. This work showcases a reverse pulse electrodeposition methodology that achieves void-free gap fill inside the microvia and uniform plating in FLS (Fine Lines and Spaces) regions with significantly higher deposition rates than traditional approaches. In order to achieve this capability, systematic experimental and simulation studies were performed. A strong correlation of independent parameters that govern the electrodeposition process such as bath temperature, reverse pulse plating parameters and the ratio of electrolyte concentrations is shown to the deposition kinetics and deposition uniformity in fine patterned regions and gap fill rate inside the microvia. Additionally, insight into the physics of via fill process is presented with secondary and tertiary current simulation efforts. Such efforts lead to show “smart” control of deposition rate at the top and bottom of via to avoid void formation. Finally, a parametric effect on grain size and the ensuing copper metallurgical characteristics of bulk copper is also shown to enable high reliability substrate packages for the IC packaging industry.
ContributorsGanesan, Kousik (Author) / Tasooji, Amaneh (Thesis advisor) / Manepalli, Rahul (Committee member) / Alford, Terry (Committee member) / Chan, Candace (Committee member) / Arizona State University (Publisher)
Created2018
Description
In our modern world the source of for many chemicals is to acquire and refine oil. This process is becoming an expensive to the environment and to human health. Alternative processes for acquiring the final product have been developed but still need work. One product that is valuable is butanol. The normal process for butanol production is very intensive but there is a method to produce butanol from bacteria. This process is better because it is more environmentally safe than using oil. One problem however is that when the bacteria produce too much butanol it reaches the toxicity limit and stops the production of butanol. In order to keep butanol from reaching the toxicity limit an adsorbent is used to remove the butanol without harming the bacteria. The adsorbent is a mesoporous carbon powder that allows the butanol to be adsorbed on it. This thesis explores different designs for a magnetic separation process to extract the carbon powder from the culture.
ContributorsChabra, Rohin (Author) / Nielsen, David (Thesis director) / Torres, Cesar (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor)
Created2015-05
Description
Organic optoelectronics include a class of devices synthesized from carbon containing ‘small molecule’ thin films without long range order crystalline or polymer structure. Novel properties such as low modulus and flexibility as well as excellent device performance such as photon emission approaching 100% internal quantum efficiency have accelerated research in this area substantially. While optoelectronic organic light emitting devices have already realized commercial application, challenges to obtain extended lifetime for the high energy visible spectrum and the ability to reproduce natural white light with a simple architecture have limited the value of this technology for some display and lighting applications. In this research, novel materials discovered from a systematic analysis of empirical device data are shown to produce high quality white light through combination of monomer and excimer emission from a single molecule: platinum(II) bis(methyl-imidazolyl)toluene chloride (Pt-17). Illumination quality achieved Commission Internationale de L’Éclairage (CIE) chromaticity coordinates (x = 0.31, y = 0.38) and color rendering index (CRI) > 75. Further optimization of a device containing Pt-17 resulted in a maximum forward viewing power efficiency of 37.8 lm/W on a plain glass substrate. In addition, accelerated aging tests suggest high energy blue emission from a halogen-free cyclometalated platinum complex could demonstrate degradation rates comparable to known stable emitters. Finally, a buckling based metrology is applied to characterize the mechanical properties of small molecule organic thin films towards understanding the deposition kinetics responsible for an elastic modulus that is both temperature and thickness dependent. These results could contribute to the viability of organic electronic technology in potentially flexible display and lighting applications. The results also provide insight to organic film growth kinetics responsible for optical, mechanical, and water uptake properties relevant to engineering the next generation of optoelectronic devices.
ContributorsBakken, Nathan (Author) / Li, Jian (Thesis advisor) / Dai, Lenore (Thesis advisor) / Adams, James (Committee member) / Alford, Terry (Committee member) / Lind, Mary (Committee member) / Arizona State University (Publisher)
Created2017
Description
Of the potential technologies for pre-combustion capture, membranes offer the advantages of being temperature resistant, able to handle large flow rates, and having a relatively small footprint. A significant amount of research has centered on the use of polymeric and microporous inorganic membranes to separate CO2. These membranes, however, have limitations at high temperature resulting in poor permeation performance. To address these limitations, the use of a dense dual-phase membrane has been studied. These membranes are composed of conductive solid and conductive liquid phases that have the ability to selectively permeate CO2 by forming carbonate ions that diffuse through the membrane at high temperature. The driving force for transport through the membrane is a CO2 partial pressure gradient. The membrane provides a theoretically infinite selectivity. To address stability of the ceramic-carbonate dual-phase membrane for CO2 capture at high temperature, the ceramic phase of the membrane was studied and replaced with materials previously shown to be stable in harsh conditions. The permeation properties and stability of La0.6Sr0.4Co0.8Fe0.2O3-δ (LSCF)-carbonate, La0.85Ce0.1Ga0.3Fe0.65Al0.05O3-δ (LCGFA)-carbonate, and Ce0.8Sm0.2O1.9 (SDC)-carbonate membranes were examined under a wide range of experimental conditions at high temperature. LSCF-carbonate membranes were shown to be unstable without the presence of O2 due to reaction of CO2 with the ceramic phase. In the presence of O2, however, the membranes showed stable permeation behavior for more than one month at 900oC. LCGFA-carbonate membranes showed great chemical and permeation stability in the presence of various conditions including exposure to CH4 and H2, however, the permeation performance was quite low when compared to membranes in the literature. Finally, SDC-carbonate membranes showed great chemical and permeation stability both in a CO2:N2 environment for more than two weeks at 900oC as well as more than one month of exposure to simulated syngas conditions at 700oC. Ceramic phase chemical stability increased in the order of LSCF < LCGFA < SDC while permeation performance increased in the order of LCGFA < LSCF < SDC.
ContributorsNorton, Tyler (Author) / Lin, Jerry Y.S. (Thesis advisor) / Alford, Terry (Committee member) / Lind, Mary Laura (Committee member) / Smith, David (Committee member) / Torres, Cesar (Committee member) / Arizona State University (Publisher)
Created2013
Description
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.
ContributorsDaly, John H (Author) / Ren, Yi (Thesis director) / Zhuang, Houlong (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
In the last two decades, fantasy sports have grown massively in popularity. Fantasy football in particular is the most popular fantasy sport in the United States. People spend hours upon hours every year building, researching, and perfecting their teams to compete with others for money or bragging rights. One problem, however, is that National Football League (NFL) players are human and will not perform the same as they did last week or last season. Because of this, there is a need to create a machine learning model to help predict when players will have a tough game or when they can perform above average. This report discusses the history and science of fantasy football, gathering large amounts of player data, manipulating the information to create more insightful data points, creating a machine learning model, and how to use this tool in a real-world situation. The initial model created significantly accurate predictions for quarterbacks and running backs but not receivers and tight ends. Improvements significantly increased the accuracy by reducing the mean average error to below one for all positions, resulting in a successful model for all four positions.
ContributorsCase, Spencer (Author) / Johnson, Jarod (Co-author) / Kostelich, Eric (Thesis director) / Zhuang, Houlong (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2023-05
Description
A Compact Linear Fresnel Reflector (CLFR) is a simple, cost-effective, and scalable option for generating solar power by concentrating the sun rays. To make a most feasible application, design parameters of the CLFR, such as solar concentrator design parameters, receiver design parameters, heat transfer, power block parameters, etc., should be optimized to achieve optimum efficiency. Many researchers have carried out modeling and optimization of CLFR with various numerical or analytical methods. However, often computational time and cost are significant in these existing approaches. This research attempts to address this issue by proposing a novel computational approach with the help of increased computational efficiency and machine learning. The approach consists of two parts: the algorithm and the machine learning model. The algorithm has been created to fulfill the requirement of the Monte Carlo Ray tracing method for CLFR collector simulation, which is a simplified version of the conventional ray-tracing method. For various configurations of the CLFR system, optical losses and optical efficiency are calculated by employing these design parameters, such as the number of mirrors, mirror length, mirror width, space between adjacent mirrors, and orientation angle of the CLFR system. Further, to reduce the computational time, a machine learning method is used to predict the optical efficiency for the various configurations of the CLFR system. This entire method is validated using an existing approach (SolTrace) for the optical losses and optical efficiency of a CLFR system. It is observed that the program requires 6.63 CPU-hours of computational time are required by the program to calculate efficiency. In contrast, the novel machine learning approach took only seconds to predict the optical efficiency with great accuracy. Therefore, this method can be used to optimize a CLFR system based on the location and land configuration with reduced computational time. This will be beneficial for CLFR to be a potential candidate for concentrating solar power option.
ContributorsLunagariya, Shyam (Author) / Phelan, Patrick (Thesis advisor) / Kwon, Beomjin (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
Phase-field (PF) models are one of the most powerful tools to simulate microstructural evolution in metallic materials, polymers, and ceramics. However, existing PF approaches rely on rigorous mathematical model development, sophisticated numerical schemes, and high-performance computing for accuracy. Although recently developed surrogate microstructure models employ deep-learning techniques and reconstruction of microstructures from lower-dimensional data, their accuracy is fairly limited as spatio-temporal information is lost in the pursuit of dimensional reduction. Given these limitations, a novel data-driven emulator (DDE) for extrapolation prediction of microstructural evolution is presented, which combines an image-based convolutional and recurrent neural network (CRNN) with tensor decomposition, while leveraging previously obtained PF datasets for training. To assess the robustness of DDE, the emulation sequence and the scaling behavior with phase-field simulations for several noisy initial states are compared. In conclusion, the effectiveness of the microstructure emulation technique is explored in the context of accelerating runtime, along with an emphasis on its trade-off with accuracy.Meanwhile, an interpolation DDE has also been tested, which is based on obtaining
a low-dimensional representation of the microstructures via tensor decomposition and subsequently predicting the microstructure evolution in the low-dimensional space using Gaussian process regression (GPR). Once the microstructure predictions are obtained in the low-dimensional space, a hybrid input-output phase retrieval algorithm will be employed to reconstruct the microstructures. As proof of concept, the results on microstructure prediction for spinodal decomposition are presented, although the method itself is agnostic of the material parameters. Results show that GPR-based DDE model are able to predict microstructure evolution sequences that closely resemble the true microstructures (average normalized mean square of 6.78 × 10−7) at time scales half of that employed in obtaining training data. This data-driven
microstructure emulator opens new avenues to predict the microstructural evolution by leveraging phase-field simulations and physical experimentation where the time resolution is often quite large due to limited resources and physical constraints, such as the phase coarsening experiments previously performed in microgravity.
Future work will also be discussed and demonstrate the intended utilization of these two approaches for 3D microstructure prediction through their combined application.
ContributorsWu, Peichen (Author) / Ankit, Kumar (Thesis advisor) / Iquebal, Ashif (Committee member) / Jiao, Yang (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2024