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- Genre: Doctoral Dissertation
Description
Locomotion of microorganisms is commonly observed in nature. Although microorganism locomotion is commonly attributed to mechanical deformation of solid appendages, in 1956 Nobel Laureate Peter Mitchell proposed that an asymmetric ion flux on a bacterium's surface could generate electric fields that drive locomotion via self-electrophoresis. Recent advances in nanofabrication have enabled the engineering of synthetic analogues, bimetallic colloidal particles, that swim due to asymmetric ion flux originally proposed by Mitchell. Bimetallic colloidal particles swim through aqueous solutions by converting chemical fuel to fluid motion through asymmetric electrochemical reactions. This dissertation presents novel bimetallic motor fabrication strategies, motor functionality, and a study of the motor collective behavior in chemical concentration gradients. Brownian dynamics simulations and experiments show that the motors exhibit chemokinesis, a motile response to chemical gradients that results in net migration and concentration of particles. Chemokinesis is typically observed in living organisms and distinct from chemotaxis in that there is no particle directional sensing. The synthetic motor chemokinesis observed in this work is due to variation in the motor's velocity and effective diffusivity as a function of the fuel and salt concentration. Static concentration fields are generated in microfluidic devices fabricated with porous walls. The development of nanoscale particles that swim autonomously and collectively in chemical concentration gradients can be leveraged for a wide range of applications such as directed drug delivery, self-healing materials, and environmental remediation.
ContributorsWheat, Philip Matthew (Author) / Posner, Jonathan D (Thesis advisor) / Phelan, Patrick (Committee member) / Chen, Kangping (Committee member) / Buttry, Daniel (Committee member) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2011
Description
Wind measurements are fundamental inputs for the evaluation of potential energy yield and performance of wind farms. Three-dimensional scanning coherent Doppler lidar (CDL) may provide a new basis for wind farm site selection, design, and control. In this research, CDL measurements obtained from multiple wind energy developments are analyzed and a novel wind farm control approach has been modeled. The possibility of using lidar measurements to more fully characterize the wind field is discussed, specifically, terrain effects, spatial variation of winds, power density, and the effect of shear at different layers within the rotor swept area. Various vector retrieval methods have been applied to the lidar data, and results are presented on an elevated terrain-following surface at hub height. The vector retrieval estimates are compared with tower measurements, after interpolation to the appropriate level. CDL data is used to estimate the spatial power density at hub height. Since CDL can measure winds at different vertical levels, an approach for estimating wind power density over the wind turbine rotor-swept area is explored. Sample optimized layouts of wind farm using lidar data and global optimization algorithms, accounting for wake interaction effects, have been explored. An approach to evaluate spatial wind speed and direction estimates from a standard nested Coupled Ocean and Atmosphere Mesoscale Prediction System (COAMPS) model and CDL is presented. The magnitude of spatial difference between observations and simulation for wind energy assessment is researched. Diurnal effects and ramp events as estimated by CDL and COAMPS were inter-compared. Novel wind farm control based on incoming winds and direction input from CDL's is developed. Both yaw and pitch control using scanning CDL for efficient wind farm control is analyzed. The wind farm control optimizes power production and reduces loads on wind turbines for various lidar wind speed and direction inputs, accounting for wind farm wake losses and wind speed evolution. Several wind farm control configurations were developed, for enhanced integrability into the electrical grid. Finally, the value proposition of CDL for a wind farm development, based on uncertainty reduction and return of investment is analyzed.
ContributorsKrishnamurthy, Raghavendra (Author) / Calhoun, Ronald J (Thesis advisor) / Chen, Kangping (Committee member) / Huang, Huei-Ping (Committee member) / Fraser, Matthew (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis outlines the development of a vector retrieval technique, based on data assimilation, for a coherent Doppler LIDAR (Light Detection and Ranging). A detailed analysis of the Optimal Interpolation (OI) technique for vector retrieval is presented. Through several modifications to the OI technique, it is shown that the modified technique results in significant improvement in velocity retrieval accuracy. These modifications include changes to innovation covariance portioning, covariance binning, and analysis increment calculation. It is observed that the modified technique is able to make retrievals with better accuracy, preserves local information better, and compares well with tower measurements. In order to study the error of representativeness and vector retrieval error, a lidar simulator was constructed. Using the lidar simulator a thorough sensitivity analysis of the lidar measurement process and vector retrieval is carried out. The error of representativeness as a function of scales of motion and sensitivity of vector retrieval to look angle is quantified. Using the modified OI technique, study of nocturnal flow in Owens' Valley, CA was carried out to identify and understand uncharacteristic events on the night of March 27th 2006. Observations from 1030 UTC to 1230 UTC (0230 hr local time to 0430 hr local time) on March 27 2006 are presented. Lidar observations show complex and uncharacteristic flows such as sudden bursts of westerly cross-valley wind mixing with the dominant up-valley wind. Model results from Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) and other in-situ instrumentations are used to corroborate and complement these observations. The modified OI technique is used to identify uncharacteristic and extreme flow events at a wind development site. Estimates of turbulence and shear from this technique are compared to tower measurements. A formulation for equivalent wind speed in the presence of variations in wind speed and direction, combined with shear is developed and used to determine wind energy content in presence of turbulence.
ContributorsChoukulkar, Aditya (Author) / Calhoun, Ronald (Thesis advisor) / Mahalov, Alex (Committee member) / Kostelich, Eric (Committee member) / Huang, Huei-Ping (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2013
Description
As one of the most promising materials for high capacity electrode in next generation of lithium ion batteries, silicon has attracted a great deal of attention in recent years. Advanced characterization techniques and atomic simulations helped to depict that the lithiation/delithiation of silicon electrode involves processes including large volume change (anisotropic for the initial lithiation of crystal silicon), plastic flow or softening of material dependent on composition, electrochemically driven phase transformation between solid states, anisotropic or isotropic migration of atomic sharp interface, and mass diffusion of lithium atoms. Motivated by the promising prospect of the application and underlying interesting physics, mechanics coupled with multi-physics of silicon electrodes in lithium ion batteries is studied in this dissertation. For silicon electrodes with large size, diffusion controlled kinetics is assumed, and the coupled large deformation and mass transportation is studied. For crystal silicon with small size, interface controlled kinetics is assumed, and anisotropic interface reaction is studied, with a geometry design principle proposed. As a preliminary experimental validation, enhanced lithiation and fracture behavior of silicon pillars via atomic layer coatings and geometry design is studied, with results supporting the geometry design principle we proposed based on our simulations. Through the work documented here, a consistent description and understanding of the behavior of silicon electrode is given at continuum level and some insights for the future development of the silicon electrode are provided.
ContributorsAn, Yonghao (Author) / Jiang, Hanqing (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Phelan, Patrick (Committee member) / Wang, Yinming (Committee member) / Yu, Hongyu (Committee member) / Arizona State University (Publisher)
Created2014
Description
The heat and mass transfer phenomena in micro-scale for the mass transfer phenomena on drug in cylindrical matrix system, the simulation of oxygen/drug diffusion in a three dimensional capillary network, and a reduced chemical kinetic modeling of gas turbine combustion for Jet propellant-10 have been studied numerically. For the numerical analysis of the mass transfer phenomena on drug in cylindrical matrix system, the governing equations are derived from the cylindrical matrix systems, Krogh cylinder model, which modeling system is comprised of a capillary to a surrounding cylinder tissue along with the arterial distance to veins. ADI (Alternative Direction Implicit) scheme and Thomas algorithm are applied to solve the nonlinear partial differential equations (PDEs). This study shows that the important factors which have an effect on the drug penetration depth to the tissue are the mass diffusivity and the consumption of relevant species during the time allowed for diffusion to the brain tissue. Also, a computational fluid dynamics (CFD) model has been developed to simulate the blood flow and oxygen/drug diffusion in a three dimensional capillary network, which are satisfied in the physiological range of a typical capillary. A three dimensional geometry has been constructed to replicate the one studied by Secomb et al. (2000), and the computational framework features a non-Newtonian viscosity model for blood, the oxygen transport model including in oxygen-hemoglobin dissociation and wall flux due to tissue absorption, as well as an ability to study the diffusion of drugs and other materials in the capillary streams. Finally, a chemical kinetic mechanism of JP-10 has been compiled and validated for a wide range of combustion regimes, covering pressures of 1atm to 40atm with temperature ranges of 1,200 K - 1,700 K, which is being studied as a possible Jet propellant for the Pulse Detonation Engine (PDE) and other high-speed flight applications such as hypersonic missiles. The comprehensive skeletal mechanism consists of 58 species and 315 reactions including in CPD, Benzene formation process by the theory for polycyclic aromatic hydrocarbons (PAH) and soot formation process on the constant volume combustor, premixed flame characteristics.
ContributorsBae, Kang-Sik (Author) / Lee, Taewoo (Thesis advisor) / Huang, Huei-Ping (Committee member) / Calhoun, Ronald (Committee member) / Phelan, Patrick (Committee member) / Lopez, Juan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Nanoparticles are ubiquitous in various fields due to their unique properties not seen in similar bulk materials. Among them, core-shell composite nanoparticles are an important class of materials which are attractive for their applications in catalysis, sensing, electromagnetic shielding, drug delivery, and environmental remediation. This dissertation focuses on the study of core-shell type of nanoparticles where a polymer serves as the core and inorganic nanoparticles are the shell. This is an interesting class of supramolecular building blocks and can "exhibit unusual, possibly unique, properties which cannot be obtained simply by co-mixing polymer and inorganic particles". The one-step Pickering emulsion polymerization method was successfully developed and applied to synthesize polystyrene-silica core-shell composite particles. Possible mechanisms of the Pickering emulsion polymerization were also explored. The silica nanoparticles were thermodynamically favorable to self-assemble at liquid-liquid interfaces at the initial stage of polymerization and remained at the interface to finally form the shells of the composite particles. More importantly, Pickering emulsion polymerization was employed to synthesize polystyrene/poly(N-isopropylacrylamide) (PNIPAAm)-silica core-shell nanoparticles with N-isopropylacrylamide incorporated into the core as a co-monomer. The composite nanoparticles were temperature sensitive and could be up-taken by human prostate cancer cells and demonstrated effectiveness in drug delivery and cancer therapy. Similarly, by incorporating poly-2-(N,N)-dimethylamino)ethyl methacrylate (PDMA) into the core, pH sensitive core-shell composite nanoparticles were synthesized and applied as effective carriers to release a rheological modifier upon a pH change. Finally, the research focuses on facile approaches to engineer the transition of the temperature-sensitive particles and develop composite core-shell nanoparticles with a metallic shell.
ContributorsSanyal, Sriya (Author) / Dai, Lenore L. (Thesis advisor) / Jiang, Hanqing (Committee member) / Lind, Mary L. (Committee member) / Phelan, Patrick (Committee member) / Rege, Kaushal (Committee member) / Arizona State University (Publisher)
Created2012
Description
Portable devices rely on battery systems that contribute largely to the overall device form factor and delay portability due to recharging. Membraneless microfluidic fuel cells are considered as the next generation of portable power sources for their compatibility with higher energy density reactants. Microfluidic fuel cells are potentially cost effective and robust because they use low Reynolds number flow to maintain fuel and oxidant separation instead of ion exchange membranes. However, membraneless fuel cells suffer from poor efficiency due to poor mass transport and Ohmic losses. Current microfluidic fuel cell designs suffer from reactant cross-diffusion and thick boundary layers at the electrode surfaces, which result in a compromise between the cell's power output and fuel utilization. This dissertation presents novel flow field architectures aimed at alleviating the mass transport limitations. The first architecture provides a reactant interface where the reactant diffusive concentration gradients are aligned with the bulk flow, mitigating reactant mixing through diffusion and thus crossover. This cell also uses porous electro-catalysts to improve electrode mass transport which results in higher extraction of reactant energy. The second architecture uses porous electrodes and an inert conductive electrolyte stream between the reactants to enhance the interfacial electrical conductivity and maintain complete reactant separation. This design is stacked hydrodynamically and electrically, analogous to membrane based systems, providing increased reactant utilization and power. These fuel cell architectures decouple the fuel cell's power output from its fuel utilization. The fuel cells are tested over a wide range of conditions including variation of the loads, reactant concentrations, background electrolytes, flow rates, and fuel cell geometries. These experiments show that increasing the fuel cell power output is accomplished by increasing reactant flow rates, electrolyte conductivity, and ionic exchange areas, and by decreasing the spacing between the electrodes. The experimental and theoretical observations presented in this dissertation will aid in the future design and commercialization of a new portable power source, which has the desired attributes of high power output per weight and volume and instant rechargeability.
ContributorsSalloum, Kamil S (Author) / Posner, Jonathan D (Thesis advisor) / Adrian, Ronald (Committee member) / Christen, Jennifer (Committee member) / Phelan, Patrick (Committee member) / Chen, Kangping (Committee member) / Arizona State University (Publisher)
Created2010
Description
Many expect renewable energy technologies to play a leading role in a sustainable energy supply system and to aid the shift away from an over-reliance on traditional hydrocarbon resources in the next few decades. This dissertation develops environmental, policy and social models to help understand various aspects of photovoltaic (PV) technologies. The first part of this dissertation advances the life cycle assessment (LCA) of PV systems by expanding the boundary of included processes using hybrid LCA and accounting for the technology-driven dynamics of environmental impacts. Hybrid LCA extends the traditional method combining bottom-up process-sum and top-down economic input-output (EIO) approaches. The embodied energy and carbon of multi-crystalline silicon photovoltaic systems are assessed using hybrid LCA. From 2001 to 2010, the embodied energy and carbon fell substantially, indicating that technological progress is realizing reductions in environmental impacts in addition to lower module price. A variety of policies support renewable energy adoption, and it is critical to make them function cooperatively. To reveal the interrelationships among these policies, the second part of this dissertation proposes three tiers of policy architecture. This study develops a model to determine the specific subsidies required to support a Renewable Portfolio Standard (RPS) goal. The financial requirements are calculated (in two scenarios) and compared with predictable funds from public sources. A main result is that the expected investments to achieve the RPS goal far exceed the economic allocation for subsidy of distributed PV. Even with subsidies there are often challenges with social acceptance. The third part of this dissertation originally develops a fuzzy logic inference model to relate consumers' attitudes about the technology such as perceived cost, maintenance, and environmental concern to their adoption intention. Fuzzy logic inference model is a type of soft computing models. It has the advantage of dealing with imprecise and insufficient information and mimicking reasoning processes of human brains. This model is implemented in a case study of residential PV adoption using data through a survey of homeowners in Arizona. The output of this model is the purchasing probability of PV.
ContributorsZhai, Pei (Author) / Williams, Eric D. (Thesis advisor) / Allenby, Braden (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2010
Description
In convective heat transfer processes, heat transfer rate increases generally with a large fluid velocity, which leads to complex flow patterns. However, numerically analyzing the complex transport process and conjugated heat transfer requires extensive time and computing resources. Recently, data-driven approach has risen as an alternative method to solve physical problems in a computational efficient manner without necessitating the iterative computations of the governing physical equations. However, the research on data-driven approach for convective heat transfer is still in nascent stage. This study aims to introduce data-driven approaches for modeling heat and mass convection phenomena. As the first step, this research explores a deep learning approach for modeling the internal forced convection heat transfer problems. Conditional generative adversarial networks (cGAN) are trained to predict the solution based on a graphical input describing fluid channel geometries and initial flow conditions. A trained cGAN model rapidly approximates the flow temperature, Nusselt number (Nu) and friction factor (f) of a flow in a heated channel over Reynolds number (Re) ranging from 100 to 27750. The optimized cGAN model exhibited an accuracy up to 97.6% when predicting the local distributions of Nu and f. Next, this research introduces a deep learning based surrogate model for three-dimensional (3D) transient mixed convention in a horizontal channel with a heated bottom surface. Conditional generative adversarial networks (cGAN) are trained to approximate the temperature maps at arbitrary channel locations and time steps. The model is developed for a mixed convection occurring at the Re of 100, Rayleigh number of 3.9E6, and Richardson number of 88.8. The cGAN with the PatchGAN based classifier without the strided convolutions infers the temperature map with the best clarity and accuracy.
Finally, this study investigates how machine learning analyzes the mass transfer in 3D printed fluidic devices. Random forests algorithm is hired to classify the flow images taken from semi-transparent 3D printed tubes. Particularly, this work focuses on laminar-turbulent transition process occurring in a 3D wavy tube and a straight tube visualized by dye injection. The machine learning model automatically classifies experimentally obtained flow images with an accuracy > 0.95.
ContributorsKang, Munku (Author) / Kwon, Beomjin (Thesis advisor) / Phelan, Patrick (Committee member) / Ren, Yi (Committee member) / Rykaczewski, Konrad (Committee member) / Sohn, SungMin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Recent advancements in the field of light wavefront engineering rely on complex 3D metasurfaces composed of sub-wavelength structures which, for the near infrared range, are challenging to manufacture using contemporary scalable micro- and nanomachining solutions. To address this demand, a novel parallel micromachining method, called metal-assisted electrochemical nanoimprinting (Mac-Imprint) was developed. Mac-Imprint relies on the catalysis of silicon wet etching by a gold-coated stamp enabled by mass-transport of the reactants to achieve high pattern transfer fidelity. This was realized by (i) using nanoporous catalysts to promote etching solution diffusion and (ii) semiconductor substrate pre-patterning with millimeter-scale pillars to provide etching solution storage. However, both of these approaches obstruct scaling of the process in terms of (i) surface roughness and resolution, and (ii) areal footprint of the fabricated structures. To address the first limitation, this dissertation explores fundamental mechanisms underlying the resolution limit of Mac-Imprint and correlates it to the Debye length (~0.9 nm). By synthesizing nanoporous catalytic stamps with pore size less than 10 nm, the sidewall roughness of Mac-Imprinted patterns is reduced to levels comparable to plasma-based micromachining. This improvement allows for the implementation of Mac-Imprint to fabricate Si rib waveguides with limited levels of light scattering on its sidewall. To address the second limitation, this dissertation focuses on the management of the etching solution storage by developing engineered stamps composed of highly porous polymers coated in gold. In a plate-to-plate configuration, such stamps allow for the uniform patterning of chip-scale Si substrates with hierarchical 3D antireflective and antifouling patterns. The development of a Mac-Imprint system capable of conformal patterning onto non-flat substrates becomes possible due to the flexible and stretchable nature of gold-coated porous polymer stamps. Understanding of their mechanical behavior during conformal contact allows for the first implementation of Mac-Imprint to directly micromachine 3D hierarchical patterns onto plano-convex Si lenses, paving the way towards scalable fabrication of multifunctional 3D metasurfaces for applications in advanced optics.
ContributorsSharstniou, Aliaksandr (Author) / Azeredo, Bruno (Thesis advisor) / Chan, Candace (Committee member) / Rykaczewski, Konrad (Committee member) / Petuskey, William (Committee member) / Chen, Xiangfan (Committee member) / Arizona State University (Publisher)
Created2022