Doctor of Philosophy (PhD), Ohio University, 2024, Civil Engineering (Engineering and Technology)

Colloidal aggregation is a critical phenomenon influencing various environmental processes. However, limited research has been conducted on the aggregation of particles with heterogeneous physical and chemical properties, which are more representative of practical environmental systems than homogeneous particles. The central hypothesis of this dissertation is that primary particle size polydispersity along with chemical and material heterogeneity of primary particles exert non-trivial effects on the aggregate growth rate and the fractal dimensions of aggregates. In this dissertation, the aggregation and stability of heterogeneous nano- and sub-micrometer particles in aqueous systems were investigated using in situ microscopy and image analysis. Initially, the study examined the growth kinetics and structures of aggregates formed by polystyrene microplastics in mono- and bidisperse systems. Findings indicated that while the primary particle size distribution did not affect the scaling behavior of aggregate growth, it delayed the onset of rapid aggregation. Structural analysis revealed a power law dependence of the aggregate fractal dimension in both mono- and bidisperse systems, with mean fractal dimensions consistent with aggregates from diffusion-limited cluster aggregation. The results also suggested that aggregate fractal dimension was insensitive to shape anisotropy. The dissertation further explored the structure of DLCA aggregates in heterogeneous systems composed of particles with varying sizes, surface charges, and material compositions. The fractal dimensions of DLCA aggregates in these heterogeneous particle systems were similar, ranging from 1.6 to 1.7, and consistent with theoretical predictions and experimental evidence for homogeneous DLCA aggregates. This confirmed the universality of aggregate structures in the DLCA regime, regardless of particle composition. Additionally, a scaling relationship was demonstrated between aggregat (open full item for complete abstract)

Committee: Lei Wu (Advisor); Guy Riefler (Committee Member); Daniel Che (Committee Member); Sumit Sharma (Committee Member); Natalie Kruse Daniels (Committee Member) Subjects: Chemical Engineering; Civil Engineering; Environmental Engineering; Physical Chemistry

Doctor of Philosophy, The Ohio State University, 2021, Aerospace Engineering

Sizing and topology optimization are the two main structural optimization tools in a wide range of applications in aerospace, mechanical, and design. An iterative process solves the sizing optimization using classical gradient-based methods, usually carried out with an integrated process including a full-scale finite element analysis (FEA) to evaluate the design performance and a gradient search step at each iteration. With a complex real-world model, the optimization process is extremely cumbersome, time-consuming, and with no guarantee for an optimal solution or design. Alternatively, global population-based methods, such as genetic algorithm and particle swarm, can achieve the global optimal design with many simulations for every iteration to evaluate different designs for searching for the best candidates. This tremendous computational effort for simulations at each iteration prevents the global method from optimizing with complex physics simulation models. As for topology optimization, state-of-the-art methods, such as the Solid Isotropic Material with Penalty (SIMP) method, uses hand-coded gradient functions for optimization and must be run repeatedly for different boundary and loading conditions. Several practical and efficient machine-learning-based data-driven approaches have been proposed to optimize structures instantaneously using the generative adversarial network. Nevertheless, a complex machine learning model is costly because of the large amount of data and long training time. This dissertation presents several new, rapid, and accurate optimal design approaches for improving current structural sizing and topology optimization methods. First, for sizing optimization, to reduce the optimization time while preserving global optimality, a new optimization framework with response surface method and global sensitivity method is presented to approximate the simulation model with high accuracy while using a minimum number of simulations. The response surfac (open full item for complete abstract)

Committee: Herman Shen (Advisor) Subjects: Engineering

Doctor of Philosophy, University of Toledo, 2009, Physics

The main objective of the work presented in this thesis is to develop new methods to extend the time and length scales of atomistic kinetic Monte Carlo (KMC) simulations. When all the relevant processes and their activation barriers are known, KMC is an extremely efficient method to carry out atomistic simulations for longer time scales. However, in some cases (ex. low barrier repetitive events) direct KMC simulations may not be sufficient to reach the experimentally relevant length and time scales. Accordingly, we have tested and developed several different parallel KMC algorithms and also developed a dynamic boundary allocation (DBA) method to improve parallel efficiency by reducing number of boundary events. Results for parallel KMC simulations of Ag(111) island coarsening at room temperature carried out using a large database of processes obtained from previous self-learning KMC simulations are also presented. We find that at long times the coarsening behavior corresponds to Ostwald ripening. We also find that the inclusion of concerted small-cluster events has a significant impact on the average island size. In addition, we have also developed a first passage time (FPT) approach to KMC simulations to accelerate KMC simulation of (100) multilayer epitaxial growth with rapid edge diffusion. In our FPT approach, by mapping edge-diffusion to a 1D random walk, numerous diffusive hops are replaced with first-passage time to make one large jump to a new location. As a test, we have applied our method to carry out multilayer growth simulations of three different models. We note that despite the additional overhead, the FPT approach leads to a significant speed-up compared to regular KMC simulations Finally, we present results obtained from KMC simulations of irreversible submonolayer island growth with strain and rapid island relaxation. Our results indicate that in the presence of large strain there is significant anisotropy in qualitative agreement with experiments o (open full item for complete abstract)

Committee: Amar Jacques (Advisor); Amar Jacques (Advisor); Collins Robert (Committee Member); Bigioni Terry (Committee Member); Anderson-huang Lawrence (Committee Member); Kvale Thomas (Committee Member) Subjects: Physics

Master of Science, Miami University, 2008, Physics

The goal of this research has been to explore the structure of tornado-like vortices by making measurements of static surface pressure. Instantaneous surface pressure profiles were obtained for five different swirl ratios. Comparisons are made between those surface pressure profiles obtained in this research and those previously obtained in similar research experiments. Estimates of the radius of the pressure core are also made and compared with values previously obtained.

Committee: Samir Bali PhD (Advisor); Jeffrey Clayhold PhD (Committee Member); Christopher Church PhD (Committee Member) Subjects: Atmosphere; Earth; Physics

Doctor of Philosophy (PhD), Ohio University, 2013, Physics and Astronomy (Arts and Sciences)

Nanoscale systems, at the intersection of bottom-up and top-down approaches to technological development, have demonstrated unique properties and applications in recent scientific studies. Scanning probe microscopy has emerged as a versatile tool for studying nanoscale interactions due to its capabilities of local measurement of spectroscopic, magneto-electric, and topographic properties in real-space with sub-nanometer resolution. Still, many physical and chemical effects have yet to be completely characterized and understood. This dissertation demonstrates the novel application of scanning tunneling microscopy to the study of local work functions through field emission resonances, surface catalyzed covalently bound chain formation, and spintronic interactions of organically coupled magnetic ions. Local work functions are found, by analyses of field emission resonances, for probe induced surface vacancies and atomic step edges on an atomically clean Ag(111) crystal. The extracted local work functions for defect locations vary significantly from the known and measured clean surface values. The local work function plays a large part in surface binding and electronic interaction of surface adsorbates. This technique for local work function measurement can be extended to more unique surface and molecular systems. A process for the formation, and topographic measurement, of covalently bound chains by surface catalysis is demonstrated with homogeneous magnetic and heterogeneous networks of molecules. The chain coupling occurs through an Ullmann-like halogen substitution and subsequent ring coupling reaction mediated by surface atoms, with application of adequate thermal energy. Individual molecules with central magnetic ions are shown to exhibit a Kondo resonance in spectroscopic measurements. Covalently bound chains of these molecules maintain the Kondo interaction while developing an antiferromagnetic coupling between the central magnetic ions as demonstrated through t (open full item for complete abstract)

Committee: Hla Saw-Wai (Advisor); Smith Arthur (Committee Member); Ulloa Sergio (Committee Member); Rack Jeffrey (Committee Member) Subjects: Condensed Matter Physics; Physics