PhD Dissertations

Textile electronics is a newly emerging field that is gaining the interest of researchers due to its wide range of applications and prospects. Despite the variety of materials and methods used to fabricate textile electronic devices, there is a lack of the necessary focus and direction required for the rapid advancement of this field. A suggested solution is concentrating the efforts on versatile methods and materials that can create multiple devices. Atomic Layer Deposition (ALD) is a suitable method for textile substrates since it can deposit solid-state materials conformally and at low temperatures. It can deposit semi-conductive Zinc Oxide (ZnO), which is used to fabricate various devices, including transistors, capacitors, and sensors. However, there is limited information about the characteristics of ZnO deposited via ALD on textile substrates. By exploring the influence of the following parameters, this research aims to optimize the deposition process for fabric-based electronics and sensors while investigating the impact of various ALD reaction parameters on the electrical properties of ZnO deposited on cotton fabric. The ALD reaction utilized two precursors, Diethylzincite and water, to facilitate the deposition of ZnO on the cotton substrate. The reaction involved a ligand exchange process with gaseous ethane as a byproduct, which was purged after each cycle. The experiment involved conducting the ALD process under different testing conditions, including varying reaction temperatures, dose time for the precursors, purge time, and the duration of holding the precursors in the chamber before purging. Consequently, the results indicated that the optimized recipe showed a low resistivity of 1.25 Ω.cm and an atomic concentration ratio of oxygen per zinc atoms of 22.8. The reaction was conducted at a temperature of 180 oC and had 100 ms DEZ dose time, 25 ms water dose time, and 1 s purge time. The findings contribute to the broader understanding of thin-film deposition processes and their impact on electronic performance, opening avenues for the development of innovative and efficient electronic systems integrated into textile materials.

Understanding the mechanisms governing earthquake motion along faults is crucial for investigating earthquake nucleation, dynamic slip evolution, and arrest. Faults have complex geometries, including a fracture network within the damage zone characterized by a low-velocity zone. This dissertation aims to provide insights into the fundamental physics underlying cascading earthquakes in geometrically complex fault systems using 3D high-resolution dynamic rupture modeling. This is achieved by (i) developing physics-based numerical models that integrate geological observations, laboratory findings, statistical measurements, and theoretical insights, and (ii) utilizing these models to explore the influence of different physical mechanisms and conditions reflected in geological, geophysical, and geodetic observations. I investigate the dynamics of complex earthquake source mechanisms observed in the 2017 Mw 5.5 Pohang earthquake and develop a new fault plane fitting technique with a priori source parameter’s information. The simulations successfully reproduce key observable characteristics of the earthquake, emphasizing the significance of perturbations in local stress conditions that lead to such an earthquake. I then construct a fracture network model comprising two intersecting families with a listric fault to examine the conditions that facilitate a self-sustained rupture cascade within the fracture network. The results demonstrate that a cascading rupture is mechanically plausible under high fracture connectivity if one fracture family is favorably and the other conditionally or favorably pre-stressed relative to ambient stress conditions. The dynamic rupture cascade occurring within geometrically complex faults presents distinct ground motion characteristics compared to a non-cascading rupture. I conduct a comparative analysis of high-resolution models to investigate this phenomenon. The simulations feature high-frequency seismic waves generated by the simultaneous slipping of multiple fractures. Finally, I investigate dynamic rupture cascades in microearthquakes using a planar fault model and fracture networks within its damage zone. The analyses incorporate considerations of elastic and inelastic material properties and a low-velocity fault zone. The results highlight that, in microearthquakes, the propensity of failure for both the fractures and the main fault outweighs the importance of material properties.

This Ph.D. dissertation explores the diverse applications of supramolecular cages and macrocycles in overcoming complex challenges in the biochemistry and chemical industry, as well as pioneering new possibilities in functional materials. This research is dedicated to discovering the potential of cages/macrocycles as biomimetic anion receptors, industrial absorbance for crucial intermediates, and smart materials towards different stimuli. The following Chapters (II-V) in this dissertation will detailly illustrate how these synthetic cages and macrocycles compounds will interact differently with different guest molecules and provide groundbreaking findings that have practical implications for various industries. In Chapter II, a novel crystalized biomimetic cage was introduced as a “protector” to overcome the Hofmeister effect---a phenomenon that interferes with the solubility and function of proteins. The study demonstrated how a synthetic anion receptor can contribute to retaining the activity of lysozyme under conditions where anion-induced precipitation would otherwise inhibit its function. Chapters III and IV addressed alternative methods for separating molecules with close physical properties. Specifically, separating methylcyclohexane (MCH) from toluene (Tol) and para-halotoluene (PHT) isomers addressed the energy-intensive and time-consuming problems in industry. These two projects utilized trianglimine (TI) and trianglamine (TA) macrocycles as “separator”, displaying unique extrinsic/intrinsic approach based on crystal conformation and outlining innovative methods for selective absorption based on different molecular interactions. In Chapter V, a smart, macrocycle-based “actuator” (M1 and M2) towards specific organic solvents was synthesized. When these smart macrocycles are exposed to hydrogen bond acceptor solvents, the amorphous compounds become crystallized, and their conformational changes upon the solvent vary at molecular level. Investigation have been done to “amplify” the changes at molecular level to macroscopic level, by composing a macrocycle-contained polymer matrix (M1@PVDF and M2@PVDF). This Chapter V revealed the potential for swift solvent-responsive actuation towards different organic vapors, marking an advancement in the development of "smart" materials that are responsive to external stimuli. This dissertation expands the existing knowledge on the utility of synthetic macrocyclic compounds, suggesting their versatility across multiple applications—from mitigating biological limitations imposed by the Hofmeister effect to chemical separation and the creation of responsive smart materials. The findings presented are not only academically noteworthy but also having significant implications for industrial applications.

Carotenoid cleavage, catalyzed by Carotenoid Cleavage Dioxygenases (CCDs), forms various apocarotenoids that act as signaling molecules and plant hormone precursors. The rice Zaxinone Synthase (ZAS), an overlooked plant CCD subfamily, catalyzes the synthesis of apocarotenoid zaxinone in vitro. Zaxinone is a growth regulator essential for normal rice growth and development. In the first project, we generated and characterized rice lines that constitutively overexpress ZAS to gain deeper insights into the role of ZAS in determining zaxinone levels and its impact on rice growth and architecture. We found that the overexpression of ZAS increased the level of endogenous zaxinone, promoted root growth, and increased the number of productive tillers. Importantly, ZAS overexpression led to about 30% higher grain yield per plant. Hormone and metabolite analysis revealed a decrease in the level of strigolactones (SLs) and an increased accumulation of monosaccharide sugars. Additionally, ZAS overexpression enhanced the uptake of nutrients, including phosphate, resulting in improved tolerance to low phosphate. Overall, ZAS plays a crucial role in regulating rice growth, yield, and various physiological and metabolic processes. The second project focused on the enzymatic and biological function of OsZAS2, which represents a separate ZAS clade. Through in vitro assay and genetic manipulation, we revealed that OsZAS2 is a further zaxinone-forming enzyme mainly localized in plastids. The loss-of-function CRISPR/Cas9-Oszas2 mutants exhibited lower levels of zaxinone in their roots, reduced root and shoot biomass, fewer tillers, and elevated levels of SLs. Furthermore, the expression of OsZAS2 is induced in arbuscule-containing cells, and in support of this, the Oszas2 mutant displayed a significant reduction in arbuscular mycorrhizal colonization. In summary, OsZAS2 is involved in zaxinone production in rice. Like OsZAS, it plays a crucial and non-redundant role in determining rice growth, architecture, and SL content and is essential for optimal mycorrhization. In the last chapter, we investigated the effect of exogenous zaxinone application on the non-mycorrhizal plant Arabidopsis that lacks ZAS orthologues but synthesizes zaxinone via an unknown mechanism. Obtained results suggest that zaxinone functions as a regulatory metabolite in Arabidopsis, stimulating the biosynthesis of the two carotenoid-derived hormones SL and abscisic acid in roots.

This thesis proposes statistical spatial and spatial-temporal methods for addressing real-world challenges within the public health and environmental domains.

Firstly, we introduce a new method that integrates compartment and spatial point process models to describe the propagation of infectious diseases over space and time. We apply this method to the analysis of COVID-19 data in Cali, Colombia, in 2020. Secondly, we implement a new class of statistical hazard models and Bayesian inference tools for studying spatially dependent survival data under the assumption of competing risks and unknown cause-of-death (named "relative survival framework"). This framework accounts for the neighboring spatial dependence among the studied sub-regions by means of a conditional autoregressive model. This method is employed in the analysis of colon cancer data in England. Thirdly, in the context of model-based geostatistics, we extend a well-known model for geostatistical data that accounts for preferential sampling by allowing the degree of preferentiality to vary over space. We use this model to analyze the levels of air pollution in the United States. Lastly, we explore different post-processing and ensemble techniques for the nowcasting of the "7-day COVID-19 hospitalization incidence" in Germany between 2021 and 2022. In this setting, additional challenges arise from the fact that the data is constantly being revised, preventing us from using common methods. We address this issue by training our models based on a modified version of the original incidence counts.

For Bayesian inference, we implement our models with Stan, as well as the integrated nested Laplace approximation (INLA) method. The latter may reduce the computational burden for parameter estimation and enable fast inference. In the corresponding chapters, we provide details of the methodology and accessible links to our scripts, ensuring the full reproducibility of our methods and results.