M.Sc. / Despite several decades of extensive research and development in pharmaceutical chemistry, the poor solubility of lipophilic compounds in aqueous media remains a major barrier to their absorption, bioavailability and clinical efficacy. This poor solubility is also a problem in other areas such as the flavour and fragrance industry. In cosmetics, for example, poor aqueous solubility and instability of oily compounds causes problems in formulation and fragrance stability. One approach to overcome these difficulties is to encapsulate oily compounds in biocompatible materials. As a drug delivery system such an approach is attractive if the size of the capsule is reduced to the micrometer or nanometer scale. Naturally occurring polysaccharides like sodium alginate (NaALG) and chitosan (CS) are generally regarded as safe (GRAS) for use in human use and have therefore gained much attention recently. As a drug delivery system, this polymer matrix can be used to prevent drug degradation in the gastro intestinal tract (GIT) and often provides controlled release of the encapsulant. Cyclodextrins (CDs) on the other hand offer an alternative approach. These cyclic oligosaccharides have the ability to form non-covalent inclusion complexes with a range of organic compounds, and in so doing alter their physiochemical properties such as solubility. This study was aimed at exploring these concepts by using ALG and CS as an entrapment matrix for an essential oil, tagette oil (used as a model oily drug) that is insoluble in aqueous media. Alginate/chitosan (ALG/CS) nanoparticles were prepared in a 3-step procedure; emulsification of tagette oil in aqueous Na-ALG solution, followed by ionotropic pre-gelation of the ALG core with CaCl2 and further crosslinking with CS. Morphology and particle size measurements were performed by scanning and transmission electron microscopy (SEM and TEM), and Malvern Zetasizer.

We propose to interpret Cryogenic Electron Microscopy (CryoEM) data as a supervision for learning parameters of CryoEM microscopes. Following this formulation, we present a differentiable version of Transmission Electron Microscopy (TEM) Simulator that provides differentiability of all continuous inputs in a simulation. We demonstrate the learning capability of our simulator with two examples, detector parameter estimation and denoising. With our differentiable simulator, detector parameters can be learned from real data without time-consuming handcrafting. Besides, our simulator enables new way to denoising micrographs. We develop this simulator with the combination of Taichi and PyTorch, exploiting kernel-based and operator-based parallel differentiable programming, which results in good speed, low memory footprint and expressive code. We call our work as Differentiable TEM Detector as there are still challenges to implement a fully differentiable transmission electron microscope simulator that can further differentiate with respect to particle positions. This work presents first steps towards a fully differentiable TEM simulator. Finally, as a subsequence of our work, we abstract out the fuser that connects Taichi and PyTorch as an open-source library, Stannum, facilitating neural rendering and differentiable rendering in a broader context. We publish our code on GitHub.

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Random Access Memory (RRAM) has emerged as a leading candidate for nonvolatile memory storage. RRAM devices typically consist of a metal/insulator/metal (MIM) structure and exhibit switching of the device resistivity state (low-to-high, highto- low) by application of electrical bias. It is now widely accepted that shunting and rupturing of local conductive paths (filaments) directly determines the resistance state. The size and composition of these filaments are very much an open question, but are usually attributed to high local concentrations of oxygen vacancies. Although there has been a huge body of research conducted in this field, the fundamental nature of the conductive path and basic switching/failure mechanisms are still under debate. This is largely due to a lack of structural analysis of existing filament size and composition in actual devices. Since the non-volatile nature and device reliability issues (i.e. retention and endurance) are directly related to the irreversible structural transformations in the device, microstructural characterization is essential for eventual commercialization of RRAM. In this study, I investigated oxygen vacancy defect dynamics under electric filed essential for resistive switching and aim to identify size, location, and chemical nature of the conductive filaments in RRAM devices by using a variety of devices and materials characterization methods: in situ transmission electron microscopy (TEM), highresolution TEM (HRTEM), scanning TEM (STEM)-electron energy loss spectroscopy (EELS), electron holography, rapid thermal annealing (RTA), transient thermometry, and electro-thermal simulation. I adopt an in situ electrical biasing TEM technique to study microstructural changes occurring during resistive switching using a model TiO2-based RRAM device, and confirmed the device is switchable inside of the TEM column. I observed extension and contraction of {011} and {121}-type Wadsley defects, crystallographic shear faults, associated with resistive switching. More specifically, emission and adsorption of oxygen vacancies under different polarity of electrical biases at the fault bounding dislocations were identified. The motion of Wadsley defects was used to track oxygen vacancy migration under electric field. Also, the microstructural changes that occur when the device experiences low electric field (~104 V/cm) was reported, akin to read disturb. Crossbar type RRAM device stacks consisting of TiN/a-HfAlOx/Hf/TiN were investigated to estimate filament size, filament temperature, and its chemical footprint using HRTEM, transient thermometry and numerical simulation. In each of the switched devices, a single crystallite ~ 8-16 nm in size embedded in an amorphous HfAlOx matrix was found. The HfAlOx crystallization temperature (Tc) of 850 K was determined by combining RTA and HRTEM imaging. In parallel, the filament size has been determined by transient thermometry. The temperature profile extracted from these measurements suggested that the peak filament temperature was > 1500 K at the center, with the hot zone (T > Tc = 850 K) extending to a radius of 7 nm around the filament. These results were consistent with the HRTEM observations of the crystallite size. The potential filament location (crystallite) in the switching devices was analyzed by STEM-EELS and identification of the filament chemical nature identification has been attempted.

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Thesis (Ph. D.)--Florida State University, 2003. / Advisor: Dr. Michael S. Chapman, Florida State University, College of Arts and Sciences, Dept. of Chemistry and Biochemistry. Title and description from dissertation home page (viewed Feb. 24, 2003). Includes bibliographical references.