Developments in scanning transmission electron microscopy (STEM) have opened up new possibilities for time-resolved imaging at the atomic scale. However, rapid imaging of single atom dynamics brings with it a new set of challenges, particularly regarding noise and the interaction between the electron beam and the specimen. This thesis develops a set of analytical tools for capturing atomic motion and analyzing the dynamic behaviour of materials at the atomic scale. Machine learning is increasingly playing an important role in the analysis of electron microscopy data. In this light, new unsupervised learning tools are developed here for noise removal under low-dose imaging conditions and for identifying the motion of surface atoms. The scope for real-time processing and analysis is also explored, which is of rising importance as electron microscopy datasets grow in size and complexity. These advances in image processing and analysis are combined with computational modelling to uncover new chemical and physical insights into the motion of atoms adsorbed onto surfaces. Of particular interest are systems for heterogeneous catalysis, where the catalytic activity can depend intimately on the atomic environment. The study of Cu atoms on a graphene oxide support reveals that the atoms undergo anomalous diffusion as a result of spatial and energetic disorder present in the substrate. The investigation is extended to examine the structure and stability of small Cu clusters on graphene oxide, with atomistic modelling used to understand the significant role played by the substrate. Finally, the analytical methods are used to study the surface reconstruction of silicon alongside the electron beam-induced motion of adatoms on the surface. Taken together, these studies demonstrate the materials insights that can be obtained with time-resolved STEM imaging, and highlight the importance of combining state-ofthe- art imaging with computational analysis and atomistic modelling to quantitatively characterize the behaviour of materials with atomic resolution.

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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.

A scanning force microscope is an instrument which can image the forces present on a sample with high resolution. These forces include magnetic, electric, and atomic forces. The scanning force microscope uses either a tunneling, capacitive or optical method of sensing the motion of a lever-tip mechanical system that reacts to the forces present on a sample. There are four optical methods used in scanning force microscopy: heterodyne, homodyne, deflection and laser feedback. In this thesis, two implementations of the laser feedback method of detecting lever motion are described: a fiber coupled laser diode feedback and a tightly coupled laser diode feedback. The theory of interactions between a tip and the magnetic or electric fields at the surface of a sample are presented, along with the theory of the laser diode feedback. The limiting noises inherent in the system are discussed and representative values are given. Results of the sensitivity of the two systems are presented, and images of electric forces acting on the tip from interdigitated electrodes are demonstrated.

No description available.

The design of a custom VUV microscope is studied. The microscope is designed around a custom high brightness, spectrally narrow VUV source operating at the Hydrogen-Lyman-α (HLA) transition characterized by the emission wavelength of 121.6 nm. The incentive for microscopy at 121.6nm is a transparent window in the air absorption spectrum coinciding with 121.6nm light. This allows for the sample to be in air while the microscope is in an enclosed vacuum or nitrogen environment. A microscope is built consisting of the VUV source, a low noise, x-ray camera, a custom 120 magnification, 0.3 numerical aperture objective lens, and an assortment of vacuum flanges, nipples, and crosses. The camera is verified to detect the HLA output from the source. The objective lens is capable of achieving an intrinsic resolution of 247 nm with a wavelength of 121.6 nm if the proposed alignment procedure is followed and the fabricated mechanical tolerances are within the specified range. The objective lens mirrors and the primary mirror cell are fabricated out of specification. Therefore, the best expected optical performance is 0.3 Strehl ratio. In order to improve the optical performance, a few design changes are discussed, including increasing the primary mirror thickness to improve surface figure error and increasing the back thickness of the primary mirror cell in order to reduce the force on the primary mirror from radial adjustment screws.

No description available.

No description available.