Emine B. Gulsoy

Catalyst incorporation at defects during nanowire growth.

Scanning and transmission electron microscopy was used to correlate the structure of planar defects with the prevalence of Au catalyst atom incorporation in Si nanowires. Site-specific high-resolution imaging along orthogonal zone axes, enabled by advances in focused ion beam cross sectioning, reveals substantial incorporation of catalyst atoms at grain boundaries in <110> oriented nanowires. In contrast, (111) stacking faults that generate new polytypes in <112> oriented nanowires do not show preferential catalyst incorporation. Tomographic reconstruction of the catalyst-nanowire interface is used to suggest criteria for the stability of planar defects that trap impurity atoms in catalyst-mediated nanowires.

TIMBIR: A Method for Time-Space Reconstruction From Interlaced Views

Synchrotron X-ray computed tomography (SXCT) is increasingly being used for 3-D imaging of material samples at micron and finer scales. The success of these techniques has increased interest in 4-D reconstruction methods that can image a sample in both space and time. However, the temporal resolution of widely used 4-D reconstruction methods is severely limited by the need to acquire a very large number of views for each reconstructed 3-D volume. Consequently, the temporal resolution of current methods is insufficient to observe important physical phenomena. Furthermore, measurement nonidealities also tend to introduce ring and streak artifacts into the 4-D reconstructions. In this paper, we present a time-interlaced model-based iterative reconstruction (TIMBIR) method, which is a synergistic combination of two innovations. The first innovation, interlaced view sampling, is a novel method of data acquisition, which distributes the view angles more evenly in time. The second innovation is a 4-D model-based iterative reconstruction algorithm (MBIR), which can produce time-resolved volumetric reconstruction of the sample from the interlaced views. In addition to modeling both the sensor noise statistics and the 4-D object, the MBIR algorithm also reduces ring and streak artifacts by more accurately modeling the measurement nonidealities. We present reconstructions of both simulated and real X-ray synchrotron data, which indicate that TIMBIR can improve temporal resolution by an order of magnitude relative to existing approaches.

The Three-Dimensional Morphology of Growing Dendrites

The processes controlling the morphology of dendrites have been of great interest to a wide range of communities, since they are examples of an out-of-equilibrium pattern forming system, there is a clear connection with battery failure processes, and their morphology sets the properties of many metallic alloys. We determine the three-dimensional morphology of free growing metallic dendrites using a novel X-ray tomographic technique that improves the temporal resolution by more than an order of magnitude compared to conventional techniques. These measurements show that the growth morphology of metallic dendrites is surprisingly different from that seen in model systems, the morphology is not self-similar with distance back from the tip, and that this morphology can have an unexpectedly strong influence on solute segregation in castings. These experiments also provide benchmark data that can be used to validate simulations of free dendritic growth.

Visualization of the Magnetic Structure of Sculpted Three-Dimensional Cobalt Nanospirals

In this work, we report on the direct visualization of magnetic structure in sculpted three-dimensional cobalt (Co) nanospirals with a wire diameter of 20 nm and outer spiral diameter of 115 nm and on the magnetic interactions between the nanospirals, using aberration-corrected Lorentz transmission electron microscopy. By analyzing the magnetic domains in three dimensions at the nanoscale, we show that magnetic domain formation in the Co nanospirals is a result of the shape anisotropy dominating over the magnetocrystalline anisotropy of the system. We also show that the strong dipolar magnetic interactions between adjacent closely packed nanospirals leads to their magnetization directions adopting alternating directions to minimize the total magnetostatic energy of the system. Deviations from such magnetization structure can only be explained by analyzing the complex three-dimensional structure of the nanospirals. These nanostructures possess an inherent chirality due to their growth conditions and are of significant importance as nanoscale building blocks in magneto-optical devices.

Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling

Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage. There is a pressing need to develop novel imaging and spectroscopic imaging methods that will minimize such sample damage as well as reduce the data acquisition time. The latter is useful for high-throughput analysis of materials structure and chemistry. In this work, we present a novel machine learning based method for dynamic sparse sampling of EDS data using a scanning electron microscope. Our method, based on the supervised learning approach for dynamic sampling algorithm and neural networks based classification of EDS data, allows a dramatic reduction in the total sampling of up to 90%, while maintaining the fidelity of the reconstructed elemental maps and spectroscopic data. We believe this approach will enable imaging and elemental mapping of materials that would otherwise be inaccessible to these analysis techniques.

Under-sampling and Image Reconstruction for Scanning Electron Microscopes

Electron Microscopes have been used to investigate materials from micron to nano scale. Scanning electron microscopes (SEM) as well as scanning transmission electron microscopes (STEM) can acquire image data relatively fast, however acquiring spectroscopic data requires longer data collection times. Depending on the desired resolution or sample area, this can make a significant difference in the duration and feasibility of the experiment. Moreover, for electron beam sensitive samples, it is necessary to acquire the image data with minimal exposure time as not to further damage the sample [1]. Here, we propose an under-sampling and reconstruction method to reduce the data collection time while maintaining imaging accuracy.