Geometric reconstruction methods for electron tomography

The benefit of thresholding carbon layers in electron tomographic tilt series by intensity downshifting

When performing electron tomography, tilt series of images are often acquired from samples that contain unwanted carbonaceous material, such as an embedding resin, a thin carbon support film or hydrocarbon contamination. The presence of such layers can introduce artefacts in reconstructions, obscuring features of interest. Here, we illustrate the benefit of preprocessing a high-angle annular dark-field tomographic tilt series by thresholding unwanted low-density materials using a simple intensity downshifting procedure. The resulting tomograms have fewer artefacts and segmentation can be performed more accurately. We present two representative examples taken from studies of catalyst nanoparticles and amyloid plaque core material from the human brain.

Electron tomography image reconstruction using data-driven adaptive compressed sensing

Electron tomography (ET) is an increasingly important technique for the study of the three-dimensional morphologies of nanostructures. ET involves the acquisition of a set of two-dimensional projection images, followed by the reconstruction into a volumetric image by solving an inverse problem. However, due to limitations in the acquisition process, this inverse problem is ill-posed (i.e., a unique solution may not exist). Furthermore, reconstruction usually suffers from missing wedge artifacts (e.g., star, fan, blurring, and elongation artifacts). Recently, compressed sensing (CS) has been applied to ET and showed promising results for reducing missing wedge artifacts. This uses image sparsity as a priori knowledge to improve the accuracy of reconstruction, and can require fewer projections than other reconstruction techniques. The performance of CS relies heavily on the degree of sparsity in the selected transform domain and this depends essentially on the choice of sparsifying transform. We propose a new image reconstruction algorithm for ET that learns the sparsifying transform adaptively using a dictionary-based approach. We demonstrate quantitatively using simulations from complex phantoms that this new approach reconstructs the morphology with higher fidelity than either analytically based CS reconstruction algorithms or traditional weighted back projection from the same dataset. SCANNING 38:251-276, 2016. © 2015 Wiley Periodicals, Inc.

Deciphering the physics and chemistry of perovskites with transmission electron microscopy

Perovskite oxides exhibit rich structural complexity and a broad range of functional properties, including ferroelectricity, ferromagnetism, and superconductivity. The development of aberration correction for the transmission electron microscope and concurrent progress in electron spectroscopy, electron holography, and other techniques has fueled rapid progress in the understanding of the physics and chemistry of these materials. New techniques based on the transmission electron microscope are first surveyed, and the applications of these techniques for the study of the structure, chemistry, electrostatics, and dynamics of perovskite oxides are then explored in detail, with a particular focus on ferroelectric materials.

Removing the effects of the "dark matter" in tomography

Electron tomography (ET) using different imaging modes has been progressively consolidating its position as a key tool in materials science. The fidelity of a tomographic reconstruction, or tomogram, is affected by several experimental factors. Most often, an unrealistic cloud of intensity that does not correspond to a real material phase of the specimen ("dark matter") blurs the tomograms and enhances artefacts arising from the missing wedge (MW). Here we show that by simple preprocessing of the background level of any tomographic tilt series, it is possible to minimise the negative effects of that "dark matter". Iterative reconstruction algorithms converge better, leading to tomograms with fewer streaking artefacts from the MW, more contrast, and increased accuracy. The conclusions are valid irrespective of the imaging mode used, and the methodology improves the segmentation and visualisation of tomograms of both crystalline and amorphous materials. We show examples of HAADF STEM and BF TEM tomography.

Self-assembly of biaxial discorectangular lead carbonate nanosheets into stacked ribbons studied by SAXS and HAADF-STEM tomographic tilt series

The self-assembling behaviour of 2.6 nm thin PbCO3 nanoplatelets with discorectangular shape and uniform width and thickness occurring after their formation in nonionic water-in-oil microemulsions has been investigated using synchrotron small angle X-ray scattering (SAXS) and (scanning) transmission electron microscopy ((S)TEM). The presence of attractive depletion forces originating from the ubiquitous microemulsion droplets triggers a new type of superstructure at low particle concentration. Instead of the universally observed formation of face-to-face assembled lamellar mesostructures, the nanosheets self-organise into extended ribbon structures, whereby each on top lying sheet is displaced by a constant shift in the length and width directions leading to a so far unprecedented staggered zigzag-type stack assembly with restricted height. This type of stacking gives rise to a complex interference pattern in the isotropic small angle scattering of the stacked ribbon assemblies (SRAs) in reverse micellar solution. Different to the, for lamellar-structured nanosheets typical, diffraction peaks at multiples of the wave vector corresponding to one particular repeat distance, the scattering peaks measured in this study are asymmetric, displaying a shoulder on their low wave vector side. The asymmetric shape of the observed face-to-face correlation peaks indicates that the SRAs do not extend in one direction only. Their scattering behaviour is analysed by expanding the Kratky-Porod structure factor for stacking plates into three dimensions. High-angle annular dark-field (HAADF)-STEM tilt series have complementary been acquired to retrieve three-dimensional structural information on the SRAs in the dry state and to confirm the model used for the refinement of the SAXS data.

Understanding the true shape of Au-catalyzed GaAs nanowires

With increasing interest in nanowire-based devices, a thorough understanding of the nanowire shape is required to gain tight control of the quality of nanowire heterostructures and improve the performance of related devices. We present a systematic study of the sidewalls of Au-catalyzed GaAs nanowires by investigating the faceting process from the beginning with vapor-liquid-solid (VLS) nucleation, followed by the simultaneous radial growth on the sidewalls, and to the end with sidewall transformation during annealing. The VLS nucleation interface of our GaAs nanowires is revealed by examining cross sections of the nanowire, where the nanowire exhibits a Reuleaux triangular shape with three curved surfaces along {112}A. These curved surfaces are not thermodynamically stable and adopt {112}A facets during radial growth. We observe clear differences in radial growth rate between the ⟨112⟩A and ⟨112⟩B directions with {112}B facets forming due to the slower radial growth rate along ⟨112⟩B directions. These sidewalls transform to {110} facets after high temperature (>500 °C) annealing. A nucleation model is proposed to explain the origin of the Reuleaux triangular shape of the nanowires, and the sidewall evolution is explained by surface kinetic and thermodynamic limitations.