Abstract: Colloidal nanoparticles (NPs) including nanowires and nanosheets made by chemical methods involve many organic ligands. When the structure of NPs is investigated via transmission electron microscopy (TEM), the organic ligands act as a source for e-beam induced deposition and this causes substantial build-up of carbon layers in the investigated areas, which is typically referred to as “contamination” in the eld of electron mi- croscopy. This contamination is often more severe for scanning TEM, a technique that is based on a focused electron beam and hence higher electron dose rate. In this paper, we report a simple and effective method to clean drop-cast TEM grids that contain NPs with ligands. Using a combination of activated carbon and ethanol, this method effectively reduces the amount of ligands on TEM grids, and therefore greatly improves the quality of electron microscopy images and subsequent analytical measurements. This ef cient and facile method can be helpful during electron microscopy investigation of different kinds of nanomaterials that suffer from ligand- induced contamination.

Abstract: Annular dark field TEM images exhibit a dominant mass-thickness contrast that can be quantified to extract single atom scattering cross sections. On top of this incoherent background, additional lattice fringes appear with a nonlinear information limit of 1.2 angstrom at 150 kV. The formation of these fringes is described by coherent nonlinear imaging theory and good agreement is found between experimental and simulated images. Calculations furthermore predict that the use of aberration corrected microscopes will improve the image quality dramatically. (c) 2005 Elsevier B.V. All rights reserved.

Abstract: The field of discrete tomography focuses on the reconstruction of samples that consist of only a few different materials. Ideally, a three-dimensional (3D) reconstruction of such a sample should contain only one grey level for each of the compositions in the sample. By exploiting this property in the reconstruction algorithm, either the quality of the reconstruction can be improved significantly, or the number of required projection images can be reduced. The discrete reconstruction typically contains fewer artifacts and does not have to be segmented, as it already contains one grey level for each composition. Recently, a new algorithm, called discrete algebraic reconstruction technique (DART), has been proposed that can be used effectively on experimental electron tomography datasets. In this paper, we propose discrete tomography as a general reconstruction method for electron tomography in materials science. We describe the basic principles of DART and show that it can be applied successfully to three different types of samples, consisting of embedded ErSi2 nanocrystals, a carbon nanotube grown from a catalyst particle and a single gold nanoparticle, respectively.

Abstract: A model-based method is proposed to relatively quantify the chemical composition of atomic columns using high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) images. The method is based on a quantification of the total intensity of the scattered electrons for the individual atomic columns using statistical parameter estimation theory. In order to apply this theory, a model is required describing the image contrast of the HAADF STEM images. Therefore, a simple, effective incoherent model has been assumed which takes the probe intensity profile into account. The scattered intensities can then be estimated by fitting this model to an experimental HAADF STEM image. These estimates are used as a performance measure to distinguish between different atomic column types and to identify the nature of unknown columns with good accuracy and precision using statistical hypothesis testing. The reliability of the method is supported by means of simulated HAADF STEM images as well as a combination of experimental images and electron energy-loss spectra. It is experimentally shown that statistically meaningful information on the composition of individual columns can be obtained even if the difference in averaged atomic number Z is only 3. Using this method, quantitative mapping at atomic resolution using HAADF STEM images only has become possible without the need of simultaneously recorded electron energy loss spectra.

Abstract: Several different projection mechanisms that all make use of inelastically scattered electrons are used for electron tomography. The advantages and the disadvantages of these methods are compared to HAADFSTEM tomography, which is considered as the standard electron tomography technique in materials science. The different inelastic setups used are energy filtered transmission electron microscopy (EFTEM), thickness mapping based on the log-ratio method and bulk plasmon mapping. We present a comparison that can be used to select the best inelastic signal for tomography, depending on different parameters such as the beam stability and nature of the sample. The appropriate signal will obviously also depend on the exact information which is requested.

Abstract: Accurate segmentation of nanoparticles within various matrix materials is a difficult problem in electron tomography. Due to artifacts related to image series acquisition and reconstruction, global thresholding of reconstructions computed by established algorithms, such as weighted backprojection or SIRT, may result in unreliable and subjective segmentations. In this paper, we introduce the Partially Discrete Algebraic Reconstruction Technique (PDART) for computing accurate segmentations of dense nanoparticles of constant composition. The particles are segmented directly by the reconstruction algorithm, while the surrounding regions are reconstructed using continuously varying gray levels. As no properties are assumed for the other compositions of the sample, the technique can be applied to any sample where dense nanoparticles must be segmented, regardless of the surrounding compositions. For both experimental and simulated data, it is shown that PDART yields significantly more accurate segmentations than those obtained by optimal global thresholding of the SIRT reconstruction.

Abstract: The 3D reconstruction of a tilt series for electron tomography is mostly carried out using the weighted backprojection (WBP) algorithm or using one of the iterative algorithms such as the simultaneous iterative reconstruction technique (SIRT). However, it is known that these reconstruction algorithms cannot compensate for the missing wedge. Here, we apply a new reconstruction algorithm for electron tomography, which is based on compressive sensing. This is a field in image processing specialized in finding a sparse solution or a solution with a sparse gradient to a set of ill-posed linear equations. Therefore, it can be applied to electron tomography where the reconstructed objects often have a sparse gradient at the nanoscale. Using a combination of different simulated and experimental datasets, it is shown that missing wedge artefacts are reduced in the final reconstruction. Moreover, it seems that the reconstructed datasets have a higher fidelity and are easier to segment in comparison to reconstructions obtained by more conventional iterative algorithms.

Abstract: The intensity levels in a three-dimensional (3D) reconstruction, obtained by electron tomography, can be influenced by several experimental imperfections. Such artifacts will hamper a quantitative interpretation of the results. In this paper, we will correct for artificial intensity variations by determining the 3D point spread function (PSF) of a tomographic reconstruction based on high angle annular dark field scanning transmission electron microscopy. The large tails of the PSF cause an underestimation of the intensity of smaller particles, which in turn hampers an accurate radius estimate. Here, the error introduced by the PSF is quantified and corrected a posteriori.

Abstract: Gold nanoparticles are studied extensively due to their unique optical and catalytical properties. Their exact shape determines the properties and thereby the possible applications. Electron tomography is therefore often used to examine the three-dimensional (3D) shape of nanoparticles. However, since the acquisition of the experimental tilt series and the 3D reconstructions are very time consuming, it is difficult to obtain statistical results concerning the 3D shape of nanoparticles. Here, we propose a new approach for electron tomography that is based on artificial neural networks. The use of a new reconstruction approach enables us to reduce the number of projection images with a factor of 5 or more. The decrease in acquisition time of the tilt series and use of an efficient reconstruction algorithm allows us to examine a large amount of nanoparticles in order to retrieve statistical results concerning the 3D shape.

Abstract: We present the ASTRA Toolbox as an open platform for 3D image reconstruction in tomography. Most of the software tools that are currently used in electron tomography offer limited flexibility with respect to the geometrical parameters of the acquisition model and the algorithms used for reconstruction. The ASTRA Toolbox provides an extensive set of fast and flexible building blocks that can be used to develop advanced reconstruction algorithms, effectively removing these limitations. We demonstrate this flexibility, the resulting reconstruction quality, and the computational efficiency of this toolbox by a series of experiments, based on experimental dual-axis tilt series.

Abstract: Solution derived La2Zr2O7 films have drawn much attention for potential applications as thermal barriers or low-cost buffer layers for coated conductor technology. Annealing and coating parameters strongly affect the microstructure of La2Zr2O7, but different film processing methods can yield similar microstructural features such as nanovoids and nanometer-sized La2Zr2O7 grains. Nanoporosity is a typical feature found in such films and the implications for the functionality of the films are investigated by a combination of scanning transmission electron microscopy (STEM), electron energy-loss spectroscopy (EELS) and quantitative electron tomography. Chemical solution based La2Zr2O7 films deposited on flexible Ni5 at.%W substrates with a {100}lang001rang biaxial texture were prepared for an in-depth characterization. A sponge-like structure composed of nanometer-sized voids is revealed by high-angle annular dark-field scanning transmission electron microscopy in combination with electron tomography. A three-dimensional quantification of nanovoids in the La2Zr2O7 film is obtained on a local scale. Mostly non-interconnected highly faceted nanovoids compromise more than one-fifth of the investigated sample volume. The diffusion barrier efficiency of a 170 nm thick La2Zr2O7 film is investigated by STEM-EELS, yielding a 1.8 ± 0.2 nm oxide layer beyond which no significant nickel diffusion can be detected and intermixing is observed. This is of particular significance for the functionality of YBa2Cu3O7 − δ coated conductor architectures based on solution derived La2Zr2O7 films as diffusion barriers.

Abstract: Controlling the morphology of organolead halide perovskite crystals is crucial to a fundamental understanding of the materials and to tune their properties for device applications. Here, we report a facile solution-based method for morphology-controlled synthesis of rod-like and plate-like organolead halide perovskite nanocrystals using binary capping agents. The morphology control is likely due to an interplay between surface binding kinetics of the two capping agents at different crystal facets. By high-resolution scanning transmission electron microscopy, we show that the obtained nanocrystals are monocrystalline. Moreover, long photoluminescence decay times of the nanocrystals indicate long charge diffusion lengths and low trap/defect densities. Our results pave the way for large-scale solution synthesis of organolead halide perovskite nanocrystals with controlled morphology for future device applications.

Abstract: In this paper electrodeposition is used to obtain Cu nanoparticles, as it allows good control over particle size and distribution. These Cu particles were deposited onto a gas diffusion electrode which increased the resulting surface area. Prior to deposition, the surface was pre-treated with NaOH, HNO3, MQ and TX100 to investigate the influence on the electrodeposition of Cu on the gas diffusion electrode (GDE). When using HNO3, the smallest particles with the most homogeneous distribution and high particle roughness were obtained. Once the optimal substrate was determined, we further demonstrated that by altering the electrodeposition parameters, the particle size and density could be tuned. On the one hand, increasing the nucleation potential led to a higher particle density resulting in smaller particles because of an increased competition between particles. Finally, the Cu particle size increased when applying a greater growth charge and growth potential. This fundamental study thus opens up a path towards the synthesis of supported Cu materials with increased surface areas, which is interesting from a catalytic point of view. Larger surface areas are generally correlated with a better catalyst performance and thus higher product yields. This research can contributed in obtaining new insides into the deposition of metallic nanoparticles on rough surfaces. [GRAPHICS] .

Abstract: The electrochemical reduction of CO2 to multi-carbon products has attracted much attention because it provides an avenue to the synthesis of value-added carbon-based fuels and feedstocks using renewable electricity. Unfortunately, the efficiency of CO2 conversion to C-2 products remains below that necessary for its implementation at scale. Modifying the local electronic structure of copper with positive valence sites has been predicted to boost conversion to C-2 products. Here, we use boron to tune the ratio of Cu delta+ to Cu-0 active sites and improve both stability and C-2-product generation. Simulations show that the ability to tune the average oxidation state of copper enables control over CO adsorption and dimerization, and makes it possible to implement a preference for the electrosynthesis of C-2 products. We report experimentally a C-2 Faradaic efficiency of 79 +/- 2% on boron-doped copper catalysts and further show that boron doping leads to catalysts that are stable for in excess of similar to 40 hours while electrochemically reducing CO2 to multi-carbon hydrocarbons.