Abstract: Nanoscience and nanotechnologies are of immense importance across many fields of science and for numerous practical applications. In this context, scanning transmission electron microscopy (STEM) and 4D-STEM are among the most powerful characterization methods at the atomic scale. Annular dark-field (ADF)-STEM can be used to quantify atomic structures in 3D by counting atoms based on a single projection image. In 4D-STEM a full diffraction pattern is recorded at each scan step, which enables more dose efficient imaging and the utilization of various advanced imaging modalities, which can however be complex and slow. Both, STEM and 4D-STEM suffer from noise and distortions. In the first section of this work the most important of these distortions are discussed and it is shown how image restoration with a dedicated convolutional neural network (CNN) can be beneficial for atomic structure quantifications in ADF-STEM. In the second part, a new 4D-STEM imaging method real-time-integrated-centre-of-mass (riCOM) is introduced, which is a very dose-efficient and fast algorithm that enables unprecedented live-imaging capabilities for 4D-STEM. It is based on the integrated centre-of-mass approach, but is reformulated with variable integration ranges and optional filters, which allows for a tunable contrast transfer function. This enables the imaging of light and heavy elements simultaneously at very low doses. In the third part another new 4D-STEM method, coined AIRPI (AI-assisted rapid phase imaging) is introduced, which uses a CNN to retrieve a patch of the specimen's phase image for each scan position, based on the diffraction patterns in the probe's immediate surroundings. This allows also live imaging in principle and surpasses comparable state-of-the-art algorithms in terms of resolution also at low doses. Different atomic columns can be reliably distinguished over a wide range of atomic numbers, enabling a very good image interpretability. Further, AIRPI can recover low frequency image components, which preserves thickness information. This is a unique and important feature which could make quantitative 4D-STEM feasible.

Abstract: Numerical simulations of flow and pollutant dispersion in urban environments have the potential to support design and policy decisions that could reduce the population's exposure to air pollution. Reynolds-averaged Navier-Stokes simulations are a common modeling technique for urban flow and dispersion, but several sources of uncertainty in the simulations can affect the accuracy of the results. The present study proposes a method to quantify the uncertainty related to variability in the inflow boundary conditions. The method is applied to predict flow and pollutant dispersion in downtown Oklahoma City and the results are compared to field measurements available from the Joint Urban 2003 measurement campaign. Three uncertain parameters that define the inflow profiles for velocity, turbulence kinetic energy and turbulence dissipation are defined: the velocity magnitude and direction, and the terrain roughness length. The uncertain parameter space is defined based on the available measurement data, and a non-intrusive propagation approach that employs 729 simulations is used to quantify the uncertainty in the simulation output. A variance based sensitivity analysis is performed to identify the most influential uncertain parameters, and it is shown that the predicted tracer concentrations are influenced by all three uncertain variables. Subsequently, we specify different probability distributions for the uncertain inflow variables based on the available measurement data and calculate the corresponding means and 95% confidence intervals for comparison with the field measurements at 35 locations in downtown Oklahoma City. (C) 2017 Elsevier Ltd. All rights reserved.

Abstract: The size, shape and distribution of Ni4Ti3 precipitates in Ni51Ti49 single crystals annealed under stress-free and 〈1 1 1〉B2 compressive conditions are studied via focused ion beam/scanning electron microscopy slice-and-view. The precipitates in the stress-free material grow in autocatalytic pockets with larger size, lower number density, flatter shape and larger inter-particle distance than in the compressed material. Nevertheless, a new quantification method called water penetration reveals that, due to the precipitate alignment, martensite can grow more easily in the compressed material perpendicular to the compression direction.

Abstract: Morphological characterization of nanoparticle assemblies and hybrid nanomaterials is critical in determining their structure-property relationships as well as in the development of structures with desired properties. Electron tomography has become a widely utilized technique for the three-dimensional characterization of nanoparticle assemblies. However, the extraction of quantitative morphological parameters from the reconstructed volume can be a complex and labor-intensive task. In this study, we aim to overcome this challenge by automating the volumetric segmentation process applied to three-dimensional reconstructions of nanoparticle assemblies. The key to enabling automated characterization is to assess the performance of different volumetric segmentation methods in accurately extracting predefined quantitative descriptors for morphological characterization. In our methodology, we compare the quantitative descriptors obtained through manual segmentation with those obtained through automated segmentation methods, to evaluate their accuracy and effectiveness. To show generality, our study focuses on the characterization of assemblies of CdSe/CdS quantum dots, gold nanospheres and CdSe/CdS encapsulated in polymeric micelles, and silica-coated gold nanorods decorated with both CdSe/CdS or PbS quantum dots. We use two unsupervised segmentation algorithms: the watershed transform and the spherical Hough transform. Our results demonstrate that the choice of automated segmentation method is crucial for accurately extracting the predefined quantitative descriptors. Specifically, the spherical Hough transform exhibits superior performance in accurately extracting quantitative descriptors, such as particle size and interparticle distance, thereby allowing for an objective, efficient, and reliable volumetric segmentation of complex nanoparticle assemblies.

Abstract: In contrast to perfectly periodic crystals, materials with short-range order produce diffraction patterns that contain both Bragg reflections and diffuse scattering. To understand the influence of short-range order on material properties, current research focuses increasingly on the analysis of diffuse scattering. This article verifies the possibility to refine the short-range order parameters in submicrometre-sized crystals from diffuse scattering in single-crystal electron diffraction data. The approach was demonstrated on Li_1.2Ni_0.13Mn_0.54Co_0.13O₂, which is a state-of-the-art cathode material for lithium-ion batteries. The intensity distribution of the 1D diffuse scattering in the electron diffraction patterns of Li_1.2Ni_0.13Mn_0.54Co_0.13O₂depends on the number of stacking faults and twins in the crystal. A model of the disorder in Li_1.2Ni_0.13Mn_0.54Co_0.13O₂was developed and both the stacking fault probability and the percentage of the different twins in the crystal were refined using an evolutionary algorithm in<italic>DISCUS</italic>. The approach was applied on reciprocal space sections reconstructed from 3D electron diffraction data since they exhibit less dynamical effects compared with in-zone electron diffraction patterns. A good agreement was achieved between the calculated and the experimental intensity distribution of the diffuse scattering. The short-range order parameters in submicrometre-sized crystals can thus successfully be refined from the diffuse scattering in single-crystal electron diffraction data using an evolutionary algorithm in<italic>DISCUS</italic>.

Abstract: Quantitative atomic resolution annular dark field scanning transmission electron microscopy (ADF STEM) has become a powerful technique for nanoparticle atom-counting. However, a lot of nanoparticles provide a severe characterisation challenge because of their limited size and beam sensitivity. Therefore, quantitative ADF STEM may greatly benefit from statistical detection theory in order to optimise the instrumental microscope settings such that the incoming electron dose can be kept as low as possible whilst still retaining single-atom precision. The principles of detection theory are used to quantify the probability of error for atom-counting. This enables us to decide between different image performance measures and to optimise the experimental detector settings for atom-counting in ADF STEM in an objective manner. To demonstrate this, ADF STEM imaging of an industrial catalyst has been conducted using the near-optimal detector settings. For this experiment, we discussed the limits for atomcounting diagnosed by combining a thorough statistical method and detailed image simulations.

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: High angle annular dark field scanning transmission electron microscopy (HAADF STEM) images provide sample information which is sensitive to the chemical composition. The image intensities indeed scale with the mean atomic number Z. To some extent, chemically different atomic column types can therefore be visually distinguished. However, in order to quantify the atomic column composition with high accuracy and precision, model-based methods are necessary. Therefore, an empirical incoherent parametric imaging model can be used of which the unknown parameters are determined using statistical parameter estimation theory (Van Aert et al., 2009, [1]). In this paper, it will be shown how this method can be combined with frozen lattice multislice simulations in order to evolve from a relative toward an absolute quantification of the composition of single atomic columns with mixed atom types. Furthermore, the validity of the model assumptions are explored and discussed.

Abstract: The microstructural and compositional evolution during initial annealing of a superconducting (Bi,Pb)(2)Sr2Ca2Cu3O10+delta/Ag tape is studied using quantitative transmission electron microscopy. Special attention is devoted to the occurrence of Pb-rich liquids, which are crucial for the Bi2Sr2CaCu2O8+delta to (Bi,Pb)(2)Sr2Ca2Cu3O10+delta transformation. Ca and/or Pb-rich (Bi,Pb)(2)Sr2CaCu2O8+delta grains dissolve into a liquid, which reacts with Ca-rich phases to increase the liquid's Ca-content. This leads to (Bi,Pb)(2)Sr2Ca2Cu3O10+delta formation. Apparently, a Ca/Sr ratio of around I is sufficient to keep (Bi,Pb)(2)Sr2Ca2Cu3O10+delta nucleation going. It is confirmed that Ag particles are transported from the Ag-sheath into the oxide core by the liquid and not by mechanical treatment of the tape.