Abstract: Electron tomography is a widely used technique for 3D structural analysis of nanomaterials, but it can cause damage to samples due to high electron doses and long exposure times. To minimize such damage, researchers often reduce beam exposure by acquiring fewer projections through tilt undersampling. However, this approach can also introduce reconstruction artifacts due to insufficient sampling. Therefore, it is important to determine the optimal number of projections that minimizes both beam exposure and undersampling artifacts for accurate reconstructions of beam-sensitive samples. Current methods for determining this optimal number of projections involve acquiring and post-processing multiple reconstructions with different numbers of projections, which can be time-consuming and requires multiple samples due to sample damage. To improve this process, we propose a protocol that combines golden ratio scanning and quasi-3D reconstruction to estimate the optimal number of projections in real-time during a single acquisition. This protocol was validated using simulated and realistic nanoparticles, and was successfully applied to reconstruct two beam-sensitive metal–organic framework complexes.

Abstract: Advances in multi-spectral detectors are causing a paradigm shift in x-ray computed tomography (CT). Spectral information acquired from these detectors can be used to extract volumetric material composition maps of the object of interest. If the materials and their spectral responses are known a priori, the image reconstruction step is rather straightforward. If they are not known, however, the maps as well as the responses need to be estimated jointly. A conventional workflow in spectral CT involves performing volume reconstruction followed by material decomposition, or vice versa. However, these methods inherently suffer from the ill-posedness of the joint reconstruction problem. To resolve this issue, we propose 'A Dictionary-based Joint reconstruction and Unmixing method for Spectral Tomography' (ADJUST). Our formulation relies on forming a dictionary of spectral signatures of materials common in CT and prior knowledge of the number of materials present in an object. In particular, we decompose the spectral volume linearly in terms of spatial material maps, a spectral dictionary, and the indicator of materials for the dictionary elements. We propose a memory-efficient accelerated alternating proximal gradient method to find an approximate solution to the resulting bi-convex problem. From numerical demonstrations on several synthetic phantoms, we observe that ADJUST performs exceedingly well compared to other state-of-the-art methods. Additionally, we address the robustness of ADJUST against limited and noisy measurement patterns. The demonstration of the proposed approach on a spectral micro-CT dataset shows its potential for real-world applications. Code is available at https://github.com/mzeegers/ADJUST.

Abstract: A detailed 3D investigation of nanoparticles at a local scale is of great importance to connect their structure and composition to their properties. Electron tomography has therefore become an important tool for the 3D characterization of nanomaterials. 3D investigations typically comprise multiple steps, including acquisition, reconstruction, and analysis/quantification. Usually, the latter two steps are performed offline, at a dedicated workstation. This sequential workflow prevents on-the-fly control of experimental parameters to improve the quality of the 3D reconstruction, to select a relevant nanoparticle for further characterization or to steer an in-situ tomography experiment. Here, we present an efficient approach to overcome these limitations, based on the real-time reconstruction of arbitrary 2D reconstructed slices through a 3D object. Implementation of this method may lead to generalized implementation of electron tomography for routine nanoparticle characterization in 3D.

Abstract: Anatase nanosheets with exposed {001} facets
have gained increasing interest for photocatalytic applications. To
fully understand the structure-to-activity relation, combined
experimental and computational methods have been exploited.
Anatase nanosheets were prepared under hydrothermal conditions
in the presence of fluorine ions. High resolution scanning
transmission electron microscopy was used to fully characterize
the synthesized material, confirming the TiO2 nanosheet
morphology. Moreover, the surface structure and composition
of a single nanosheet could be determined by annular bright-field
scanning transmission electron microscopy (ABF-STEM) and
STEM electron energy loss spectroscopy (STEM-EELS). The photocatalytic activity was tested for the decomposition of organic
dyes rhodamine 6G and methyl orange and compared to a reference TiO2 anatase sample. The anatase nanosheets with exposed
{001} facets revealed a significantly lower photocatalytic activity compared to the reference. In order to understand the
mechanism for the catalytic performance, and to investigate the role of the presence of F−, light-induced electron paramagnetic
resonance (EPR) experiments were performed. The EPR results are in agreement with TEM, proving the presence of Ti3+
species close to the surface of the sample and allowing the analysis of the photoinduced formation of paramagnetic species.
Further, ab initio calculations of the anisotropic effective mass of electrons and electron holes in anatase show a very high effective
mass of electrons in the [001] direction, having a negative impact on the mobility of electrons toward the {001} surface and thus
the photocatalysis. Finally, motivated by the experimental results that indicate the presence of fluorine atoms at the surface, we
performed ab initio calculations to determine the position of the band edges in anatase slabs with different terminations of the
{001} surface. The presence of fluorine atoms near the surface is shown to strongly shift down the band edges, which indicates
another reason why it can be expected that the prepared samples with a large amount of {001} surface, but with fluorine atoms
near the surface, show only a low photocatalytic activity.

Abstract: Electron tomography is an essential imaging technique for the investigation of morphology and 3D structure of nanomaterials. This method, however, suffers from well-known missing wedge artifacts due to a restricted tilt range, which limits the objectiveness, repeatability and efficiency of quantitative structural analysis. Discrete tomography represents one of the promising reconstruction techniques for materials science, potentially capable of delivering higher fidelity reconstructions by exploiting the prior knowledge of the limited number of material compositions in a specimen. However, the application of discrete tomography to practical datasets remains a difficult task due to the underlying challenging mathematical problem. In practice, it is often hard to obtain consistent reconstructions from experimental datasets. In addition, numerous parameters need to be tuned manually, which can lead to bias and non-repeatability. In this paper, we present the application of a new
iterative reconstruction technique, named TVR-DART, for discrete electron tomography. The technique is capable of consistently delivering reconstructions with significantly reduced missing wedge artifacts for a variety of challenging data and imaging conditions, and can automatically estimate its key parameters. We describe the principles of the technique and apply it to datasets from three different types of samples acquired under diverse imaging modes. By further reducing the available tilt range and number of projections, we show that the
proposed technique can still produce consistent reconstructions with minimized missing wedge artifacts. This new development promises to provide the electron microscopy community with an easy-to-use and robust tool for high-fidelity 3D characterization of nanomaterials.

Abstract: Electron tomography is a powerful technique for the 3D characterization of the morphology of nanostructures. Nevertheless, resolving the chemical composition of complex nanostructures in 3D remains challenging and the number of studies in which electron energy loss spectroscopy (EELS) is combined with tomography is limited. During the last decade, dedicated reconstruction algorithms have been developed for HAADF-STEM tomography using prior knowledge about the investigated sample. Here, we will use the prior knowledge that the experimental spectrum of each reconstructed voxel is a linear combination of a well-known set of references spectra in a so-called direct spectroscopic tomography technique. Based on a simulation experiment, it is shown that this technique provides superior results in comparison to conventional reconstruction methods for spectroscopic data, especially for spectrum images containing a relatively low signal to noise ratio. Next, this technique is used to investigate the spatial distribution of Fe dopants in Fe:Ceria nanoparticles in 3D. It is shown that the presence of the Fe2+ dopants is correlated with a reduction of the Ce atoms from Ce4+ towards Ce3+. In addition, it is demonstrated that most of the Fe dopants are located near the voids inside the nanoparticle.

Abstract: The three-dimensional (3D) atomic structure of nanomaterials, including strain, is crucial to understand their properties. Here, we investigate lattice strain in Au nanodecahedra using electron tomography. Although different electron tomography techniques enabled 3D characterizations of nanostructures at the atomic level, a reliable determination of lattice strain is not straightforward. We therefore propose a novel model-based approach from which atomic coordinates are measured. Our findings demonstrate the importance of investigating lattice strain in 3D.

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: In electron tomography, the fidelity of the 3D reconstruction strongly depends on the employed reconstruction algorithm. In this paper, the properties of SIRT, TVM and DART reconstructions are studied with respect to having only a limited number of electrons available for imaging and applying different angular sampling schemes. A well-defined realistic model is generated, which consists of tubular domains within a matrix having slab-geometry. Subsequently, the electron tomography workflow is simulated from calculated tilt-series over experimental effects to reconstruction. In comparison with the model, the fidelity of each reconstruction method is evaluated qualitatively and quantitatively based on global and local edge profiles and resolvable distance between particles. Results show that the performance of all reconstruction methods declines with the total electron dose. Overall, SIRT algorithm is the most stable method and insensitive to changes in angular sampling. TVM algorithm yields significantly sharper edges in the reconstruction, but the edge positions are strongly influenced by the tilt scheme and the tubular objects become thinned. The DART algorithm markedly suppresses the elongation artifacts along the beam direction and moreover segments the reconstruction which can be considered a significant advantage for quantification. Finally, no advantage of TVM and DART to deal better with fewer projections was observed.

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: 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: Quantitative electron tomography is proposed to characterize porous materials at a nanoscale. To achieve reliable three-dimensional (3D) quantitative information, the influence of missing wedge artifacts and segmentation methods is investigated. We are presenting the Discrete Algebraic Reconstruction Algorithm as the most adequate tomography method to measure porosity at the nanoscale. It provides accurate 3D quantitative information, regardless the presence of a missing wedge. As an example, we applied our approach to nanovoids in La2Zr2O7 thin films.

Abstract: Nano-sized ZnO particles with a narrow size distribution and high crystallinity were prepared from aqueous solutions with high concentrations of Zn2+ containing salts and citric acid in a conventional spray pyrolysis setup. Structure, morphology and size of the produced material were compared to ZnO material produced by simple spray pyrolysis of zinc nitrates in the same experimental setup. Using transmission electron microscopy and electron tomography it has been shown that citric acid-assisted spray pyrolysed material is made up of micron sized secondary particles comprising a shell of lightly agglomerated, monocrystalline primary ZnO nanoparticles with sizes in the 2030 nm range, separable by a simple ultrasonic treatment step.