ICP-MS Assisted EDX Tomography: A Robust Method for Studying Electrolyte Penetration Phenomena in Gas Diffusion Electrodes Applied to CO2 Electrolysis.

A carbon paper-based gas diffusion electrode (GDE) is used with a bismuth(III) subcarbonate active catalyst phase for the electrochemical reduction of CO2 in a gas/electrolyte flow-by configuration electrolyser at high current density. It is demonstrated that in this configuration, the gas and catholyte phases recombine to form K2CO3/KHCO3 precipitates to an extent that after electrolyses, vast amount of K+ ions is found by EDX mapping in the entire GDE structure. The fact that the entirety of the GDE gets wetted during electrolysis should, however, not be interpreted as a sign of flooding of the catalyst layer, since electrolyte perspiring through the GDE can largely be removed with the outflow gas, and the efficiency of electrolysis (toward the selective production of formate) can thus be maintained high for several hours. For a full spatial scale quantitative monitoring of electrolyte penetration into the GDE, (relying on K+ ions as tracer) the method of inductively coupled plasma-mass spectrometry (ICP-MS) assisted energy dispersive X-ray (EDX) tomography is introduced. This new, cheap and robust tomography of non-uniform aspect ratio has a large planar span that comprises the entire GDE surface area and a submicrometer depth resolution, hence it can provide quantitative information about the amount and distribution of K+ remnants inside the GDE structure, in three dimensions.

Low-Dose Sparse-View HAADF-STEM-EDX Tomography of Nanocrystals Using Unsupervised Deep Learning.

High-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) can be acquired together with energy dispersive X-ray (EDX) spectroscopy to give complementary information on the nanoparticles being imaged. Recent deep learning approaches show potential for accurate 3D tomographic reconstruction for these applications, but a large number of high-quality electron micrographs are usually required for supervised training, which may be difficult to collect due to the damage on the particles from the electron beam. To overcome these limitations and enable tomographic reconstruction even in low-dose sparse-view conditions, here we present an unsupervised deep learning method for HAADF-STEM-EDX tomography. Specifically, to improve the EDX image quality from low-dose condition, a HAADF-constrained unsupervised denoising approach is proposed. Additionally, to enable extreme sparse-view tomographic reconstruction, an unsupervised view enrichment scheme is proposed in the projection domain. Extensive experiments with different types of quantum dots show that the proposed method offers a high-quality reconstruction even with only three projection views recorded under low-dose conditions.

Gradient-based and wavelet-based compressed sensing approaches for highly undersampled tomographic datasets.

Electron tomography is widely employed for the 3D morphological characterization at the nanoscale. In recent years, there has been a growing interest in analytical electron tomography (AET) as it is capable of providing 3D information about the elemental composition, chemical bonding and optical/electronic properties of nanomaterials. AET requires advanced reconstruction algorithms as the datasets often consist of a very limited number of projections. Total variation (TV)-based compressed sensing approaches were shown to provide high-quality reconstructions from undersampled datasets, but staircasing artefacts can appear when the assumption about piecewise constancy does not hold. In this paper, we compare higher-order TV and wavelet-based approaches for AET applications and provide an open-source Python toolbox, Pyetomo, containing 2D and 3D implementations of both methods. A highly sampled STEM-HAADF dataset of an Er-doped porous Si sample and a heavily undersampled STEM-EELS dataset of a Ge-rich GeSbTe (GST) thin film annealed at 450°C are used to evaluate the performance of the different approaches. We show that polynomial annihilation with order 3 (HOTV3) and the Bior4.4 wavelet outperform the classical TV minimization and the related Haar wavelet.

Hardness and microstructural variation of Al-Mg-Mn-Sc-Zr alloy.

Variations of Vickers hardness were observed in Al-Mg-Mn alloy and Al-Mg-Mn-Sc-Zr alloy at different ageing times, ranging from a peak value of 81.2 HV at 54 ks down to 67.4 HV at 360 ks, below the initial hardness value, 71.8 HV at 0 ks for the case of Al-Mg-Mn-Sc-Zr alloy. Microstructures of samples at each ageing stage were examined carefully by transmission electron microscopes (TEMs) both in two-dimensions and three-dimensions. The presence of different types, densities, and sizes of particles were observed dispersed spherical Al3Sc1-xZrx and also block-shaped Al3Sc precipitates growing along <100>Al with facets {100} and {110} of the precipitates. TEM analysis both in two-dimensions and three-dimensions, performed on various samples, confirmed the direct correlation between the hardness and the density of Al3Sc.

The Dark Side of EDX Tomography: Modeling Detector Shadowing to Aid 3D Elemental Signal Analysis.

A simple model is proposed to account for the loss of collected X-ray signal by the shadowing of X-ray detectors in the scanning transmission electron microscope. The model is intended to aid the analysis of three-dimensional elemental data sets acquired using energy-dispersive X-ray tomography methods where shadow-free specimen holders are unsuitable or unavailable. The model also provides a useful measure of the detection system geometry.