Recent developments in X-ray diffraction/scattering computed tomography for materials science

X-ray diffraction/scattering computed tomography (XDS-CT) methods are a non-destructive class of chemical imaging techniques that have the capacity to provide reconstructions of sample cross-sections with spatially resolved chemical information. While X-ray diffraction CT (XRD-CT) is the most well-established method, recent advances in instrumentation and data reconstruction have seen greater use of related techniques like small angle X-ray scattering CT and pair distribution function CT. Additionally, the adoption of machine learning techniques for tomographic reconstruction and data analysis are fundamentally disrupting how XDS-CT data is processed. The following narrative review highlights recent developments and applications of XDS-CT with a focus on studies in the last five years. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 2)'

Removing multiple outliers and single-crystal artefacts from X-ray diffraction computed tomography data

This paper reports a simple but effective filtering approach to deal with single-crystal artefacts in X-ray diffraction computed tomography (XRD-CT). In XRD-CT, large crystallites can produce spots on top of the powder diffraction rings, which, after azimuthal integration and tomographic reconstruction, lead to line/streak artefacts in the tomograms. In the simple approach presented here, the polar transform is taken of collected two-dimensional diffraction patterns followed by directional median/mean filtering prior to integration. Reconstruction of one-dimensional diffraction projection data sets treated in such a way leads to a very significant improvement in reconstructed image quality for systems that exhibit powder spottiness arising from large crystallites. This approach is not computationally heavy which is an important consideration with big data sets such as is the case with XRD-CT. The method should have application to two-dimensional X-ray diffraction data in general where such spottiness arises

Interlaced X-ray diffraction computed tomography

An X-ray diffraction computed tomography data-collection strategy that allows, post experiment, a choice between temporal and spatial resolution is reported. This strategy enables time-resolved studies on comparatively short timescales, or alternatively allows for improved spatial resolution if the system under study, or components within it, appear to be unchanging. The application of the method for studying an Mn–Na–W/SiO2 fixed-bed reactor in situ is demonstrated. Additionally, the opportunities to improve the data-collection strategy further, enabling post-collection tuning between statistical, temporal and spatial resolutions, are discussed. In principle, the interlaced scanning approach can also be applied to other pencil-beam tomographic techniques, like X-ray fluorescence computed tomography, X-ray absorption fine structure computed tomography, pair distribution function computed tomography and tomographic scanning transmission X-ray microscopy

X-ray physico-chemical imaging during activation of cobalt-based Fischer-Tropsch synthesis catalysts

The imaging of catalysts and other functional materials under reaction conditions has advanced significantly in recent years. The combination of the computed tomography (CT) approach with methods such as X-ray diffraction (XRD), X-ray fluorescence (XRF) and X-ray absorption near-edge spectroscopy (XANES) now enables local chemical and physical state information to be extracted from within the interiors of intact materials which are, by accident or design, inhomogeneous. In this work, we follow the phase evolution during the initial reduction step(s) to form Co metal, for Co-containing particles employed as Fischer–Tropsch synthesis (FTS) catalysts; firstly, working at small length scales (approx. micrometre spatial resolution), a combination of sample size and density allows for transmission of comparatively low energy signals enabling the recording of ‘multimodal’ tomography, i.e. simultaneous XRF–CT, XANES–CT and XRD–CT. Subsequently, we show high-energy XRD–CT can be employed to reveal extent of reduction and uniformity of crystallite size on millimetre-sized TiO2 trilobes. In both studies, the CoO phase is seen to persist or else evolve under particular operating conditions and we speculate as to why this is observed

The Detection of Monoclinic Zirconia and Non-Uniform 3D Crystallographic Strain in a Re-Oxidized Ni-YSZ Solid Oxide Fuel Cell Anode

The solid oxide fuel cell (SOFC) anode is often composed of nickel (Ni) and yttria-stabilized zirconia (YSZ). The yttria is added in small quantities (e.g., 8 mol %) to maintain the crystallographic structure throughout the operating temperatures (e.g., room-temperature to >800 °C). The YSZ skeleton provides a constraining structural support that inhibits degradation mechanisms such as Ni agglomeration and thermal expansion miss-match between the anode and electrolyte layers. Within this structure, the Ni is deposited in the oxide form and then reduced during start-up; however, exposure to oxygen (e.g., during gasket failure) readily re-oxidizes the Ni back to NiO, impeding electrochemical performance and introducing complex structural stresses. In this work, we correlate lab-based X-ray computed tomography using zone plate focusing optics, with X-ray synchrotron diffraction computed tomography to explore the crystal structure of a partially re-oxidized Ni/NiO-YSZ electrode. These state-of-the-art techniques expose several novel findings: non-isotropic YSZ lattice distributions; the presence of monoclinic zirconia around the oxidation boundary; and metallic strain complications in the presence of variable yttria content. This work provides evidence that the reduction–oxidation processes may destabilize the YSZ structure, producing monoclinic zirconia and microscopic YSZ strain, which has implications upon the electrode’s mechanical integrity and thus lifetime of the SOFC

Multi-length scale 5D diffraction imaging of Ni-Pd/CeO2-ZrO2/Al2O3 catalyst during partial oxidation of methane

A 5D diffraction imaging experiment (with 3D spatial, 1D time/imposed operating conditions and 1D scattering signal) was performed with a Ni–Pd/CeO2–ZrO2/Al2O3 catalyst. The catalyst was investigated during both activation and partial oxidation of methane (POX). The spatio-temporal resolved diffraction data allowed us to obtain unprecedented insight into the behaviour and fate of the various metal and metal oxide species and how this is affected by the heterogeneity across catalyst particles. We show firstly, how Pd promotion although facilitating Ni reduction, over time leads to formation of unstable Ni–Pd metallic alloy, rendering the impact of Pd beyond the initial reduction less important. Furthermore, in the core of the particles, where the metallic Ni is primarily supported on Al2O3, poor resistance towards coke deposition was observed. We identified that this preceded via the formation of an active yet metastable interstitial solid solution of Ni–C and led to the exclusive formation of graphitic carbon, the only polymorph of coke observed. In contrast, at the outermost part of the catalyst particle, where Ni is predominantly supported on CeO2–ZrO2, the graphite formation was mitigated but sintering of Ni crystallites was more severe

A multi-scale study of 3D printed Co-Al2O3 catalyst monoliths versus spheres

This study demonstrates the characteristics of two model packing configurations: 3D printed (3DP) catalyst monoliths on the one hand, and their conventional counterparts, packed beds of spheres, on the other. Cobalt deposited on alumina is selected as a convenient model system for this work, due to its wide spread use in many catalytic reactions. 3DP constructs were produced from alumina powder impregnated with cobalt nitrate while the alumina spheres were directly impregnated with the same cobalt nitrate precursor. The form of the catalyst, the impregnation process, as well as the thermal history, were found to have a significant effect on the resulting cobalt phases. Probing the catalyst bodies in situ by XRD-CT indicated that the level of dispersion of identified Co phases (Co3O4 reduced to CoO) across the support is maintained under reduction conditions. The packed bed of spheres exhibits a non-uniform distribution of cobalt phases, including a core-shell morphology with an average crystallite size of 10–14 nm across the sphere, while the 3DP monolith exhibits a uniform distribution of cobalt phases with an average crystallite size of 5–12 nm upon reduction from Co3O4 to CoO. Computational Fluid Dynamics (CFD) modelling was carried out to develop digital twins and assess the effect of the geometry of both configurations on the pressure drop and velocity profiles. Finally, the activity of both Cobalt-based catalyst geometries was assessed in terms of their conversion, selectivity and turn over frequencies under model multiphase (selective oxidation) reaction conditions, which showed that the desired 3D printed monolithic geometries can offer distinct advantages to the reactor design

A deep convolutional neural network for real-time full profile analysis of big powder diffraction data

We present Parameter Quantification Network (PQ-Net), a regression deep convolutional neural network providing quantitative analysis of powder X-ray diffraction patterns from multi-phase systems. The network is tested against simulated and experimental datasets of increasing complexity with the last one being an X-ray diffraction computed tomography dataset of a multi-phase Ni-Pd/CeO2-ZrO2/Al2O3 catalytic material system consisting of ca. 20,000 diffraction patterns. It is shown that the network predicts accurate scale factor, lattice parameter and crystallite size maps for all phases, which are comparable to those obtained through full profile analysis using the Rietveld method, also providing a reliable uncertainty measure on the results. The main advantage of PQ-Net is its ability to yield these results orders of magnitude faster showing its potential as a tool for real-time diffraction data analysis during in situ/operando experiments

Multi‐scale studies of 3d printed Mn–Na–W/SiO2 catalyst for oxidative coupling of methane

This work presents multi-scale approaches to investigate 3D printed structured Mn–Na–W/SiO_{2} atalysts used for the oxidative coupling of methane (OCM) reaction. The performance of the 3D printed catalysts has been compared to their conventional analogues, packed beds of pellets and powder. The physicochemical properties of the 3D printed catalysts were investigated using scanning electron microscopy, nitrogen adsorption and X-ray diffraction (XRD). Performance and durability tests of the 3D printed catalysts were conducted in the laboratory and in a miniplant under real reaction conditions. In addition, synchrotron-based X-ray diffraction computed tomography technique (XRD-CT) was employed to obtain cross sectional maps at three different positions selected within the 3D printed catalyst body during the OCM reaction. The maps revealed the evolution of catalyst active phases and silica support on spatial and temporal scales within the interiors of the 3D printed catalyst under operating conditions. These results were accompanied with SEM-EDS analysis that indicated a homogeneous distribution of the active catalyst particles across the silica support

A scalable neural network architecture for self-supervised tomographic image reconstruction

We present a lightweight and scalable artificial neural network architecture which is used to reconstruct a tomographic image from a given sinogram. A self-supervised learning approach is used where the network iteratively generates an image that is then converted into a sinogram using the Radon transform; this new sinogram is then compared with the sinogram from the experimental dataset using a combined mean absolute error and structural similarity index measure loss function to update the weights of the network accordingly. We demonstrate that the network is able to reconstruct images that are larger than 1024 × 1024. Furthermore, it is shown that the new network is able to reconstruct images of higher quality than conventional reconstruction algorithms, such as the filtered back projection and iterative algorithms (SART, SIRT, CGLS), when sinograms with angular undersampling are used. The network is tested with simulated data as well as experimental synchrotron X-ray micro-tomography and X-ray diffraction computed tomography data