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

Chemical imaging of Fischer-Tropsch catalysts under operating conditions

Although we often understand empirically what constitutes an active catalyst, there is still much to be understood fundamentally about how catalytic performance is influenced by formulation. Catalysts are often designed to have a microstructure and nanostructure that can influence performance but that is rarely considered when correlating structure with function. Fischer-Tropsch synthesis (FTS) is a well-known and potentially sustainable technology for converting synthetic natural gas (“syngas”: CO + H2) into functional hydrocarbons, such as sulfur- and aromatic-free fuel and high-value wax products. FTS catalysts typically contain Co or Fe nanoparticles, which are often optimized in terms of size/composition for a particular catalytic performance. We use a novel, “multimodal” tomographic approach to studying active Co-based catalysts under operando conditions, revealing how a simple parameter, such as the order of addition of metal precursors and promoters, affects the spatial distribution of the elements as well as their physicochemical properties, that is, crystalline phase and crystallite size during catalyst activation and operation. We show in particular how the order of addition affects the crystallinity of the TiO2 anatase phase, which in turn leads to the formation of highly intergrown cubic close-packed/hexagonal close-packed Co nanoparticles that are very reactive, exhibiting high CO conversion. This work highlights the importance of operando microtomography to understand the evolution of chemical species and their spatial distribution before any concrete understanding of impact on catalytic performance can be realized

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

In situ X-ray Diffraction Computed Tomography studies examining the thermal and chemical stabilities of working Ba0.5Sr0.5Co0.8Fe0.2O3-δ membranes during oxidative coupling of methane

In this study we present the results from two in situ X-ray diffraction computed tomography experiments of catalytic membrane reactors (CMRs) using Ba0.5Sr0.5Co0.8Fe0.2O3−δ (BSCF) hollow fibre membranes and Na-Mn-W/SiO2 catalyst during the oxidative coupling of methane (OCM) reaction. The negative impact of CO2, when added to the inlet gas stream, is seen to be mainly related to the C2+ yield, while no evidence of carbonate phase(s) formation is found during the OCM experiments. The main degradation mechanism of the CMR is suggested to be primarily associated with the solid-state evolution of the BSCF phase rather than the presence of CO2. Specifically, in situ XRD-CT and post-mortem SEM/EDX measurements revealed a collapse of the cubic BSCF phase, formation of secondary phases, which include needle-like structures and hexagonal Ba6Co4O12, and formation of a BaWO4 layer, the latter being a result of chemical interaction between the membrane and catalyst materials at high temperatures

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

3D printed SrNbO2N photocatalyst for degradation of organic pollutants in water

Organic pollutants in water are a major concern for the environment and human health, and require urgent attention. Here, we developed for the first time monolithic structures by 3D printing of perovskite metal oxynitride, SrNbO2N, for photocatalytic degradation of organic pollutant in water. Advanced, synchrotron-based XRD-CT measurements were employed to gain structural insight into photocatalyst formulation and assess the fidelity of design in terms of both the chemical and physical form of the photocatalysts to be imaged. Our 3D printed material showed excellent photocatalytic activity, degrading 100% of methylene blue (MB) as well as good stability for three cycle operations. This is due to high adsorption of the 3D printed oxynitride towards MB which enhanced its photoredox reactivity. It is also evident from the excellent charge transfer demonstrating a charge transfer rate of (1.5 ± 0.2) × 108 s−1. We performed Time-Dependent Density Functional Theory (TD-DFT) calculations to understand the photocatalyst structure and degradation pathways. Our calculated band gap (at Γ) of 1.88 eV is in good agreement with the experimental values. We found that the highest valence bands were contributed by N p orbitals and the lowest conduction bands corresponded to Nb d orbitals offering avenues for fine-tuning the band gap. Hence, the ability to tailor photocatalyst monoliths by 3D printing renders their water treatment application more facile compared to their powder suspension counterparts

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