This work investigates 3D-printed monometallic (Ni) and bimetallic (NiCo) electrocatalysts fabricated via Direct Ink Writing (DIW) and systematically evaluates their performance in a model electrochemical oxidation reaction, in both batch and flow configurations. The selected reaction, electrochemical conversion of biomass-derived 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA), the building block of bio-based plastics, is an attractive process that leverages renewable electrons and carbon resources. The process offers the potential to increase energy and cost efficiency by combining mild reaction conditions with high activity. Despite extensive research on nickel-based catalysts, the use of 3D-printed bimetallic electrodes for this type of reactions has never been reported. Chronoamperometric experiments performed at 1.8-2.2 V with 50-200 mM HMF demonstrated that electrodes prepared from fine powders (1.6-7 mu m) exhibit superior activity compared to those based on the coarse powders (44 mu m), while the electrode composed of NiCo powders in [50:50] weight percentage ratio delivered the best overall performance among the bimetallic systems. When implemented in a parallel-plate flow reactor, both the electrodes composed of fine Ni and Co powders in [75:25] ratio and the electrode composed of coarse NiCo powders in [50:50] ratio achieved complete HMF conversion and a 95% FDCA yield within 60 min, outperforming the batch configuration. Alongside SEM and EDS characterization, XRD-CT slices were collected at different heights throughout the electrode providing further evidence on microstructure and phase distribution before and after HMF oxidation.

nDTomo is a Python-based software suite for the simulation, reconstruction and analysis of X-ray chemical imaging and computed tomography data. It provides a collection of Python function-based tools designed for accessibility and education as well as a graphical user interface. Prioritising transparency and ease of learning, nDTomo adopts a function-centric design that facilitates straightforward understanding and extension of core workflows, from phantom generation and pencil-beam tomography simulation to sinogram correction, tomographic reconstruction and peak fitting. While many scientific toolkits embrace object-oriented design for modularity and scalability, nDTomo instead emphasises pedagogical clarity, making it especially suitable for students and researchers entering the chemical imaging and tomography field. The suite also includes modern deep learning tools, such as a self-supervised neural network for peak analysis (PeakFitCNN) and a GPU-based direct least squares reconstruction (DLSR) approach for simultaneous tomographic reconstruction and parameter estimation. Rather than aiming to replace established tomography frameworks, nDTomo serves as an open, function-oriented environment for training, prototyping, and research in chemical imaging and tomography.

Battery research increasingly relies on advanced imaging, yet open access to such data remains rare, scattered across various sources, and difficult to find. The Battery Imaging Library (BIL) is the first open, curated collection of multi-modal and multi-length scale battery imaging datasets, accompanied by a searchable, FAIR-compliant website. Distinctive features include the release of raw experimental data (radiographs, sinograms, X-ray and electron diffraction patterns) together with rare operando and multi-resolution datasets. Each dataset is linked to Zenodo DOIs with metadata, ensuring persistence and citability; open-source Python scripts for preprocessing and reconstruction are also provided for each modality. BIL enables algorithm benchmarking, machine learning, and teaching using realistic, industrially relevant data. By combining coverage across modalities, length scales, and chemistries with raw data accessibility and a FAIR-aligned web platform, the Battery Imaging Library provides a foundation for openness and reproducibility in battery imaging. The library is available here: https://www.batteryimaginglibrary.com

The electrochemical degradation of Li-ion batteries occurs over different spatial and temporal scales. This work demonstrates how operando synchrotron X-ray diffraction can be used to study degradation in Li-ion battery materials by mapping component-specific crystallographic structure evolution across a single-layer pouch full cell in real-time, enabling quantification of structural degradation processes in Li-ion battery cells from the atomic to macroscopic scale.

Chemical imaging datasets, particularly those from techniques like X-ray powder diffraction (XRPD) imaging and tomography (XRD-CT), are challenging to analyse due to their high dimensionality, complexity, and spatial heterogeneity. BeamStop is a specialised software platform developed to address these challenges through an integrated, tab-based environment tailored to chemical imaging workflows. Created at Finden Ltd to meet the needs we routinely face in synchrotron-based experiments, BeamStop brings together our extensive experience in XRD-CT data processing and analysis. At its core is a powerful hyperspectral imaging explorer, enabling users to visually inspect chemical images and interactively examine spatially resolved spectra or diffraction patterns. The software also supports workflows such as unsupervised clustering, peak fitting, and mask creation. For full-profile structural refinement, it integrates the TOPAS engine to perform Rietveld analysis on individual patterns or across entire images. This paper presents an overview of BeamStop’s architecture and capabilities, illustrating how it facilitates reproducible, high-throughput interpretation of complex chemical imaging data.

nDTomo is a Python-based software suite for the simulation, reconstruction and analysis of X-ray chemical imaging and computed tomography data. It provides a collection of Python function-based tools designed for accessibility and education as well as a graphical user interface (GUI). Prioritising transparency and ease of learning, nDTomo adopts a function-centric design that facilitates straightforward understanding and extension of core workflows, from phantom generation and pencil-beam tomography simulation to sinogram correction, tomographic reconstruction and peak fitting. While many scientific toolkits embrace object-oriented design for modularity and scalability, nDTomo instead emphasises pedagogical clarity, making it especially suitable for students and researchers entering the chemical imaging and tomography field. The suite also includes modern deep learning tools, such as a self-supervised neural network for peak analysis (PeakFitCNN) and a GPU-based direct least squares reconstruction (DLSR) approach for simultaneous tomographic reconstruction and parameter estimation. Rather than aiming to replace established tomography frameworks, nDTomo serves as an open, function-oriented environment for training, prototyping, and research in chemical imaging and tomography

Ethylenebis(dithiocarbamates) (EBDTCs) have been extensively used as fungicides in agriculture for nearly 80 years. Modern fungicides based on EBDTCs contain metal ions, such as zinc in Zineb and Metiram, manganese in Maneb, or combinations of both, such as in Mancozeb. Despite being commercially available since the 1940s, the molecular structure of the metal complexes of EBDTCs was not described in detail until the crystal structure of Zineb was published in 2020. Zineb (C4H6N2S4Zn) is a single-phase crystalline material. In this study, we present a comprehensive multimethod structural characterization of Metiram (C12H27N9S12Zn3), which is the active ingredient of Polyram WG, a highly effective and plant-compatible organic contact fungicide. Our findings reveal that Metiram comprises two distinct phases. The primary phase, phase I, of Metiram is ethylenebis(dithiocarbamate) zinc(II) ammine, which constitutes 81 wt % of the material, or three-quarters of EBDTC. It is a zinc-coordinating crystalline phase. The crystal structure of this phase was determined by powder X-ray diffraction, revealing 1D S-shaped chains of EBDTC connected by strongly distorted Zn[NH3][S-4] tetragonal pyramids. These pyramids share their sulfur atoms, and ammonia molecules occupy the apex of the pyramids, pointing alternately up and down. The secondary phase, phase II, constitutes 19 wt % of the material or one-quarter of EBDTC, and is amorphous. Using a combination of different techniques, including microscopy, diffraction, and spectroscopy, we have concluded that phase II consists of Zn-free EBDTC. We infer that the primary structure of this secondary constituent aligns with previous assumptions, notably the absence of a significant amount of Zn and the presence of disulfide bonds.

In this study, we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples. These parallax artefacts manifest as artificial peak shifting, broadening and splitting, leading to inaccurate physicochemical information, such as lattice parameters and crystallite sizes. Our approach integrates a 3D artificial neural network architecture with a forward projector that accounts for the experimental geometry and sample thickness. It is a self-supervised tomographic volume reconstruction approach designed to be chemistry-agnostic, eliminating the need for prior knowledge of the sample's chemical composition. We showcase the efficacy of this method through its application on both simulated and experimental X-ray powder diffraction tomography data, acquired from a phantom sample and an NMC532 cylindrical Lithium-ion battery.

In this study, we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples. These parallax artefacts manifest as artificial peak shifting, broadening and splitting, leading to inaccurate physicochemical information, such as lattice parameters and crystallite sizes. Our approach integrates a 3D artificial neural network architecture with a forward projector that accounts for the experimental geometry and sample thickness. It is a self-supervised tomographic volume reconstruction approach designed to be chemistry-agnostic, eliminating the need for prior knowledge of the sample's chemical composition. We showcase the efficacy of this method through its application on both simulated and experimental X-ray powder diffraction tomography data, acquired from a phantom sample and an NMC532 cylindrical lithium-ion battery.

Understanding how the microstructure of the active Cu component in the commercially applicable Cu/ZnO/ Al2O3( Cs2O) low-temperature water-gas shift catalyst evolves under various H2 partial pressures in the presence/absence of a Cs promoter during thermal activation has been investigated. Time-resolved XRD and spatially-resolved XRD-CT data were measured as a function of H2 concentration along a packed bed reactor to elucidate the importance of the zincite support and the effect of the promoter on Cu sintering mechanisms, dislocation character and stacking fault probability. The rate of Cu reduction showed a dependency on [Cs], [H2] and bed height; lower [Cs] and higher [H2] led to a greater rate of metallic copper nanoparticle formation. A deeper analysis of the XRD line profiles allowed for determining a greater edge character to the dislocations and subsequent stacking fault probability was also observed to depend on higher [H2], smaller Cu (and ZnO) crystallite sizes, increased [ZnO] (30 wt.%, sCZA) and lower temperature. The intrinsic activity of Cu/ZnO/Al2O3 methanol synthesis catalysts has been intimately linked to the anisotropic behaviour of copper, and thus the presence of lattice defects; to the best knowledge of the authors, this study is the first instance in which this type of analysis has been applied to LT-WGS catalysts.

Industrial heterogeneous catalysts show high performance coupled with high material complexity. Deconvoluting this complexity into simplified models eases mechanistic studies. However, this approach dilutes the relevance because models are often less performing. We present a holistic approach to reveal the origin of high performance without losing the relevance by pivoting the system at an industrial benchmark. Combining kinetic and structural analyses, we show how the performance of Bi-Mo-Co-Fe-K-O industrial acrolein catalysts occurs. The surface BiMoO ensembles decorated with K supported on β-Co1−xFexMoO4 perform the propene oxidation, while the K-doped iron molybdate pools electrons to activate dioxygen. The nanostructured vacancy-rich and self-doped bulk phases ensure the charge transport between the two active sites. The features particular to the real system enable the high performance.

3D printed SrNbO2N photocatalyst, its reconstructed XRD-CT image, band structure and photo-oxidation process.

Understanding of complex structure-function relationships is crucial in designing catalytic materials with optimized properties. The past 20 years have seen significant progress in the development of imaging techniques (i.e., acquisition methodology, sample environment, data handling) for performing experiments under industrially relevant operating conditions (i.e., temperature, pressure, chemical environment). These can now provide invaluable insight into the nature and structure of active catalyst components. In this chapter a variety of chemical and structural imaging techniques are discussed, using exemplar recent studies where it has been investigated how the catalyst activity and stability can be affected by the interplay of micro−/macrostructure, distribution, and nature of active components/sites.

XRD-CT data presented in the manuscript: "A multi-scale study of 3D printed Co-Al2O3 catalyst monoliths versus spheres" by Clement Jacquot et al. The h5 files contained the integrated and reshaped diffraction datas as well as a native 2theta x axis.

We present a lightweight and scalable artificial neural network architecture which is used to reconstruct a tomographic image from a given sinogram.

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.

The Front Cover shows how X-rays can be used to obtain spatially resolved chemical imaging insight from within an industrial catalytic reactor. Understanding how the microstructure of the active Cu0 component in the commercially applicable Cu/ZnO/Al2O3(−Cs2O) low-temperature water-gas shift catalyst evolves under various H2 partial pressures in the presence/absence of a Cs promoter during thermal activation has been the subject of the present investigation. More information can be found in the Research Article by Daniela M. Farmer et al..

Simulated and experimental xrd-ct sinogram data containing parallax artefacts (0-180 deg scans).

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)'.