DLSR: a solution to the parallax artefact in X-ray diffraction computed tomography data

Neural networks for rapid phase quantification of cultural heritage X-ray powder diffraction data

Recent developments in synchrotron radiation facilities have increased the amount of data generated during acquisitions considerably, requiring fast and efficient data processing techniques. Here, the application of dense neural networks (DNNs) to data treatment of X-ray diffraction computed tomography (XRD-CT) experiments is presented. Processing involves mapping the phases in a tomographic slice by predicting the phase fraction in each individual pixel. DNNs were trained on sets of calculated XRD patterns generated using a Python algorithm developed in-house. An initial Rietveld refinement of the tomographic slice sum pattern provides additional information (peak widths and integrated intensities for each phase) to improve the generation of simulated patterns and make them closer to real data. A grid search was used to optimize the network architecture and demonstrated that a single fully connected dense layer was sufficient to accurately determine phase proportions. This DNN was used on the XRD-CT acquisition of a mock-up and a historical sample of highly heterogeneous multi-layered decoration of a late medieval statue, called 'applied brocade'. The phase maps predicted by the DNN were in good agreement with other methods, such as non-negative matrix factorization and serial Rietveld refinements performed with TOPAS, and outperformed them in terms of speed and efficiency. The method was evaluated by regenerating experimental patterns from predictions and using the R-weighted profile as the agreement factor. This assessment allowed us to confirm the accuracy of the results.

In Situ and Operando X-ray Scattering Methods in Electrochemistry and Electrocatalysis

Electrochemical and electrocatalytic processes are of key importance for the transition to a sustainable energy supply as well as for a wide variety of other technologically relevant fields. Further development of these processes requires in-depth understanding of the atomic, nano, and micro scale structure of the materials and interfaces in electrochemical devices under reaction conditions. We here provide a comprehensive review of in situ and operando studies by X-ray scattering methods, which are powerful and highly versatile tools to provide such understanding. We discuss the application of X-ray scattering to a wide variety of electrochemical systems, ranging from metal and oxide single crystals to nanoparticles and even full devices. We show how structural data on bulk phases, electrode-electrolyte interfaces, and nanoscale morphology can be obtained and describe recent developments that provide highly local information and insight into the composition and electronic structure. These X-ray scattering studies yield insights into the structure in the double layer potential range as well as into the structural evolution during electrocatalytic processes and phase formation reactions, such as nucleation and growth during electrodeposition and dissolution, the formation of passive films, corrosion processes, and the electrochemical intercalation into battery materials.

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)'.

Very large-scale diffraction investigations enabled by a matrix-multiplication facilitated radial and azimuthal integration algorithm: MatFRAIA

As synchrotron facilities continue to generate increasingly brilliant X-rays and detector speeds increase, swift data reduction from the collected area detector images to more workable 1D diffractograms becomes of increasing importance. This work reports an integration algorithm that can integrate diffractograms in real time on modern laptops and can reach 10 kHz integration speeds on modern workstations using an efficient pixel-splitting and parallelization scheme. This algorithm is limited not by the computation of the integration itself but is rather bottlenecked by the speed of the data transfer to the processor, the data decompression and/or the saving of results. The algorithm and its implementation is described while the performance is investigated on 2D scanning X-ray diffraction/fluorescence data collected at the interface between an implant and forming bone.

Cycling Rate-Induced Spatially-Resolved Heterogeneities in Commercial Cylindrical Li-Ion Batteries

Synchrotron high-energy X-ray diffraction computed tomography has been employed to investigate, for the first time, commercial cylindrical Li-ion batteries electrochemically cycled over the two cycling rates of C/2 and C/20. This technique yields maps of the crystalline components and chemical species as a cross-section of the cell with high spatiotemporal resolution (550 × 550 images with 20 × 20 × 3 µm³ voxel size in ca. 1 h). The recently developed Direct Least-Squares Reconstruction algorithm is used to overcome the well-known parallax problem and led to accurate lattice parameter maps for the device cathode. Chemical heterogeneities are revealed at both electrodes and are attributed to uneven Li and current distributions in the cells. It is shown that this technique has the potential to become an invaluable diagnostic tool for real-world commercial batteries and for their characterization under operating conditions, leading to unique insights into "real" battery degradation mechanisms as they occur.