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X‐rays offer high penetration with the potential for tomography of centimetre‐sized specimens, but synchrotron beamlines often provide illumination that is only millimetres wide. Here an approach is demonstrated termed Tomosaic for tomographic imaging of large samples that extend beyond the illumination field of view of an X‐ray imaging system. This includes software modules for image stitching and calibration, while making use of existing modules available in other packages for alignment and reconstruction. The approach is compatible with conventional beamline hardware, while providing a dose‐efficient method of data acquisition. By using parallelization on a distributed computing system, it provides a solution for handling teravoxel‐sized or larger datasets that cannot be processed on a single workstation in a reasonable time. Using experimental data, the package is shown to provide good quality three‐dimensional reconstruction for centimetre‐sized samples with sub‐micrometre pixel size. [ABSTRACT FROM AUTHOR]

A unique correlative approach for automated segmentation of large 3D nanotomography datasets obtained using Transmission X-ray Microscopy (TXM) in an Al-Cu alloy has been introduced. Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. This extremely versatile technique is capable of emulating the manual segmentation process effectively. Coupling this technique with post-scanning SEM imaging ensured precise estimation of 3D morphological parameters from nanotomography. The segmentation process as well as subsequent analysis was expedited by several orders of magnitude. Quantitative comparison between segmentation performed manually and using the CNN architecture established the accuracy of this automated technique. Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis, possibly opening up novel avenues for performing 4D characterization experiments with finer time steps. [ABSTRACT FROM AUTHOR]

In this work, we identify a new potential Mg battery cathode structure Mo3(PO4)3O, which is predicted to exhibit ultra-fast Mg2+ diffusion and relatively high voltage based on first-principles density functional theory calculations. Nudged elastic band calculations reveal that the migration barrier of the percolation channel is only ∼80 meV, which is remarkably low, and comparable to the best Li-ion conductors. This low barrier is verified by ab initio molecular dynamics and kinetic Monte Carlo simulations. The voltage and specific energy are predicted to be ∼1.98 V and ∼173 W h kg−1, respectively. If confirmed by experiments, this material would have the highest known Mg mobility among inorganic compounds. [ABSTRACT FROM AUTHOR]