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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.

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.

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]

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]

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]

Synchrotron-based X-ray tomography offers the potential for rapid large-scale reconstructions of the interiors of materials and biological tissue at fine resolution. However, for radiation sensitive samples, there remain fundamental trade-offs between damaging samples during longer acquisition times and reducing signals with shorter acquisition times. We present a deep convolutional neural network (CNN) method that increases the acquired X-ray tomographic signal by at least a factor of 10 during low-dose fast acquisition by improving the quality of recorded projections. Short-exposure-time projections enhanced with CNNs show signal-to-noise ratios similar to long-exposure-time projections. They also show lower noise and more structural information than low-dose short-exposure acquisitions post-processed by other techniques. We evaluated this approach using simulated samples and further validated it with experimental data from radiation sensitive mouse brains acquired in a tomographic setting with transmission X-ray microscopy. We demonstrate that automated algorithms can reliably trace brain structures in low-dose datasets enhanced with CNN. This method can be applied to other tomographic or scanning based X-ray imaging techniques and has great potential for studying faster dynamics in specimens.