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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.

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This paper addresses the integrated attitude and position control problem for the final phase proximity operations of spacecraft autonomous rendezvous and docking, in which important motion constraints of the chaser spacecraft are considered. On the one hand, to ensure reliable real-time measurements of the relative attitude and position information between two spacecraft, the relevant sensor system of the chaser spacecraft is required to continuously point toward the target; on the other hand, for the proximity safety concerns, the chaser also needs to follow a specified approach path constraint. A special dual-quaternion-based artificial potential function is presented to encode information regarding these motion constraints. Using this potential function, a novel six-degree-of-freedom control method is proposed to ensure the arrival of the chaser at the docking port of the target with a desired relative attitude, while strictly complying with all constraints. The closed-loop stability is demonstrated by a Lyapunov-based method in conjunction with the special properties of the artificial potential function. The local minimum problem associated with the artificial potential function can be addressed by selection of control parameters that satisfies a mild condition. Simulation results of prototypical spacecraft rendezvous and docking missions are provided to illustrate the effectiveness of the proposed method.

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.