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The general-purpose powder diffractometer beamline (BL2-1) at the Stanford Synchrotron Radiation Lightsource (SSRL) is described. The evolution of design and performance of BL2-1 are presented, in addition to current operating specifications, applications and measurement capabilities. Recent developments involve a robotic sample changer enabling high-throughput X-ray diffraction measurements, applicable to mail-in and remote operations. In situ and operando capabilities to measure samples with different form factors ( e.g. capillary, flat plate or thin film, and transmission) and under variable experimental conditions are discussed. Several example datasets and accompanying Rietveld refinements are presented., (© Kevin H. Stone et al. 2023.)

In situ techniques are essential to understanding the behavior of electrocatalysts under operating conditions. When employed, in situ synchrotron grazing-incidence X-ray diffraction (GI-XRD) can provide time-resolved structural information of materials formed at the electrode surface. In situ cells, however, often require epoxy resins to secure electrodes, do not enable electrolyte flow, or exhibit limited chemical compatibility, hindering the study of non-aqueous electrochemical systems. Here, a versatile electrochemical cell for air-free in situ synchrotron GI-XRD during non-aqueous Li-mediated electrochemical N 2 reduction (Li-N 2 R) has been designed. This cell not only fulfills the stringent material requirements necessary to study this system but is also readily extendable to other electrochemical systems. Under conditions relevant to non-aqueous Li-N 2 R, the formation of Li metal, LiOH and Li 2 O as well as a peak consistent with the α-phase of Li 3 N was observed, thus demonstrating the functionality of this cell toward developing a mechanistic understanding of complicated electrochemical systems., (open access.)

A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution., (© Sathya R. Chitturi et al. 2021.)