J. Appl. Cryst.の発刊に際して

The amount of data collected during synchrotron X-ray diffraction (XRD) experiments is constantly increasing. Most of the time, the data are collected with image detectors, which necessitates the use of image reduction/integration routines to extract structural information from measured XRD patterns. This step turns out to be a bottleneck in the data processing procedure due to a lack of suitable software packages. In particular, fast-running synchrotron experiments require online data reduction and analysis in real time so that experimental parameters can be adjusted interactively. Dioptas is a Python-based program for on-the-fly data processing and exploration of two-dimensional X-ray diffraction area detector data, specifically designed for the large amount of data collected at XRD beamlines at synchrotrons. Its fast data reduction algorithm and graphical data exploration capabilities make it ideal for online data processing during XRD experiments and batch post-processing of large numbers of images.

DIOPTAS: a program for reduction of two-dimensional X-ray diffraction data and data exploration

Tandem solar cells that pair silicon with a metal halide perovskite are a promising option for surpassing the single-cell efficiency limit. We report a monolithic perovskite/silicon tandem with a certified power conversion efficiency of 29.15%. The perovskite absorber, with a bandgap of 1.68 electron volts, remained phase-stable under illumination through a combination of fast hole extraction and minimized nonradiative recombination at the hole-selective interface. These features were made possible by a self-assembled, methyl-substituted carbazole monolayer as the hole-selective layer in the perovskite cell. The accelerated hole extraction was linked to a low ideality factor of 1.26 and single-junction fill factors of up to 84%, while enabling a tandem open-circuit voltage of as high as 1.92 volts. In air, without encapsulation, a tandem retained 95% of its initial efficiency after 300 hours of operation.

Monolithic perovskite/silicon tandem solar cell with >29% efficiency by enhanced hole extraction

In the near future, the targets for lithium-ion batteries concerning specific energy and cost can advantageously be met by introducing layered LiNixCoyMnzO2 (NCM) cathode materials with a high Ni content (x ≥ 0.6). Increasing the Ni content allows for the utilization of more lithium at a given cell voltage, thereby improving the specific capacity but at the expense of cycle life. Here, the capacity-fading mechanisms of both typical low-Ni NCM (x = 0.33, NCM111) and high-Ni NCM (x = 0.8, NCM811) cathodes are investigated and compared from crystallographic and microstructural viewpoints. In situ X-ray diffraction reveals that the unit cells undergo different volumetric changes of around 1.2 and 5.1% for NCM111 and NCM811, respectively, when cycled between 3.0 and 4.3 V vs Li/Li+. Volume changes for NCM811 are largest for x(Li) < 0.5 because of the severe decrease in interlayer lattice parameter c from 14.467(1) to 14.030(1) A. In agreement, in situ light microscopy reveals that delithiation leads to differe...

Anisotropic Lattice Strain and Mechanical Degradation of High- and Low-Nickel NCM Cathode Materials for Li-Ion Batteries

The high-temperature, all-inorganic CsPbI3 perovskite black phase is metastable relative to its yellow, nonperovskite phase at room temperature. Because only the black phase is optically active, this represents an impediment for the use of CsPbI3 in optoelectronic devices. We report the use of substrate clamping and biaxial strain to render black-phase CsPbI3 thin films stable at room temperature. We used synchrotron-based, grazing incidence, wide-angle x-ray scattering to track the introduction of crystal distortions and strain-driven texture formation within black CsPbI3 thin films when they were cooled after annealing at 330°C. The thermal stability of black CsPbI3 thin films is vastly improved by the strained interface, a response verified by ab initio thermodynamic modeling.

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Thermal unequilibrium of strained black CsPbI3 thin films

pyFAI is an open-source software package designed to perform azimuthal integration and, correspondingly, two-dimensional regrouping on area-detector frames for small- and wide-angle X-ray scattering experiments. It is written in Python (with binary submodules for improved performance), a language widely accepted and used by the scientific community today, which enables users to easily incorporate the pyFAI library into their processing pipeline. This article focuses on recent work, especially the ease of calibration, its accuracy and the execution speed for integration.This article will form part of a virtual special issue of the journal, presenting some highlights of the 12th Biennial Conference on High-Resolution X-ray Diffraction and Imaging (XTOP2014).

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The fast azimuthal integration Python library: pyFAI .

The pyFAI package has been designed to reduce X-ray diffraction images into powder diffraction curves to be further processed by scientists. This contribution describes how to convert an image into a radial profile using the Numpy package, how the process was accelerated using Cython. The algorithm was parallelised, needing a complete re-design to benefit from massively parallel devices like graphical processing units or accelerators like the Intel Xeon Phi using the PyOpenCL library.

PyFAI: a Python library for high performance azimuthal integration on GPU

Air pollution is detrimental to the environment and contributes to the global burden of disease. Accurate prediction of surface concentrations of atmospheric pollutants, including fine particulate matter ( 𝑃𝑀 2.5 ) and gaseous 𝑁𝑂 2 , is key to mitigating these harmful effects. However, most forecasts produced by state-of-the-art models exhibit biases and limitations due to low-resolution (typically ∼ 40𝑘𝑚 ) and intense computational requirements. While deep learning-based approaches promise cheap inference, and can inherently scale and assimilate large amounts of data, ground truth measurements are obtained by station networks that are costly and sparsely distributed spatially - making the training of data-hungry deep learning methods tedious. In this context, we propose a novel deep learning framework that can accurately downscale the resolution of surface concentrations down to 1 × 1 km by leveraging additional data sources of static and time-varying nature (such as elevation and land use type). To do so, we carefully design a tailored data alignment and sampling process that balances the distribution across land use type, enhancing model generalization. To handle ground truth sparsity, we propose a tailored loss function that leads the network to model correlations across data sources close to ground station, while encouraging it to approximate model-based outputs elsewhere. With a set of qualitative and quantitative experiments, we demonstrate that the proposed method outperforms all compared modeling- and learning-based approaches, in both the aerosol and gaseous phase, with a significant increase in accuracy (up to 19% for 𝑁𝑂 2 and 13% for 𝑃𝑀 2.5 ) - while generalizing the improvement across the various land use types.

Giannis Ashiotis

Papers

The fast azimuthal integration Python library: pyFAI .

PyFAI: a Python library for high performance azimuthal integration on GPU

AI for Air Quality: Leveraging Data Fusion for Deep Downscaling of Atmospheric Pollutants