We propose a machine-learning method to recommend successful processing conditions for new compounds on the basis of parallel experiments. Initially, an experimental database was constructed for 67 pseudobinary oxides registered in the Inorganic Crystal Structure Database (ICSD) by parallel experiments using 23 starting materials and 23 cation mixing ratios. Precursor powders were obtained by four synthesis methods (solid-state reaction, polymerized complex, cyclic ether sol–gel, and spray coprecipitation), which were fired at five different temperatures. This resulted in 1648 unique chemical synthesis conditions and database entries. The reactants were characterized sequentially using powder X-ray diffraction equipment with an automatic sample exchanger. The synthesis results were rated as a score, which was placed into a fifth-order tensor with 243 340 elements. The Tucker decomposition method was used to predict yet-to-be-rated scores for unexperimented processing conditions. Good predictive performance of the present model was demonstrated by cross validation. It was further evaluated by examining the presence of highly rated compositions in another database, ICDD-PDF (International Center for Diffraction Data-Powder Diffraction File). Successful processing conditions for unexperimented compositions were found to be well recommended.

Fossils, including those that occasionally preserve decay-prone soft-tissues, are mostly made of minerals. Accessing their chemical composition provides unique insight into their past biology and/or the mechanisms by which they preserve, leading to a series of developments in chemical and elemental imaging. However, the mineral composition of fossils, particularly where soft-tissues are preserved, is often only inferred indirectly from elemental data, while X-ray diffraction that specifically provides phase identification received little attention. Here, we show the use of synchrotron radiation to generate not only X-ray fluorescence elemental maps of a fossil, but also mineralogical maps in transmission geometry using a two-dimensional area detector placed behind the fossil. This innovative approach was applied to millimetre-thick cross-sections prepared through three-dimensionally preserved fossils, as well as to compressed fossils. It identifies and maps mineral phases and their distribution at the microscale over centimetre-sized areas, benefitting from the elemental information collected synchronously, and further informs on texture (preferential orientation), crystallites size and local strain. Probing such crystallographic information is instrumental in defining mineralization sequences, reconstructing the fossilization environment and constraining preservation biases. Similarly, this approach could potentially provide new knowledge on other (bio)mineralization processes in environmental sciences. We also illustrate that mineralogical contrasts between fossil tissues and/or the encasing sedimentary matrix can be used to visualize hidden anatomies in fossils.

Methods Data were collected at the DiffAbs beamline of the SOLEIL Synchrotron source (France). Synchronous synchrotron rapid scanning X-ray fluorescence and diffraction mapping (SRS-XRFD) was performed using an incident X-ray beam of 16.2 or 18 keV, monochromatised using a Si(111) double-crystal monochromator, with a beam size diameter reduced down to 50 or 100 µm using platinum pinholes, or focused down to ~10 µm using Kirkpatrick-Baez mirrors. XRF was collected using a 4-element silicon drift detector (SDD, Vortex ME4, Hitachi High-Technologies Science America, Inc., total active area: 170 mm2) oriented at 90° to the incident beam, in the horizontal plane. XRD was collected in transmission geometry using a 2D hybrid pixel detector (XPAD S140, 240×560 pixels of 130 µm each), placed behind the sample at a distance of typically 200–300 mm such to intercept diffraction rings over an angular range of ~7° in scattering angle (2θ). Two-dimensional scanning was done by moving laterally the fossils in a plane rotated around the vertical axis by 20° to the primary beam (i.e., incident angle), to limit X-ray beam footprint on the sample but also such that the sample exhibits its surface to the SDD detector (no shadowing of the reflected XRD signal, figure 1a). Mapping over the entire fossils at a 35–100 µm lateral resolution was performed on the fly using the FLYSCAN platform. A full XRF spectrum and one or several XRD images were collected at each pixel.

The present dataset includes 6 types of data:

(1) Synchrotron X-Ray Fluorescence elemental maps

Methods: All elemental distributions presented in the paper correspond to integrated intensities around emission lines of elements of interest (XRF peaks), represented using linear (expect figure 1b, logarithmic) grey or color scales that go from dark to light, respectively for low to high intensities.

Data: figure1b_AsPb-map_DATA_XRF.txt; figure1b_Mn-map_DATA_XRF.txt; figure1b_Zn-map_DATA_XRF.txt; figure1b_stackRGB_DATA_XRF.tif; figure2b_Ca-map_DATA_XRF.txt; figure2b_Fe-map_DATA_XRF.txt; figure2b_Y-map_DATA_XRF.txt; figure2b_stackRGB_DATA_XRF.tif; figure5f_Y-map_DATA_XRF.txt

(2) Synchrotron X-Ray Diffraction detector images

Methods: A few XPAD detector images are shown in the paper, either simply after flat correction (figure 1c) or after conversion to (2θ-Ѱ) coordinates (figure 2e). These images are represented using logarithmic color scales that go from dark to light, respectively for low to high intensities.

Data: figure1c_left_DATA_XRD.txt; figure1c_right_DATA_XRD.txt; figure2e_DATA_XRD.xlsx

(3) Diffractograms

Methods: XPAD detector images were processed (azimuthal data regrouping along y direction) to extract their respective diffractograms (Intensity vs. 2θ profiles). Phase identification and 2θ calibration were performed using powder XRD diffractograms obtained on fragments of the sedimentary matrix (and of the fossil when possible) using the Match! software (Crystal Impact) making use of the International Centre for Diffraction Data (ICDD)- PDF 2015 database. Additional peaks in the XRD maps could then be identified using Match/ICDD database, as well as from the elemental information provided by the XRF data.

Data: figure1d_DATA_XRD.xlsx; figure2f_DATA_XRD.xlsx

(4) Synchrotron X-Ray Diffraction mineral maps

Methods: During XPAD detector images processing 4D datasets (x, y, 2θ, intensity) were also generated, and then particular XRD contrast maps. Phase identification and 2θ calibration is discussed above. All phase distributions presented in the paper correspond to integrated intensities of XRD peaks of interest, represented using linear grey or color scales that go from dark to light, respectively for low to high intensities.

Data: figure1e_left_DATA_XRD.txt; figure1e_center_DATA_XRD.txt; figure1e_right_DATA_XRD.txt; figure2c_A211-map_DATA_XRD.txt; figure2c_C006-map_DATA_XRD.txt; figure2c_Q101-map_DATA_XRD.txt; figure2c_stackRGB_DATA_XRD.tif; figure2d_C012-map_DATA_XRD.txt; figure2d_C113-map_DATA_XRD.txt; figure2d_C202-map_DATA_XRD.txt; figure2d_stackRGB_DATA_XRD.tif; figure4a_17p97-map_DATA_XRD.txt; figure4a_25p09-map_DATA_XRD.txt; figure4a_26p01-map_DATA_XRD.txt; figure4a_stackRGB_DATA_XRD.tif; figure4b_22p51-map_DATA_XRD.txt; figure4b_26p97-map_DATA_XRD.txt; figure4b_27p63-map_DATA_XRD.txt; figure4b_stackRGB_DATA_XRD.tif; figure5b_FAp002-map-head_DATA_XRD.txt; figure5b_FAp211-map-head_DATA_XRD.txt; figure5b_phyll-map-head_DATA_XRD.txt; figure5b_stackRGB-head_DATA_XRD.tif; figure5b_FAp002-map-tail_DATA_XRD.txt; figure5b_FAp211-map-tail_DATA_XRD.txt; figure5b_phyll-map-tail_DATA_XRD.txt; figure5b_stackRGB-tail_DATA_XRD.tif; figure5c_FAp002-map-cropped_DATA_XRD.txt; figure5e_FAp002-map_DATA_XRD.txt

(5) Crystallite sizes

Methods: By Gaussian fitting the 2θ profile of XRD peaks attributed to different crystalline phases, corresponding crystallite sizes were extracted (for each pixel of the maps) by converting their full width at half maximum (FWHM) using Scherrer’s formula. It was assumed that only the crystallite size is contributing to the broadening, and an instrument resolution function measured as ~0.035° (amounting several 10 %, and up to 50 % of the measured peak FWHM) was also taken into account for FWHM deconvolution. Crystallite size distributions are represented in the paper using linear color scales that go from dark to light, respectively for low to high intensities.

Data: figure2g_A211-crystSize-map_DATA_XRD.txt; figure2g_C006-crystSize-map_DATA_XRD.txt; figure2g_Q101-crystSize-map_DATA_XRD:txt; figure2g_stackRGB_DATA_XRD.tif

(6) Local texture measurements

Methods: In order to confirm some microstructure results obtained using the local probe XRD approach, supplementary local texture measurements were performed. This was done by scanning it in azimuth (Φ, rotation around the sample surface normal) and elevation (Ѱ, rotation around the projection of the impinging X-ray beam on the sample surface), while recording, at each position, the X-ray scattered signal. The resulting intensity is represented in a map, in polar coordinates (azimuth angle and elevation, e.g. figures 3f–h). In this way, when one or several crystallites are oriented such that the Bragg law is fulfilled for the particular inter-reticular distance probed (or the particular Bragg angle 2θ), high signal is found in the particular corresponding regions of the polar map, allowing: i) to retrieve the particular orientation of the grains (j, y), and ii) to possibly quantify the volume ratio of that particular orientation, compared to other orientations on the map. Rapid texture measurements were performed using the XPAD area detector. The sample was illuminated by the impinging X-ray beam (of size ~ 150 × 150 µm2 in this case) and the azimuth (Φ) and elevation (Ѱ) angles were scanned, the first one continuously. An image was recorded in each point, then texture maps for various 2θ angles (i.e. volumes) were reconstructed. Then, a similar dataset was recorded at the next vertical position on the sample. Local texture measurements are represented in the paper using logarithmic color scales that go from dark to light, respectively for low to high intensities.

Data: Figure3b-d_DATA_XRD.xlsx; figure3f_left_DATA_XRD.txt; figure3f_right_DATA_XRD.txt; figure3g_left_DATA_XRD.txt; figure3g_right_DATA_XRD.txt; figure3h_left_DATA_XRD.txt; figure3h_right_DATA_XRD.txt

A solid-state combinatorial chemistry approach, which used the A–Ge–O (A = Li, K, Rb) system doped with a small amount of Mn4+ as an activator, was adopted in a search for novel red-emitting phosphors. The A site may have been composed of either a single alkali metal ion or of a combination of them. This approach led to the discovery of a novel phosphor in the above system with the chemical formula Li3RbGe8O18:Mn4+. The crystal structure of this novel phosphor was solved via direct methods, and subsequent Rietveld refinement revealed a trigonal structure in the P3̅1m space group. The discovered phosphor is believed to be novel in the sense that neither the crystal structure nor the chemical formula matches any of the prototype structures available in the crystallographic information database (ICDD or ICSD). The measured photoluminescence intensity that peaked at a wavelength of 667 nm was found to be much higher than the best intensity obtained among all the existing A2Ge4O9 (A = Li, K, Rb) compounds in the alkali-germanate system. An ab initio calculation based on density function theory (DFT) was conducted to verify the crystal structure model and compare the calculated value of the optical band gap with the experimental results. The optical band gap obtained from diffuse reflectance measurement (5.26 eV) and DFT calculation (4.64 eV) results were in very good agreement. The emission wavelength of this phosphor that exists in the deep red region of the electromagnetic spectrum may be very useful for increasing the color gamut of LED-based display devices such as ultrahigh-definition television (UHDTV) as per the ITU-R BT.2020-2 recommendations and also for down-converter phosphors that are used in solar-cell applications.

This repository provides two use cases for infrastructure asset management using information containers according to the Information Container for linked Document Delivery standard (ISO 25197). Use Case 1 demonstrates the data preparation and result collection of bridge visual inspection with requirement- and delivery container. Use Case 2 demonstrates the pavement maintenance plan based on the existing condition data. Therefore, an additional connection to a relational database in the information container is provided in Use Case 2, which is registered within the container using an extension EXDOC:Extension for document types for the ISO 21597 ICDD Part 1 Container ontology.

Full Changelog: https://github.com/RUB-Informatik-im-Bauwesen/ams-icdd-usecases/commits/v0.1

Olivine is a dynamic and important mineral in the crust and mantle with relevance to processes that are important to climate change technology, such as geologic carbon storage and critical mineral recovery. In this work, we critically evaluated and compiled a new database of olivine diffraction data, lattice parameters, and compositions to enable rapid Ni–Mg–Fe olivine composition determination. A compilation of olivine X-ray diffraction data and chemical compositions from both the literature and the International Centre for Diffraction Data (ICDD) powder database was assembled to plot both the forsterite–fayalite and forsterite–liebenbergite solid-solution lines. We present an expanded data set to delineate equations and relationships used for quantifying the correlations between olivine lattice parameters and chemical compositions in Mg2SiO4–Fe2SiO4 (forsterite–fayalite) and Mg2SiO4–Ni2SiO4 (forsterite–liebenbergite) olivine solid-solution series.

The authors calculated a complete lattice parameters characterization by Rietveld refinement using Fullprof suite software. Input information, such as cell parameters, atomic positions, and space groups, was collected from the International Centre for Diffraction Data (ICDD) PDF-4+2015 database. The background was modeled by a 5-coefficient polynomial approach and the Pseudo-Void function was used in the profile. The folder contains two files (ZnO_A & ZnO_B), these files can be opened by notepad app or text edition software.

The National Institute of Standards and Technology and the International Centre for Diffraction Data co-hosted a workshop on 17-18 October 2023 to identify and prioritize the goals, challenges, and opportunities for critical and emerging technology needs within industry, with an emphasis on leveraging artificial intelligence, data-driven methodologies, and high-throughput and automated workflows for accelerating x-ray-based structural analysis for materials development and manufacturing. Participants, predominantly from industry, gathered in-person at ICDD headquarters in Newtown Square, Pennsylvania. The data collected during this workshop is published in this data publication. This data is interpreted in the workshop report, which cites this dataset. Certain equipment, instruments, software, or materials, commercial or non-commercial, are identified in this dataset. Such identification does not imply recommendation or endorsement of any product or service by NIST, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.

The cement specimens, after hardening, were crushed and ground in a mortar and then analyzed by a Phillips X’Pert Pro X-ray diffractometer (Almelo, The Netherlands) using Cu-Kα radiation. Data were collected from 2θ = 20–50° with a step size of 0.02°, a 40 kV voltage and a 40 mA current. The phase identification has been undertaken using HighScore Plus software with the International Centre for Diffraction Data (ICDD) database.