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10 results for “X-ray powder diffraction”
Data set for "Quantification of amorphous siliceous fly ash in hydrating blended cement pastes by X-ray powder diffraction"
<p>The main data is XRD patterns originally collected as xrdml and converted into rd format.</p> <p>The data set for the manuscript:</p> <p>Quantification of amorphous siliceous fly ash in hydrating blended cement pastes by X-ray powder diffraction</p> <p>Xuerun Li<sup>a</sup>, Ruben Snellings<sup>b</sup> and Karen L. Scrivener<sup>a</sup></p> <p><sup>a</sup>Laboratory of Construction Materials, Swiss Federal Institute of Technology in Lausanne (EPFL), Station 12, CH-1015 Lausanne, Switzerland</p> <p><sup>b</sup>Sustainable Materials Management, Flemish Institute of Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium<br> </p>
Zinc-doped Zeolite 13X, Partially Zinc-doped Zeolite 13X, and pure Zeolite 13X X-Ray Powder Diffraction
<p>This repository holds X-Ray Powder Diffraction data for three different Zeolite 13X samples to allow for characterisation of the diffraction pattern for zinc-doped Zeolite 13X to perform accurate phase-based diffraction-tomography reconstructions using the data from 10.5281/zenodo.13329639.</p> <p>The three samples are fully Zinc-doped Zeolite 13X, partially Zinc-doped 13X, and pure Zeolite 13X. An empty borosilicate glass capillary is provided to remove scattering from the capillary the samples were housed in.</p> <p>Data in all instances is provided in ASCII format as a .asc file. A basic jupyter notebook is provided to perform the analysis used to determine powder peaks.</p> <p>Data was collected on a Rigaku SmartLab Diffractometer with a copper x-ray source of wavelength 1.5406 angstroms at the ISIS Neutron & Muon Source Materials Characterisation Lab.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p>
[Data] Acoustic emission signature of martensitic transformation in Laser Powder Bed Fusion of Ti6Al4V-Fe, supported by operando X-ray diffraction
<p>The dataset for this study focuses on investigating Acoustic Emission (AE) monitoring in the Laser Powder Bed Fusion (LPBF) process, using premixed Ti6Al4V-(x wt%) Fe, where x = 0, 3, and 6. By employing a structure-borne AE sensor, we analyze AE data statistically, uncovering notable discrepancies within the 50-750 kHz frequency range. Leveraging Machine Learning (ML) methodologies, we accurately predict composition for particular processing conditions. These fluctuations in AE signals primarily arise from unique microstructural alterations linked to martensitic phase transformation, corroborated by operando synchrotron X-ray diffraction and post-mortem SEM and EBSD analysis. Moreover, cracks are evident at the periphery of the printed parts, stemming from local inadequate heat input during the blending of Ti6Al4V with added Fe powder. These cracks are discerned via AE signals subsequent to the cessation of the laser beam, correlating with the presence of brittle intermetallics at their junction. This study highlights for the first time the potential of AE monitoring in reliably detecting footprints of martensitic transformations during the LPBF process. Additionally, AE is shown to prove valuable for assessing crack formations, particularly in scenarios involving premixed powders and necessitating precise selection of processing parameters, notably at part edges.</p>
Specimen displacement correction for powder x-ray diffraction in Debye-Scherrer geometry with a flat area detector
<p>This is a repository of synchrotron, powder XRD data including area detector images (.tiff) and integrated intensity vs 2theta files (.xye) for an experiment determining a sample displacement correction equation for powder x-ray diffraction in Debye-Scherrer geometry with a flat area detector. The accuracy of this equation and the corresponding corrections were verified by comparing it with corrections based on finding new integration parameters from an internal standard reference material.</p> <p>This work was published in the Journal of Applied Crystallography, the citation is shown below:</p> <p>Hulbert, B. S. & Kriven, W. M. (2023). J. Appl. Cryst. 56.</p> <p><a href="https://doi.org/10.1107/S1600576722011360">https://doi.org/10.1107/S1600576722011360</a></p>
Dataset for "Mix and measure - combining in situ X-ray powder diffraction and microtomography for accurate hydrating cement studies" paper
<p>Dataset for paper (doi: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.cemconres.2023.107370" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.cemconres.2023.107370</a>) with the abstract: "It is reported an innovative methodology based on in situ MoKa1 laboratory X-ray powder diffraction (LXRPD) and microtomography (μCT) avoiding any sample conditioning. The pastes are injected in 2.0 mm capillaries and the extremes are just sealed. The measurements take place in the same region of the hydrating paste. Thick capillaries are key to avoiding self-desiccation, which dictates the need of high-energy X-ray radiation for the diffraction study. This approach has been tested with a PC 42.5 R paste having w/c=0.50. μCT data were collected at 12 hours and 1, 3, 7 and 79 days. LXRPD data were acquired at 1, 3, 7 and 77 days. In this proof-of-principle research, the same paste was also cured ex situ. Portlandite contents obtained by thermal analysis, ex situ powder diffraction, in situ mass balance calculation and in situ powder diffraction were 13.8, 13.1, 13.1 and 12.5 wt%, respectively. From the μCT study, the grey value histogram evolution with time showed a crossing point which allowed us to distinguish (appearing) hydrated products from (dissolving) unhydrated cement particles. Segmentations were carried out by global thresholding and the random forest approach (one type of supervised Machine Learning). The comparison of the segmented results for the unhydrated cement fraction and the Rietveld quantitative phase analysis outputs gave an agreement of 2%. The potential of this methodology to deal with more complex binders is also presented."</p>
X-ray powder diffraction patterns of Bi2-xSbxTe3 (x=0, 0.2, 0.5, 1.5, 1.8, 2.0) nanoparticles
<p>The data set contains X-ray powder diffraction patterns of Bi<sub>2-x</sub>Sb<sub>x</sub>Te<sub>3</sub> (x=0, 0.2, 0.5, 1.5, 1.8, 2.0) nanoparticles, synthesized utilizing microwave-assisted heating. Diffraction data were collected at room temperature using a benchtop Rigaku MiniFlex 600 diffractometer with Bragg-Brentano θ-2θ geometry. An X-ray tube with a copper anode (Cu Kα radiation, λ =1.5418 Å), operated at U = 40 kV and I = 15 mA, was used as a source. Bi<sub>2</sub>Te<sub>3</sub> and Sb<sub>2</sub>Te<sub>3</sub> are isostructural and crystallize in a rhombohedral crystal system with the space group R-3m (No. 166).</p> <p> </p>
X-ray powder diffraction data of a dried Pepto-Bismol suspension (bismuth subsalicylate) collected at ambient temperature
<p>PXRD data of dried Pepto-Bismol suspension loaded in a Kapton tube. Data collected under ambient conditions. Calibrated wavelength = 0.458092 Å.</p> <p>Use of the Advanced Photon Source at Argonne National Laboratory was supported by the U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.</p>
opXRD: Open Experimental Powder X-ray Diffraction Database
<p>In this publication, we provide a new open powder X-ray diffraction (opXRD) dataset that collects a broad range of patterns from experiments. Our opXRD dataset has been curated by collecting the accumulated powder data from multiple large research groups and institutions with high-throughput XRD facilities. It contains pXRD patterns from single and multiphase materials from a wide variety of materials classes. </p>
Raw data and Figures for the article "Strain Wave Pathway to Semiconductor-to-Metal Transition Revealed by Time Resolved X-ray Powder Diffraction"
<p>- Raw data from Jungfrau Integrating pixel detector (together with azimuthally averaged curves)</p> <p>- Script to create time resolved data from raw data</p> <p>- Scripts to generates main figures of the article.</p>
Raw data for "Accuracy in cement hydration investigations: combined X-ray microtomography and powder diffraction analyses" paper
<p>Raw data for "Accuracy in cement hydration investigations: combined X-ray microtomography and powder diffraction analyses" paper, including:</p> <p>- TG-DTA</p> <p>- X-ray diffraction data.</p> <p>- micro-CT data.</p>
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