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746 results for “Powder”
Neutron powder diffraction data from solid nitrogen in the range 6K-70K
<p>Neutron powder diffraction patterns from nitrogen solidified in-situ. This data accompanies a submitted paper.</p>
Feeding with plant powders increases longevity and body weight of Western honeybee workers (Apis mellifera)
<p>Beekeepers routinely substitute honey from managed western honeybees, <em>Apis mellifera</em>, colonies with sugar water post-harvest, potentially leading to malnutrition. Although nutritional supplements have been created, a general consensus on proper colony nutrition for beekeeping has yet to be reached. Thus, finding easily obtainable fortified <em>A. mellifera</em> food alternatives is still of interest. Here, we test plant powder-enriched food supplements since <em>a priori</em> evidence suggests plant extracts can enhance dry body weight and longevity of workers. Freshly emerged workers were kept in hoarding cages (N=69 days) and fed either with 50 % (w/v) sucrose solution alone or additionally with one of 12 powders: <em>Laurus nobilis, Quercus </em>spp<em>., Curcuma longa, Hypericum </em>spp<em>., Spirulina platensis, Calendula officinalis, Chlorella vulgaris, Melissa officinalis, Moringa oleifera, Rosa canina, Trigonella foenum-graecum, </em>and<em> Urtica dioica </em>(N=2028 workers total). The dry body weight was significantly increased in <em>Quercus</em> spp., <em>Hypericum</em> spp., <em>Spirunlina platensis, Mellisa officinalis, Moringa oelifera</em>, and <em>Trigonella foenum-graecum</em> treatments. Further, the longevity was significantly increased in <em>Quercus </em>spp., <em>Curcuma longa, Calendulae officinalis, Chlorella vulgaris, Melissa officinalis, Rosa canina, Trigonella foenum-graecum, </em>and<em> Urtica diocia</em> treatments<em>.</em> Given that plant extracts can enhance <em>A. mellifera</em> health, plant powders possibly provide additional macro- (i.e. proteins, lipids, peptides) and micronutrients (minerals and vitamins) thereby enhancing nutrient availability. Further investigations into the mechanisms underlying these effects and field studies are recommended to validate these findings in real-hive scenarios.</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>
Process parameters and properties of laser powder bed fusion alloys
<p>List of process paramaters and resulting properties of printed samples after laser powder bed fusion.</p> <p>Process parameters include: manufacturer of LPBF equipment, equipment model and laser type of the printer, laser power (P), scan speed (v), nominal powder layer thickness (t), hatch spacing (h), laser beam diameter (spot size (d), focus offset distance of the laser beam, scanning strategy, rotation angle of scanning strategy between layers and the build plate temperature.</p> <p>Information about the post-processing of the alloys was collected as to whether the alloy was heat-treated, heat treatment type, temperature and duration of each heat treatment step.</p> <p><br> Properties include consolidation, hardness, yield stress, elongation to failure, tensile strength</p> <p><br> Dataset was collected from peer-reviewed publicaions. Sources of the original data are provided within the datasheet.</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>
Figure 2 in Turmeric powder: biostimulator from expired lettuce seeds?
Figure 2. Dead seeds - DS (A) and germination speed index - GSI (B) of iceberg lettuce seeds, cultivar Great lakes 659, batch expired five years ago, under doses of turmeric powder.
Figure 1. Germination - G in Turmeric powder: biostimulator from expired lettuce seeds?
Figure 1. Germination - G (A) and abnormal seedlings - AS (B), obtained from iceberg lettuce seeds, cultivar Great lakes 659, batch expired five years ago, under doses of turmeric powder.
Figure 3 in Turmeric powder: biostimulator from expired lettuce seeds?
Figure 3. Seedling length - SL (A) and seedling fresh mass - SFM (B), obtained from iceberg lettuce seeds, cultivar Great lakes 659, batch expired five years ago, under doses of turmeric powder.
Dataset: Modeling the Dielectric Properties of Minerals from Crystals to Bulk Powders for Improved Interpretation of Asteroid Radar Observations
<p>Data (measurements of scattering parameters of samples) presented in: Hickson ,D.C., Boivin, A.L., Tsai, C.A., Daly, M.G. and Ghent, R.R. (2020) Modeling the Dielectric Properties of Minerals from Crystals to Bulk Powders for Improved Interpretation of Asteroid Radar Observations. <em>Journal of Geophysical Research: Planets, 125, </em>e2019JE006141. https://doi.org/10.1029/2019JE006141</p>
Figure 3 in Insecticidal effect of diatomaceous earth and dolomite powder against Corn weevil Sitophilus zeamais Motschulsky, 1855 (Coleoptera: Curculionidae)
Figure 3. Pictures of Sitophilus zeamais control taken by scanning electron microscopy (SEM). A. Dorsal view: trichoid sensilla (Se), sensilla (S), antenna (A), rostrum (R), elytrum (E). Bar = 500 µm. B. Rostrum and antenna: trichoid sensilla (Se), sensilla (S). Bar = 100 µm. C. Antenna: trichoid sensilla (Se). Bar = 20 µm. D. Elytrum: sensilla (S), suture (Su). Bar = 20 µm. E. Elytrum: sensilla (S), suture (Su). Bar = 50 µm. F. Abdomen, ventral view: sensilla (S). Bar = 10 µm.
Figure 2 in Insecticidal effect of diatomaceous earth and dolomite powder against Corn weevil Sitophilus zeamais Motschulsky, 1855 (Coleoptera: Curculionidae)
Figure 2. Mortality at different concentrations (mg) of diatomaceous earth and dolomite powder used for control of Sitophilus zeamais Motschulsky, 1855 (Coleoptera: Curculionidae), after different exposure times. DE:Diatomaceous earth and DOL: Dolomite powder.
Figure 5 in Insecticidal effect of diatomaceous earth and dolomite powder against Corn weevil Sitophilus zeamais Motschulsky, 1855 (Coleoptera: Curculionidae)
Figure 5. Pictures of Sitophilus zeamais exposed to inert dusts taken by scanning electron microscopy (SEM). A. Elytrum (E) of insect exposed to diatomaceous earth: sensilla (S), suture (Su). Bar = 200 µm. B. Elytrum (E) of insect exposed to dolomite powder: sensilla (S), suture (Su). Barra = 20 µm. C. Leg of insect exposed to dolomite powder: sensilla (S). Bar = 100 µm. D. Claw of insect exposed to dolomite powder. Bar = 50 µm.
Al-Co-Cu alloy - melt-spun ribbons and powder - SEM and TEM microstructure
<p>This set contains SEM and TEM images with EDS chemical composition data for Al-Co-Cu alloy in a melt-spun ribbon form, which was applied as a catalyst for the phenylacetylene hydrogenation reaction. </p> <p>The material preparation and microstructural analyses were performed at the Institute of Metallurgy and Materials Science of the Polish Academy of Sciences.</p> <p>The experimental procedure for material preparation, instrumentation, data collection and results analysis were described in the work: https://doi.org/10.1007/s43452-024-00904-x</p> <p> </p> <p>Preparation of materials: Amelia Zięba</p> <p>TEM images collection (FEI Tecnai G2, ThermoFisher Titan Themis G2 200 Probe Cs-Corrected): Amelia Zięba, Lidia Lityńska-Dobrzyńska</p> <p>SEM images acquisition (FEI E-SEM XL-30): Amelia Zięba</p> <p> </p> <p>Files description code:</p> <p>sem_rib_2000_1 - SEM BSE image of a melt-spun ribbon_magnification_image no</p> <p>sem_pwdr_1000_1 - SEM BSE image of pulverised melt-spun ribbons_magnification_image no</p> <p>sem_pwdr_ar_1000_1 - SEM BSE image of pulverised melt-spun ribbons recovered after use as a catalyst in the phenylacetylene hydrogenation reaction_magnification_image no</p> <p>tem_bf_1 - TEM bright field image of a melt-spun ribbon sample (FIB lamella)_image no</p> <p>tem_dyf_5 - selected area electron diffraction of a melt-spun ribbon sample - the number indicates a corresponding image number</p> <p>EDS-HAADF_img_1 - STEM image of a melt-spun ribbon sample (FIB lamella) with EDS corresponding maps and line analyses</p> <p>TEM_eds_point_analysis.txt - results of point analyses for EDS-HAADF_img_x series</p> <p>stem_pwdr_ar_1 - STEM images of powder recovered after reaction with EDS analysis results: eds_spec_stem_pwdr_ar_1</p> <p> </p> <p><em><strong>Acknowledgements</strong></em></p> <p><strong><em>The work was financially supported by the National Science Centre (NCN), Poland, project No. 2021/41/N/ST8/02533.</em></strong></p> <p> </p>
Data of "Effect of sample dimensions on the stiffness of PA12 Lattice materials fabricated using Powder Bed Fusion"
<div> </div> <div> <pre>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "Effect of sample dimensions on the stiffness of PA12 Lattice materials fabricated using Powder Bed Fusion", journal = "Additive Manufacturing", pages = " ", year = "2024", issn = "", doi = "https://doi.org/10.1016/j.addma.2024.104382", author = "L. Cobian, E. Maire, J. Adrien, U. Freitas, J.P. Fernandez-Blazquez, M.A. Monclus, J. Segurado"</pre> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 862015</p> </div>
Optical Particle Tracking in the Pneumatic Conveying of Metal Powders through a Thin Capillary Pipe
<p>An experimental setup utilizing high-speed cameras and specialized optics was constructed to collect the conveying flow characteristics. The data here presented is pre-processed using ImageJ/Fiji, and uses the TrackMate package (see https://github.com/trackmate-sc/TrackMate/pull/296). The videos can be loaded to Fiji using the FFMPG package.</p>
Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"
<p>Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading".</p> <p>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz.</p> <p>Date of data collection: The data were collected between 2021 and 2024.</p> <p>File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt."</p> <p>See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.</p>
Data for the conference poster "TiAl6V4 bistable mechanism produced by Laser Powder Bed Fusion"
<p>The dataset contains raw data for conference poster "TiAl6V4 bistable mechanism produced by Laser Powder Bed Fusion" presented at the 9th Metal Additive Manufacturing Conference.</p>
Dataset for the research paper "Computational and experimental investigation of thermally auxetic multi-metal lattice structures produced by Laser Powder Bed Fusion"
<p>The aim of this study is to investigate the potential of tailoring the structural thermal expansion properties of a multi-metal re-entrant lattice structure made of 316L stainless steel and CuCr1Zr copper alloy. Several geometric configurations with different layout of parent materials were designed and tested for their ability to thermally expand at elevated temperature. The study showed that one of the geometric configurations with the chosen material layouts allows to exceed the expansion range that can be achieved by both parent materials. The prediction of the finite element analysis was thus confirmed by experimental measurements. In addition, the influence of manufacturing imperfections in the form of geometric deviations and non-optimal material deposition was also investigated, and the results showed that this has a significant influence on the overall expansion. In conclusion, it was found that it is possible to tailor multi-metal lattice structures to a specific expansion, but the disadvantages associated with manufacturing must first be eliminated.</p>
Dataset for "Influence on micro-geometry and surface characteristics of laser powder bed fusion built 17-4 PH miniature spur gears in laser shock peening"
<p>The dataset represents the experimental data for publication "Influence on micro-geometry and surface characteristics of laser powder bed fusion built 17-4 PH miniature spur gears in laser shock peening".</p>
A comparative study of red brick powder and lime as soft soil stabilizer (Dataset)
<p>This data is the result of laboratory CBR testing in soaked and unsoaked conditions with or without additional stabilization</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.