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342 results for “fabric”
Dataset for scanning electron microscopy based local fiber volume fraction analysis of non-crimp fabric glass fiber reinforced composites
<p>SEM data sets, Matlab codes, and output from the local fiber volume fraction analysis from the publications:</p> <p>Mortensen, U.A., Rasmussen, S., Mikkelsen, L.P., Fraisse, A., Andersen, T.L. The impact of the fibre volume fraction on the fatigue performance of glass fiber composites used in the wind turbine industry, <em>Composites Part A</em>, submitted Dec 2022.</p> <p>Mikkelsen, Lars P., Fæster, S., Dahl, V.A. Dataset for scanning electron microscopy based local fiber volume fraction analysis of non-crimp fabric glass fiber reinforced composites. <em>Data in Brief</em>, Submitted, 2023.</p> <p>to where a reference should be given. </p> <p>The files are structured in the following way.</p> <ul> </ul> <p>L1, L2: Low FVF cases 1 and 2; see the publication.<br> H1, H2: High FVF case 1 and 2; see the publication.</p> <p>SEM images:</p> <ul> <li>*.bmp: Single SEM scan</li> <li>*.bmp.hdr: settings for single SEM scan</li> <li>*.tif: Stitched SEM scan</li> <li>*.tif.hdr: settings for stitched SEM scan</li> </ul> <p>*_BundleAnalysis.m: Script for manual segmentation of the individual bundles.</p> <ul> <li>*.zip files: Functions used by the ..._BundleAnalysis.m scripts</li> <li>*-CURVES_AND_AREAS.mat: Matlab saving of bundle definitions used by the ...BUndlesAnalysis.m script</li> <li>*BWbundles.mat: Output from the _BundleAnalysis.m script</li> </ul> <p>*.m: Local fiber volume fraction analysis script of SEM-scan based on output from BundleAnalysis tool</p> <ul> <li>Figxx.png: Reference to specific figures in the publications shown for all 4 cases</li> <li>Fig7.m: Matlab script for plotting figure 7</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Fabrication of low loss III-V Bragg-reflection waveguides for parametric down-conversion
<p>Measurements of sidewall roughness (AFM), optical losses (transmission) and photon pair coincidence counts (coinc) of Bragg-reflection waveguides.</p> <p>Data are contained in .csv files. Example plots are .pdf or .png format.</p> <p>RF: with reflow step</p> <p>No RF: without reflow step</p> <p>Coinc: Coincidence Counts</p>
Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits
<p>Most soft robots are pneumatically actuated and fabricated by molding and assembling processes that typically require many manual operations and limit complexity. Furthermore, complex control components (for example, electronic pumps and microcontrollers) must be added to achieve even simple functions. Desktop fused filament fabrication (FFF) three-dimensional printing provides an accessible alternative with less manual work and the capability of generating more complex structures. However, because of material and process limitations, FFF-printed soft robots often have a high effective stiffness and contain a large number of leaks, limiting their applications. We present an approach for the design and fabrication of soft, airtight pneumatic robotic devices using FFF to simultaneously print actuators with embedded fluidic control components. We demonstrated this approach by printing actuators an order of magnitude softer than those previously fabricated using FFF and capable of bending to form a complete circle. Similarly, we printed pneumatic valves that control a high-pressure airflow with low control pressure. Combining the actuators and valves, we demonstrated a monolithically printed electronics-free autonomous gripper. When connected to a constant supply of air pressure, the gripper autonomously detected and gripped an object and released the object when it detected a force due to the weight of the object acting perpendicular to the gripper. The entire fabrication process of the gripper required no posttreatment, postassembly, or repair of manufacturing defects, making this approach highly repeatable and accessible. Our proposed approach represents a step toward complex, customized robotic systems and components created at distributed fabricating facilities.</p>
Clement Fabric Impressed Jar (620p16-1)
**Clement Fabric Impressed jar rim** Location: Gaston site (31Hx7), Halifax County, North Carolina. Period: Middle Woodland (AD 300-1000). Material: ceramic. Dimensions: length, 159.0 mm; width, 135.5 mm; thickness, 8.4 mm. Notes: Catalog no. 620p16 (specimen 1). North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Chris LaMack. Source: Objaverse 1.0 / Sketchfab
3D orthogonal woven carbon fibre fabric and composites - Micro-computed tomography scans
<p>Computed tomography images of a 3D orthogonal woven carbon fibre fabric and composites. Detailed description can be found in the attached metadata files as well as in the associated paper: <a href="https://doi.org/10.1016/j.compositesa.2013.10.004">https://doi.org/10.1016/j.compositesa.2013.10.004</a></p> <p>The sample was used for geometrical analysis and for creating a TexGen model with the subsequent permeability and mechanical modelling.</p>
In Vivo Evaluation of the Accuracy of Immediate Screw-Retained Provisional Crowns Fabricated Using Digital Planning and Guided Surgery
ClinicalTrials.gov study NCT07315607. IPD Sharing: NO. Countries: 1. Publications: 19.
Basic Study of Fabricating Biomorphic Crowns
ClinicalTrials.gov study NCT02609178. IPD Sharing: UNDECIDED. Countries: 1. Publications: 17.
The Effect of Dynamic Elastomeric Fabric Orthosis (DEFO) on Sitting Balance and Gross Manuel Dexterity in Cerebral Palsy
ClinicalTrials.gov study NCT03191552. IPD Sharing: NO. Countries: 1. Publications: 8.
New Fabric Attenuation Device in Endovascular Interventional Radiology II (PREFER II)
ClinicalTrials.gov study NCT01611454. IPD Sharing: NO. Countries: 1. Publications: 1.
Facile fabrication of polymer network using click chemistry and their computational study
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Lithographic Crystallinity Regulation in Additive Fabrication of Thermoplastics (CRAFT)
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Data from: Preparation and properties of nano-TiO₂/SiO₂ composite sol-modified cotton fabrics
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Data for: Fabrication and characterization of antibacterial coatings using an amphoteric condensed tannin,Tanfloc
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Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits
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Induced pluripotent stem cell-derived cardiomyocyte in vitro models: tissue fabrication protocols, assessment methods, and quantitative maturation metrics for benchmarking progress
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Loading-dependent microscale measures control bulk properties in granular material: an experimental test of the Stress-Force-Fabric relation
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Data from: Advancing neural interfaces: A framework for the fabrication and characterization of freestanding micro-nanodevices
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Easy and fast fabrication of self-cleaning and anti-icing perfluoroalkyl silane film on aluminium
<p><strong>Easy and fast fabrication of self-cleaning and anti-icing perfluoroalkyl silane film on aluminium</strong></p> <p><strong>Peter Rodič<sup>a,</sup></strong><sup><a href="#_ftn1">[1]</a></sup><strong>, Barbara Kapun<sup>a</sup>, Matjaž Panjan<sup>b</sup>, Ingrid Milošev<sup>a</sup></strong></p> <p><sup>a</sup> Jožef Stefan Institute, Department of Physical and Organic Chemistry, Jamova c. 39, SI-1000 Ljubljana, Slovenia</p> <p><sup>b </sup>Jožef Stefan Institute, Department of Thin Films and Surfaces, Jamova c. 39, SI-1000 Ljubljana, Slovenia</p> <p> </p> <p>Appendix B:</p> <p>The following videos complement the data and Figures 9-11 in this article. They are available in the online version.</p> <p>Appendix 2 = Video A1. Bouncing dynamic characterisation</p> <p>Appendix 3 = Video A2. Water rolling off the surface</p> <p>Appendix 4 = Video A3. Self-cleaning of solid pollutants</p> <p>Appendix 5 = Video A4. Anti-icing properties and melting delay</p> <p>Appendix 6 = Video A5. Ice adhesion on ground and treated aluminium</p> <p> </p> <p><sup><a href="#_ftnref1">[1]</a> </sup>Corresponding author; e-mail: <a href="mailto:peter.rodic@ijs.si">peter.rodic@ijs.si</a> (P. Rodič); tel.: + 386 1 4773 261</p> <p> </p>
Deterministically fabricated spectrally-tunable quantum dot based single-photon source
<p>Deterministically fabricated spectrally-tunable quantum dot based single-photon source<br> Spectrally-tunable quantum light sources are key elements for the realization of long-distance quantum communication. A deterministically fabricated single-photon source with a photon extraction efficiency of η =(20 ± 2) %, a maximum tuning range of ΔE =  2.5 meV and a minimum g(2)(τ = 0) = 0.03 ± 0.02 is presented. The device consists of a single pre-selected quantum dot (QD) monolithically integrated into a microlens that is bonded onto a piezoelectric actuator via gold thermocompression bonding. Here, a thin gold layer simultaneously provides strain transfer and acts as a backside mirror for the QD-microlens to maximize the photon extraction efficiency. The QD-microlens structure is patterned via 3D in-situ electron-beam lithography (EBL), which allows us to pre-select and integrate suitable QDs based on their emission intensity and energy with a spectral accuracy of 1 meV for the final device. Together with strain fine-tuning, this enables the scalable realization of single-photon sources with identical emission energy. Moreover, we show that the emission energy of the source can be stabilized to µeV accuracy by closed-loop optical feedback. Thus, the combination of deterministic fabrication, spectral-tunability and high broadband photon-extraction efficiency makes the QD-microlens single-photon source an interesting building block for the realization of quantum communication networks.</p>
Three tomographic CT datasets of a woven fabric
<p><strong>Summary</strong><br> This submission contains three tomographic datasets of a fragment of fabric woven in tapestry weave. The data is collected at three different zoom levels to achieve different reconstructed image resolution.<br> The data is made available as part of [Bossema 2020]. </p> <p><strong>Apparatus</strong><br> The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].</p> <p><strong>Sample Information</strong><br> The sample is a fragment of woven fabric approximately 7cm x 15cm in size. The fabric was hung vertically on a piece of foam, held on the top by a wooden stick through one of the holes and on the bottom by a piece of plastic tape. See Figure 5 in [Bossema 2020] for a picture of the object and examples of the reconstruction. <br> </p> <p><strong>Experimental Plan</strong><br> The data in this submission was collected to illustrate the use of zooming for the investigation of cultural heritage objects. Three region-of-interest scans of the lower part, containing a hole, were collected at different zoom levels. For each scan, the sample was rotated 360° in circular and continuous motion, with a dark-field (closed-shutter), and flat-field (open-shutter) image taken before the acquisition. Each dataset consists of 1200 projections. The source-detector distance was kept at 1098mm. At the first level of zooming, the object was placed at 963mm from the source yielding a magnification of 1.14 and 131 micron resolution. For the second level the object was moved closer to the source so that the source-object distance was 603mm, yielding a magnification of 1.82 and 82 micron resolution. At the third level, the source-object distance was reduced to 243, yielding a magnification of 4.5 and 33 micron resolution.<br> All raw data (i.e. no corrections) is made available in .tif format.</p> <p> </p> <p><strong>List of Contents</strong></p> <p>The content of the submission is given below.</p> <ul> <li><strong>level1: </strong>lowest resolution scan.</li> <li><strong>level2:</strong> higher resolution scan.</li> <li><strong>level3:</strong> highest resolution scan.</li> </ul> <p>Each data folder contains:</p> <ul> <li>dark-field (or closed-shutter) image, <em>di000000.tif</em>,</li> <li>flat-field (or open-shutter before acquisition) image, <em>io000000.tif</em>,</li> <li>raw (unprocessed or uncorrected) projections,<em> scan_*.tif</em>,</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata,</li> <li><em>scan settings.txt</em>, a text file with scanner metadata in a human readable format, and</li> <li><em>data settings XRE.ini</em>, a snapshot text file of basic geometry information at the start of a scan.</li> <li><em>script_executed.txt</em>, the text file containing the list of commands the apparatus has executed.</li> </ul> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a href="https://www.cwi.nl/research/groups/computational-imaging">Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p><strong>Contact Details</strong><br> For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><strong>Acknowledgments</strong><br> We thank Suzan Meijer of the Rijksmuseum for providing this sample.</p>
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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)
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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.