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149 results for “Viscosity”
Ionic conductivity, viscosity, and self-diffusion coefficients of novel imidazole salts for lithium-ion battery electrolytes
<p>This entry contains the data related to the publication<br><strong>A. Szczęsna-Chrzan <em>et al.</em>, “Ionic conductivity, viscosity, and self-diffusion coefficients of novel imidazole salts for lithium-ion battery electrolytes,”<em> J. Mater. Chem. A</em>, vol. 11, no. 25, pp. 13483–13492, 2023, doi: 10.1039/D3TA01217D.</strong><br><br>It contains experimentally determined conductivity, viscosity and self-diffusion coefficients of anions of the Hückel-type salts lithium 4,5-dicyano-2-(trifluoromethyl)imidazolide (LiTDI), lithium 4,5-dicyano-2-(pentafluoroethyl)imidazolide (LiPDI) and lithium 4,5-dicyano-2-(n‑heptafluoropropyl)imidazolide (LiHDI) for various concentrations of the conducting salts (0 M - 1.5 M) in a solvent mixture containing ethylene carbonate (EC) and ethyl methyl carbonate (EMC) in a ratio of 3:7 by weight.</p> <p>The Python scripts used for the analysis of the NMR data are also included in the dataset.</p>
The effect of solvent on convectively-driven silica particle assembly: Decoupling surface tension,viscosity, and evaporation rate
<p>Dataset associated with 'The effect of solvent on convectively-driven silica particle assembly: Decoupling surface tension, viscosity, and evaporation rate’.</p> <p>The data is based on the figures below, published in the linked article (see the doi).</p> <p><strong>- Figure 1. S</strong>egmented and raw images of dip-coated films. <strong>(images, .TIF)</strong></p> <p><strong>- Figure 2. </strong>Calculated surface coverages <strong>(data, .csv)</strong></p> <p><strong>- Figure 3. </strong>Rheology on SiO<sub>2</sub>-iPrOH-Glycerol mixtures & SEM micrographs of particle films. <strong>(data, .csv; images, .TIF)</strong></p> <p><strong>- Figure 4. </strong>SEM micrographs of silica helices films. <strong>(images, .TIF)</strong></p> <p><strong>- Figure S1. </strong>Measured evaporated masses of each solvent as a function of time. <strong>(data, .csv)</strong></p> <p><strong>- Figure S2. </strong>TEM micrographs of SiO2 seeds and measured particle diameters. <strong>(data, .csv; images, .TIF)</strong></p> <p><strong>- Figure S3. </strong>TEM micrographs of SiO particles and measured particle diameters. <strong>(data, .csv; images, .TIF)</strong></p> <p><strong>- Figure S4. </strong>Calculated solvent fractions as a function of time. <strong>(data, .csv)</strong></p> <p><strong>- Figure S5.</strong> Rheology of i-PrOH-glycerol mixtures.<strong> (data, .csv)</strong></p>
Constraints on mantle viscosity and Laurentide ice sheet evolution from pluvial paleolake shorelines in the western United States: Datasets
<p>*********** Please view the README.txt file for detailed documentation of data. ***********</p> <p><strong>Title:</strong> Constraints on mantle viscosity and Laurentide ice sheet evolution from pluvial paleolake shorelines in the western United States: Datasets</p> <p><strong>Version: </strong>1.0</p> <p><strong>Date of Release: </strong>2019/12/16</p> <p><strong>Identifier: </strong>10.5281/zenodo.3576251</p> <p><strong>Associated publication:</strong> Austermann, J., Chen, C.Y., Lau, H.C.P., Maloof, A.C., and Latychev, K. (2019) Constraints on mantle viscosity and Laurentide ice sheet evolution from pluvial paleolake shorelines in the western United States. <em>Earth and Planetary Science Letters</em>. doi: 10.1016/j.epsl.2019.116006</p> <p><strong>Link to publication: </strong><a href="https://doi.org/10.1016/j.epsl.2019.116006">https://doi.org/10.1016/j.epsl.2019.116006</a></p> <p><strong>Suggested citation: </strong>Please reference the associated publication above when using any datasets or materials described in the README file.</p> <p><strong>Contact information:</strong> Jacky Austermann (jackya@ldeo.columbia.edu) and Christine Y. Chen (cychen.earth@gmail.com)</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>This directory contains the following datasets:</p> <p>SHORELINE FEATURE ELEVATION DATA</p> <ul> <li><strong>Bonneville_Provo_Sehoo_shoreline_feature_elev_Austermann2019_EPSL.xlsx</strong>: shoreline feature elevation measurements of the Bonneville, Provo, and Sehoo lake stages of Lake Bonneville and Lake Lahontan; original measurements were made by Adams et al. (1999), Chen and Maloof (2017), and Currey (1982)</li> </ul> <p>MODELED RECONSTRUCTIONS OF LAKE VOLUME AND PALEOTOPOGRAPHY</p> <ul> <li><strong>LakeBonneville_NAICE_l20.ump02p25.lmp5VM5.mat:</strong> model output for Lake Bonneville, including reconstructions of lake volume and paleotopography</li> <li><strong>LakeLahontan_NAICE_l20.ump02p25.lmp5VM5.mat</strong>: model output for Lake Lahontan, including reconstructions of lake volume and paleotopography</li> </ul>
Tables of results for lower mantle grain size and viscosity estimates
<p>Data on diffusivity, grain size, and viscosity calculations are shown in Figs. 7-10 and Figs. S4-S5 in Okamoto and Hiraga's "A Common Diffusional Mechanism for Creep and Grain Growth in Polycrystalline Rocks: Application to Lower Mantle Viscosity Estimates".</p>
PEPT data for Understanding the effect of fluid viscosity in Vertical Stirred Mills using the Positron Emission Particle Tracking (PEPT) approach
<p>The raw PEPT data collected for the paper "Understanding the effect of fluid viscosity in vertical stirred mills using the positron emission particle tracking (PEPT) approach." The paper is the first to use the PEPT technique to investigate the effect of fluid viscosity on the efficiency of the grinding process.</p> <p>This data can be post-processed using the PEPT-ML library and used in isolation or it can be used to calibrate an equivalent simulation. The simulation template is available on GitHub and the link to this is under the Software tab. Each file is labelled by the fluid viscosity and attritor speed used in the experiment, The data for a single run is often split across files but can be combined by the PEPT-ML library.</p>
Data for: Klein et al., Viscosity of aqueous ammonium nitrate--organic particles: Equilibrium partitioning may be a reasonable assumption for most tropospheric conditions, egusphere-2024-1459
<p><strong>Experimental data </strong></p> <p>This folder contains the experimental and modelled data to the figures shown in the main manuscript and Appendix.</p> <p>Figure 3B AIOMFAC-VISC (AIOMFAC-VISC modelling of sucrose)</p> <p>Figure 3B Experimental (Viscosity measurements of sucrose)</p> <p>Figure 4 (Viscosity measurements of ammonium nitrate - sucrose - water mixtures)</p> <p>Figure 5 A and C (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using mixing rules)</p> <p>Figure 5 B and D (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using AIOMFAC-VISC)</p> <p>Figure 6 (Viscosity estimations of inorganic - sucrose - water mixtures using mixing rules)</p> <p>Figure 7 (Mixing times for ammonium nitrate - sucrose - water and Toluene SOA - sucrose - water aerosol particles for varies cities) </p> <p>Figure A2 (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using a mass fraction based mixing rules)</p>
Data to Single fibre coating of viscose filaments with cellulose acetate
<p>Evaluation data for the manuskript with the preliminary title:</p> <p>Single fibre coating of viscose filaments with cellulose acetate for partially hydrophobic hybrid fibres</p>
Dataset for "Toward closure between predicted and observed particle viscosity over a wide range temperature and relative humidity"
<p>Raw data and processing scripts associated with the paper "Toward closure between predicted and observed particle viscosity over a wide range temperature and relative humidity". Please see README.md for details. </p>
Artificial viscosity model to mitigate numerical artefacts at fluid interfaces with surface tension (Supporting data)
<p>This data accompanies the paper "Artificial viscosity model to mitigate numerical artefacts at fluid interfaces with surface tension", published in Computers & Fluids.</p>
Dataset for "Experimental determination of the relationship between organic aerosol viscosity and ice nucleation at upper free tropospheric conditions"
<p><strong>Data and scripts used to create the figures in the manuscript titled: "Experimental determination of the relationship between organic aerosol viscosity and ice nucleation at upper free tropospheric conditions"</strong></p>
Figure 3: Viscosity variation depending on speed rate Table-STUDIES TOWARDS OBTAINING A PHOTOPROTECTIVE DERMO-COSMETIC COMPLEX PRODUCT WITH NATURAL EXTRACTS OF INORGANIC AND ORGANIC SUBSTANCES-
<p>Microbiological characteristics The total number of aerobic germs and fungi<br> was determined by the decimal dilution method or the multiple tube test. For<br> identifying pathogenic species we have used speci¯c tests (table6).<br> The evaluation of photodegradation was carried out through the variation<br> of photoprotective factor of the studied product kept 60 minutes under UV<br> radiation at 65±C. The tests revealed the preservation or even a slight increase<br> in the SPF. The use of screen substances in the formulation of photoprotective<br> products results in undesirable sensory characteristics: visible white ¯lm on the<br> skin surface, unpleasant sensation, dry or oily skin after application.</p>
Data for Glacial isostatic adjustment reveals Mars' interior viscosity structure
<p>Present-day Martian interior models used in Broquet et al. (2024). All models use the following naming convention: Profile_NorthPole_Mars-TAYAK-dc-rho_south[-rho_north], where dc is the crustal thickness at the InSight landing site in km, rho_north and rho_south are the bulk density of the northern and southern hemisphere crust in g cm^-3. If added, XGRS provides the crustal heat producing element enrichment factor (X) with respect to the nominal Gamma Ray measured average of 49 pW kg^-1. </p> <p>Files with _60deg provide quantities averaged over the northern regions (>60°N) and _AVG give averages for the whole planet. Models with case numbers are from Plesa et al. (2018) [https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018GL080728]. </p> <p>Data Columns:<br>------------------------------<br>Column 1: Radius [m]<br>Column 2: Temperature [K]<br>Column 3: Viscosity [Pa s]<br>Column 4: Shear Velocity [m/s]<br>Column 5: Density [kg/m3]<br>Column 6: Shear Modulus [Pa]</p>
Dataset for: Using the quasi-chemical model beyond the quadruplet approximation: Density and Viscosity Models for Molten Salt Fuel Systems
<p>Contains data plotted in the figures of the manuscript with the same title (submitted, 2021). </p>
Hysteresis of viscosity in a solid-liquid two-phase system with shear
<p>In this study, the role of fluids in a slow slip events (SSEs) regime was examined by investigating the rheological properties of the solid-liquid two-phase system on a laboratory scale. We specifically investigated how the rheological properties change with the shear rate when the granular layer is fluid-rich. We used a velocity-controlled rheometer to apply shear to a liquid-saturated granular layer. The results show that the liquid-saturated granular layer’s shear viscosity depended on the liquid viscosity and exhibited power-law like behavior. Additionally, the saturated granular layered exhibited hysteresis, which increased as the viscosity of the liquid decreased. After a quantitative discussion through Jop et al. (2006), Bagnold (1954) and Perrin et al. (2019) framework, we discuss the role of fluid in (SSE) regime. (Abstract in this study)</p>
Viscosity calibration data
<p>Data used in the preprint https://arxiv.org/abs/2208.13854</p>
The role of blood viscosity in hovering flight of hawkmoths
<p>Viscosity determines the resistance of hemolymph flow through vessels. For flying insects, viscosity is a major physiological parameter limiting flight performance by controlling the flow rate of fuel to the flight muscles, circulating nutrients, and rapidly removing metabolic waste products. The more viscous the hemolymph, the greater the metabolic energy needed to pump it through body cavities and hemolymph vessels. By employing Magnetic Rotational Spectroscopy with nickel nanorods, we showed that viscosity of hemolymph in resting hawkmoths (Sphingidae) depends on wing size non-monotonically. Viscosity increases for small hawkmoths with high wingbeat frequencies, reaches a maximum for middle-sized hawkmoths with moderate wingbeat frequencies, and decreases in large hawkmoths with slower wingbeat frequencies but greater lift. Accordingly, hawkmoths with small and large wings have viscosities approaching that of water, whereas hawkmoths with mid-sized wings have more than twofold greater viscosity. The metabolic demands of flight correlate with significant changes in circulatory strategies via modulation of hemolymph viscosity. Thus, the evolution of hovering flight would require fine-tuned viscosity adjustments to balance the need for the hemolymph to carry more fuel to the flight muscles while decreasing the viscous dissipation associated with its circulation.</p>
Data set used in "Effect of turbulence and viscosity models on wall shear stress derived biomarkers for aorta simulations"
<p>Data set used in "Effect of turbulence and viscosity models on wall shear stress derived biomarkers for aorta simulations"</p> <p>Includes the data for 20 heartbeats. Divided into external and internal walls regions. </p>
Shear viscosity coefficient of acqueous glycerol from non-equilibrium Molecular Dynamics simulations
<p>This dataset contains the results of non-equilibrium atomistic Molecular Dynamics simulations of water-glycerol liquid mixtures, at various relative concentrations. The goal of the simulations is to quantify the shear viscosity coefficient of said mixtures using the periodic perturbation technique [1]. </p> <p>The pattern "Glycerol***" refers to the mass fraction of glycerol ("000": pure water, "100": pure glycerol). Each folder contains three sets of simulations, with different perturbation force parameters ("Em*"), where configuration files necessary to reproduce molecular simulations simulations are provided. Maps of density and velocity field are in "Em*"->"Flow".</p> <p>A small self-contained Python script to fit the velocity fields to a periodic cosine perturbation is provided (fit-periodic.py). Alternatively, viscosity can be obtained from energy outputs by running:</p> <pre><code>gmx energy -f ener.edr</code></pre> <p>and selecting "1/Viscosity". Simulations are performed with Gromacs. We refer to the code documentation for further information (<a href="https://manual.gromacs.org/">https://manual.gromacs.org/</a>).</p> <p>References:</p> <p>[1] B. Hess, Determining the shear viscosity of model liquids from molecular dynamics simulations, J. Chem. Phys. 116, 209–217 (2002) <a href="https://doi.org/10.1063/1.1421362">https://doi.org/10.1063/1.1421362</a></p>
Shear viscosity coefficient of acqueous glycerol from equilibrium Molecular Dynamics simulations
<p>This dataset contains the results of equilibrium atomistic Molecular Dynamics simulations of water-glycerol liquid mixtures, at various relative concentrations. The goal of the simulations is to quantify the shear viscosity coefficient of said mixtures using linear response theory (Einstein relations) [1]. The post-processing of simulation results is inspired by the method of Zhang et al. [2].</p> <p>The pattern "Glycerol***" refers to the mass fraction of glycerol, being "000" pure water (0%) and "100" pure glycerol (100%). Zip folders contain</p> <ul> <li>Expected value and integral of the square of the off-diagonal components of the pressure gradient ("EnergyOutputs")</li> <li>Initial configurations used to start the ensemble of replicas from which viscosity is computed ("InitConfReplicas")</li> <li>Output, state and topology of replica simulations ("MdrunOutputs")</li> </ul> <p>A self-contained Python script (compute-visco.py) for the computation of viscosity from the output of an ensemble of simulations is provided. Integrals used to quantify viscosity via Einstein's relation can be obtained from energy output files (.edr) by running:</p> <pre><code>gmx energy -f ener.edr -evisco -eviscoi -vis</code></pre> <p>Simulations are performed with Gromacs. We refer to the code documentation for further information (<a href="https://manual.gromacs.org/">https://manual.gromacs.org/</a>).</p> <p>References:</p> <p>[1] B. Hess, Determining the shear viscosity of model liquids from molecular dynamics simulations, J. Chem. Phys. 116, 209–217 (2002) <a href="https://doi.org/10.1063/1.1421362">https://doi.org/10.1063/1.1421362</a></p> <p>[2] Y. Zhang et al., Reliable Viscosity Calculation from Equilibrium Molecular Dynamics Simulations: A Time Decomposition Method, J. Chem. Theory Comput. 2015, 11, 3537−3546, <a href="https://doi.org/10.1021/acs.jctc.5b00351">https://doi.org/10.1021/acs.jctc.5b00351</a></p>
Physical link between effective viscosity and electrical resistivity for dislocation creep in upper mantle and its application in Northwest Xinjiang, China
<p>Cross-section of electrical resistivity extracted from the preferred 3-D resistivity model from Liu (2022)</p> <p>Format: X (Km), Z (Km), rho (ohm-m), T (K)</p> <p>Notes: Temperature(T) extracted from Sun et al., 2022, available at https://doi.org/10.5281/zenodo.6459746</p> <p> (lat, lon) of the ends of the profile: (,40.71,79.8300), -->, (,46.84,86.0700)</p>
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