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48 results for “Diffusion coefficient”
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>
Diffusion coefficients on amorphous polystyrene and modelling of migration levels from plastic packaging
<p>This dataset is actually supplementary data of the scientific article:</p> <p>Martinez-Lopez, Brais; Gontard, Natalie and Peyron, Stephane "Worst case prediction of additives migration from polystyrene for food safety purposes: a model update" in Food Additives and Contaminants Part A, doi:10.1080/19440049.2017.1402129.</p> <p>If you use it, please cite it using the reference file we have provided.</p> <p>This description is the same as in the file "readme.txt", included in the upload.</p> <p>List of files:</p> <ul> <li>The file database_D contains the experimental diffusivity data for amorphous polystyrene used for the figure 1b. It is a spreadsheet file with two tabs. In the first tab, the diffusion coefficients can be found by choosing molecule family (and the publication were they were found) and temperature in celsius degrees. The second tab contains the same diffusivity data, but they are ranged by increasing molecular weight and temperature. This file is available in open document (.ods) and microsoft excel (.xlsx) formats.</li> <li>The file migration modelling is also a spreadsheet file, and contains several tabs. The first tab (diffusion coefficient) is an implementation of equation 1, the predictive model for overestimated diffusion coefficients. The given Ap and tau parameter sets are the ones specified in Table 2 for amorphous polystyrene. The second tab (migration levels) is an implementation of equation 3, the solution to Fick's second law that is used to predict migration levels in food, for pre-selected values of alpha (equation 5). The tabs labeled alpha =... contain the sums used in the equation, whereas the tab "roots" contains the first 200 roots of trascendental equation 4, needed to calculate the sum or terms. This file is also available in open document (.ods) and microsoft excel (.xlsx) formats.</li> <li>The file "table.pdf" sums the main characteristics of the molecule families, together with the references where they were found (in the second page).</li> <li>The file reference.bib contains the reference that should be cited if you use this dataset for your own work.</li> <li>Finally, the file readme.txt contains this very same description.</li> </ul> <p>These files have undergone thorough check, so there should not be any mistakes. In the rare event that you find one, please report it to the author so it can get fixed.</p> <p>bramar@food.dtu.dk</p> <p>Brais Martínez López, PhD<br> Assistant professor<br> DTU Fødevareinstituttet<br> Danmarks Tekniske Universitet<br> Søltofts Plads<br> Bygning 227<br> 2800 Kgs. Lyngby</p> <p> </p> <p> </p> <p> </p>
Data and code for gmd-2023-113 "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"
<p>Data and code for the paper "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"</p> <p>includes: </p> <p>The model is Community Earth System Model (v1.2.1) (provided by www.cesm.ucar.edu)</p> <p>Data assimilation code is initially provided by Data Assimilation Research Testbed (DART) (https://dart.ucar.edu/), some modifications are made to enable parameter estimation function of ocean background vertical diffusivity coefficients. And the programs and scripts for deal with OISST and EN4 profiles are also developed.</p> <p>The parameter sensitivity experiment results are saved as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012.nc">sensitive2008-2012.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012salt.nc">sensitive2008-2012salt.nc</a> for temperature and salinity, respectively. And the python script to draw the results is </p> <p>The state estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>The parameter estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>the estimated paremeter ensemble is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/parameters.nc">parameters.nc</a></p> <p>the python script for comparing the SE and PE results is <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/plot_analysis.py">plot_analysis.py</a></p> <p>the nino3.4 indices computed by the forecast experiment is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/fcst_correlation.nc">fcst_correlation.nc</a></p> <p> </p>
ULF Wave Radial Diffusion Coefficients
<p>Hourly radial diffusion coefficients, DLL, derived from ground-based magnetometer measurements from March 16 to March 20, 2015 in units of days<sup>-1</sup>. The DLL values are plotted out at 100 equally spaced L* values from L*=1 to L*=5 derived from the TS05 magnetic field model at K=0 G<sup>1/2</sup>Re. These DLL values are used to reproduce the the evolution of the electron phase space density profiles in the Earth's outer radiation belt during the March 2015 geomagnetic storm presented in:</p> <p>Ozeke et al., The March 2015 Superstorm Revisited: Phase Space Density Profiles and Fast ULF Wave Diffusive Transport, Journal of Geophysical Research, 2019, submitted.</p> <p>The attached plot shows a comparison of these event specific diffusion coefficient with those obtained from the empirical models of:</p> <p>Brautigam, D. H., and J. M. Albert (2000), Radial diffusion analysis of outer radiation belt electrons during the October 9, 1990, magnetic storm, <em>J. Geophys. Res.</em>, 105(A1), 291–309, doi:10.1029/1999JA900344.</p> <p>and</p> <p>Ozeke, L. G., I. R. Mann, K. R. Murphy, I. Jonathan Rae, and D. K. Milling (2014), Analytic expressions for ULF wave radiation belt radial diffusion coefficients, <em>J. Geophys. Res. Space Physics</em>, 119, 1587–1605, doi:10.1002/2013JA019204.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Diffusion Coefficient Analysis by Dynamic Light Scattering Enables Determination of Critical Micelle Concentration
<p>This upload contains dynamic light scattering data files obtained from the work described in the manuscript that is published by Lena Nielinger and co-workers in ChemPlusChem (<a href="https://doi.org/10.1002/cplu.202400645">https://doi.org/10.1002/cplu.202400645</a>) (WILEY). The files in this repository contain dynamic light scattering data obtained from the analysis of different detergents series and can be downloaded and analysed with a Zetasizer software according to the instructions procied in the manuscript. For information on how to obtain the the Zetasizer software, we refer to the customer support and/or website of the company Malvern Panalytical.</p>
Pitch-angle and energy diffusion coefficients calculated for ions interacting with kinetic Alfven waves near the magnetopause
<p>In each file:<br> the first row (starting with the second column) contains pitch-angle grid in degrees<br> the first column (starting with the second row) contains the energy grid in keV<br> "x_to_L" value in the file name denotes the position in space along the normal to the magnetopause relative to the current sheet center (see description file).<br> energy diffusion coefficients are measured in keV^2/s, and pitch-angle coefficients are measured in rad^2/s.</p>
Data for diffusion coefficient and offset figures
Data for a figure that shows the distributions of the mean and varance of the diffusion coefficient and offset that are found from the analysis of 1024 unique random walks using kinisi. This is performed for both a truely random walk and a temporally anti-correlated random walk.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/75fc5afb67cd43425ac5554449aba4bddab52e84">this GitHub repo</a>.
msd-errors - Data for diffusion coefficient and offset figures3
Data for a figure that shows the distributions of the mean and varance of the diffusion coefficient and offset that are found from the analysis of 1024 unique random walks using kinisi. This is performed for both a truely random walk and a temporally anti-correlated random walk.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/900ea562a3de64ba9ec05ddfdf2d4c41f611c37c">this GitHub repo</a>.
msd-errors - Data for diffusion coefficient and offset figures
Data for a figure that shows the distributions of the mean and varance of the diffusion coefficient and offset that are found from the analysis of 1024 unique random walks using kinisi. This is performed for both a truely random walk and a temporally anti-correlated random walk.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/900ea562a3de64ba9ec05ddfdf2d4c41f611c37c">this GitHub repo</a>.
Supporting data for "Rapid determination of solid-state diffusion coefficients in Li-based batteries via intermittent current interruption method"
<p>This is the dataset of electrochemical and operando X-ray diffraction experiments for our publication "Rapid determination of solid-state diffusion coefficients in Li-based batteries via intermittent current interruption method". This archive contains the raw data and scripts written in R used in the analysis and presentation of the results in this manuscript.</p> <p><strong>Abstract of the manuscript:</strong></p> <p>The galvanostatic intermittent titration technique (GITT) is considered the go-to method for determining the Li<sup>+</sup>diffusion coefficients in insertion electrode materials. However, GITT-based methods are either time-consuming, prone to analysis pitfalls or require sophisticated interpretation models. Here, we propose the intermittent current interruption (ICI) method as a reliable, accurate and faster alternative to GITT-based methods. Using Fick’s laws, we prove that the ICI method renders the same information as the GITT within a certain duration of time since the current interruption. Via experimental measurements, we also demonstrate that the results from ICI and GITT methods match where the assumption of semi-infinite diffusion applies. Moreover, the benefit of the non-disruptive ICI method to operando materials characterization is exhibited by correlating the continuously monitored diffusion coefficient of Li<sup>+</sup> in a LiNi<sub>0.8</sub>Mn<sub>0.1</sub>Co<sub>0.1</sub>O<sub>2</sub>-based electrode to its structural changes captured by operando X-ray diffraction measurements.</p>
Diffusion MRI (Magnetic Resonance Imaging) Using ADC (Apparent Diffusion Coefficient) Histograms in the Evaluation of Adnexal Tumor Aggressiveness
ClinicalTrials.gov study NCT02742870. IPD Sharing: Not stated. Countries: 1. Publications: 2.
ECCO Ocean 3D Gent-Mcwilliams, Redi, and Background Vertical Diffusivity Coefficients for the Lat-Lon-Cap 90 (llc90) Native Model Grid (Version 4 Release 4)
This dataset provides 3D coefficients for the Gent-McWilliams and Redi parameterizations and background vertical diffusivity on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Each of these three time-invariant, spatially-varying terms are estimated during the ECCO V4r4 optimization. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Ocean 3D Gent-Mcwilliams, Redi, and Background Vertical Diffusivity Coefficients for the 0.5 degree Lat-Lon Model Grid (Version 4 Release 4)
This dataset provides 3D coefficients for the Gent-McWilliams and Redi parameterizations and background vertical diffusivity interpolated to a regular 0.5-degree grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Each of these three time-invariant, spatially-varying terms are estimated during the ECCO V4r4 optimization. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
Data from: Alkaline phosphatase, lactic dehidrogenase, inflammatory variables and apparent diffusion coefficients from MRI for prediction of chemotherapy response in osteosarcoma
<p><span><b><i>Background</i></b><b>. </b>This present study aimed to assess if clinical, laboratory and MRI were an accurate benchmark in assessing the effectiveness of neoadjuvant chemotherapy in osteosarcoma patients.<b> <i>Methods. </i></b>This was an observational analytic study with a cross-sectional design. Research subjects were selected using the total sampling method from osteosarcoma patients who underwent neoadjuvant chemotherapy during the period between January 2017– July 2019. <b><i>Results</i>.</b>Of the 58 patients included in this study, 38 were male and 20 were female aged 5 - 67 years (mean,16-year-old. 37(63.8%) patients underwent neoadjuvant chemotherapy with CAI regimens and 13 (36.2%) with CA. The tumors were classified as stage <b>IIB </b>in 43 (74.1%) patients and stage III in15 (25.9%) patients. After undergoing neoadjuvant chemotherapy, 4 patients had poor MSTS, 30 patients had fair MSTS, 17 and 7 patients had good and excellent MSTS score, respectively. Spearman's test revealed no correlation between tumor necrosis after neoadjuvant chemotherapy with a reduction in tumor size and MSTS score. Wilcoxon test showed significant differences between ALP, ESR, and NLR before and after neoadjuvant chemotherapy in the poor-response group. We found no significant difference between L<b>DH</b> and LMR before and after neoadjuvant chemotherapy in the good-response group. We had 9 patients for ADC value only. No significant statistical differences were found in tumor volumes after chemotherapy in both groups. <b><i>Conclusion.</i></b> We demonstrated that ALP level after neoadjuvant chemotherapy was markedly decreased, and was statistically significant in the poor-response group. We also demonstrated that LDH value before neoadjuvant chemotherapy had a strong correlation with degree of necrosis and could be used as a predictive indicator. NLR and LMR cannot be independent prognostic factors. MRI plays an important role in evaluating tumor volumes and preoperative radiological changes, using DWI and water diffusion to predict histological necrosis. </span></p> <p> </p> <p> </p>
Fiber-tract localized diffusion coefficients highlight patterns of white matter disruption induced by proximity to glioma
Gliomas account for 26.5% of all primary central nervous system tumors. Recent studies have used diffusion tensor imaging (DTI) to extract white matter fibers and the diffusion coefficients derived from MR processing to provide useful, non-invasive insights into the extent of tumor invasion, axonal integrity, and gross differentiation of glioma from metastasis. Here, we extend this work by examining whether a tract-based analysis can improve non-invasive localization of tumor impact on white matter integrity. This study retrospectively analyzed preoperative magnetic resonance sequences highlighting contrast enhancement and DTI scans of 13 subjects that were biopsy confirmed to have either high or low-grade glioma. We reconstructed the corticospinal tract and superior longitudinal fasciculus by applying atlas-based regions of interest to fibers derived from whole-brain deterministic streamline tractography. Within-subject comparison of hemispheric diffusion coefficients (e.g., fractional anisotropy and mean diffusivity) indicated higher levels of white matter degradation in the ipsilesional hemisphere. Novel application of along-tract analyses revealed that tracts traversing the tumor region showed significant white matter degradation compared to the contralesional hemisphere and ipsilesional tracts displaced by the tumor.
Data from: Experimental study of the supercritical CO2 diffusion coefficient in porous media under reservoir conditions
Reliable measurement of CO2 diffusion coefficient in consolidated oil-saturated porous media is critical for design, performance of carbon capture and storage (CCS) and CO2 enhanced oil recovery (EOR) project. In this study, a thorough experimental investigation on supercritical CO2 diffusion in n-decane saturated Berea core (50 and 100 mD) was conducted at elevated pressure (10 to 25 MPa) and temperature (333.15 to 373.15 K), which simulated the real reservoir condition. Supercritical CO2 diffusion coefficients in Berea core were calculated by a model appropriate for diffusion in porous media based on Fick's law. The results show that with an increase in pressure, temperature and permeability, the supercritical CO2 diffusion coefficient increases correspondingly. The supercritical CO2 diffusion coefficient first increases slowly at 10 MPa and then grow significantly with the increase of pressure. The effect of temperature becomes weaker at elevated pressure condition. And the effect of permeability keeps steady despite of temperature change in experiments. The effect of gas state and porous media on supercritical CO2 diffusion coefficient is further discussed by comparing the results in this study with previous research. Based on the experimental results, an empirical correlation for supercritical CO2 diffusion coefficient in n-decane saturated porous media is developed. The experimental results contribute to the study of supercritical CO2 diffusion in compact porous media.
Lithium diffusion coefficient
<p>this is an example data. </p>
Experimental data for chloride diffusion coefficient of concrete by rapid chloride migration test
<p>This database collects the chloride ion diffusion coefficient measured by RCM method (including the method adopted by the Nordic standard NT Build 492, the IBAC method of Germany's Anchen University of technology, as well as the methods adopted by China's GB/T 50082-2009 and JTG/T B07-01-2006); The curing method is standard curing.</p>
Experimental data for chloride diffusion coefficient of concrete by rapid chloride migration test
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Data from: Experimental study of the supercritical CO2 diffusion coefficient in porous media under reservoir conditions
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