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251 results for “Electrolyte”
Detection of neurofilament light chain with label-free electrolyte-gated organic field-effect transistors
<p>The uploaded data include</p> <p>i) transfer curves registered at every NF-L concentration;</p> <p>ii) transfer curves for control experiments;</p> <p>iii) raw AFM data acquired after electrode incubation in 0 M, 1 pM, 100 pM and 10 nM [NF-L].</p> <p>Electrical measurements were acquired in 50 mM PBS, pH 7.4 under static conditions.</p> <p>Anti-NF-L antibodies were immobilized on the gate electrode through cys-Protein G. The gate surface has been subsequently passivated through 11-mercaptoundecyl-triethylene glycol SAM.</p>
Datasets to Poly(ethylene oxide)-based Electrolytes for Solid-State Potassium Metal Batteries with Prussian Blue Positive Electrode
<p>This dataset provides the raw data to the manuscript</p> <p>"<strong>Poly(ethylene oxide)-based Electrolytes for Solid-State Potassium Metal Batteries with Prussian Blue Positive Electrode"</strong></p> <p>published in ACS Appl. Polym. Mater. (DOI: <a href="https://doi.org/10.1021/acsapm.2c00014">10.1021/acsapm.2c00014</a> ) / <a href="https://doi.org/10.1021/acsapm.2c00014">https://doi.org/10.1021/acsapm.2c00014</a></p> <p>Specifically, the following measurements are provided:</p> <p>Electrochemical cell tests of liquid and solid electrolytes ("CYCLING_" & Ratecapability test)</p> <p>Solid electrolyte characterization:</p> <p>Differential Scanning Calorimetry ("DSC_")</p> <p>Electrochemical Impedance Spectroscopy ("EIS_")</p> <p>Rheological measurements ("RHEO_")</p> <p>X-ray diffraction data ("XRD_")</p>
Inferring global dynamics from local structure in liquid electrolytes.
<p>Molecular dynamics simulation data (obtained using the HOOMD-blue package) used to generate results in "Inferring global dynamics from local structure in liquid electrolytes". </p> <p>108 bulk electrolytic systems were simulated with twelve concentrations ranging from 0.0005 σ−3 to 0.05 σ−3 and nine Bjerrum lengths in the range 2.5 σ to 10.0σ. Each simulation consisted of cations, anions, and solvent, all of which are modelled as beads of diameter σ (the Lennard-Jones unit of distance) and unit mass. If the size of each bead is mapped to the size of a water molecule (2.75 Å), the concentration range of 0.0005 σ−3 to 0.05 σ−3 approximately corresponds to 0.04 M to 4 M in real units. </p> <p>For example, the system with concentration 0.01 σ−3 and Bjerrum length 4.0σ has the following relevant file names: </p> <p>"conc0.01_lb4.0.dcd" contains the MD trajectory, and "initial_config_conc0.01.gsd" contains the initial configuration.</p> <p>The jupyter notebook "sample_data_analysis.ipynb" illustrates how to load these trajectories as well as the computed molar conductivities for each system.</p> <p>"transport_coefficients.zip" contains the computed Onsager transport coefficients for each of the simulated systems. </p>
Dataset for publication "Operando Observation of (Bi)carbonate Precipitation during Electrochemical CO2 Reduction in Strongly Acidic Electrolytes"
<p>Broad topic: electrochemical reduction of CO2 using Ag/Cu-based gas diffusion electrodes in neutral and acid electrolyte, operando characterisation using synchrotron radiation (wide-angle X-ray scattering), (bi)carbonate precipitation.</p> <p>Data is devided in subfolders named after the figure of the paper.</p> <p>Raw data, processed data, and Origin/PowerPoint files are all contained in the subfolders.</p> <p>Synchrotron raw data are linked to the experiment numbers MA5874 and INHC1776 at ESRF</p> <p>A subfolder corresponding to a sample contains: data from a potentiostat, gas chromatograms, recording of flow, pressure and temperature, tables of calculated Faradaic efficiency (FE), png image of the FE vs t, zipped raw files.</p> <p>.json file was created using a yadg scheme (https://dgbowl.github.io/yadg/master/index.html), and data was processed by a dgpost scheme (<a href="https://pypi.org/project/dgpost/">https://dgbowl.github.io/dgpost/master/index.html</a>)</p> <p>Synchrotron data are analysized using this python library https://github.com/EmpaEconversion/Twaxs</p>
Data - Effect of electrolytes as adjuvants in GFP and LPS partitioning on aqueous two-phase systems: 1. Polymer-polymer systems
<p><strong>Overview</strong></p> <p>The production of recombinant biopharmaceuticals is highly dependent of a proper choice of the downstream processing stages. Particularly, the purification that must ensure that all the endotoxins (lipopolysaccharide - LPS) are efficiently removed from the final product. This dataset contains the raw data and statistical analysis for the research entitled - "Effect of electrolytes as adjuvants in GFP and LPS partitioning on aqueous two-phase systems: 1. Polymer-polymer systems". </p> <p><strong>Info</strong></p> <p>ANOVA_Turkey_Sub.R <- code for ANOVA analysis in R statistic 3.3.3 <br> glm.R <- code for GLM analysis in R statistic 3.3.3<br> K&REC_LPS_PEG_NaPA.xlsx <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) and recover (REC) for ANOVA analysis<br> K&REC_LPS_PEG_NaPA_K.docx <- File with ANOVA result of partition coefficient (K) for GFP<br> K&REC_LPS_PEG_NaPA_REC.docx <- File with ANOVA result of recover (REC) for GFP <br> K_GFP_Pol_005.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.05M salt assays <br> K_GFP_Pol_005.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.05M salt assays <br> K_GFP_Pol_005_QQ.png <- Residual quantile plot of GLM analysis for partition coefficient (K) in 0.05M salt assays <br> K_GFP_Pol_025.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.25M salt assays <br> K_GFP_Pol_025.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.25M salt assays <br> K_GFP_Pol_025_QQ.png <- Residual quantile plot of GLM analysis for partition coefficient (K) in 0.25M salt assays <br> REC_GFP_Pol_005.csv <- File with raw values organized in a spreadsheet of GFP recover (REC) for GLM analysis in 0.05M salt assays <br> REC_GFP_Pol_005.doc <- File with GLM analysis of GFP recover (REC) in 0.05M salt assays <br> REC_GFP_Pol_005_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.05M salt assays <br> REC_GFP_Pol_025.csv <- File with raw values organized in a spreadsheet of GFP recover (REC) for GLM analysis in 0.25M salt assays <br> REC_GFP_Pol_025.doc <- File with GLM analysis of GFP recover (REC) in 0.25M salt assays <br> REC_GFP_Pol_025_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.25M salt assays <br> REM_LPS_PEG_NaPA.docx <- File with ANOVA result of LPS removal <br> REM_LPS_PEG_NaPA.xlsx <- File with raw values organized in a spreadsheet of LPS removal for ANOVA analysis<br> Stability_GFP_PEG_NaPA.docx <- File with ANOVA result of GFP stability<br> Stability_GFP_PEG_NaPA.xlsx <- File with raw values organized in a spreadsheet of GFP stability results for ANOVA analysis</p> <p>REM_LPS_Pol_005.csv <- File with raw values organized in a spreadsheet of LPS removal (REM) for GLM analysis in 0.05M salt assays <br> REM_LPS_Pol_005.doc <- File with GLM analysis of LPS removal (REM) in 0.05M salt assays <br> REM_LPS_Pol_005_QQ.png <- Residual quantile plot of GLM analysis of LPS removal (REM) in 0.05M salt assays <br> REM_LPS_Pol_025.csv <- File with raw values organized in a spreadsheet of LPS removal (REM) for GLM analysis in 0.25M salt assays <br> REM_LPS_Pol_025.doc <- File with GLM analysis of LPS removal (REM) in 0.25M salt assays <br> REM_LPS_Pol_025_QQ.png <- Residual quantile plot of GLM analysis of LPS removal (REM) in 0.25M salt assays</p> <p>K_GFP_Pol_025_NaCl_Li2SO4.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.25M salt assays comparing NaCl and Li2SO4 effect <br> K_GFP_Pol_025_NaCl_Li2SO4.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.25M salt assays comparing NaCl and Li2SO4 effect <br> K_GFP_Pol_025_NaCl_Li2SO4_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.25M salt assays comparing NaCl and Li2SO4 effect </p> <p>REM_LPS_Pol_KI_0.05_vs_0.25.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in KI assays comparing salt concentration effect <br> REM_LPS_Pol_KI_0.05_vs_0.25.doc <- File with GLM analysis of GFP partition coefficient (K) in KI assays comparing salt concentration effect<br> REM_LPS_Pol_KI_0.05_vs_0.25_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in KI assays comparing salt concentration effect<br> REM_LPS_Pol_KNO3_0.05_vs_0.25.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in KNO3 assays comparing salt concentration effect <br> REM_LPS_Pol_KNO3_0.05_vs_0.25.doc <- File with GLM analysis of GFP partition coefficient (K) in KNO3 assays comparing salt concentration effect<br> REM_LPS_Pol_KNO3_0.05_vs_0.25_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in KNO3 assays comparing salt concentration effect<br> REM_LPS_Pol_Li2SO4_0.05_vs_0.25.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in Li2SO4 assays comparing salt concentration effect <br> REM_LPS_Pol_Li2SO4_0.05_vs_0.25.doc <- File with GLM analysis of GFP partition coefficient (K) in Li2SO4 assays comparing salt concentration effect<br> REM_LPS_Pol_Li2SO4_0.05_vs_0.25_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in Li2SO4 assays comparing salt concentration effect<br> REM_LPS_Pol_NaCl_0.05_vs_0.25.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in NaCl assays comparing salt concentration effect <br> REM_LPS_Pol_NaCl_0.05_vs_0.25.doc <- File with GLM analysis of GFP partition coefficient (K) in NaCl assays comparing salt concentration effect <br> REM_LPS_Pol_NaCl_0.05_vs_0.25_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in NaCl assays comparing salt concentration effect</p> <p> </p> <p><strong>Annotation</strong></p> <p>12/12 - Concentration of 12% of each polymer PEG/NaPA</p> <p>16/16 - Concentration of 16% of each polymer PEG/NaPA</p> <p>P/N - PEG/NaPA</p> <p>10e4, 10e5, 10e6 - Concentration of LPS in scientific notation - 10000, 100000, 100000 EU/mL</p> <p>poly - Polymer</p> <p>salt - Salt concentration in the assay</p> <p>tsalt - Type of salt in the assay (NaCl, KNO3, KI and Li2SO4)</p> <p>lps - lipopolysaccharide</p> <p>K - GFP partition coefficient</p> <p>REM - LPS removal</p> <p>REC - GFP recover</p> <p>wo_salt - Assay without salt addition</p> <p><strong>Acknowledgements</strong></p> <p>The authors are grateful for financial support from FAPESP (São Paulo Research Foundation, Brazil) through the following projects: 2005/60159-7; 2007/51978-0; 2014/16424-7; and 2014/19793-3. The authors also acknowledge the support from CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil) through the process #0366/09-9 and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil).</p> <p><strong>Consider citing our work. </strong></p> <p>1. Work in progress...</p>
Data - Effect of electrolytes as adjuvants in GFP and LPS partitioning on aqueous two-phase systems: 2. Nonionic micellar systems
<p><strong>Overview</strong></p> <p>The production of recombinant biopharmaceuticals is highly dependent of a proper choice of the downstream processing stages. Particularly, the purification that must ensure that all the endotoxins (lipopolysaccharide - LPS) are efficiently removed from the final product. This dataset contains the raw data and statistical analysis for the research entitled - "Effect of electrolytes as adjuvants in GFP and LPS partitioning on aqueous two-phase systems: 2. Nonionic micellar systems". </p> <p><strong>Info</strong></p> <p>ANOVA_Turkey_Sub.R <- code for ANOVA analysis in R statistic 3.3.3 <br> glm.R <- code for GLM analysis in R statistic 3.3.3<br> K&REC_ORG_ANOVA.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) and recover (REC) for ANOVA analysis</p> <p>K_ORG_ANOVA.docx <- File with ANOVA result of partition coefficient (K) for GFP</p> <p>REC_ORG_ANOVA.docx <- File with ANOVA result of partition coefficient (REC) for GFP</p> <p>REM_LPS_ORG_ANOVA.csv <- File with raw values organized in a spreadsheet of LPS removal for ANOVA analysis</p> <p>REM_LPS_ORG_ANOVA.docx <- File with ANOVA result of removal of LPS</p> <p>Stability__ORG_ANOVA.csv <- File with raw values organized in a spreadsheet of GFP stability for ANOVA analysis</p> <p>Stability__ORG_ANOVA.docx <- File with ANOVA result of GFP stability</p> <p>K_ORG_glm_005.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.05M salt assays </p> <p>K_ORG_glm_005.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.05M salt assays </p> <p>K_ORG_glm_005_QQ.png <- Residual quantile plot of GLM analysis for partition coefficient (K) in 0.05M salt assays </p> <p>K_ORG_glm_025.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.25M salt assays </p> <p>K_ORG_glm_025.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.25M salt assays </p> <p>K_ORG_glm_025_QQ.png <- Residual quantile plot of GLM analysis for partition coefficient (K) in 0.25M salt assays </p> <p>REC_ORG_glm_005.csv <- File with raw values organized in a spreadsheet of GFP recover (REC) for GLM analysis in 0.05M salt assays</p> <p>REC_ORG_glm_005.doc <- File with GLM analysis of GFP recover (REC) in 0.05M salt assays </p> <p>REC_ORG_glm_005_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.05M salt assays </p> <p>REC_ORG_glm_025.csv <- File with raw values organized in a spreadsheet of GFP recover (REC) for GLM analysis in 0.25M salt assays</p> <p>REC_ORG_glm_025.doc <- File with GLM analysis of GFP recover (REC) in 0.25M salt assays </p> <p>REC_ORG_glm_025_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.25M salt assays </p> <p>REM_ORG_glm_005.csv <- File with raw values organized in a spreadsheet of LPS removal (REM) for GLM analysis in 0.05M salt assays</p> <p>REM_ORG_glm_005.doc <- File with GLM analysis of LPS removal (REM) in 0.05M salt assays </p> <p>REM_ORG_glm_005_QQ.png <- Residual quantile plot of GLM analysis of LPS removal (REM) in 0.25M salt assays</p> <p>REM_ORG_glm_025.csv <- File with raw values organized in a spreadsheet of LPS removal (REM) for GLM analysis in 0.25M salt assays</p> <p>REM_ORG_glm_025.doc <- File with GLM analysis of LPS removal (REM) in 0.25M salt assays </p> <p>REM_ORG_glm_025_QQ.png <- Residual quantile plot of GLM analysis of LPS removal (REM) in 0.25M salt assays</p> <p> </p> <p><strong>Annotation</strong></p> <p>12/12 - Concentration of 12% of each polymer PEG/NaPA</p> <p>16/16 - Concentration of 16% of each polymer PEG/NaPA</p> <p>P/N - PEG/NaPA</p> <p>10e4, 10e5, 10e6 - Concentration of LPS in scientific notation - 10000, 100000, 100000 EU/mL</p> <p>poly - Polymer</p> <p>salt - Salt concentration in the assay</p> <p>tsalt - Type of salt in the assay (NaCl, KNO3, KI and Li2SO4)</p> <p>lps - lipopolysaccharide</p> <p>K - GFP partition coefficient</p> <p>REM - LPS removal</p> <p>REC - GFP recover</p> <p>wo_salt - Assay without salt addition</p> <p><strong>Acknowledgements</strong></p> <p>The authors are grateful for financial support from FAPESP (São Paulo Research Foundation, Brazil) through the following projects: 2005/60159-7; 2007/51978-0; 2014/16424-7; and 2014/19793-3. The authors also acknowledge the support from CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil) through the process #0366/09-9 and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil).</p> <p><strong>Consider citing our work. </strong></p> <p>1. Work in progress...</p>
Supporting Data for "Proton transfer in nonpolar solvents: an approach to generate electrolytes in aprotic media" (Phys. Chem. Chem. Phys., doi:10.1039/c8cp02349b)
<p>Conductivity data for all cation-anion pairs [units given in the header for each column].</p> <p>Small-angle neutron scattering (SANS) data (Q [1/Å], I(Q) [SAXS - arbitrary, SANS - 1/cm], error I(Q) [same units]) of PLMA48 as a 2 wt % solution in n-dodecande-d26.</p>
Dataset From: Unexpectedly Large Decay Lengths of Double Layer Forces in Solutions of Symmetric, Multivalent Electrolytes
<p>The dataset for the publication "Unexpectedly Large Decay Lengths of Double Layer Forces in Solutions of Symmetric, Multivalent Electrolytes". DOI: 10.1021/acs.jpcb.8b12246.</p> <p>Files containing data have .dat extension and are in text format.</p>
Data for the publication "MgB2Se4 Spinels (B = Sc, Y, Er, Tm) as Potential Mg-Ion Solid Electrolytes – Partial Ionic Conductivity and the Ion Migration Barrier"
<h1><strong>Description Datasets</strong></h1> <p>Datasets are obtained from X-ray diffraction (XRD), Rietveld analysis, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), transmission electron microscopy (TEM), nuclear magnetic resonance (NMR) spectroscopy, electrochemical impedance spectroscopy (EIS), chronoamperometry (CA), chronopotentiometry (CP) and linear sweep voltammetry (LSV).</p> <h1><strong>Abstract</strong></h1> <p>The magnesium chalcogenide spinel MgSc~2~Se~4~ with high Mg-ion room-temperature conductivity has recently attracted interest as solid electrolyte for magnesium ion batteries. Its ionic/electronic mixed-conducting nature and the influence of the spinel composition on the conductivity and Mg^2+^ migration barrier are yet not well understood. Here, results from a combined experimental and computational study on four MgB~2~Se~4~ spinels (B = Sc, Y, Er, Tm) are presented. The room-temperature ionic conductivities (<em>σ</em>~ion~ = 2x10^–5^–7x10^–5^ S cm^–1^) of the spinels are accurately measured, as electron transport is effectively suppressed by purely Mg-ion conducting electrode interlayers. Using the same approach, reversible Mg plating/stripping as well as good electrochemical stability are achieved. Driven by the good accordance of the computationally and experimentally obtained Mg^2+^ migration barriers <em>E</em>~a~(th) and <em>E</em>~a~, respectively, further periodic density functional calculations are performed on the MgB~2~Se~4~ spinel system, revealing the role of trigonal distortion on the migration path geometry and <em>E</em>~a~(th). These findings provide deeper understanding how to reach small Mg^2+^ migration barriers <em>E</em>~a~ in the MgB~2~Se~4~ spinels.</p>
Data Supplement for "Impact of Charged Surfaces on the Structure and Dynamics of Polymer Electrolytes: Insights from Atomistic Simulations"
<p>Data set containing the molecular dynamics simulation data used for the journal article "Impact of Charged Surfaces on the Structure and Dynamics of Polymer Electrolytes: Insights from Atomistic Simulations" (<span>Andreas Thum, </span><span>Diddo Diddens, </span><span>Andreas Heuer, </span><em>J. Phys. Chem. C</em> <strong>2021</strong>, <em>125</em>, 25392−25403, <a href="https://doi.org/10.1021/acs.jpcc.1c07751">https://doi.org/10.1021/acs.jpcc.1c07751</a>).</p>
Dataset of "Transient current responses of organic electrochemical transistors by vertical ionic diffusion and electrolyte resistance"
<p>This dataset supports the article </p> <p>"<span>Transient current responses of organic electrochemical transistors by vertical ionic diffusion and electrolyte resistance</span>"</p> <p> </p> <p>Raw data for the article "<span>Transient current responses of organic electrochemical transistors by vertical ionic diffusion and electrolyte resistance</span>". For further details see the readme.txt file.</p>
Dataset for publication: "Magnesium and Aluminum in Contact with Liquid Battery Electrolytes: Ion Transport through Interphases and in the Bulk"
<div> </div> <div> <p>This is the experimental raw data set associated with the following publication: M. Löw, J. Grill, MM May, and J. Popovic-Neuber, Magnesium and Aluminium in Contact with Liquid Battery Electrolyte: Ion Transport through Interphases and in the Bulk, ACS Material Letters (2024). DOI:10.1021/acsmaterialslett.4c01589</p> <p>The data set is organized according to the publication's figures. </p> </div>
Collection: Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes Confined Between Electrodes
<p>Metadata record collecting related data sets that contain molecular dynamics simulations of PEO-LiTFSI polymer electrolytes confined between model electrodes.</p> <p>Related data sets:</p> <ul> <li>Uncharged electrodes: <ul> <li><a href="https://doi.org/10.5281/zenodo.13164944">https://doi.org/10.5281/zenodo.13164944</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths Confined Between Uncharged Electrodes</li> <li><a href="https://doi.org/10.5281/zenodo.13165450">https://doi.org/10.5281/zenodo.13165450</a>:<br>Molecular Dynamics Simulations of Monoglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Uncharged Electrodes</li> <li><a href="https://doi.org/10.5281/zenodo.13165725">https://doi.org/10.5281/zenodo.13165725</a>:<br>Molecular Dynamics Simulations of Tetraglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Uncharged Electrodes</li> <li><a href="https://doi.org/10.5281/zenodo.13166024">https://doi.org/10.5281/zenodo.13166024</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Salt Concentrations Confined Between Uncharged Electrodes</li> </ul> </li> <li>Charged electrodes: <ul> <li><a href="https://doi.org/10.5281/zenodo.13166152">https://doi.org/10.5281/zenodo.13166152</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167128">https://doi.org/10.5281/zenodo.13167128</a>:<br>Molecular Dynamics Simulations of Monoglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167338">https://doi.org/10.5281/zenodo.13167338</a>:<br>Molecular Dynamics Simulations of Tetraglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167551">https://doi.org/10.5281/zenodo.13167551</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Salt Concentrations Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167614">https://doi.org/10.5281/zenodo.13167614</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths Confined Between Charged Electrodes With Various Surface Charges</li> </ul> </li> <li>Plots: <ul> <li><a href="https://doi.org/10.5281/zenodo.13168242">https://doi.org/10.5281/zenodo.13168242</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths and Salt Concentrations Confined Between Charged Electrodes With Various Surface Charges: Plots</li> </ul> </li> </ul>
Collection: Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes in the Bulk and Confined Between Electrodes
<p>Metadata record collecting related data sets that contain molecular dynamics simulations of PEO-LiTFSI polymer electrolytes in the bulk and confined between model electrodes.</p> <p>Related data sets:</p> <ul> <li>In the Bulk: <ul> <li><a href="https://doi.org/10.5281/zenodo.13144737">https://doi.org/10.5281/zenodo.13144737</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths and Salt Concentrations in the Bulk</li> </ul> </li> <li>Confined Between Electrodes: <ul> <li><a href="https://doi.org/10.5281/zenodo.13169120">https://doi.org/10.5281/zenodo.13169120</a>:<br>Collection: Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes Confined Between Electrodes</li> </ul> </li> </ul>
Data set: Modeling of Electron-Transfer Kinetics in Magnesium Electrolytes: Influence of the Solvent on the Battery Performance
<p>Dataset of the continuum simulations generated and used within the paper "<span>Modeling of Electron-Transfer Kinetics in Magnesium Electrolytes: Influence of the Solvent on the Battery Performance</span>", published in ChemSusChem (<span>2021</span><span>, </span><span>14 (21)</span><span>, 4820-4835, DOI: <span>10.1002/cssc.202101498</span></span>).</p> <p><span>The performance of rechargeable magnesium batteries is strongly dependent on the choice of electrolyte. The desolvation of multivalent cations usually goes along with high energy barriers, which can have a crucial impact on the plating reaction. This can lead to significantly higher overpotentials for magnesium deposition compared to magnesium dissolution. In this work we combine experimental measurements with DFT calculations and continuum modeling to analyze magnesium deposition in various solvents. Jointly, these methods provide a better understanding of the electrode reactions and especially the magnesium deposition mechanism. Thereby, a kinetic model for electrochemical reactions at metal electrodes is developed, which explicitly couples desolvation to electron transfer and, furthermore, qualitatively takes into account effects of the electrochemical double layer. The influence of different solvents on the battery performance is studied for<br>the state-of-the-art magnesium tetrakis(hexafluoroisopropyloxy)borate electrolyte salt. It becomes apparent that not necessarily a whole solvent molecule must be stripped from the</span> <span>solvated magnesium cation before the first reduction step can take place. For magnesium reduction it seems to be sufficient to have one coordination site available, so that the magnesium cation is able to get closer to the electrode surface. Thereby, the initial desolvation of the magnesium cation determines the deposition reaction for mono-, tri- and tetraglyme, whereas the influence of the desolvation on the plating reaction is minor for diglyme and<br>tetrahydrofuran. Overall, we can give a clear recommendation for diglyme to be applied as solvent in magnesium electrolytes</span>.<br><br></p>
Data set: Modeling of Ion Agglomeration in Magnesium Electrolytes and its Impacts on Battery Performance
<p>Dataset of the continuum simulations generated and used within the paper "Modeling of Ion Agglomeration in Magnesium Electrolytes and its Impacts on Battery Performance", published in ChemSusChem (<span>2020</span><span>, </span><span>13 (14)</span><span>, 3599-3604, </span>DOI: 10.1002/cssc.202001034).</p> <p><br>The choice of electrolyte has a crucial influence on the performance of rechargeable magnesium batteries. In multivalent electrolytes an agglomeration of ions to pairs or bigger clusters may affect the transport in the<br>electrolyte and the reaction at the electrodes. In this work the formation of clusters is included in a general model for magnesium batteries. In this model, the effect of cluster formation on transport, thermodynamics and kinetics is consistently taken into account. The model is used to analyze the effect of ion clustering in magnesium tetrakis(hexafluoroisopropyloxy)borate in dimethoxyethane as electrolyte. It becomes apparent that ion agglomeration is able to explain experimentally observed phenomena at high salt concentrations. </p>
Revisiting the diffuse layer polarization of a spherical grain in electrolytes with numerical solutions of Nernst-Planck-Poisson equations
<p>These datasets contain the Comsol files that were used in the publication "Revisiting the diffuse layer polarization of a spherical grain in electrolytes with numerical solutions of Nernst-Planck-Poisson equations", which is submitted to the Journal of Geophysical Research: Solid Earth. </p> <p>Note:</p> <ol> <li>file parallel plate_static.mph is the model used to generate numerical results in Figure 2</li> <li>file parallel plate_dynamic.mph is the model used to generate numerical results in Figure 3</li> <li>file grain_mechanism.mph is the model used to generate numerical results in Figures 4, 5, 6, 7, and 8</li> <li>file grain_salinity.mph is the model used to generate numerical results in Figures 9, 10, 11, and 12</li> <li>the solutions in the mph files are cleared to reduce file size. The solutions can be reproduced by conducting "solve" </li> </ol>
Electrocatalysis at the Polarised Interface between Two Immiscible Electrolyte Solutions (dataset)
<p>This is a dataset used to prepare Figure 4 in the review "<strong>Electrocatalysis at the Polarised Interface between Two Immiscible Electrolyte Solutions</strong>", prepared for the journal Current Opinion in Electrochemistry.</p>
Evolutionary footprints of cold adaptation in arctic-alpine Cochlearia (Brassicaceae) – evidence from freezing experiments and electrolyte leakage
<p><span>As </span><span>global warming progresses, plants may be forced to adapt to drastically changing environmental conditions. Arctic-alpine plants have been among the first to experience the effects of climate change. As a result, cold acclimation and freezing tolerance may become increasingly crucial for the survival as winter warming events and earlier snowmelt will cause increased exposure to occasional frost. The tribe </span><span>Cochlearieae in the mustard family (Brassicaceae) </span><span>offers an instructive system for studying cold adaptation in evolutionary terms, as the two sister genera </span><em><span>Ionopsidium</span></em> <span>and </span><em><span>Cochlearia</span></em> <span>are distributed among different ecological habitats throughout the European continent and the far north into circumarctic regions. By applying an electrolyte leakage assay to leaves obtained from plants cultivated under controlled temperature regimes in growth chambers, the freezing tolerance of different </span><em><span>Ionopsidium</span></em> <span>and </span><em><span>Cochlearia</span></em> <span>species was assessed measuring lethal freezing temperature values (</span><em><span>LT</span><span>50</span></em> <span>and </span><em><span>LT</span><span>100</span></em><span>), thereby allowing for a comparison across different species and accessions in their responses to cold. We hypothesized that, owing to varying selection pressures, geographically distant species would differ in freezing tolerance. Despite </span><em><span>Ionopsidium</span></em> <span>occurring under warm and dry Mediterranean conditions and </span><em><span>Cochlearia</span></em> <span>species distributed often at cold habitats, all accessions exhibited similar cold responses. The results may indicate that physiological adaptations of primary metabolic pathways to different stressors, such as salinity and drought, may confer an additional tolerance to cold; this is because all these stressors induce osmotic challenges. </span></p>
Cellulose nanofiber-reinforced solid polymer electrolytes with high ionic conductivity for lithium batteries
<p>The data contained herein support both the results described in the research article entitled "Cellulose nanofiber-reinforced solid polymer electrolytes with high ionic conductivity for lithium batteries" with the following DOI: <a href="https://doi.org/10.1039/D3TA00380A">10.1039/D3TA00380A</a>, and the corresponding supporting information.</p>
ScienceDex guides
Understand access before you commit
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.