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251 results for “Electrolyte”
Dataset: Electrolyte-dependent deposition morphology on magnesium metal utilizing MeMgCl, Mg[B(hfip)4]2 and Mg(HMDS)2–2AlCl3 electrolytes
<p>This is a collection featuring the data generated and used within the paper: 'Electrolyte-dependent deposition morphology on magnesium metal utilizing MeMgCl, Mg[B(hfip)4]2 and Mg(HMDS)2–2AlCl3 electrolytes'. The deposition behavior of two state-of-the-art electrolytes, magnesium tetrakis(hexafluoroisopropyloxy)borate (Mg[B(hfip)~4~]~2~) in dimethoxyethane (DME) and magnesium bis(hexamethyldisilazide) with two equivalents of aluminum chloride (Mg(HMDS)~2~-2AlCl~3~) in tetrahydrofuran (THF) was investigated. Using symmetric flooded magnesium-magnesium cells with different electrolyte concentrations and current densities the deposition process was monitored optically in-situ by a video microscope. The depositions were characterized by scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) and compared to depositions from methylmagnesium chloride (MeMgCl) in THF, known for its dendritic growth. In this work, MeMgCl showed unidirectional growth and for the harshest applied conditions, mossy depositions, but no branching dendrites as reported in previous literature. Mg[B(hfip)~4~]~2~ and Mg(HMDS)~2~-2AlCl~3~ did not show the formation of dendrites or a dendrite preform but also did not result in a desired smooth layer but in spherical depositions. For the Mg[B(hfip)~4~]~2~ electrolyte, the influence of magnesium borohydride (Mg(BH~4~)~2~) as an additive was additionally tested resulting in a more planar growth.</p>
Data associated with the following publication: "Giant thermoelectric response of confined electrolytes with thermally activated charge carrier generation"
<p>Data associated with the following publication: "Giant thermoelectric response of confined electrolytes with thermally activated charge carrier generation" (DOI: <a title="" href="https://doi.org/10.48328/tudatalib-1376">https://doi.org/10.48328/tudatalib-1376</a>)</p>
Dataset- Advancements in surface finish for additive manufacturing of metal parts: A comprehensive review of Plasma Electrolytic Polishing (PEP)
<p>This repository collects all the data (Figures and Tables) presented in the review article "Advancements in surface finish for additive manufacturing of metal parts: A comprehensive review of Plasma Electrolytic Polishing (PEP)"</p>
Influence of Anode Immersion Speed on Current and Power in Plasma Electrolytic Polishing
<p><span>Plasma electrolytic polishing (PeP) is mainly used to improve the surface quality and thus </span><span>the performance of electrically conductive parts. It is usually used as an anodic process, i.e., the </span><span>workpiece is positively charged. However, the process is susceptible to high current peaks during</span> <span>the formation of the vapour–gaseous envelope, especially when polishing workpieces with a large </span><span>surface area. In this study, the influence of the anode immersion speed on the current peaks and the</span> <span>average power during the initialisation of the PeP process is investigated for an anode the size of a </span><span>microreactor mould insert. Through systematic experimentation and analysis, this work provides</span> <span>insights into the control of the initialisation process by modulating the anode immersion speed. The </span><span>results clarify the relationship between immersion speed, peak current, and average power and</span> <span>provide a novel approach to improve process efficiency in PeP. The highest peak current and average </span><span>power occur when the electrolyte splashes over the top of the anode and not, as expected, when the </span><span>anode touches the electrolyte. By immersion of the anode while the voltage is applied to the anode</span> <span>and counterelectrode, the reduction of both parameters is over 80 %.</span></p>
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>
Raw data for the article "The role of ionomers in the electrolyte management of zero-gap MEA-based CO2 electrolysers: A Fumion vs. Nafion comparison''
<p>Raw data for the article "The role of ionomers in the electrolyte management of zero-gap MEA-based CO2 electrolysers: A Fumion vs. Nafion comparison'', published in Applied Catalysis B: Environmental 2023 335:122885, doi: <a href="https://doi.org/10.1016/j.apcatb.2023.122885">10.1016/j.apcatb.2023.122885</a></p> <p>Folder names describe the type of data content.</p>
Supplementary files for "Influence of the Artificial Nanostructure on the LiF Formation at the Solid−Electrolyte Interphase of Carbon-Based Anodes"
<p>Databases containing DFT optimized structures used for the paper: ''Influence of the Artificial Nanostructure on the LiF Formation at the Solid−Electrolyte Interphase of Carbon-Based Anodes". For further details of the computational setup we refer to this paper.</p> <p>Each database contains structures for one carbon substrate. The structures can be retrieved using the Atomic Simulation Environment (ASE).</p>
Data for the publication "Sodium Triflate Water-in-Salt Electrolyte in Advanced Battery Applications: A First-principles Based Molecular Dynamics Study"
<p>The datasets 'CONTCAR_aiMLMD' and 'CONTCAR_AIMD' represent the final structures obtained from the aiMLMD and AIMD simulations, respectively. These simulations were conducted using VASP at T=333K and c=9.25 m.</p> <p>The datasets 'NP.rdf' and 'MSD_NP.xlsx' represent the radial pair distribution functions at different time steps and the time-dependent variations of mean squared displacement for sodium in 10 segments of the classical MD trajectory. The associated MD simulation was performed using a nonpolarizable force field in the LAMMPS package at T=333K and c=9.25 m. The file 'dataNP.lmp' includes the initial configuration for this simulation. The GROMOS parameters were employed for LJ interactions of sodium and all other force field parameters were set according to Table 1 in the manuscript.</p> <p>The datasets 'P.rdf' and 'MSD_P.xlsx,' respectively, represent the radial pair distribution functions at different time steps and the time-dependent variations of mean squared displacement for sodium in 10 segments of the classical MD trajectory. These data were obtained employing the Drude oscillator model in the LAMMPS package at T=333K and c=10 m. The file 'dataP.lmp' includes the initial configuration for this simulation. The simulation was conducted using the optimal force field parameters 'Sys. 1,' as described in table 3 of the manuscript.</p> <p>The second column in the files 'NP.rdf' and 'NP.rdf' represents the distance from sodium. The subsequent odd columns display the radial distribution functions for the Na-C, Na-F, Na-S, Na-O, Na-Na, Na-Hw, and Na-Ow pairs, while the even columns present the coordination numbers for the same atom pairs.</p>
Choice of the right supporting electrolyte in electrochemical reductions: a principal component analysis
<h2>Introduction</h2> <p>This dataset contains the raw data as well as an HTML-based visualization of our dataset using Python Bokeh. We have also added a feature to highlight commercially available supporting electrolytes. The data is taken from the PubChem database. For each of the 6650 cations, the known neutral compounds in the PubChem dataset were identified with their corresponding anions. For each of these compounds, the vendor information stored in PubChem was queried.</p> <h2>Directory structure</h2> <ul> <li>Raw Data <ul> <li>[<a href="../records/10813969/files/raw_data.tar.xz?download=1" target="_blank" rel="noopener">raw_data.tar.xz</a>] Compressed directory with the output from the automated feature calculation.</li> <li>[<a href="../records/10813969/files/raw_data.csv?download=1" target="_blank" rel="noopener">raw_data.csv</a>] CSV file with the values of the calculated properties of all cations.</li> </ul> </li> <li>Visualization <ul> <li>[<a href="../records/10813969/files/pca_qac_tool_QC.html?download=1" target="_blank" rel="noopener">pca_qac_tool_QC.html</a>] HTML page with Javascript to display PC1 and PC2 for the quantum chemical PCA model.</li> <li>[<a href="../records/10813969/files/pca_qac_tool_RDKit.html?download=1" target="_blank" rel="noopener">pca_qac_tool_RDKit.html</a>] HTML page with Javascript to display PC1 and PC2 for the PCA model based on non empirical RDKit descriptors.</li> </ul> </li> <li>Tools <ul> <li>[<a href="../records/10813969/files/pca_qac_tool.py?download=1" target="_blank" rel="noopener">pca_qac_tool.py</a>] Python script to generate the HTML output using Bokeh. Depends on the data_pca_qac.csv and the data_commercial.json file.</li> <li>[<a href="../records/10813969/files/PubChem_get_Vendor_information.py?download=1" target="_blank" rel="noopener">PubChem_get_Vendor_information.py</a>] Crawler that checks a list of PubChem CIDs for net-neutral compounds and whether they are commercially available.</li> <li>[<a href="../records/10813969/files/RDKit_Descriptor-2D.py?download=1" target="_blank" rel="noopener">RDKit_Descriptor-2D.py</a>] Python script to calculate all available 2D RDkit descriptors based on a list of SMILES strings.</li> <li>[<a href="../records/10813969/files/RDKit_Descriptor-3D.py?download=1" target="_blank" rel="noopener">RDKit_Descriptor-3D.py</a>] Python script to calculate the RDKit 3D descriptors based on the CREST and ORCA GeoOpt geometries.</li> </ul> </li> </ul>
Data related to the article "Impedance of nanocapacitors from molecular simulations to understand the dynamics of confined electrolytes"
<p>Contains input files and data used to generate the figures of the article:</p> <p>Impedance of nanocapacitors from molecular simulations to understand the dynamics of confined electrolytes<br>(Giovanni Pireddu, Connie J. Fairchild, Samuel P. Niblett, Stephen J. Cox and Benjamin Rotenberg)</p> <p>ChemRxiv: https://doi.org/10.26434/chemrxiv-2023-2ccrw</p> <p>Published version: to be inserted upon publication</p> <p>The folder EXAMPLE_INPUT_FILES contains typical [MetalWalls](https://doi.org/10.21105/joss.02373) ([repository](https://gitlab.com/ampere2/metalwalls)) and [LAMMPS]([repository](https://github.com/lammps/lammps)) input files used to perform the molecular simulations.</p> <p>The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper (see below).</p> <p><br>Notes: <br>1) In the file names, the notation 'M01', 'M05', 'M10' and 'M15' refers to the salt concentration in each system (0.1, 0.5, 1.0 and 1.5, respectively). 'W' refers to pure water (0 M) systems.<br>2) In the file names, the notation 'd1', 'd2', 'd3', 'd4', refers to different interelectrode distances (d1= 2.56 nm; d2= 5.07 nm; d3= 9.80 nm; d4= 19.84 nm) <br>3) The files containing the polarization cross-correlation are marked with 'AxB' indicating the cross-correlation between the contributions A and B. Specifically A and B can be: <br> - T = total<br> - I = ion<br> - W = water</p> <p><br>Figure 1:<br>- Panel B<br> - 'Fig1_CapConcentration': Differential capacitance scaled by electrode area as a function of NaCl concentration<br>- Panel C<br> - 'Fig1_QACF_*': Electrode charge autocorrelation function<br>- Panel D<br> - 'Fig1_Norm_QACF_*': Normalized electrode charge autocorrelation function<br> - 'Fig1_NormChar_*': Normalized non-equilibrium charge response</p> <p>Figure 2:<br>- Panel A: <br> - 'Fig2_ReZ_*': Real part of impedance<br>- Panel B:<br> - 'Fig2_nImZ_*': Negative imaginary part of impedance<br>- Panel C:<br> - 'Fig2_ReZint_*': Real part of interfacial impedance<br> - 'Fig2_Resistivities.dat': Resistivity as a function of NaCl concentration (bulk, confined, Nernst-Einstein)<br>- Panel D:<br> - 'Fig2_nImZint_*': Negative imaginary part of interfacial impedance<br> - 'Fig2_ECM*': Capacitor contributions to the imaginary part of interfacial impedance (finite concentrations)<br> - 'Fig2_ECW1.dat': Capacitor contributions to the imaginary part of interfacial impedance (pure water). Full cell capacitance taken into account<br> - 'Fig2_ECW2.dat': Capacitor contributions to the imaginary part of interfacial impedance (pure water). Interfacial capacitance taken into account </p> <p>Figure 3:<br>- Panel A:<br> - 'Fig3_ReCond_Peyman_M10.dat': Real part of conductivity (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))<br> - 'Fig3_ReCond_Querry_M10.dat': Real part of conductivity (data from: MR Querry, RC Waring, WE Holland, GM Hale, W Nijm, Optical Constants in the Infrared for Aqueous Solutions of NaClt. J. Opt. Soc. Am. 62 (1972)) <br> - 'Fig3_ReCond_Vinh_M10.dat': Real part of conductivity (data from: NQ Vinh, et al., High-precision gigahertz-to-terahertz spectroscopy of aqueous salt solutions as a probe of the femtosecond-to-picosecond dynamics of liquid water. The J.<br>Chem. Phys. 142, 164502 (2015).)<br> - 'Fig3_ReCond_M10.dat': Real part of conductivity from MD simulations<br>- Panel B:<br> - 'Fig3_ReCond_M*/W.dat': Real part of conductivity from MD simulations<br> - 'Fig3_ReCond_Peyman_M*': Real part of conductivity (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))<br>- Panel C:<br> - 'Fig3_Cond0.dat': Static conductivity as a function of concentration (MD data)<br> - 'Fig3_Cond0_Buchner.dat': Static conductivity as a function of concentration (data from: R Buchner, GT Hefter, PM May, Dielectric relaxation of aqueous nacl solutions. The J. Phys. Chem. A 103, 1–9 (1999))<br> - 'Fig3_Cond0_Peyman.dat': Static conductivity as a function of concentration (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))</p> <p>Figure 4:<br>- Panel A: <br> - 'Fig4_ReZ_d*': Real part of impedance (MD simulations)<br> - 'Fig4_ReZEC_d*': Real part of impedance (equivalent circuit model)<br>- Panel B:<br> - 'Fig4_nImZ_d*': Negative imaginary part of impedance (MD simulations)<br> - 'Fig4_nImZEC_d*': Negative imaginary part of impedance (equivalent circuit model)</p> <p>Figure 5:<br>- 'Fig5_TauQ.dat': timescales from the total charge autocorrelation functions<br>- 'Fig5_iontot.dat': timescales from the TxI autocorrelation function<br>- 'Fig5_RC.dat': timescales from the RC estimates<br>- 'Fig5_RbulkC.dat': timescales from the RbulkC estimates<br>- 'Fig5_Taudiff.dat': timescales from the difference between electrolyte and pure water QACFs<br>- 'Fig5_taud.dat': tau_d analytical timescales<br>- 'Fig5_tauDebye.dat': tau_Debye analytical timescales<br>- 'Fig5_taumix.dat': tau_mix analytical timescales</p> <p>Figure 6:<br>- Panel A:<br> - 'Fig6_Static_*: Static correlation between polarization contributions as a function of salt concentration<br>- Panel B:<br> - 'Fig6_Dynamic_EQ_*_M01' Dynamical correlations between polarization contributions (equilibrium MD results)<br> - 'Fig6_Dynamic_NEQ_*_M01' Dynamical correlations between polarization contributions (non-equilibrium MD results)<br>- Panel C:<br> - 'Fig6_Dynamic_EQ_*_M10' Dynamical correlations between polarization contributions (equilibrium MD results)<br> - 'Fig6_Dynamic_NEQ_*_M10' Dynamical correlations between polarization contributions (non-equilibrium MD results)</p> <p> </p> <p> </p>
Dataset for Dynamics of Solid-Electrolyte Interphase Formation on Silicon Electrodes Revealed by Combinatorial Electrochemical Screening
<p>This dataset provides the raw data to the manuscript</p> <p>"<strong>Dynamics of Solid-Electrolyte Interphase Formation on Silicon Electrodes Revealed by Combinatorial Electrochemical Screening"</strong></p> <p>published in Angewandte Chemie International Edition (2022): <a href="https://doi.org/10.1002/anie.202207184">https://doi.org/10.1002/anie.202207184</a></p> <p>Specifically, the following measurements are provided:</p> <ul> <li>Electrochemical measurements for combinatorial preparation of solid-electrolyte layers under different conditions and repetitions ("SECCM/")</li> <li>Raman spectra obtained using SHINERS for all the prepared conditions and repetitions ("SHINERS/")</li> <li>Atomic force microscopy data for each SEI layer ("AFM/")</li> <li>Energy-dispersive X-ray spectroscopy data ("EDX/")</li> </ul>
Raw data for the plot in the article entitled "On the electrophoretic deposition of Bi2Te3 nanoparticles through electrolyte optimization and substrate design"
<p>raw data of transport presented in Fig1a of the open access article with the following details:</p> <p>On the electrophoretic deposition of Bi2Te3nanoparticles through electrolyte optimization and substrate design</p> <p><a href="https://www.sciencedirect.com/journal/colloids-and-surfaces-a-physicochemical-and-engineering-aspects">Colloids and Surfaces A: Physicochemical and Engineering Aspects</a></p> <p><a href="https://www.sciencedirect.com/journal/colloids-and-surfaces-a-physicochemical-and-engineering-aspects/vol/649/suppl/C">Volume 649</a>, 20 September 2022, 129537</p> <p><a href="https://doi.org/10.1016/j.colsurfa.2022.129537">https://doi.org/10.1016/j.colsurfa.2022.129537</a></p>
Data associated to: Analytical Physical Model for Organic Metal-Electrolyte-Semiconductor Capacitors
<p>Data associated to the manuscript entitled: Analytical Physical Model for Organic Metal-Electrolyte-Semiconductor Capacitors by Larissa Huetter, Adrica Kyndiah and Gabriel Gomila</p>
Dataset: Analytical Physical Model for Electrolyte Gated Organic Field Effect Transistors in the Helmholtz Approximation
<p>Data corresponding to the figures of the manuscript "Analytical Physical Model for Electrolyte Gated Organic Field Effect Transistors in the Helmholtz Approximation" by Larissa Huetter, Adrica Kyndiah and Gabriel Gomila</p>
Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software
<pre>Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software Dataset structure: <strong>- Dynamic_PEFC_data.h5</strong> # Raw projection data for dynamic tomography imaging of PEFC catalyst hydration. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /humidity_readout # Relative humidity value at the time each projection is measured, 1D array with axis (Nangle). - /Deform_X # X/Y/Z components for the deformation vector field which characterize nonrigid deformation of the sample. - /Deform_Y - /Deform_Z <strong>- liquid_simulation.h5</strong> # Numerical simulation of dynamic liquid filling process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). <strong>- phasetran_simulation.h5</strong> # Numerical simulation of gradual linear density change process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). Reconstruction codes: <strong>- astra_nonrigid.zip</strong> # Compressed python package of modified version of astra-toolbox with nonrigid computed tomography implementation. - /astra # Python package folder, need to be added to Python import search path (sys.path). # If the pre-compiled version doesn't work, source code of the pacakge can be downloaded: # https://github.com/zr-gao/astra-toolbox-nonrigid # Follow the instructions and requirements on the website to compile and install the package. <strong>- reconstruction_PEFC.py</strong> # Python script for sparse dynamic tomography of the PEFC dataset. # Need to be in the same folder with Dynamic_PEFC_data.h5 to load data. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra(with nonrigid)*, h5py # * <strong>!!!</strong> Nonrigid computed tomography is used for the reconstruction, therefore the astra package with nonrigid implementation (in astra_nonrigid.zip) is required. <strong>- reconstruction_simulation.py</strong> # Python script for sparse dynamic tomography of numerical simulations. # Need to be in the same folder with liquid_simulation.h5 or phasetran_simulation.h5, loaded filename is selected in the code. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra**, h5py # ** Reconstruction of numerical simulations does not use nonrigid computed tomography, therefore both the astra_nonrigid.zip and the official astra-toolbox package will work. # To download and install the official astra-toolbox refer to the repository: # https://github.com/astra-toolbox/astra-toolbox</pre>
Databases with structures used for "Improving the Activity of M-N4 Catalysts for the Oxygen Reduction Reaction by Electrolyte Adsorption"
<p>DFT optimised structures used for the paper "Improving the Activity of M-N<sub>4</sub> Catalysts for the Oxygen Reduction Reaction by Electrolyte Adsorption". There is a separate database for structures with Cr, Mn, Fe and Co as the central metal atom in the M-N4 motif, and one with the molecular references. The structures can be retrieved using the Atomic Simulation Environment (ASE).</p>
DATASET: Electric Potential Distribution Inside the Electrolyte During High Voltage Electrolysis
<p>This project contains all the data shown in the figures of the manuscript (and the supporting information) entitled:<br> 'Electric Potential Distribution Inside the Electrolyte During High Voltage Electrolysis'<br> (doi:10.26434/chemrxiv-2022-nw4sp).</p> <p>The data to each figure is provided in a subfolder with further information.</p>
Data related to the article "Frequency-dependent impedance of nanocapacitors from electrode charge fluctuations as a probe of electrolyte dynamics"
<p>Contains input files and data used to generate the figures of the article:</p> <p>Frequency-dependent impedance of nanocapacitors from electrode charge fluctuations as a probe of electrolyte dynamics<br> (Giovanni Pireddu and Benjamin Rotenberg)</p> <p>arXiv: https://arxiv.org/abs/2206.13322</p> <p>The folder EXAMPLE_INPUT_FILES contains typical <a href="https://doi.org/10.21105/joss.02373">MetalWalls</a> (<a href="https://gitlab.com/ampere2/metalwalls">repository</a>) input files used to perform the simulations.</p> <p>The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper (see below).<br> </p> <p>The 'd*' labels are used in the directory or file names to refer to the following interelectrode distances considered:<br> - 'd1' corresponds to 2.51 nm;<br> - 'd2' corresponds to 4.94 nm;<br> - 'd3' corresponds to 9.76 nm;<br> - 'd4' corresponds to 19.42 nm;</p> <p><br> Figure 1:<br> - 'Fig1_Continuum.dat': <br> - Capacitance calculated considering a continuum approximation<br> - 'Fig1_DDS.dat':<br> - Capacitance calculated considering the DDS model (three capacitors in series)<br> - 'Fig1_MD.dat':<br> - Capacitance calculated from MD simulations. Includes the capacitance of the empty capacitor.</p> <p>Figure 2:<br> Panel A<br> - 'Fig2_QACFd*.dat':<br> - Time autocorrelation function of the electrode charge fluctuations<br> Panel B<br> - 'Fig2_Qrampd*.dat':<br> - Charge profile upon a step in voltage (0 to 1 V)<br> - 'Fig2_Vramp.dat':<br> - Voltage ramp related to the charging profiles<br> Panel C<br> - 'Fig2_QACFNormd*.dat':<br> - Normalized charge autocorrelation function<br> -'Fig2_QrampNormd*.dat'<br> - Normalized charge profile</p> <p>Figure 3:<br> - 'Fig3_MDd*.dat':<br> - Real and imaginary parts of impedance as estimated from MD simulations<br> - 'Fig3_ECd*.dat'<br> - Real and imaginary parts of impedance as calculated from the equivalent circuit models</p> <p>Figure 4:<br> - 'Fig4_MDd*.dat':<br> - Magnitude of admittance as estimated from MD simulations<br> - 'Fig4_Debyed*.dat':<br> - Magnitude of admittance as calculated from the Debye relaxation model<br> Inset<br> - 'Fig4_MDTau.dat':<br> - Relaxation time estimated from MD simulations<br> - 'Fig4_TauFit.dat':<br> - Fit of the relaxation time</p>
Polymer Electrolyte Membrane Water Electrolyzer Oxygen Bubble Evolution Optical Video Recording For Deep Learning-Enhanced Characterization of Bubble Dynamics in Proton Exchange Membrane Water Electrolyzer by André Colliard-Granero, Keusra A. Gompou, Christian Rodenbücher, Kourosh Malek, Michael H. Eikerling, and Mohammad J. Eslamibidgoli
<p>Dataset used for the training of the segmentation model employed in the work "Deep Learning-Enhanced Characterization of Bubble Dynamics in Proton Exchange Membrane Water Electrolyzer" by André Colliard-Granero, Keusra A. Gompou, Christian Rodenbücher, Kourosh Malek, Michael H. Eikerling, and Mohammad J. Eslamibidgoli. This dataset consists in 35 images and the corresponding manual annotated masks of diverse bubbly scenarios extracted from the optical video recording of a PEMWE with a transparent flow field.</p>
Dataset for manuscript entitled "Insights into the LiMn2O4 Cathode Stability in Aqueous Electrolyte"
<p>Dataset for manuscript entitled <em>Insights into the LiMn2O4 Cathode Stability in Aqueous Electrolyte</em>. This dataset includes:</p> <p>-XRD of as-deposited films and bare substrate</p> <p>-TERS average spectra and maps of the as-deposited and cycled samples</p> <p>-Macro-Raman spectra of the as-deposited and cycled samples.</p> <p>-PALS data of the as-deposited and cycled films, as a function of depth and extracted for 2 keV</p> <p> </p> <p> </p>
ScienceDex guides
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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.