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371 results for “Electrode”
Enhanced performance of Pr_4Ni_3O_10+_\delta - Ce_0.75Gd_0.1Pr_0.15O_2-\delta composite electrode via particle size grading
<p>The zipped file consists of original data of impedance for electrodes measured at 625 degrees and pO2 of 0.21 atm, and the corresponding DRT result, as well as peak fitting results based on DRT results. A txt file of specification is prepared for illustration.</p>
Data set related to the manuscript "Dynamics and energetics of ion adsorption at the interface between a pure ionic liquid and carbon electrodes"
<p>Text files with data for plotting figures in the quoted manuscript. Example input files for simulations.</p>
Data-Driven Discovery of Carbonyl Organic Electrode Molecules: Machine Learning and Experiment
<p>Prediction model of organic molecular electrode</p>
Dataset for publication Reaction Mechanism and Performance of Innovative 2D Germanane-Silicane Alloys SixGe1−xH Electrodes in Lithium-Ion Batteries
<p>A dataset for publication Datase for publication Reaction Mechanism and Performance of Innovative 2D Germanane-Silicane Alloys SixGe1−xH Electrodes in Lithium-Ion Batteries including all relevant data used in the manuscript. Information on how to use the dataset are included in the readme file.</p>
The amplitudes of electric fields generated by TI stratrgy under different electrode montages
<p>This dataset containes the amplitudes of electric fields generated by extraocular temporally interfering electrical stimulation under all the electrode mongtages mentiond in our research.There also original figures of our research. </p>
Dataset: Mesoporous Electrodes Enhance the Electrocatalytic Performance of [FeFe]-Hydrogenase
<p>Dataset for: <a href="https://doi.org/10.1002/anie.202416658">https://doi.org/10.1002/anie.202416658</a> </p> <p> </p>
Tailored nano-columnar La2NiO4 cathodes for improved electrode performance
<p>La<sub>2</sub>NiO<sub>4</sub> is a promising cathode material for intermediate and low temperature solid oxide cell applications, due to good electronic and ionic conductivity and high oxygen exchange activity with a low activation energy. Oxygen incorporation and transport in La<sub>2</sub>NiO<sub>4 </sub>(LNO) thin films is limited by surface reactions. Hence, tailoring the morphology is expected to lead to an overall improvement of electrode performance. We report on the growth of to nano-architectured La<sub>2</sub>NiO<sub>4</sub> thin film electrodes by PI-MOCVD, achieving vertically gapped columns with multi-fold active surface area, leading to faster oxygen exchange. This nano-rod structure is rooted in a dense bottom layer serving as good electronic and ionic conduction pathway. The microstructure is tuned by modification of the growth temperature and characterised by SEM, TEM and XRD. We studied the effect of surface activity by electrical conductivity relaxation measurements in fully dense and columnar-structured La<sub>2</sub>NiO<sub>4</sub> thin films of various thicknesses on single crystal substrates. Our results indicate that the increased surface area, in combination with the opening of different surface terminations, leads to a significant enhancment of the total exchange activity in columnar structured films.</p> <p> </p>
Supporting data for "Towards reliable three-electrode cells for lithium–sulfur batteries"
<p>This is the dataset of electrochemical measurements for our publication "Towards reliable three-electrode cells for lithium–sulfur batteries". 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>Abstract of the manuscript:</p> <p>Three-electrode measurements are valuable to the understanding of the electrochemical processes in a battery system. However, their application in lithium–sulfur chemistry is difficult due to the complexity of the system and thus rarely reported. Here, we present a simple three-electrode cell format with relatively good life time and minimum interference with the cell operation. </p>
Dataset for the paper: "Carbon Aerogel Based Thin Electrodes for Zero-Gap all Vanadium Redox Flow Batteries – Quantifying the Factors Leading to Optimum Performance"
<p>The data in this spreadsheet was used to produce the figures in the paper </p> <p>Andres Parra-Puerto, Javier Rubio-Garcia, Matthew Markiewicz, Zhuo Zheng and Anthony Kucernak</p> <p>Carbon Aerogel Based Thin Electrodes for Zero-Gap all Vanadium Redox Flow Batteries – Quantifying the Factors Leading to Optimum Performance </p> <p>DOI: https://doi.org/10.1002/celc.202101617 </p> <p>Please cite the above reference if you wish to use this data </p> <p>DOI of this data file is: 10.5281/zenodo.6261512</p>
High-density electrode recordings reveal strong and specific connections between retinal ganglion cells and midbrain neurons.
<p>This is the source file to be use for debugging problems on your own set-up. This files is a good example to both the repository provided for visual processing on the Kremow GitHub:</p> <p>https://github.com/KremkowLab/Axon-on-Neuropixels-in-Kilosort</p> <p>https://github.com/KremkowLab/tangential_recording</p>
Long-term testing of alkaline electrolyser single cell using Raney Ni electrodes
<p>Chronopotentiometry and polarization curves of alkaline electrolyser single cell using Raney Ni electrodes.</p>
Dataset from J. Am. Chem. Soc. paper: Hydrogen-Induced Restructuring of a Cu(100) Electrode in Electroreduction Conditions
<p>Dataset of the article "Hydrogen-Induced Restructuring of a Cu(100) Electrode in Electroreduction Conditions" accepted in J. Am. Chem. Soc.</p> <p>- ensemble_GCGA_all.db: The ASE database of all unique surface states from the grand canonical genetic algorithm (GCGA) search.</p> <p>- ensemble_LELM_refined.db: The ASE database of low-energy local minima surface states after refinement.</p> <p>- ensemble_LELM_GCDFT.db: The ASE database of low-energy local minima surface states with energy-potential relations obtained from grand canonical density functional theory (GCDFT) calculations.</p> <p>- input_GCGA.zip: input files of the GCGA search.</p> <p>- input_GCDFT.zip: input files of the GCDFT calculation.</p> <p>GCGA and GCDFT calculations are performed using the workflow or wrapper in the GOCIA package (https://github.com/zishengz/gocia)</p>
Glass electrode and silicon probe recordings from THY-Tau22 mice
<p>This repository contains code used to analyse electrophysiological data obtained from THY-Tau22 mice. Example datasets are included.</p>
Randomised material parameter piezoelectric impedance dataset with structured electrodes (RaPIDstruc)
<h1>Description of the dataset</h1> <p>This dataset contains the results of 652517 finite-element simulations of the complex, frequency-dependent electrical impedance of a piezoelectric disc with structured electrodes and randomised material parameters. Each impedance consists of 1500 samples in the frequency domain up to 9.37 MHz. The electrode structure is the result of a topology optimisation process with the aim to enhance the sensitivity of the impedance with respect to all material parameters (DOI: <a href="https://jsss.copernicus.org/articles/12/163/2023/">10.5194/jsss-12-163-2023</a>). The piezoelectric disc has a radius of 5 mm and a thickness of 1 mm. The transverse isotropic material parameters are sampled from independent uniform distributions with ranges that are intended to represent the behaviour of hard as well as soft piezoceramic materials. The parameters of the Rayleigh damping model (alpha_M and alpha_K) are sampled from a logarithmic distribution to account for the larger parameter range.</p> <table> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Min</strong></td> <td><strong>Max</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>c11</td> <td>115</td> <td>150</td> <td>GPa</td> <td>Elastic stiffness </td> </tr> <tr> <td>c12</td> <td>70</td> <td>110</td> <td>GPa</td> <td>Elastic stiffness </td> </tr> <tr> <td>c13</td> <td>65</td> <td>95</td> <td>GPa</td> <td>Elastic stiffness </td> </tr> <tr> <td>c33</td> <td>95</td> <td>125</td> <td>GPa</td> <td>Elastic stiffness </td> </tr> <tr> <td>c44</td> <td>18</td> <td>25</td> <td>GPa</td> <td>Elastic stiffness </td> </tr> <tr> <td>eps11</td> <td>5</td> <td>18</td> <td>nF/m</td> <td>Dielectric permittivity</td> </tr> <tr> <td>eps33</td> <td>6</td> <td>12</td> <td>nF/m</td> <td>Dielectric permittivity</td> </tr> <tr> <td>e15</td> <td>7</td> <td>19</td> <td>C/m^2</td> <td>Piezoelectric coupling</td> </tr> <tr> <td>e31</td> <td>1</td> <td>7.5</td> <td>C/m^2</td> <td>Piezoelectric coupling</td> </tr> <tr> <td>e33</td> <td>13</td> <td>23</td> <td>C/m^2</td> <td>Piezoelectric coupling</td> </tr> <tr> <td>alpha_M</td> <td>2</td> <td>150</td> <td>1/ms</td> <td>Mass-proportional damping</td> </tr> <tr> <td>alpha_K</td> <td>0.1</td> <td>0.65</td> <td>ns</td> <td>Stiffness-proportional damping</td> </tr> <tr> <td>density</td> <td>7600</td> <td>7850</td> <td>kg/m^3</td> <td>Density</td> </tr> </tbody> </table> <h1>Files</h1> <p>The dataset contains the following files:</p> <ul> <li><strong>dataset.hdf5</strong>: The main dataset file in HDF5 format. Refer to the next section on how to load the dataset.</li> <li><strong>simulation.xml</strong>: The file describing the parameters of the finite element simulation. This file can be used with <a href="https://www.opencfs.org/">openCFS</a> along with the mesh and material file to simulate the behaviour of the piezoelectric ceramic in the frequency domain. This simulation file was used with randomised material parameters to generate the dataset.</li> <li><strong>material.xml</strong>: The material file used for the simulation with exemplary material parameters values.</li> <li><strong>piezo.geo</strong>: The gmsh geometry file with the axisymmetric representation of the piezoelectric disc and the electrode structure.</li> <li><strong>piezo.msh</strong>: The mesh file generated with gmsh using the piezo.geo file.</li> </ul> <h1>Loading the dataset</h1> <p>The dataset is stored as a HDF5 file, which can be opened with all libraries that support that format, e.g. in Python using the h5py library:</p> <pre><code>import h5py # Open the dataset in read mode. file = h5py.File("dataset.hdf5", "r") # Impedances as a 652517 x 1500 array of complex numbers. impedances = file["impedances"] # Material parameter values as a 652517 x 13 array of real numbers. parameters = file["parameters"] # Frequency vector of the impedance with length 1500. frequencies = file["meta"]["frequencies"] # 13 strings with the identifiers of the material parameters. parameter_labels = file["meta"]["parameter_labels"]</code></pre> <h1>Simulating impedances</h1> <p>To generate a result for the electrical impedance using the supplied simulation files, download and install openCFS and call the executable with the simulation.xml, but omit the file extension, e.g.:</p> <pre><code>cfsbin.exe simulation</code></pre> <p>The path to the executable of openCFS will depend on your operating system and installation. Running the simulation will result in the creation of several files and folders. Among those files will be the result for the electric charge on one of the electrodes of the sample, which will be placed in the 'history' subfolder. We can determine the current by taking the time derivative of the charge and already know the voltage because we excited the piezoceramic with an electric potential of 1 V in the simulation. Because the simulation is conducted in the frequency regime, all we have to do is to divide voltage by current to get the frequency dependent electrical impedance. The loading of the result and calculation of the impedance is implemented in the following Python script as an example:</p> <pre><code>import numpy as np # Load result file for electric charge result_path = 'history/simulation-elecCharge-surfRegion-ground.hist' data = np.loadtxt(result_path) frequency = data[:, 0] # Convert polar representation from file to complex numbers. charge = data[:, 1] * np.exp(1j * 2 * np. pi / 360 * data[:, 2]) # Excitation potential is 1 V in simulation. potential = 1 # Determine impedance by applying Z = V / I = V / (j omega Q). impedance = potential / (1j * 2 * np.pi * frequency * charge)</code></pre>
Dataset for publication "Composite MAX phase/MXene/Ni electrodes with a porous 3D structure for hydrogen evolution and energy storage application
<p>Dataset for publication "Composite MAX phase/MXene/Ni electrodes with a porous 3D structure for hydrogen evolution and energy storage application". The dataset contains relevant data and figures from the publication. Description on how to work with the dataset is given in the readme file. DOI for the original paper: DOI <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3RA07335A">https://doi.org/10.1039/D3RA07335A</a>.</p>
Unveiling the Mechanisms of Solid-State Dewetting in Solid Oxide Cells with Novel 2D Electrodes
<p>Supporting videos as a part of the Supplementary Material of the paper published by the Journal of Power Sources, Volume 420, 30 April 2019, Pages 124-133 doi: 10.1016/j.jpowsour.2019.02.068</p>
Wired for Stability: Evaluating Electrical Performance in Zinc Oxide-Modified Silver Nanowire Solution-Processed Transparent Electrode
<p>Characterisation dataset for “<span>Wired for Stability: Evaluating Electrical Performance in Zinc Oxide-Modified Silver Nanowire Solution-Processed Transparent Electrode</span>”, DOI:xx. Data for main and supporting figures provided as *.xlsx and *.txt files.</p>
A Novel Integrated Reference-Counter Electrode for Electrochemical Measurements of HOMO and LUMO Levels in Small-Molecule Thin-Film Semiconductors for OLEDs
<p>This dataset <span>includes all raw data, processed data, and analysis scripts necessary to replicate the findings reported in the published paper.</span></p>
Optimization and scalability assessment of supercapacitor electrodes based on hydrothermally grown MoS2 on carbon cloth
<p>This dataset contains the measurement data for figures (graphs) published in journal article:</p> <p>Optimization and scalability assessment of supercapacitor electrodes based on hydrothermally grown MoS2 on carbon cloth </p> <p>by Jasna Mannayil, Olli Pitkänen, Minna Mannerkorpi, and Krisztian Kordas</p>
Research Data for "In-vacuo scratching yields undisturbed insight into the bulk of lithium-ion battery positive electrode materials"
<p>This are the datasets supporting the figures and tables in the manuscript and supporting information of the publication "In-vacuo scratching yields undisturbed insight into the bulk of lithium-ion battery positive electrode materials".</p> <p>Available in ACS Energy Letters under <span><a href="https://doi.org/10.1021/acsenergylett.4c02106"><span>https://doi.org/10.1021/acsenergylett.4c02106</span></a></span></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)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.