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501 results for “Charging”
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: Laser spectroscopy of fermium isotopes probing trends in nuclear charge radii
<p>In this dataset, a collection of data supporting the findings in the publication J. Warbinek et al. (2024) is presented along with python scripts for the analysis of the data. In the README.txt file, mor einformation on the analysis scripts are provided. </p> <p> </p> <p>The data was collected as parts of on-line expeirmental campaigns between 2019 and 2022 at GSI Helmholtzzentrum für Schwerionenforschung GmbH. The experiments used the RADRIS laser spectroscopy setup coupled to the SHIP separator.</p> <p>More data was collected at Johannes Gutenberg-Universität Mainz between 2019 and 2023 using the RISIKO mass separator. </p> <p> </p> <p> </p>
Collinear laser spectroscopy data of singly charged natural uranium isotopes
<div> <div>This dataset contain the raw data produced in an offline experiment at the IGISOL facility, University of Jyväskylä</div> <div>Accelerator laboratory JYFL, using the Collinear Laser Spectroscopy (CLS) line to measure a set of ten ionic transitions in 234,235,238U.</div> </div>
Data for device simulation in the article "Analysing the impact of the hole transport layer on the space charge distribution and hysteresis in perovskite solar cells using capacitance-voltage profiling"
<p>This repository contains the data used to perform device simulation in the article "Analysing the impact of the hole transport layer on the space charge distribution and hysteresis in perovskite solar cells using capacitance-voltage profiling", submitted in September 2024 to the journal Sustainable Energy and Fuels.</p> <p><br>The authors of this data and the article are E. Regalado-Pérez, Evelyn B. Díaz-Cruz, and J. Villanueva-Cab </p> <p><br>The scripts (.m) and input files (.csv) hosted here are based on the files created by the authors of the Driftfusion code, which can be found in the GitHub repository "barnesgroupICL/Driftfusion" at https://github.com/barnesgroupICL/Driftfusion.</p> <p> </p>
Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs
<p>This dataset is associated with the study <em>"Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs."</em> Detailed instructions for installation, usage, and example scripts for the PACMOF2 models can be found on our GitHub <a href="https://github.com/snurr-group/pacmof2">repository</a>.</p> <div> <div> <div> <div> <p>The dataset includes the following files:</p> <ul> <li><strong>DDEC6_data.zip</strong>: Contains the crystal structures of MOFs with DDEC6 partial charges.</li> <li><strong>PACMOF2_prediction_cifs.zip</strong>: Contains the crystal structures of MOFs with DDEC6 charges as predicted by the PACMOF2 models.</li> <li><strong>PACMOF2_ionic.gz</strong>: A machine learning model designed to predict charges in ionic MOFs with a non-zero formal charge.</li> <li><strong>PACMOF2_neutral.gz</strong>: A machine learning model designed to predict charges in neutral MOFs.</li> </ul> <p>For more details about each file, please refer to the accompanying <code>README.md</code> file.<br><br><br>Updates: <br>- September 2024 (version 1.0.1): Added README.md file.</p> </div> </div> </div> </div>
Data for: Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning
<p>The dataset accompanies the Journal of Energy Storage publication by Shuquan Wang et al. (2024), Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning, DOI 10.1016/j.est.2024.112571. </p> <h2><strong>Experimental Description:</strong></h2> <p>The dataset comprises results from two experimental tests: pulse testing and driving cycle testing. These tests were conducted on two types of sodium-ion batteries—one with a capacity of 3.2 Ah (battery numbers: 1, 2, and 5) and another with a capacity of 10 Ah (battery numbers: 3, 4, and 6).</p> <h3><strong>Pulse Testing:</strong></h3> <p>The pulse tests were carried out using a battery test platform, consisting of an Arbin battery testing system, a temperature-controlled chamber, and a computer. The tests were performed on two 3.2 Ah and two 10 Ah sodium-ion batteries from Transimage and HiNa, respectively, with a nominal voltage of 3.0 V. The upper and lower cut-off voltages were set at 3.9 V and 1.5 V.</p> <p>Enhanced pulse tests were conducted at six different temperatures: -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. The state-of-charge (SOC) was varied in 10% intervals, with pulse currents escalating incrementally from 0.25C to 3C at 0.25C intervals. Each pulse lasted for 5 seconds, followed by a 15-second rest. After completing each set of pulses, the current was increased, and the process was repeated with a two-minute pause between sets of pulses.</p> <h3><strong>Driving Cycle Testing:</strong></h3> <p>The driving cycle tests were designed to simulate real-world driving conditions using various standard test methods, including the Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), and Dynamic Stress Test (DST). These tests were performed in a temperature-controlled chamber using both the 3.2 Ah and 10 Ah sodium-ion batteries.</p> <p>As with the pulse tests, driving cycle tests were carried out at temperatures of -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. Before each test, the batteries were charged with a 0.5C constant current-constant voltage (CC-CV) charging protocol up to 3.9 V, with a cut-off current of 0.02C. After a 30-minute rest, the driving cycle protocol was performed for seven iterations.</p> <h2><strong>File Naming Conventions:</strong></h2> <p>The dataset files are named based on the experimental conditions, as follows:</p> <ul> <li><strong>Pulse_data_tempX_batY</strong>: Data from the pulse tests, where X represents the testing temperature and Y denotes the battery number.</li> <li><strong>Driving_cycle_data_tempX_batY</strong>: Data from the driving cycle tests, where X represents the testing temperature and Y denotes the battery number.</li> </ul>
Dataset package for the Manuscript "Absence of bulk charge density wave order in the normal state of UTe2"
<p>The attached dataset contains raw data, normalized to the respective attenuater, reported in the manusript: </p> <p>"Absence of bulk charge density wave order in the normal state of UTe2".</p> <p>The files "Figure4a.dat", "Figure4b.dat", and "Figure4c.dat" contain data that were presented in Figure 4a, Figure4b, and Figure4c of the manuscript. The first columns contain the x-axis values, the second columns the intensities, and the third column the errorbars.</p> <p>The files "Figure3_N.dat" present the data in Figure 3 c. Here, N labels the (K,L)-coordinates. These are orivuded in "Figure3_KL.dat", where for a number N the N-th row presents the K and L values in the first and second column, respectively.</p> <p>The files "Fig2a.dat" and "Fig2b.dat" contain the datapoints presented in Figure 2a and Figure 2b, where the first column corresponds to the x-axis coordinate and the second column to the recorded intensity.</p>
Validation of heart rate measurement of Fitbit Charge 4 and Xiaomi Mi Band 5
<p>Database containig data from heart rate validation study of 2 wristbands: Fitbit Charge 4 and Xiaomi Mi Band 5.</p>
Fig. 1 in Scientific Note Stirring, charging, and picking: hunting tactics of potamotrygonid rays in the upper Paraná River
Fig. 1. Two hunting behaviors of potamotrygonid rays. Potamotrygon falkneri undulating its disc close to the bottom, stirring the substrate and uncovering hidden prey (a), and Potamotrygon orbignyi approaching a tree stump to pick snails adhered above water surface (not visible on the photograph) (b). The former species is from the study area mentioned in this paper, whereas the latter species was observed in the Maranhão River in Goiás State, Central Brazil.
Fig. 2 in Scientific Note Stirring, charging, and picking: hunting tactics of potamotrygonid rays in the upper Paraná River
Fig. 2. Hunting tactics of Potamotrygon falkneri and Potamotrygon motoro at the Paraná River. Settling close to the bottom, undulating the disc to stir the substrate, and engulfing uncovered prey trapped under the disc (a); approaching the shallows, charging towards concentrated preys, and engulfing prey trapped under the disc (b); approaching a submerged tree stump, ascending towards surface, and exposing anterior part of the disc to pick snails adhered slightly above water surface (c).
BeMAGIC_Nanoporous films and ultra-thin films for magnetoionics and surface charging experiments
<p>BeMAGIC ITN (GA861145)_Nanoporous films and ultra-thin films for magnetoionics and surface charging experiments. Results from UAB, IFW, TUC, KIT, SPIN-ION, TTS</p>
Unravelling the Nature of the Spin Excitations Disentangled from the Charge Contributions in a Doped Cuprate Superconductor
<p>The nature of the spin excitations in superconducting cuprates is a key question toward a unified understanding of the cuprate physics from long-range antiferromagnetism to superconductivity. The intense spin excitations up to the over-doped regime revealed by resonant inelastic X-ray scattering bring new insights as well as problems—like understanding their persistence or their relation to the collective excitations in ordered magnets (magnons). Here, we study the evolution of the spin excitations upon hole-doping the superconducting cuprate Bi<sub>2</sub>Sr<sub>2</sub>CaCu<sub>2</sub>O<sub>8+δ</sub> by disentangling the spin from the charge excitations in the experimental cross section. We compare our experimental results against density matrix renormalization group calculations for a <em>t-J</em>-like model on a square lattice. Our results unambiguously confirm the persistence of the spin excitations, which are closely connected to the persistence of short-range magnetic correlations up to high doping. This suggests that the spin excitations in hole-doped cuprates are related to magnons—albeit short-ranged.</p>
Data for article "Assembling diuranium complexes in different states of charge with a bridging redox-active ligand"
<p>This upload contains raw data (NMR, X-Ray, Elemental Analysis, AC & DC SQUID, EPR) files for the article.</p>
Performance analysis & optimization of inverted inorganic CsGeI3 perovskite cells with carbon/copper charge transport materials using SCAPS‑1D
<p>Hybrid perovskite solar cells (PSC) have achieved efficiencies (PCE) of more than 25%. However, the organic compound is causing structural degradation due to heat and moisture. This has led to the exploration of inorganic perovskites. Inorganic-PSC such as cesium has seen a breakthrough by achieving highly stable PSC with PCE exceeding 15%. In this work, the inorganic non-toxic PSC of cesium germanium tri-iodide (CsGeI<sub>3</sub>) is numerically modeled in SCAPS-1D with two carbon-based and two copper-based charge transport layers(CTL). This study introduces in-depth modelling and analysis of CsGeI<sub>3</sub> through continuity and Poisson equations. Cu-CTL are selected to increase the electric conductivity of the cell, while carbon-CTL is used to increase the thermal conductivity. Four structures are designed and presented. A systematic approach is adopted to obtain the optimized design parameters for maximum performance. From the results it is observed that the C<sub>60</sub>/CsGeI<sub>3</sub>/CuSCN structure has the highest performance, with open-circuit voltage of 1.0169V, short-circuit current of 19.653 mA/Cm<sup>2</sup>, fill factor of 88.13% and PCE of 17.61%. Moreover, the effect of quantum efficiency, electric field, interface recombination, interface defects, layer thickness, defect density, doping concentration, working temperature and reflection coating on the cell performance are studied in detail.</p>
BeMAGIC_Nanoporous films and ultra-thin films for magneto-ionics and surface charging experiments
<p>BeMAGIC ITN (GA861145) Nanoporous films and ultra-thin films for magneto-ionics and surface charging experiments. Results from TUC, UCAM, AALTO and KIT</p>
Dataset for the Charged multivesicular body protein 2b antibody screening study
<p>This project contains the following underlying data included in a study which characterized antibodies for Charged multivesicular body protein 2b. The study is available on Zenodo (https://doi.org/10.5281/zenodo.6370501).</p>
Vertical profiling of the electrical properties of charged desert dust during the pre-ASKOS campaign: Dataset
<p>The zipped files contain the datasets used to produce Figure 1 of the following conference proceedings paper:</p> <p>Vasiliki Daskalopoulou, George Hloupis, Sotirios A. Mallios, Ilias Makrakis, Evangelos Skoubris, Maria Kezoudi, Zbigniew Ulanowski, & Vassilis Amiridis. (2021, July 6). <em>Vertical profiling of the electrical properties of charged desert dust during the pre-ASKOS campaign</em>. 15th International Conference on Meteorology, Climatology and Atmospheric Physics (COMECAP 2021), Ioannina, Greece. https://doi.org/10.5281/zenodo.5076042</p> <p>The repository contains overall:</p> <ol> <li>the ground-based JCI 131 Fieldmill Electrometer data that were acquired during the campaign (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Fieldmill_Ion_counter_Cyprus_campaign.rar">Fieldmill_Ion_counter_Cyprus_campaign.rar</a>)</li> <li>Data from an Alphalab Air Ion counter co-located with the fieldmill (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Fieldmill_Ion_counter_Cyprus_campaign.rar">Fieldmill_Ion_counter_Cyprus_campaign.rar</a>)</li> <li>Data from the five MiniMill electrometers that were launched (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/MiniMills_Cyprus_campaign.rar">MiniMills_Cyprus_campaign.rar</a>)</li> <li>Data from the two of the charge sensors that were launched (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Charge_sensors_Cyprus_campaign.rar">Charge_sensors_Cyprus_campaign.rar</a>)</li> <li>Data from the eleven ion counters that were launched, tethered together with the MiniMills or the charge sensors (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Ion_counters_Cyprus_campaign.rar">Ion_counters_Cyprus_campaign.rar</a>)</li> <li>A campaign calendar with the launches schedule (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Cyprus_campaign_November2019_calendar.pdf">Cyprus_campaign_November2019_calendar.pdf</a>).</li> </ol>
Surface Charge Boundary Condition Often Misused in CO2 Reduction Models (raw data for graphs)
<p>This contains the raw data, in text file format, for the figures in the journal article "Surface Charge Boundary Condition Often Misused in CO2 Reduction Models" published in Journal of Physical Chemistry C, DOI 10.1021/acs.jpcc.3c05364.</p> <p>The files are in directories corresponding to their figure number, and the files are named with the capacitance. </p> <p> </p> <p> </p>
Field and Thermal Emission Limited Charge Injection in Au–C60–Graphene van der Waals Vertical Heterostructures for Organic Electronics (Dataset)
<p>Dataset of the vertical Au-C60-Gr stacks measurements related to the publication: "Field and Thermal Emission Limited Charge Injection in Au–C60–Graphene van der Waals Vertical Heterostructures for Organic Electronics", ACS, Appl. Nano Mater., 2023.</p> <p>The dataset includes:</p> <ol> <li>AFM raw data</li> <li>Raman spectroscopy raw data</li> <li>Room temperature measurements</li> <li>Impedance analysis measurements</li> <li>Temperature dependent measurements</li> </ol> <p> </p>
Charge Transport Across Au–P3HT–Graphene van der Waals Vertical Heterostructures (Dataset)
<p>Dataset of the vertical Au-P3HT-Gr stacks measurements related to the publication: "Charge Transport Across Au–P3HT–Graphene van der Waals Vertical Heterostructures", ACS, Appl. Mater. Interfaces, 2022.</p> <p>The dataset includes:</p> <ol> <li>AFM raw data</li> <li>Raman spectroscopy raw data</li> <li>Room temperature measurements</li> <li>Impedance analysis measurements</li> <li>Temperature dependent measurements</li> </ol>
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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.