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1,491 results for “Ione”
Molecular dynamics simulations of lipid bilayers containing POPC and POPS with the lipid17 force field and ff99 ions
<p>Classical molecular dynamics simulations of various mixtures of POPC:POPS lipid bilayers in water solution at various NaCl, KCl and CaCl2 concentrations, with Na+ counterions (and K+ counterions when noted with "_KCl" suffix).</p> <p>Lipid17 force field parameters used for lipids, TIP3p water model and ff99 ions.</p> <p>The file names report the number of additional cations.</p> <p>simulations performed with Gromacs 2018.0 (*.xtc files)</p> <p>simulation length 1000 ns = 1 microsecond</p> <p>temperature 298 K</p> <p>Gromacs simulation setting is in the file npt_lipid_bilayer.mdp</p>
Fast Ion Transport in the Quasi-Single Helical Reversed-Field Pinch Raw Figure Data
<p>The raw figure data is in a .xls or .xslx file. Each figure or sub-figure is denoted by sheet title in the .xls file. For 2D data plots, the data for the x-array, y-array, x-error, and y-error are provided. For 3D data plots, the data for x-array, y-array, and z-array are provided. Each dataset within a figure or sub-figure is appropriately labeled with an identifier as the column header.</p>
CHARMM36, NB-Fix approaches, without NBFIX, POPC membrane, Ca, Na ions,
<p>Different versions of NB-Fix from CHARMM-Gui and simulations without NB-Fix (called original charmm)</p> <p>200 POPC lipids, TIP3P - charrmm water model,</p> <p> </p>
Is lithium from geothermal brines the sustainable solution for Li-ion batteries?
<p>The rising demand for Li, paramount for energy storage, necessitates expanded supply. As the supply is concentrated in a few countries, this poses supply chain risks for Li-ion battery makers. To diversify suppliers, alternative Li <a title="Learn more about ore deposits from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/ore-deposit">ore deposits</a> such as geothermal brines are being explored. However, Li extraction from geothermal brines is challenging due to the unique chemistry and elevated temperatures. Since Li-extraction from geothermal brines is in its infancy, data availability and quality are still poor, hampering <a title="Learn more about life cycle assessments from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/life-cycle-assessment">life cycle assessments</a>. Hence, this study provides a parametrized life cycle inventory model of Li carbonate production from geothermal brines. The model accounts for site-specific environmental conditions and technological features. Life cycle impacts at the Salton Sea in the US (1686 cases) and the Upper Rhine Graben in Germany (1982 cases) are quantified. The high case numbers are chosen to mitigate the high uncertainties in input parameters. Specifically, the brine chemistry, adsorption yield, drilling required and energy inputs are varied. <a title="Learn more about Climate change impacts from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/climate-change-impact">Climate change impacts</a> of selected cases vary within 18–59 kg CO<sub>2</sub>eq/kg Li carbonate at the Salton Sea and within 5.3–46 kg CO<sub>2</sub>eq/kg Li carbonate at the Upper Rhine Graben, compared to 2.1–11 kg CO<sub>2</sub>eq/kg Li carbonate in existing ecoinvent data sets. The wide range of potential impacts underscore the necessity of early-stage assessments of the technologies. In case of high drilling demand and use of <a title="Learn more about fossil from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/fossil">fossil</a> energy, climate change impacts of Li-ion batteries using Li carbonate from geothermal brines can increase by 30–41 % compared to literature values.</p>
Source files for training the NN-based calibration model of Swarm LP ion densities
<p>Data files used for training the NN-based calibration model for Swarm Langmuir Probe ion densities.</p>
data for "Observations of significant ion energy outflows associated with cusp ion outflows and the role of Poynting flux as an energy source"
Open the record for dataset details and reuse information.
Supplemental Video for "Direct Two-Dimensional Goniometric Steering of Vacuum Electrospray Ion Beams for Angular Time-of-Flight Studies"
<p>Supplemental video for the paper submitted to RSI titled "Direct Two-Dimensional Goniometric Aiming of Vacuum Electrospray Ion Beams for Angular Time-of-Flight Studies". </p> <p>A dual-axis goniometer is used to directly aim a vacuum electrospray ionization source (vESI). This device enables the ion beam to be aimed onto a target to dramatically increase the SNR of downstream diagnostics while also enable angular time-of-flight studies of the vESI plumes.</p>
Supporting Data for "Potassium Alloy Reference Electrodes for Potassium-Ion Batteries: The K-In and K-Bi Systems"
<p>This is the dataset of the data for our publication "Potassium Alloy Reference Electrodes for Potassium-Ion Batteries: The K-In and K-Bi Systems". This archive contains all the raw data gathered and used to produce the results presented in this manuscript.</p> <p>Abstract of manuscript:</p> <div> <div> <div> <div> <p>Potassium-ion batteries (KIBs) are a promising alternative to conventional lithium- ion batteries with reduced critical mineral dependency, but accurate three-electrode characterization is hindered by the lack of a suitable reference electrode. Potassium metal is frequently used as a reference electrode out of necessity, but its high reactivity and unstable potential limit its reliability. Here we investigate the K-In and K-Bi alloy systems, synthesize two-phase In-In4K and Bi-Bi2K alloys, and identify Bi-Bi2K as a promising material owing to its stable potential of 1.07 V vs. K+/K. We prove the use of Bi-Bi2K as a reference electrode by cycling graphite in three-electrode cells and demonstrate that it results in significantly less electrolyte reduction than potassium metal, facilitating the accurate electrochemical characterization necessary to accelerate KIB development.</p> </div> </div> </div> </div>
Feature Engineered Dataset from HPPC Files for Lithium-Ion Battery Cells
<p>This dataset contains time-series data from Hybrid Pulse Power Cycle (HPPC) tests of lithium-ion battery cells, specifically designed for evaluating cell performance and degradation over multiple cycles. The dataset originally published by (Popp et al., 2024) includes information from both charge and discharge tests for each cell, with additional engineered features derived from the raw data. The cells in this dataset were evaluated under different States of Health (SoH), providing insights into performance across various degradation levels.</p> <h2>Dataset Overview</h2> <p>The dataset comprises charge and discharge cycles from 256 individual cells. Each test involves the following procedures:</p> <ol> <li>Cells were equilibrated at 25 °C inside a thermal chamber to ensure a stable temperature before testing.</li> <li>Cells were charged using constant current/constant voltage (CC/CV) mode with a charging rate of C/2 and a cutoff current of 98 mA.</li> <li>After a resting period to allow thermal stabilization, the cells were discharged at a rate of C/5 until the voltage dropped to 2.5 V, capturing the full discharge capacity.</li> <li>A second resting period was conducted until the cells reached thermal equilibrium.</li> <li>HPPC cycles were performed at multiple stages of the charge and discharge cycles to assess cell performance at different State of Charge (SOC) levels.</li> </ol> <p>The dataset also includes a micro HPPC cycle performed at every 10% SOC decrement, starting from 100% SOC down to 10% SOC, with a final HPPC test at 0% SOC. Cells were tested using a combination of charge and discharge pulses to simulate real-world usage patterns. All cycles were recorded at 100 Hz for standard charge/discharge cycles and 1 kHz for the HPPC tests, allowing for high-resolution analysis of the cell behavior.</p> <h2>Data Features</h2> <p>The dataset includes the following features recorded by (Popp. et al., 2024) during the tests:</p> <ul> <li><strong>Time</strong>: Timestamp for each data point.</li> <li><strong>ClimaTemp</strong>: Temperature recorded from the climate chamber.</li> <li><strong>I</strong>: Current applied to the cell (in Amps).</li> <li><strong>Itarget</strong>: Setpoint for the applied current (in Amps).</li> <li><strong>P</strong>: Power output of the cell (in Watts).</li> <li><strong>Q</strong>: Total charge accumulated in the cell (in Amp-seconds).</li> <li><strong>Qneg</strong>: Negative charge accumulated (in Amp-seconds).</li> <li><strong>Qpos</strong>: Positive charge accumulated (in Amp-seconds).</li> <li><strong>Temp_Cell</strong>: Temperature measured at the cell (in °C).</li> <li><strong>U</strong>: Cell voltage (in Volts).</li> </ul> <p>In addition to these core features, engineered features have been included to facilitate analysis of battery performance and degradation trends:</p> <ul> <li><strong>Cumulative_Cycles</strong>: Running count of cycles completed by the cell.</li> <li><strong>Avg_Voltage</strong>: The average voltage of the cell up to the current cycle.</li> <li><strong>Capacity_Fade_Rate</strong>: The rate of capacity degradation over time.</li> <li><strong>Avg_Temperature</strong>: Average temperature experienced by the cell up to the current cycle.</li> <li><strong>Temp_Variation</strong>: Maximum difference in cell temperature over time.</li> <li><strong>High_Temp_Flag</strong>: Binary flag indicating whether the cell temperature exceeded 40°C.</li> <li><strong>Internal_Resistance</strong>: Internal resistance of the cell, calculated from voltage and current data.</li> <li><strong>Power_Consumption_Rate</strong>: Rate at which power is consumed by the cell.</li> <li><strong>Energy_Efficiency</strong>: Efficiency of energy storage, calculated from the ratio of positive to negative charge.</li> <li><strong>Rolling_Avg_Voltage</strong>: Rolling average of the cell voltage over recent cycles.</li> <li><strong>Std_Dev_Voltage</strong>: Standard deviation of cell voltage across cycles.</li> <li><strong>Max_Voltage</strong>: Maximum voltage observed during the test.</li> <li><strong>Min_Voltage</strong>: Minimum voltage observed during the test.</li> <li><strong>Dynamic_Resistance</strong>: Resistance change calculated from voltage and current differentials.</li> <li><strong>Impedance</strong>: Impedance calculated from voltage and current changes.</li> <li><strong>Temp_Coefficient</strong>: Rate of change in power with respect to temperature.</li> <li><strong>Thermal_Runaway_Risk</strong>: Flag indicating the potential for thermal runaway conditions.</li> <li><strong>Effective_Capacity</strong>: Net charge retained by the cell after each cycle.</li> <li><strong>Energy_Throughput</strong>: Total energy delivered by the cell over time, calculated from power and time.</li> </ul> <p>This dataset is suitable for research into lithium-ion battery degradation, performance modeling, and State of Health prediction. The engineered features provide a robust foundation for predictive modeling, including the estimation of Remaining Useful Life (RUL) and optimization of battery usage in practical applications.</p> <p>The dataset has been structured for easy integration with machine learning workflows, with all features provided in CSV format for each test cycle.</p> <h3>Citation:</h3> <p>Popp, A., Spaeth, U., & Schmuelling, B. (2024). Samsung INR21700-50E Capacity and HPPC tests (V1.0) [Data set]. 2024 IEEE Transportation Electrification Conference and Expo (ITEC), Rosemont, IL, USA. Zenodo. <a href="https://doi.org/10.5281/zenodo.10891871" target="_new" rel="noopener">https://doi.org/10.5281/zenodo.10891871</a></p>
Simulation dataset for Off-diagonal Ion Pressure Linked to Hall Fields in Collisionless Magnetic Reconnection
<p>The dataset contains simulation data used in the paper of "Off-diagonal Ion Pressure LInked to Hall Fields in Collisionless Magnetic Reconnection". Data are generated from a particle-in-cell simulation using the VPIC code. Please see the paper for the descriptions of the simulation setup.</p> <p>The .gda files in fiels_moments_gda_files.zip are fields and plasma moments data from the time step presented in the paper. Each file contains float-type data arrays with a size of 6720x1x2240, corresponding to x-y-z dimensions, respectively. Data can be read by softwares like IDL, python, matlab, etc., using the standard data reading methods.</p> <p>The file of harris_m100_x60_Hparticle.89586 contains ion particle data for this time step, for the domain of x=[0,60]di, z=[-8,8]di. The file is written in BINARY, for the information of indivial particles. Each data chunk constitues of the following variables in order: x, z, ux, uy, uz, q. Here x and z are positions of the particles, in unit of de; ux, uy, uz are relativistic velocities in unit of the speed of light (c), i.e., ux = gamma*vx, gamma=sqrt(1+ux^2+uy^2+uz^2); q represents the weight of the particles, so the contribution of a particle to the phase space density needs to multiply by this q factor (In the paper, q factors are re-normalized while the relative weights between particles remain the same).</p> <p>An example IDL sentence of reading particle data: readu, lun, x, z, ux, uy, uz, q. Repeat this sentence for different particles.</p>
Dataset: Stack pressure on lithium-ion pouch cells: a comparative study of constant pressure and fixed displacement devices
<p>Dataset for the paper Experimental Investigation of Power Available in Lithium-Ion Batteries.</p> <ul> <li>Battery: Melasta LCO 6.8Ah pouch cell</li> </ul> <p>Static Pressure Loss:</p> <ul> <li>Test 01: Fixed displacement device</li> <li>Test 02: Constant pressure (springs)</li> <li>Test 03: Constant pressure (pneumatic)</li> </ul> <p>Dynamic pressure variation:</p> <ul> <li>Test 04</li> </ul> <p>Spring stiffness investigation:</p> <ul> <li>Test 05</li> </ul> <p> </p>
Boosting large scale capacitive harvesting of osmotic power by dynamical matching of ion exchange kinetics
<p>Raw data for the article "Boosting large scale capacitive harvesting of osmotic power by dynamical matching of ion exchange kinetics" by N. Chapuis and L.Bocquet.</p>
Ion sensors based on organic semiconductors acting as quasi-reference electrodes
<p>We report electric double-layer transistors with organic semiconductors in single-crystal forms for ion sensing. High operational stability of our transistors enabled them to serve as quasi-reference electrodes, which show one-to-one relationship between the source electrode potential and device resistance. Differential measurements between our sensing and reference transistors demonstrated ion concentration sensing without a conventional reference electrode.</p>
Experimental data of "Analysis of battery-like and pseudocapacitive ion intercalation kinetics via distribution of relaxation times"
<p><span>Improving the kinetics of electrochemical ion intercalation processes is of interest for realizing high-power electrochemical energy storage. This includes classical battery-like intercalation and pseudocapacitive intercalation processes with a capacitor-like electrochemical signature. Electrochemical methods are needed to probe the kinetics of such complex multistep processes in detail. Here, we present the use of the Distribution of Relaxation Times (DRT) analysis of electrochemical impedance data to identify the kinetic limits of intercalation reactions. We study the lithium intercalation reaction in TiS<sub>2 </sub>from organic and aqueous electrolytes as a model system. The material can exhibit both battery-like and pseudocapacitive intercalation regimes depending on the potential range, variable diffusion lengths by adjusting its particle size, and a tunable degree of solvent cointercalation by choosing the electrolyte solvent. Using DRT, we can distinguish between the kinetic limitations imposed by solid-state ion diffusion, interfacial ion adsorption and transport, and ion desolvation processes. Thus, DRT analysis can complement existing methods, such as voltammetry or 3D-Bode analysis, to better understand the kinetics of intercalation reactions</span>.</p>
DFT data from article "Oxide Ion Mobility in V- and P-doped Bi2O3-Based Solid Electrolytes: Combining Quasielastic Neutron Scattering with Ab Initio Molecular Dynamics"
<p>DFT data from article: "Oxide Ion Mobility in V- and P-doped Bi2O3-Based Solid Electrolytes: Combining Quasielastic Neutron Scattering with Ab Initio Molecular Dynamics" (<span><a href="https://pubs.acs.org/doi/full/10.1021/acs.chemmater.2c03103">https://pubs.acs.org/doi/full/10.1021/acs.chemmater.2c03103</a>). Published by 'creators' listed above. </span></p>
Cartesian Coordinates of Unraveling Effects of Counter-Anions and Intramolecular Interaction on the Initial Coordination Stage of Ni(II) Ions with a Flexible Tripeptide
<p>Cartesian Coordinates of Unraveling Effects of Counter-Anions and Intramolecular Interaction on the Initial Coordination Stage of Ni(II) Ions with a Flexible Tripeptide</p>
Dataset for "Dynamical Study of the Reaction of N+ Ions with O2 Neutrals"
<p>Dataset for "Dynamical Study of the Reaction of N+ Ions with O2 Neutrals"</p>
Diagnosis of lithium-ion batteries degradation with P2D model parameters identification: a case study on low temperature charging
<p>Dataset and code of the equilibrium model. They refer to publication <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.fub.2024.100006" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.fub.2024.100006</span></span></a></p>
Dataset for "Imaging the ion-molecule reaction dynamics of O- + CD4"
<p>Dataset for "Imaging the ion-molecule reaction dynamics of O- + CD4"</p>
MeV Ion beam analysis of granite in 10000 points
<p><span>The dataset presents 10000 ion-beam-analysis (IBA) measurements of a granite using 2970+-20 keV protons. Figure 1 shows an example of the result. 10,000 points are acquired in a regular 100x100 point grid with 50 µm beam spot size and step-size. Four detectors acquire the data simultaneously for RBS, NRA, PIGE, and PIXE. The description of the end-station and detectors can be found here: </span><span><a href="https://doi.org/10.3390/instruments5010010"><span>https://doi.org/10.3390/instruments5010010</span></a></span><span>. </span></p> <p><span>Each point on the sample consists of 4 files, four detector spectra. The filename consists of the sample name, three positions in units of nm (X, Y, Z), a rotation in units of µ° followed by an underscore ( _ ) and the type of data contained in the file (RBS, NRA, PIXE, PIGE, or Meta). Only X and Z positions are relevant in this case as indicated in the attached figure.</span></p> <p><span>The RBS, NRA, and PIGE detector files contain a header specifying the dead- and live-time followed by two columns. The first column represents the digital channel of the detector. The second column represents the counts acquired in this channel. This format is readable as ASCI by SimNRA7. The PIXE file contains the number of channels in the first row/header and only the counts in the subsequent rows. The channel is given by the row number. This format is readable by GUPIXWIN3.</span></p> <p><span>The channels of the detector files can be re-calculated using a calibration shown in the following table 1. Certain drifts in the calibrations are possible, but should be limited to below 2% of the given value. All points are integrated to the same ion dose of 0.07 µC. This corresponds to a Particle*Sr of about 5.0E+09 (RBS detector), 5.4E+10 (NRA).</span></p> <p><span>RBS Spectra can be empty or feature a strong low energy background due to excessive scintillation light. Empty spectra can be ignored. The background can be mitigated by ignoring channels <150.</span></p> <p> </p> <p><span>Table </span><span><span>1</span></span><span>: Detector calbration enabling a recalculation of channels to energy. Units are in keV</span></p> <table> <tbody> <tr> <td> <p><span>Detector</span></p> </td> <td> <p><span>Linear (multiply with channel)</span></p> </td> <td> <p><span>Offset (add to first result)</span></p> </td> </tr> <tr> <td> <p><span>RBS</span></p> </td> <td> <p><span>2.77</span></p> </td> <td> <p><span>10</span></p> </td> </tr> <tr> <td> <p><span>NRA</span></p> </td> <td> <p><span>5.5</span></p> </td> <td> <p><span>10</span></p> </td> </tr> <tr> <td> <p><span>PIXE</span></p> </td> <td> <p><span>0.184</span></p> </td> <td> <p><span>1.5</span></p> </td> </tr> <tr> <td> <p><span>PIGE</span></p> </td> <td> <p><span>0.85</span></p> </td> <td> <p><span>0</span></p> </td> </tr> </tbody> </table>
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.