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501 results for “Charging”
Role of net charges and charge clustering in a dynamic disordered complex between an IDP and a folded domain.
<p>Simulation input files and trajectories for the paper "Role of net charges and charge clustering in a dynamic disordered complex between an IDP and a folded domain". </p> <p>Files here:</p> <p>cg_prota_multigd.tgz -- coarse-grained simulations of prothymosin alpha with multiple globular domains</p> <p>gd_prota_cg_umbrella.tgz -- coarse grained umbrella sampling simulations of prothymosin alpha associating with a single globular domain</p> <p>prota_aa.tgz -- all-atom simulations in explicit water of prothymosin alpha alone, with several force fields</p> <p>gd_amber03ws.tgz -- all-atom simulations in explicit water of globular domain alone, with amber ff03ws force field</p> <p>gd_des-amber.tgz-- all-atom simulations in explicit water of globular domain alone, with des-amber force field</p> <p>prota+gd_des-amber-SF1.0.tgz -- all-atom simulations in explicit water of prothymosin alpha + single globular domain, des-amber-SF1.0 force field</p> <p>prota+gd_des-amber.tgz-- all-atom simulations in explicit water of prothymosin alpha + single globular domain, des-amber force field</p> <p>prota+gd_amber_ff99sbws.tgz -- all-atom simulations in explicit water of prothymosin alpha + single globular domain, amber ff99sbws force field</p> <p>prota+gd_amber_ff99sb-disp.tgz -- all-atom simulations in explicit water of prothymosin alpha + single globular domain, amber ff99sb-disp force field</p> <p>prota+gd_amber_ff03ws.tgz -- all-atom simulations in explicit water of prothymosin alpha + single globular domain, amber ff03ws force field</p> <p> </p>
Electric Vehicle Fast-Charging Software: Architectural Considerations Towards Trustworthiness
Open the record for dataset details and reuse information.
Energies, forces, and Born effective charges for water and MAPbI3 + Born effective charges and polarization data for a water dimer
<p>Datasets containing energies, forces, and Born effective charges for water and MAPbI3 in the VASP format (ML_AB, OUTCAR, and POSCAR), polarization data and Born effective charges for a water dimer are also included.</p> <p><strong>Energies and forces:</strong><br>H2O/MLFF/RPBE-D3/ML_AB – 64 water molecules, 654 configurations.<br>H2O/MLFF/SCAN/ML_AB – 64 water molecules, 972 configurations.<br>MAPbI3/MLFF/ML_AB – 8(MAPbI3), 1414 configurations, tetragonal and orthorhombic phase, SCAN.</p> <p><strong>Born effective charges:</strong><br>dimer/BEC/ – 2 water molecules, 150 configurations.<br>H2O/BEC/ – 64 water molecules, 100 configurations.<br>MAPbI3/BEC/ – 8(MAPbI3), 300 configurations, tetragonal, orthorhombic, and cubic phase.</p> <p><strong>Polarization:<br></strong>dimer/P/ – 2 water molecules, 1000 configurations.</p> <p>See preprint for more details: </p> <table> <tbody> <tr> <td><a href="https://doi.org/10.48550/arXiv.2404.19674">https://doi.org/10.48550/arXiv.2404.19674</a></td> </tr> </tbody> </table>
Exploring the chemical and structural change of copper porphyrins upon charging by means of synchrotron X-ray absorption spectroscopy
<h2><span>Abstract</span></h2> <p><span>In response to the growing demand for battery materials, researchers explore alternative resources with a focus on sustainability. Among these, organic electrode materials—including porphyrins—have emerged as promising candidates due to their advantageous properties, such as rapid charging capabilities and high energy densities. However, despite their potential, the precise charging mechanism of these alternatives remains elusive. To address this gap, our study delved into copper porphyrins, with a primary focus on [5,15-bis(ethynyl)-10,20-diphenylporphinato] copper(II) (CuDEPP). Employing synchrotron X-ray absorption spectroscopy in <em>operando</em> mode, we probed the evolution in chemical and electronic structure of Cu in CuDEPP. Our findings unequivocally demonstrate the participation of copper as a redox center during reversible charge storage, shedding light on its superior electrochemical performance. Furthermore, a combined approach involving extended X-ray absorption fine structure (EXAFS) studies and theoretical calculations provided deeper insights into the observed structural distortion during the charge storage process. Notably, our results support the hypothesis that redox processes, specifically those involving the aromatic porphyrin ring, drive the electrochemical activity of CuDEPP. In summary, our investigation offers important insights into the charging mechanism of copper porphyrins—an essential step toward advancing sustainable organic materials for batteries. </span></p>
E2E-differentiable Charged Particle Tracking Data
<div> <div>In this data repository, we extend an earlier dataset (https://zenodo.org/records/7426388) generated for the Bergen pCT DTC by various additional Monte Carlo (MC) simulations (generated using the Gate 9.2 simulation toolkit [1, 2] built upon Geant4 [3,4,5]) with different setups and phantom materials with particular focus on an E2E-differentiable tracking algorithm provided on <a href="https://github.com/SIVERT-pCT/e2e-tracking">GitHub</a>. The data repository is further extended by trained model checkpoints and results of addtional analysis required for running the code, without re-training and re-evaluation.</div> <br> <h3><strong>Training Files</strong></h3> <strong>Files</strong>: We provide multiple simulations for different phantom geometries and simulation setups, each generated with a mono-energetic pencil beam (230 MeV, 2 sigma). The supplied files include single beam spots for water phantoms of various thicknesses (100, 150 and 200 mm): <ul> <li>water_{100,150,200}_5k.npz (validation data)</li> </ul> <div> <ul> <li>water_{100,150,200}_10k.npz (test data, taken from https://zenodo.org/records/7426388)</li> </ul> </div> <div> <ul> <li>water_{100,150,200}_100k.npz (training data)</li> </ul> </div> <br> <div><strong>Columns</strong>: All above-mentioned simulation files contain MC simulated data of a single simulation run in tabular form, where each row represents a single particle hit inside the detector. Furthermore, each particle hit is parametrized by the following columns:</div> <ul> <li><strong>posX, posY, posZ</strong>: Measured x, y, z position (in millimeter) of the particle hit relative to the simulation origin defined by the center of the phantom.</li> </ul> <div> <ul> <li><strong>edep</strong>: Amount of energy (in MeV) deposited by a particle while interacting with the sensitive area of the detector.</li> </ul> </div> <div> <ul> <li><strong>eventID</strong>: Each primary is simulated in its own isolated "event" and gets an incremental ID. Everything that happens during the simulation of said primary is grouped under the same eventID. Events are simulated independent of each other. trackIDs are only unique within their respective event.</li> </ul> </div> <div> <ul> <li><strong>trackID</strong>: A track describes a single particle throughout its entire lifetime in the simulation. In any given event, the first track (trackID = 1) is always associated with the primary particle. Every subsequently produced secondary particle has an incremental trackID.</li> </ul> </div> <div> <ul> <li><strong>parentID</strong>: The parentID specifies the trackID in the current event that caused this track to exist. If the parentID is 0, the particle is a primary, i.e., generated by the particle beam. Otherwise, the row describes a secondary which was generated through interactions of a primary with the traversed matter.</li> </ul> </div> <div> <ul> <li><strong>volumeID[2]</strong>: Incremental numerical identifier of layer containing particle hit inside GATE volume 2 defined within the detector geometry. 0 for tracking layers, 1 for calorimeter layers.</li> </ul> </div> <div> <ul> <li><strong>volumeID[3]</strong>: Incremental numerical identifier of layer containing particle hit inside GATE volume 3 defined within the detector geometry. Unique identifiers (starting from zero) for tracking layer (0, 1) and calorimeter layer (0, 1, …, 40).</li> </ul> </div> <br> <h3><strong>Model Checkpoints and Analysis Data</strong></h3> <strong>Files</strong>: Additionaly to the simulated track data, we provide the trained model checkpoints and additional analysis data, generated using the source code for end-to-end differentiable charged particle tracking published on <a href="https://github.com/SIVERT-pCT/e2e-tracking">GitHub</a>. The compressed directory contains the following directories:<br> <div> <ul> <li><strong>cka</strong>: Calculated CKA similarities [6] estimated for all trained model combinations .</li> </ul> </div> <div> <ul> <li><strong>mode</strong>: Minimum energy connecting curves (mode connectivity) [7] of Bezier splines optimized for all trained model combinations.</li> </ul> </div> <div> <ul> <li><strong>pat_lambda_{25,50,75}</strong>: All trained end-to-end differentiable tracking networks.</li> </ul> </div> <div> <ul> <li><strong>ptt</strong>: All trained two-step tracking networks.</li> </ul> </div> <br><br> <div>[1] S. Jan, G. Santin, D. Strul et al., “GATE -Geant4 Application for Tomographic Emission: a simulation toolkit for PET and SPECT,”Phys Med Biol. Phys Med Biol, vol. 49, no. 19, pp. 4543–4561, 2004.</div> <br> <div>[2] S. Jan, D. Benoit, E. Becheva et al., “GATE V6: A major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy, ”Physics in Medicine and Biology, vol. 56, no. 4,pp. 881–901, 2011.</div> <br> <div>[3] S. Agostinelli, J. Allison, K. Amako et al., “GEANT4 - A simulation toolkit, ”Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 506, no. 3, pp. 250–303, 2003.</div> <br> <div>[4] J. Allison, K. Amako, J. Apostolakis et al., “Geant4 developments and applications, ”IEEE Transactions on Nuclear Science, vol. 53, no. 1, pp. 270–278, 2006.</div> <br> <div>[5] J. Allison, K. Amako, J. Apostolakis et al., “Recent developments in geant4”, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 835, pp. 186–225, 201</div> <br> <div>[6] Kornblith, S., Norouzi, M., Lee, H., & Hinton, G. (2019). Similarity of Neural Network Representations Revisited. 36th International Conference on Machine Learning, pp 6156–6175, 2019.</div> <br> <div>[7] Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D., & Wilson, A. G. (2018). Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. Advances in Neural Information Processing Systems, pp. 8789–8798, 2018.</div> </div>
Data Related to "Charge-Transfer Contacts for the Measurement of Correlated States in Monolayer WSe2"
<p>Data related to "Charge-Transfer Contacts for the Measurement of Correlated States in Monolayer WSe2"</p>
Dynamical decoding of the competition between charge density waves in a kagome superconductor
<p>P1, P2, P3 denote the peaks at (0 -1.5 2.5), (-0.5 -1 2), (0 -1.5, 3), respectively.</p>
Data for "Leader Polarity-Reversal Feature and Charge Structure of Three Upward Bipolar Lightning Flashes"
<p>In a manuscript entitled “Leader Polarity-Reversal Feature and Charge Structure of Three Upward Bipolar Lightning Flashes”,lightning locations,electric field and current data can be obtained through the following attachment.These files can be opened by matlab 2016(or later). The data supports the aforementioned manuscript and can be used freely for scientific purposes with appropriate citation.<br> One thing should be noted that in the files associated with LMA data,five columns represent x,y,z,time and radiation power, respectively.</p>
GROMOS-CKP POPS/POPC simulations (versions 1 and 2) 298 K with GROMOS NH3 charges and PME
<p>GROMOS-CKP POPS/POPC simulations (298 K, starting structure from the CHARMM-GUI with the appropriate hydrogen atoms removed and with 22 POPS and 110 POPC lipids) performed with 1.4 nm cut-offs, PME and a long range dispersion correction. The charges for the NH3 part of the head group are taken from the standard GROMOS force field parameters. Two different simulations generated with different starting velocities are provided (the files are named v1 and v2 for these different simulations). The trajectories contain only the data from 400-500 ns of the simulations (as per the analysis provided on the nmrlipids blog) and additionally they have been processed with trjconv -skip 10 to keep the upload small.</p>
GROMOS-CKP POPS/POPC simulations (versions 1 and 2) 298 K with Berger/Chiu NH3 charges and PME
<p>GROMOS-CKP POPS/POPC simulations (298 K, starting structure from the CHARMM-GUI with the appropriate hydrogen atoms removed and with 22 POPS and 110 POPC lipids) performed with 1.4 nm cut-offs, PME and a long range dispersion correction. The charges for the NH3 part of the head group are the same as in the Berger PS simulations (originally taken from the Berger/Chiu PC head group). Two different simulations generated with different starting velocities are provided (the files are named v1 and v2 for these different simulations). The trajectories contain only the data from 400-500 ns of the simulations (as per the analysis provided on the nmrlipids blog) and additionally they have been processed with trjconv -skip 10 to keep the upload small.</p>
Charge regions indicated by LMA lightning flashes in Hokuriku's winter thunderstorms
<p>The attached is the dataset assoicated with the paper titled "Charge regions indicated by LMA lightning flashes in Hokuriku’s winter thunderstorms" which was submitted to Journal of Geophysical Research: Atmospheres.</p> <p>The charge distribution of some cells in three winter thunderstorms in the Hokuriku region of Japan is investigated based on Lightning Mapping Array (LMA) flash data. The vertical arrangements of charge regions involved in lightning discharges suggest diverse charge patterns, including quad-polar, tripole, positive dipole, inverted dipole, and inverted tripole. The riming electrification between graupel and ice crystals or their aggregations are thought to be responsible for the electrification of most cells. The charging process between snow/aggregates and ice crystals may be responsible for some inverted charge structure occurred above 0 ℃ isotherm and accompanying with weak radar echoes. Convection indicated by the vertical development of radar reflectivity appears crucial to shaping the diverse charge distribution patterns by determining which charging mechanisms occur and where; it also influences changes in height or even the disappearance of the charge regions. The charged cores are distributed from 0.7 to 5.3 km heights and 2 to –31 ℃ temperatures, while the distances between adjacent charged cores with opposite polarities change between 0.2 and 3.4 km, with a mean of 1.3 km. The mean flash duration, and horizontal distance are 425.0 ms and 19.8 km, respectively. The average height, temperature, and power of flash initiations are 2.8 km, −11.9 ℃ and 15.6 dBW, respectively.</p>
NanoValid D.5.47 Annex 2: Inter-laboratory comparison on measurand Surface Charge (Zeta Potential) of 15 nm Lys-SNPs-B1 Silica Nanoparticles Particles
<p>An inter-laboratory comparison on the surface charge, expressed as zeta potential <em>ζ</em>, of nanoscaled SiO<sub>2</sub> has been performed using #14 BAM ~15 nm Lys-SNPs-B1 Silica nanoparticles (see D.5.41/5.42). The comparability of results delivered by participants has been tested.</p> <p>The Task 5.4 of NanoValid is designed to test, compare and validate current methods to measure and characterize physicochemical properties of selected engineered nanoparticles. The measurand is <em>Surface charge expressed as zeta-Potential</em>. The measurements are to be accompanied by estimates of the uncertainties at a confidence level of 95%, deduced from the standard uncertainties. Therefore an uncertainty budget comprising statistical (Type A) and systematic (Type B) errors has to be established and delivered for the measurand.</p>
Descriptors for Electron and Hole Charge Carriers in Metal Oxides: Calculations Dataset
<p>Calculation dataset required in order to carry out the full analysis in <a href="http://github.com/FaradayInstitution/charge_carriers_data">This repository</a>. Outputs from hybrid Density Functional Theory calculations using the Vienna Ab-initio Simulation Package (VASP) are provided. See the main repository for details on analysis steps. </p>
Data accompanying: Impact of interface traps on charge noise and low-density transport properties in Ge/SiGe heterostructures
<p>These are the data accompanying the publication titled: <em>Impact of interface traps on charge noise and low-density transport properties in Ge/SiGe heterostructures</em></p> <p>The repository contains:</p> <ul> <li>FigurePlotting.ipynb : JupyterNotebook used to load the data and plot the Figures.</li> <li>FigureData : folder that contains the raw/analysed data needed for plotting the Figures.</li> </ul> <p>The JupyterNotebook is organised in sections corresponding to the Figures and Supplementary Figures of the last version (published) of the paper. For each Figure/SuppFigure the needed data (stored in .txt or .pkl files in the FigureData folder) is loaded and plotted.</p> <p> </p>
Data Files for P. Mai et al., "Fluctuating charge-density-wave correlations in the three-band Hubbard model" (2024)
<p>These are the data for P. Mai et al., "Fluctuating charge-density-wave correlations in the three-band Hubbard model" (2024)</p> <p>arXiv reference: https://arxiv.org/abs/2405.13164</p> <p>This work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award Number DE-SC0022311. This research used resources of the Oak Ridge Leadership Computing Facility, a DOE Office of Science User Facility supported under Contract No. DE-AC05-00OR22725.</p>
Dataset for: "Spectrally Resolving the Phase and Amplitude of Coherent Phonons in the Charge Density Wave State of 1T-TaSe2"
Open the record for dataset details and reuse information.
SPARCS_WP4_Leipzig_City_Number of EV charging points
<p>Number of EV charging points in the city of Leipzig with annually captured data for the period between 2019 and 2024</p>
Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
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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.