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441 results for “Battery”
Battery discharge characteristics for IEEE 802.15.4 based radio load profile
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Data from: An infrared, Raman, and X-ray database of battery interphase components
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Data from: US-Mexico second-hand electric vehicle trade: Battery circularity and end-of-life policy implications
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3D microCT of lithium metal battery after charge and discharge
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Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
<p>Dataset accompanying the paper: "Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning"</p>
Germanium tin alloy nanowires as anode materials for high performance Li-ion batteries
<p><strong>Abstract</strong><br> The combination of two active Li-ion materials (Ge and Sn) can result in improved conduction paths and higher capacity retention. Here we report for the first time, the implementation of Ge<sub>1–x</sub>Sn<sub>x</sub> alloy nanowires as anode materials for Li-ion batteries. Ge<sub>1−x</sub>Sn<sub>x</sub> alloy nanowires have been successfully grown via vapor–liquid–solid technique directly on stainless steel current collectors. Ge<sub>1−x</sub>Sn<sub>x</sub> (x = 0.048) nanowires were predominantly seeded from the Au<sub>0.80</sub>Ag<sub>0.20</sub> catalysts with negligible amount of growth was also directly catalyzed from stainless steel substrate. The electrochemical performance of the the Ge<sub>1−x</sub>Sn<sub>x</sub> nanowires as an anode material for Li-ion batteries was investigated via galvanostatic cycling and detailed analysis of differential capacity plots (DCPs). The nanowire electrodes demonstrated an exceptional capacity retention of 93.4% from the 2nd to the 100th charge at a C/5 rate, while maintaining a specific capacity value of ∼921 mAh g−1 after 100 cycles. Voltage profiles and DCPs revealed that the Ge<sub>1−x</sub>Sn<sub>x</sub> nanowires behave as an alloying mode anode material, as reduction/oxidation peaks for both Ge and Sn were observed, however it is clear that the reversible lithiation of Ge is responsible for the majority of the charge stored.</p>
Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling
<p>This supplementary material includes data and code for the research described in the paper "Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling". The code containts an interface between the output files of the agent-based simulation model CURRENT and the energy system optimization model REMix as well as some scripts for analyzing REMix results. The data folder contains input data for REMix, the complete list of all model runs analyzed in the paper in the GAMS format .gdx as well as Excel files containing annual results of the sensitivity runs and respective pivot tables and figures for respective analysis.</p>
Dataset for: "Dipentamethylene Thiuram Tetrasulfide Based Cathodes for Rechargeable Magnesium Batteries"
<p>Dataset for computational contributions to paper with DOI: 10.1021/acsaem.0c01655</p> <p>data-structure: </p> <p>.<br> ├── bondstrengths<br> │ ├── 1<br> │ │ ├── left<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── right<br> │ │ └── output -> ../../7/right/output/<br> │ ├── 2<br> │ │ ├── left<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── right_quasi_newton<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 3<br> │ │ ├── left<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── right<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 4<br> │ │ ├── left<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── right<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 5<br> │ │ ├── left<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── right<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ ├── 6<br> │ │ ├── left<br> │ │ │ └── output<br> │ │ │ ├── CONTCAR<br> │ │ │ ├── INCAR<br> │ │ │ ├── KPOINTS<br> │ │ │ ├── OUTCAR<br> │ │ │ └── POSCAR<br> │ │ └── right<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── 7<br> │ ├── left<br> │ │ └── output<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ └── POSCAR<br> │ └── right<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> └── other<br> ├── MgPMDTC2<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── Mg_atom<br> │ └── output<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ └── POSCAR<br> ├── PMTT<br> │ ├── output_HOMO_LUMO_isosurface<br> │ │ ├── CONTCAR<br> │ │ ├── INCAR<br> │ │ ├── KPOINTS<br> │ │ ├── OUTCAR<br> │ │ ├── PARCHG.0057.ALLK<br> │ │ ├── PARCHG.0058.ALLK<br> │ │ ├── POSCAR<br> │ │ └── PROCAR<br> │ └── output_geometric_relaxation<br> │ ├── CONTCAR<br> │ ├── INCAR<br> │ ├── KPOINTS<br> │ ├── OUTCAR<br> │ ├── POSCAR<br> │ └── PROCAR<br> └── S2<br> └── output<br> ├── CONTCAR<br> ├── INCAR<br> ├── KPOINTS<br> ├── OUTCAR<br> └── POSCAR</p> <p>46 directories, 94 files</p>
NMR spectroscopy of coin cell batteries with metal casings
<p>Raw NMR data in Bruker format for experiments on coin cells with metal casings.</p> <p>Operando experiment on COTS cell: 20201112-LiAlCell3/19</p>
Datasets to Impact of Nano-sized Inorganic Fillers on PEO-based Electrolytes for Potassium Batteries
<p>This dataset provides the raw data to the manuscript</p><p><strong>"Impact of Nano‐Sized Inorganic Fillers on PEO‐Based Electrolytes for Potassium Batteries"</strong></p><p>published in Batteries & Supercaps, <strong>2023</strong>, e202300404. https://doi.org/10.1002/batt.202300404</p><p> </p><p>Specifically, the following measurements are provided in separate zip folders:</p><p>Solid polymer electrolytes characterization:</p><p>Differential Scanning Calorimetry ("DSC.zip")</p><p>Rheological measurements ("Rheology.zip")</p><p>Electrochemical Impedance Spectroscopy ("PEIS.zip")</p><p>Plating and Stripping Experiments ("Plating-Stripping.zip")</p><p>Galvanostatic Cycling with Potential Limitations (GCPL.zip)</p><p> </p><p>Experimental and sample details, including assignment to filenames are provided in the respective README files.</p>
Dataset of article : A phenazine-based conjugated microporous polymer as high performing cathode for aluminium-organic batteries
<p>Data used for preparation of the article : A phenazine-based conjugated microporous polymer as high performing cathode for aluminium-organic batteries</p><p> Abstract of article: </p><p>Here, we present one of the first examples of a phenazine-based hybrid microporous polymer, referred to as IEP-27-SR, utilized as an organic cathode in an aluminium battery with an AlCl3-EMIMCl ionic liquid electrolyte. The preliminary redox and charge storage mechanism of IEP-27-SR was confirmed by ex situ ATR-IR and EDS analyses. The introduction of phenazine active units in a robust microporous framework resulted in a remarkable rate-capability (specific capacity of 116 mAh g-1 at 0.5C with 77% capacity retention at 10C) and notable cycling stability, maintaining 75% of their initial capacity after 3440 charge-discharge cycles at 1C (127 days of continuous cycling). This superior performance compared to reported Al//n-type organic cathode RABs is attributed to the stable 3D porous microstructure and the presence of micro/mesoporosity of IEP-27-SR, which facilitates electrolyte permeability and improves kinetics.</p>
Original data of electrochemical performace in "Chemo-Mechanical Failure Mechanisms of the Silicon Anode in Solid-State Batteries "
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A Stable High-Potential Na7V4(P2O7)4(PO4) Cathode for Sodium-Ion Batteries Developed from a Water-Based Slurry
<p>These are the corresponding raw data related to the publication:</p> <p>A Stable High-Potential Na7V4(P2O7)4(PO4) Cathode for Sodium-Ion Batteries Developed from a Water-Based Slurry</p> <p> </p> <p>Ruihao Gong,<sup>+a</sup> Fabio Maroni,<sup>+a</sup> Mario Marinaro<sup>*a</sup></p> <p> </p> <p><sup>a</sup>Zentrum für Sonnenenergie- und Wasserstoff- Forschung, Baden-Württemberg (ZSW)</p> <p>Helmholtzstraße 8 - 89081 Ulm, Germany</p> <p> </p> <p><sup>+</sup>: The authors contribute equally to this study.</p> <p>*: Corresponding: <a href="mailto:mario.marinaro@zsw-bw.de">mario.marinaro@zsw-bw.de</a></p> <h3> </h3>
Quantitative analysis techniques for evaluating the reliability of Li-ion battery challenges and solutions
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Thermal modeling of a high-energy prismatic lithium-ion battery cell and module based on a new thermal characterization methodology
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State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network
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Novel thermal management methods to improve the performance of the Li-ion batteries in high discharge current applications
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A novel liquid cooling plate concept for thermal management of lithium-ion batteries in electric vehicles
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A novel hybrid thermal management approach towards high-voltage battery pack for electric vehicles
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A comparative study between air cooling and liquid cooling thermal management systems for a high-energy lithium-ion battery module
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ScienceDex guides
Understand access before you commit
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.