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441 results for “Battery”
Online Repository for "Sorting lithium-ion battery electrode materials using dielectrophoresis"
<p>Please see the readme file.</p> <p>The matlab script for evaluating the measurements is called “Eval_Fluoro.m” and can be found in this repository.</p> <p>The excel sheet “20221028_photometric_iron.xlsx” contaiins the data from the chemical analysis.</p> <p>The manufacturing data for the electrodes is provided in the zip folder: PCB_boards_Giesler.zip and can be uploaded to a manufacturer of choice.</p>
Dataset: Addressing Practical Use of Viologen-Derivatives in Redox Flow Batteries through Molecular Engineering
<p>Dataset for the results shown in the publications "Addressing Practical Use of Viologen-Derivatives in Redox Flow Batteries through Molecular Engineering"</p>
Dataset for: Exploring Battery Cathode Materials in the Li-Ni-O Phase Diagrams using Structure Prediction
<p>The Li-Ni-O phase diagram contains several electrochemically active ternary phases. Many compositions and structures in this phase space can easily be altered by (electro-)chemical processes, yielding many more (meta-)stable structures with interesting properties. In this study, we use<em> ab initio </em>random structure searching (AIRSS) to accelerate materials discovery of the Li-Ni-O phase space. We demonstrate that AIRSS can efficiently explore structures (e.g. LiNiO<sub>2</sub>) displaying dynamic Jahn-Teller effects. A thermodynamically stable Li<sub>2</sub>Ni<sub>2</sub>O<sub>3</sub> phase which reduces the thermodynamic stability window of LiNiO<sub>2</sub> was discovered. AIRSS also encountered many dynamically stable structures close to the convex hull. Therefore, we confirm the presence of metastable Li-Ni-O phases by revealing their structures and properties. This work will allow Li-Ni-O phases to be more easily identified in future experiments and help to combat the challenges in synthesizing Li-Ni-O phases.</p> <p>This dataset contains the raw research data and key analysis files for "Exploring Battery Cathode Materials in the Li-Ni-O Phase Diagrams using Structure Prediction". </p> <ul> <li>`known_phases_aiida_data.zip` and `new_phases_exports.aiida.zip` contain the archives exported from the <a href="https://www.aiida.net">AiiDA framework</a> which was used to perform parts of the DFT calculations for this project. </li> <li>`search_data.zip` contains search seed files and the structures generated by the searches.</li> <li>`data_analysis.zip` contains the data and notebooks to reproduce the figures and tables shown in the manuscript.</li> </ul>
Neutron Imaging Radiographs in Battery and Electrolyte Experiments
<p>This dataset consists of raw neutron imaging radiographs acquired during experiments on batteries and electrolytes at PSI SINQ neutron source. The radiographs were obtained using a wavelength-resolved neutron imaging technique (time-of-flight neutron imaging). Additionally, the dataset provides Python functions and Jupyter notebooks for processing and analyzing the raw images.</p> <p>Proper citation of this dataset is appreciated to acknowledge the authors and promote reproducibility in scientific research.</p>
The redox mediated – scanning droplet cell system for evaluation of the solid electrolyte interphase in Li-ion batteries
<p>Dataset of articles "The redox mediated – scanning droplet cell system for evaluation of the solid electrolyte interphase in i-ion batteries"</p>
Local observability of an augmented battery model
<p>This document represents a text file including a code for computing the local observability of an augmented battery model used for an OCV estimation method. This code is designed to be used in the computer algebra system (CAS) software Maxima.</p>
CT Data of Battery Pouch Cell with Defects
<p>These are CT slices as TIF files.</p> <p>Data: cubic voxels of (22 µm)³</p> <p>You may also open the included project file in VGStudio or the free myVGL viewer (https://www.volumegraphics.com/en/download-viewer.html) or other compatible software.</p> <p> </p> <p>The sample was prepared by Johannes Münch. For further details see the following paper:</p> <p><a href="https://doi.org/10.1002/ente.202300323">https://doi.org/10.1002/ente.202300323</a></p>
Residential Power and Battery Data
<p><strong>Overview</strong></p> <p>The Residential Power and Battery data is an open-source dataset designed to facilitate the advancement of predictive and optimisation algorithms. It features anonymised, minute-by-minute real-world customer data on energy consumption, solar generation, and battery measurements. This dataset was compiled by SwitchDin and made available through Monash University on the Zenodo platform.</p> <p>Open-sourcing the Residential Power and Battery data offers numerous benefits to researchers, developers, and industry stakeholders. By providing access to comprehensive, real-world data on energy consumption, solar generation, and battery usage, the dataset enables the development of more accurate and efficient algorithms for energy management systems. For example, the dataset could facilitate the development of machine learning models that forecast energy consumption patterns, enabling better demand-side management strategies. These improved algorithms contribute to more effective demand response, grid stability, and renewable energy integration, helping to build a more resilient and sustainable energy future.</p> <p>Furthermore, open-sourcing this dataset fosters collaboration and knowledge sharing among researchers and professionals in the energy sector. By making the data freely available, researchers from various backgrounds and organisations can work together to identify patterns, trends, and innovative solutions to pressing challenges in energy management. This collaborative approach accelerates the pace of innovation, as diverse perspectives can generate novel ideas and methods that might not emerge in isolation. As a result, the open-sourcing of the Residential Power and Battery data has the potential to significantly advance the pursuit of a more efficient, reliable, and environmentally friendly energy landscape.</p> <p><strong>Data Structure</strong></p> <p><code>anonymous_public_power_data.rds</code></p> <ul> <li>utc: The date-time in UTC, formatted as <code>yyyy-mm-dd hh:mm:ss</code>.</li> <li>unit: A categorical label denoting the unique identifier for the unit.</li> <li>metric: A categorical label indicating whether the data point corresponds to load or solar power generation.</li> <li>max: A numerical variable denoting the peak value of load or solar power generation in kilowatts (kW) within a one-minute interval.</li> </ul> <p><code>anonymous_public_power_data_per_unit.zip</code></p> <ul> <li>Same as above, but files are split by units.</li> </ul> <p><code>anonymous_public_battery_data.rds</code></p> <ul> <li>unit: A categorical label denoting the unique identifier for the unit.</li> <li>batt_kwh: A numerical variable representing battery kilowatt-hour rating.</li> <li>batt_p_ch: A numerical variable representing battery charge power rating.</li> <li>batt_p_dch: A numerical variable representing battery discharge power rating.</li> </ul>
Mixed Ion–Electron-Conducting Polymer Complexes as High-Rate Battery Binders
<div class="t-landing__text-wall ">This data set accompanies the article "Mixed Ion–Electron-Conducting Polymer Complexes as High-Rate Battery Binders" published in Chemistry of Materials in 2023 (<a href="https://doi.org/10.1021/acs.chemmater.3c01587">https://doi.org/10.1021/acs.chemmater.3c01587</a>). The article demonstrates multicomponent conjugated polymer complexes as a promising platform design strategy for conducting battery binders. This data set contains comprehensive characterization information, including cyclic voltammetry, variable rate cycling, and ionic/electronic conductivity measurements. Data is presented in non-proprietary txt and csv formats. Technique and sample identification are contained in the file titles, which correspond to figure numbers. Columns in each data file contain a header indicating the data that was recorded and the unit in which it was recorded.</div>
Henry Trough Battery
Smithsonian source data can be found [here](https://ids.si.edu/ids/media_view?id=3d_package:1f7d20f0-6b3f-4780-b754-91c2f9f91602) This media file is in the public domain (free of copyright restrictions). You can copy, modify, and distribute this work without contacting the Smithsonian. For more information and to review the 3D disclaimer, visit the Smithsonian's [Terms of Use](https://www.si.edu/Termsofuse) page. Henry trough battery Measurements: overall: 1 7/8 in x 8 in x 2 5/8 in; 4.7625 cm x 20.32 cm x 6.6675 cm Object Name: Battery Date Made: 1832 Credit Line: from Mary A. Henry ID Number: EM.181178 Catalog Number: 181178 Accession Number: 27225 Data Source: National Museum of American History EDAN-URL: edanmdm:nmah_703292 Source: Objaverse 1.0 / Sketchfab
A complete energy community dataset with photovoltaic generation, battery energy storage systems and electric vehicles (v1.5)
<p>This dataset represents a complete European energy community based on actual data. In this scenario, a community of 250 households was built using real energy consumption and solar generation data obtained in homes throughout Europe. In total, 200 community members were assigned solar generation, while 150 were assigned a battery storage system. From the acquired sample, new profiles were created and randomly assigned to each end-user while also receiving two electric cars with information on their capacity, state-of-charge, and usage. Furthermore, it is provided the electric vehicle chargers’ information on their location, type, and cost of operation.</p> <p> </p> <p>Version 1.5 update: <span>on the Sheet EVs, lines 29 (Capacity kW), 30 (Charge kW), and 31 (Discharge kW) were updated to the correct values.</span></p> <p> </p> <p>This work has been published in Elsevier's Data in Brief journal:<br><em> Ricardo Faia, Calvin Goncalves, Luis Gomes, Zita Vale<br> Dataset of an energy community with prosumer consumption, photovoltaic generation, battery storage, and electric vehicles<br> Data in Brief, 2023, 109218, ISSN 2352-3409<br> <a href="https://doi.org/10.1016/j.dib.2023.109218.">https://doi.org/10.1016/j.dib.2023.109218</a><br> (<a href="https://www.sciencedirect.com/science/article/pii/S2352340923003372)">https://www.sciencedirect.com/science/article/pii/S2352340923003372)</a></em></p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Data in Brief publication to cite this work.</p> <p> </p> <p>Reference data used to create this dataset:</p> <ul> <li>Filtered energy profiles and renewable energy production profiles: <a href="../record/6778401">https://zenodo.org/record/6778401</a></li> </ul> <ul> <li>Battery storage systems and electric vehicles: <a href="../record/4737293">https://zenodo.org/record/4737293</a></li> </ul>
Lisdexamfetamine Dimesylate (LDX) Pilot Cognition Study to Evaluate the Utility of a Standardized Battery of Tests in Adults With Attention-Deficit Hyperactivity Disorder (ADHD)
ClinicalTrials.gov study NCT01010750. IPD Sharing: Not stated. Countries: 1. Publications: 1.
5-Cog Battery for Detecting Cognitive Impairment and Dementia
ClinicalTrials.gov study NCT03816644. IPD Sharing: YES. Countries: 1. Publications: 3.
Feasibility and Validity of A Novel Computer Based Battery of Assessments in the Elderly
ClinicalTrials.gov study NCT02109419. IPD Sharing: NO. Countries: 1. Publications: 2.
United States recycled content standards for lithium-ion batteries
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A coacervate-based mixed-conducting binder for high-power, high-energy batteries
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Destination labels for battery electric vehicles in eVMT dataset
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Mixed Ion–Electron-Conducting Polymer Complexes as High-Rate Battery Binders
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Directing selective solvent presentations at the electrochemical interfaces to enable initially anode-free sodium metal batteries
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Data from: Catalytic promotion of transition-metal-doped graphene cathodes in Li-CO<sub>2</sub> batteries
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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.