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73 results for “lithium-ion”
Data from: Electrochemical performance of ZnO-coated Li4Ti5O12 composite electrodes for lithium-ion batteries with the voltage ranging from 3 to 0.01 V
Oxide is widely used in modifying cathode and anode materials for lithium ion batteries. In this work, a facial method of radio magnetron sputtering is introduced to deposit a thin film on Li4Ti5O12 composite electrodes. The pristine and modified Li4Ti5O12 electrodes are characterized at an extended voltage range of 3-0.01 V. The reversible capacity reach a high level of 286 mAh g-1, which is a little less than its theoretical capacity (293 mAh g-1). Electrodes modified by ZnO thin films with various thickness show elevated rate capability and improved cycle performance.
Static electric equivalent circuit of commercial lithium-ion battery cells using genetic algorithms
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Dataset for publication Synthesis of tubular MXenes with carbon fiber template and use as anodes in lithium-ion batteries
<p>The dataset contains all relevant data and figures regarding the manuscript "Synthesis of tubular MXenes with carbon fiber template and use as anodes in lithium-ion batteries".</p> <p>All Figures are in tiff format and all relevant data are in csv formats. </p> <p>The data in csv format are labelled as specified in the corresping images (e.g. Figure 1a csv file corresponds to data used to plot graphs from Figure 1a etc.). </p> <p>Axis labeling and units are always specified at the beginning of individual columns. If more than one curve was plotted from the csv file, the conditions can also be found at the beginning of corresponding columns.</p>
One-Step Grown Carbonaceous Germanium Nanowires and Their Application as Highly Efficient Lithium-Ion Battery Anodes
<p>Developing a simple, cheap, and scalable synthetic method for the fabrication of functional nanomaterials is crucial. Carbon-based nanowire nanocomposites could play a key role in integrating group IV semiconducting nanomaterials as anodes into Li-ion batteries. Here, we report a very simple, one-pot solvothermal-like growth of carbonaceous germanium (C-Ge) nanowires in a supercritical solvent. C-Ge nanowires are grown just by heating (380–490 °C) a commercially sourced Ge precursor, diphenylgermane (DPG), in supercritical toluene, without any external catalysts or surfactants. The self-seeded nanowires are highly crystalline and very thin, with an average diameter between 11 and 19 nm. The amorphous carbonaceous layer coating on Ge nanowires is formed from the polymerization and condensation of light carbon compounds generated from the decomposition of DPG during the growth process. These carbonaceous Ge nanowires demonstrate impressive electrochemical performance as an anode material for Li-ion batteries with high specific charge values (>1200 mAh g<sup>–1</sup> after 500 cycles), greater than most of the previously reported for other “binder-free” Ge nanowire anode materials, and exceptionally stable capacity retention. The high specific charge values and impressively stable capacity are due to the unique morphology and composition of the nanowires.</p>
EU's Recycled Content Targets of Lithium-ion Batteries Are Likey to Compromise Critical Metal Circularity
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Lithium-ion battery end-of-life life cycle assessment
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Data from: Electrochemical performance of ZnO-coated Li4Ti5O12 composite electrodes for lithium-ion batteries with the voltage ranging from 3 to 0.01 V
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Nanostructured tin phosphite SnHPO3: A high-capacity anode material for lithium-ion batteries
<p>Figures and data of the article "Nanostructured tin phosphite SnHPO<sub>3</sub>: A high-capacity anode material for lithium-ion batteries" by Wissal Tout<sup>1,2</sup>, Mickael Mateos<sup>1</sup>, Junxian Zhang<sup>1</sup>, M’hamed Oubla<sup>2</sup>, Nicolas Emery<sup>1</sup>, Eric Leroy<sup>1</sup>, Pierre Dubot<sup>1</sup>, Fouzia Cherkaoui El Moursli<sup>2</sup>, Zineb Edfouf<sup>2</sup>, Fermin Cuevas<sup>1</sup></p> <p><em>(1) </em><em>Univ Paris-Est Créteil, CNRS, ICMPE (UMR 7182), 2 rue Henri Dunant, F-94320 Thiais, France</em></p> <p><em>(2) </em><em>MANAPSE, Faculty of Sciences, Mohammed V<sup>th</sup>, University in Rabat, Morocco</em></p> <p> </p> <p><strong>Images</strong> (Figures 2 and 5) are in tif format (<strong>*.tif</strong>). Open with any visualisation image software</p> <p><strong>Graphs </strong>(Figures 1, 2h, 3, 4, 6, 7, 8, 9) in Origin software program (<strong>*.opj</strong>). Their <strong>corresponding data are provided in ASCII files (*.dat) </strong> that can be opened with any text-reading software</p> <p> </p> <p> </p> <p> </p>
An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries
Prognostics is an emerging science of predicting the health condition of a system (or its components) based upon current and previous system states. A reliable predictor is very useful to a wide array of industries to predict the future states of the system such that the maintenance service could be scheduled in advance when needed. In this paper, an adaptive recurrent neural network (ARNN) is proposed for system dynamic state forecasting. The developed ARNN is constructed based on the adaptive/recurrent neural network architecture and the network weights are adaptively optimized using the recursive Levenberg-Marquardt (RLM) method. The effectiveness of the proposed ARNN is demonstrated via an application in remaining useful life prediction of lithium-ion batteries.*
Optimal sizing and lifetime investigation of second life lithium-ion battery for grid-scale stationary application
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Data for Development of a lifetime model for large format nickel-manganese-cobalt oxide-based lithium-ion cell validated using a real-life profile
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Data for "Electro-aging model development of nickel-manganese-cobalt lithium-ion technology validated with light and heavy-duty real-life profiles"
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A novel methodology to determine the specific heat capacity of lithium-ion batteries
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.