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414 results for “lithium”
Lithium-Ion Batteries in Automated Guided Vehicles (AGVs) dataset for article "Automated Battery Power Fade Estimation for Fast Charge and Discharge Operations"
<p>Dataset of aggregated information related to discharge-only cycles of lithium-ion battery packs employed in Automated Guided Vehicle systems.</p> <p>The dataset supports the study in conference article "Automated Battery Power Fade Estimation for Fast Charge and Discharge Operations"</p>
Fast Method for Calibrated Self-Discharge Measurement of Lithium-Ion Batteries including Temperature Effects and Comparison to Modelling
<p>Self-discharge data related to the manuscript entitled: 'Fast Method for Calibrated Self-Discharge Measurement of Lithium-Ion Batteries including Temperature Effects and Comparison to Modelling', submitted to Energy Reports on 26 April 2023.</p>
Maximum temperature data from thermal safety assessment of type 21700 lithium-ion batteries with NMC, NCA and LFP cathodes by means of Accelerating Rate Calorimetry (ARC)
<p>Data of safety investigation and thermal abuse behavior of commercial type 21700 LIB cells is provided.</p> <p>It has been acquired with Accelerating Rate Calorimetry (ARC), using a Thermal Hazard Technology type ES ARC.</p> <p>Moreover, thermal abuse was done by means of the so-called Heat-Wait-Seek (HWS) test, at different states of charge (SOC) from 0 to 100.</p> <p>Different cathode chemistries are compared (NMC, NCA and LFP), as well as for NCA chemistry, the high energy (HE) and high power (HP) cell design.</p> <p>For each cell, data includes the maximum temperature measured during thermal abuse at the surface on the center of the cell. Additionally, the mean value and standard deviation for each cell type and state of charge is provided.</p> <p>This data is supporting this article in the journal Batteries:</p> <p><a href="https://doi.org/10.3390/batteries9050237">https://doi.org/10.3390/batteries9050237</a></p> <p>Additional supporting material to this article are the exothermal data for thermal abuse, that are published here:</p> <p><a href="https://doi.org/10.5281/zenodo.7707929">https://doi.org/10.5281/zenodo.7707929</a></p> <p> </p>
Ageing Data of Lithium-ion "Panasonic NCR18650B" Battery Cells
<p>A large-scale battery aging experiment dataset is presented in this dataset.<br> In total, 116 Lithium-ion cells were cycled in a controlled temperature ambient until they reached the end-of-life.<br> The cycling includes 37 different aging load profiles and more detailed reference tests throughout the whole lifespan of the cells.<br> The load profiles vary in seven parameters (i.e., temperature, charge current, average discharge current, peak discharge current, frequency, average state of charge, and state of charge swing) and were set up by a design of experiment.<br> Different approaches for the estimation of state-of-charge and state-of-health can be provided and analyzed by the offered data set.<br> Thus this dataset forms a broad basis for battery modeling or battery management algorithms for electric vehicles and hybrid electric vehicles.</p> <p>For further details, see the linked publication.</p>
Visualization of Dissolution-Precipitation Processes in Lithium-Sulfur Batteries: Supporting Data
<ul> <li>Contours_1.gif: 0 mA/g - pristine state</li> <li>Contours_2.gif: 30 mA/g</li> <li>Contours_3.gif: 80 mA/g</li> <li>Contours_4.gif: 130 mA/g</li> <li>Contours_5.gif: 180 mA/g</li> <li>Contours_6.gif: 230 mA/g</li> <li>Contours_7.gif: 330 mA/g - no remaining solid sulphur</li> </ul>
Wave function for the lithium electride ROGDAS
<p>The wave function for the lithium electride ROGDAS at the b3lyp/6-31+G(d) level, analysed as a molecular electrostatic potential, a spin density, a molecular orbital and as a reduced density gradient (NCI) distribution.</p>
High-power intracavity single-cycle THz pulse generation using thin lithium niobate
<p>This dataset is accompanying the paper "High-power intracavity single-cycle THz pulse generation using thin lithium niobate"<br><br><strong>Autocorrelation.txt:</strong> second harmonic generation noncollinear autocorrelation trace data. (measurement device: Femtochrome FR-103XL)</p><p><strong>Spectrum.txt:</strong> optical spectrum (measurement device: APE wavescan)</p><p><strong>RF_1Mspan.txt:</strong> radio frequency spectrum with 1 MHz span (measurement device: ROHDE & SCHWARZ FPC1000)</p><p><strong>RF_1Gspan.txt:</strong> radio frequency spectrum with 1 GHz span (measurement device: ROHDE & SCHWARZ FPC1000)</p><p><strong>EOS_THz_raw.h5: </strong>electro-optic sampling raw data of the THz measurement in HDF-5 format (measurement device: ROHDE & SCHWARZ RTM3004)</p><p><strong>EOS_noise_raw.h5: </strong>electro-optic sampling raw data of the noise measurement in HDF-5 format (measurement device: ROHDE & SCHWARZ RTM3004)</p><p><strong>THz_time.csv:</strong> processed electro-optic sampling data of the THz measurement in time</p><p><strong>THz_freq.csv:</strong> processed electro-optic sampling data of the THz measurement in frequency</p><p><strong>Dark_time.csv:</strong> processed electro-optic sampling data of the noise measurement in time</p><p><strong>Dark_freq.csv:</strong> processed electro-optic sampling data of the noise measurement in frequency</p>
Dataset: Pyroelectric Influence on Lithium Niobate During the Thermal Transition for Cryogenic Integrated Photonics
<p>Dataset of the publication "Pyroelectric Influence on Lithium Niobate During the Thermal Transition for Cryogenic Integrated Photonics", F. Thiele, et al., in the journal Materials for Quantum Technology (2023).</p> <p>Abstract:</p> <blockquote> <p>Lithium niobate has emerged as a promising platform for integrated quantum optics, enabling efficient generation, manipulation, and detection of quantum states of light. However, integrating single-photon detectors requires cryogenic operating temperatures, since the best performing detectors are based on narrow superconducting wires. While previous studies have demonstrated the operation of quantum light sources and electro-optic modulators in LiNbO<sub>3</sub> at cryogenic temperatures, the thermal transition between room temperature and cryogenic conditions introduces additional effects that can significantly influence device performance. In this paper, we investigate the generation of pyroelectric charges and their impact on the optical properties of lithium niobate waveguides when changing from room temperature to 25 K, and vice versa. We measure the generated pyroelectric charge flow and correlate this with fast changes in the birefringence acquired through the Sénarmont-method. Both electrical and optical influence of the pyroelectric effect occur predominantly at temperatures above 100 K.</p> </blockquote>
Underlying dataset for battery pack degradation - Understanding aging in parallel-connected lithium-ion batteries under thermal gradients
<p>This record constitutes the raw data underlying the paper "<i>Battery pack degradation - Understanding aging in parallel-connected lithium-ion batteries under thermal gradients</i>" (<a href="https://www.researchsquare.com/article/rs-2535223/v1">preprint link</a>)</p><p>The dataset contains all raw data, processed data and analysis codes used to generate figures in the publication. Abstract is as follows:</p><blockquote><p>Practical lithium-ion battery systems require parallelisation of tens to hundreds of cells, however understanding of how pack-level thermal gradients influence lifetime performance remains a research gap. Here we present an experimental study of surface cooled parallel-string battery packs (temperature range 20-45 °C), and identify two main operational modes; convergent degradation with homogeneous temperatures, and (the more detrimental) divergent degradation driven by thermal gradients. We attribute the divergent case to the, often overlooked, cathode impedance growth. This was negatively correlated with temperature and can cause positive feedback where the impedance of cells in parallel diverge over time; increasing heterogeneous current and state-of-charge distributions. These conclusions are supported by current distribution measurements, decoupled impedance measurements and degradation mode analysis. From this, mechanistic explanations are proposed, alongside a publicly available aging dataset, which highlights the critical role of capturing cathode degradation in parallel-connected batteries; a key insight for battery pack developers.</p></blockquote>
Porous Organic Polymers with Heterocyclic Crown Ethers for Selective Lithium-Ion Capture
<p>Abstract of the publication: Lithium is a key resource of the 21st century. Despite that, Li is traditionally mined rather than obtained from maritime brines or secondary sources such as spent energy-storage devices owing to the difficulties in Li recovery. Herein, we present a porous organic polymer capable of capturing Li ions from aqueous solutions through highly pre-organized heterocyclic crown ether-like pores in the polymer backbone. These features enable Li+ uptake capacities over 120 mg g-1 and selectivity versus highly competitive ions such as Na+, Ca2+, and Mg2+.</p> <p> </p>
Ultrabroadband thin-film lithium tantalate modulator for high-speed communications
<p>The data sets contain the data and the scripts for generating all the plots of the manuscript "Ultrabroadband thin-film lithium tantalate modulator for high-speed communications" </p> <p>The data and the scripts are separated into sub-folders corresponding to the figures. These include the main text Fig.1-3. The scripts are in the .m format. The color and size of some curves are edited in Adobe AI.</p>
Dataset for the publication entitled: "Assessment of lithium ion battery ageing by combined impedance spectroscopy, functional microscopy and finite element modelling""
<p>Related to the publication: <a href="https://doi.org/10.1016/j.jpowsour.2021.230459">https://doi.org/10.1016/j.jpowsour.2021.230459</a></p> <p>Datasets for the following Figures:</p> <p>Figure 2.</p> <p>Figure 3.</p> <p>Figure 5.</p> <p>Figure 7.</p>
Long-Term Lithium Abundance Signatures following Planetary Engulfment [Dataset]
<p>MESA inlist files and chemical network used for the published article Sevilla et. al (2022).</p> <p>The inlist files given are for a 1 solar mass model. To generate the other models in the paper with a different stellar mass, the initial mass in the pre-ZAMS inlist was changed.</p> <p>We first used prezams.inlist to generate the model star, with mixing processes besides convection. This model saved after this run would be used for all other model runs for the same stellar mass.</p> <p>For a model run without planetary engulfment, we used one inlist to evolve the star through the main-sequence, and then obtained the desired data from the resulting profile and history files.</p> <p>For model runs with planetary engulfment, we first ran the model using an accretion inlist to simulate the engulfment process by turning on accretion, and then we used a main-sequence (MS) inlist to evolve the star through the main-sequence to produce the desired data.</p> <p>The corresponding inlists for each model run are as follows:</p> <p>10 Earth mass engulfment with included all mixing processes considered in our paper, which were convection, overshoot, thermohaline mixing, elemental diffusion, and a min_D_mix coefficient: 10earth_accretion.inlist for accretion, and 10earth_postaccretion.inlist for MS.</p> <p>10 Earth mass engulfment with all mixing processes except thermohaline mixing: 10earth_accretion_nothermohaline.inlist for accretion, and 10earth_postaccretion_nothermohaline.inlist for MS.</p> <p>10 Earth mass engulfment with all mixing processes except elemental diffusion: 10earth_accretion_nodiffusion.inlist for accretion, and 10earth_postaccretion_nodiffusion.inlist for MS.</p> <p>1 Earth mass engulfment with all mixing processes: 1earth_accretion.inlist for accretion, and 1earth_postaccretion.inlist for MS.</p> <p>100 Earth mass engulfment with all mixing processes: 100earth_accretion.inlist for accretion, and 100earth_postaccretion.inlist for MS.</p> <p>10 Earth mass engulfment with all mixing processes occurring 1 gyr after ZAMS: We first ran the model with evolve_1gyr.inlist to save a model of the star 1 gyr after ZAMS. Then, we used 1gyr_accretion.inlist for accretion, and 1gyr_postaccretion.inlist for MS.</p> <p>No planetary engulfment: No accretion inlist was used; we immediately ran no_accrete.inlist as the MS inlist.</p> <p> </p> <p>We also uploaded the chemical network used for our model as the file accrete.net.</p>
Neural network for Lithium Metal Battery
<p>Electric cars are an integral part of our clean energy future – every time one replaces a gas-powered vehicle, it can save 1.5 tons of carbon dioxide per year. But to truly expand the population and reach of electric cars, new energy storage solutions must be developed to produce lighter vehicles with longer ranges and more powerful batteries.</p> <p>A team of researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Irvine recently moved this effort forward with the development of deep-learning algorithms to automate the quality control and assessment of new battery designs.</p> <p>The research team, led by Berkeley Lab’s Daniela Ushizima, a staff scientist in the Applied Mathematics and Computational Research Division and a Berkeley Institute for Data Science Research Affiliate, includes scientists from the National Fuel Cell Research Center (NFRC) at UC Irvine and collaborators from UC Berkeley’s Department of Electrical Engineering and Computer Sciences and School of Information. Together, they created these deep-learning algorithms to automate the inspection of batteries with data acquired using advanced instruments, including those at Berkeley Lab’s Advanced Light Source (ALS). By using X-ray tomography as the input data, as well as prototypes defined by battery experts, the research team developed automated methods to detect battery defects in rechargeable lithium metal batteries and measure their growth during battery cycling.</p> <p>The researchers focused on solid-state lithium metal batteries (LMB), which are different from traditional lithium-ion batteries in that they use solid electrodes and electrolytes, providing superior electrochemical performance and high energy density. Some of the challenges of this new technology are predicting battery cycling stability and preventing the formation of lithium dendrite growth, Ushizima noted. This harmful phenomenon may occur during LMB charge and discharge, when lithium can deposit irregularly, building up dendrites (lithium plating) that lead to failures, such as short-circuiting. These morphologies are key to the LMB quality, and they can be captured and analyzed using X-ray microtomography (XRT) scans. Machine learning algorithms and multiscale representation of XRT from LMB samples enable the quantification of LMB defects.</p>
Dataset for High-throughput combinatorial analysis of the spatiotemporal dynamics of nanoscale lithium metal plating
<p>This is a dataset for the manuscript High-throughput combinatorial analysis of the spatiotemporal dynamics of nanoscale lithium metal plating. This mansucript is currently under peer-review in ACS Nano. </p>
Dataset: Foremost Lithium Resource & Technology Ltd. (FMSTW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Foremost Lithium Resource & Technology Ltd. (FMST) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Atlas Lithium Corporation (ATLX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: American Lithium Corp. (AMLI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: American Lithium Corp. (AMLI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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International Brain Laboratory public data
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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.