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414 results for “Lithium”
Dataset: Sigma Lithium Corporation (SGML) 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: Piedmont Lithium Inc. (PLL) 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: Sprott Lithium Miners ETF (LITP) 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: Ishares Lithium Miners And Producers ETF (ILIT) 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.
Data on Effects of the Incorporation of Luminescent Vanadate Nanoparticles in Lithium-borate Glass Matrices by Various Methods
<p><span>The glass-ceramic materials studied in this work are designed using combinations of lithium-vanadate-borate glass matrices and lanthanum/rare earth (RE) vanadate nanoparticles. Three different techniques of sintering of the glass matrix and vanadate nanoparticles are investigated. Morphological characteristics and spectral properties of the glass-ceramic samples obtained by different techniques are investigated and analyzed in comparison with the properties of the original glass matrices. The luminescence spectra of all glass-ceramic samples consist of a wideband glass matrix emission and the characteristic line emission of the RE ions incorporated into the glass matrices as nanoparticles. The RE luminescence of these glass-ceramics is promising for various optoelectronic applications. Recommendations for the next development of new glass-ceramic techniques and materials are discussed.</span><span> </span></p>
BRAIN Journal-Isolating the Norepinephrine Pathway Comparing Lithium in Bipolar Patients to SSRIs in Depressive Patients-Figure 2. Resting state neuroimaging findings illustrating the action of Lithium following whole brain
<p>The axial, saggital, and coronal MRI activation maps illustrate increased delta frequency band neuronal activity in the 46 patients<br> diagnosed with Bipolar Affective Disorder compared to 32 female patients diagnosed with Major Depressive Disorder of Depressive<br> Episode. The Yellow/Orange shades indicate increased neuronal activity in the right Superior Frontal Gyrus (t=0.920, p=0.05060,<br> BA 6, MNI X=20, Y=0, Z=70) and in the right Cingulate Gyrus (t=0.0846, BA 24, MNI X= 5, Y=0, Z=51). Structural anatomy is<br> shown in grey scale (A – anterior; S – superior; P – posterior; L – left; R – right).</p>
BRAIN Journal-Isolating the Norepinephrine Pathway Comparing Lithium in Bipolar Patients to SSRIs in Depressive Patients-Figure 1. Resting state neuroimaging findings illustrating the action of Lithium following whole brain
<p>The axial, saggital, and coronal MRI activation maps illustrate neuronal activity of 46 patients diagnosed with Bipolar Affective<br> Disorder compared to 16 male patients diagnosed with Major Depressive Disorder of Depressive Episode. The Yellow/Orange<br> shades indicate increased neuronal activity in the right Superior Temporal Gyrus (t=1.403, p=0.00780, BA 41, MNI X=45, Y= -35,<br> Z=10) with activation also in the Fusiform Gyrus (t=1.26, BA 20, MNI X= 45, Y= -35, Z=10), the Parahippocampal Gyrus (t=1.29,<br> BA 36, MNI X=45, Y= -35, Z=10). (b) Increased neuronal activity in the Cingulate Gyrus (t=1.06, BA 32, MNI X=45, Y= -35,<br> Z=10). Structural anatomy is shown in grey scale (A – anterior; S – superior; P – posterior; L – left; R – right).</p>
Diffraction Contrast Tomography reconstruction of an Aluminium-Lithium tension specimen
<p>This data set is the reconstruction of the polycrystalline microstructure of a small tension specimen made of Aluminium-Lithium alloy. The specimen is approximately 0.5 mm in cross section. The data is the result of 3 DCT scans merged together. The spatial resolution is 1.4 micrometer. The data was acquired at the ID11 beamline at The European Synchrotron Radiation Facility and reconstructed using the DCT software available at https://sourceforge.net/projects/dct/</p> <p>The data is provided in HDF5 format, compatible with open source packages such as Paraview and DREAM3D. The laboratory coordinate system XYZ corresponds to the sample position when all the diffractometer rotations are zero.</p>
Catalog of 2,330 lithium-rich giant stars
<p>Theoretical models of stellar evolution predict that most of the lithium inside a star is destroyed as the star becomes a red giant. However, observations reveal that about 1% of red giants are peculiarly rich in lithium, often exceeding the amount in the interstellar medium or predicted from the Big Bang. With only about 150 lithium-rich giants discovered in the past four decades, and no distinguishing properties other than lithium enhancement, the origin of lithium-rich giant stars is one of the oldest problems in stellar astrophysics. Here we report the discovery of 2,330 low-mass (1 to 3$\,M_\odot$) lithium-rich giant stars, which we argue are consistent with internal lithium production that is driven by tidal spin-up by a binary companion. Our sample reveals that most lithium-rich giants have helium-burning cores ($80^{+7}_{-6}\%$), and that the frequency of lithium-rich giants rises with increasing stellar metallicity. We find that while planet accretion may explain some lithium-rich giants, it cannot account for the majority that have helium-burning cores. We rule out most other proposed explanations as the primary mechanism for lithium-rich giants, including all stages related to single star evolution. Our analysis shows that giants remain lithium-rich for only about two million years. A prediction from this lithium depletion timescale is that most lithium-rich giants with a helium-burning core have a binary companion.</p>
Figure 5 in Investigation of protective effects of lithium borate on spermatogenesis and testes histopathology against cadmium-induced acute toxicity in rats
Figure 5. Testes tissue, control and LTB groups, negative COX-2 expression (AC), Cd group, severe COX-2 expressions in damaged tubules and intertubular intervals (B), LTB + Cd group, mild COX-2 expression in interstitial tissue (arrowheads) (D), IHC - P, Bar: 20µm.
Figure 4 in Investigation of protective effects of lithium borate on spermatogenesis and testes histopathology against cadmium-induced acute toxicity in rats
Figure 4. Testes tissue, Control and LTB groups, negative 8-OHdG expression (AC), Cd group, severe cytoplasmic 8-OHdG expressions in spermatocytes and spermatogonium in damaged tubules (arrowheads) (B), LTB + Cd group, mild cytoplasmic 8-OHdG expression in spermatocytes (arrowheads) (D), IHC - P, Bar: 20 µm.
Figure 3. Negative Caspase-3 in Investigation of protective effects of lithium borate on spermatogenesis and testes histopathology against cadmium-induced acute toxicity in rats
Figure 3. Negative Caspase-3 expressions in testes tissues of control and LTB groups (A and C), severe Caspase-3 expression in spermatocytes of Cd group (arrowheads) (B), mild Caspase- 3 expression in spermatocytes (arrowheads) of LTB + Cd group (D), IHC - P, Bar: 20 µm.
Figure 1 in Investigation of protective effects of lithium borate on spermatogenesis and testes histopathology against cadmium-induced acute toxicity in rats
Figure 1. Testicular tissue, normal anatomical appearance (A), oedema, hyperaemia, congestion and haemorrhage (B), normal anatomical appearance (C), moderate oedematous and mild hyperaemic (D).
Data for Partial Volume Deflagration Experiments with Synthesized Lithium-Ion Battery Thermal Runaway Effluent Gas.
<p>Refer to Readme.pdf or Readme.md for information on this dataset.<br><br>v1.1 - cleanup<br>v1.2 - readme updates</p>
Dataset and code: Decarbonizing lithium-ion battery primary raw materials supply chain: Available strategies, mitigation potential and challenges
<p>Repository to share the data and code associated with the scientific article <strong>Istrate et al. Decarbonizing lithium-ion battery primary raw materials supply chain: Available strategies, mitigation potential and challenges. Joule (2024)</strong>. The repository contains data files and code to import the life cycle inventories (LCIs), reproduce the results, and generate the figures presented in the article.</p>
Lithium-Ion Battery Field Data: 28 LFP battery systems with 8 cells in series, up to 5 years of operation
<div> <div> <div>This data set contains data from 28 portable 24V lithium iron phosphate (LFP) battery systems with approximately 160Ah nominal capacity. Each system's specific use case is unknown, but battery systems of this size are typically used as power sources for recreational vehicles, solar energy storage, and more.</div> <br> <div>All battery systems in this data set showed some form of unsatisfactory behavior and were returned to the manufacturer. Many reasons can cause a consumer to return a battery to the manufacturer for maintenance. The user's individual decisions may be motivated by personal judgment, BMS warnings, or customer support advice. This data set comprises a very small fraction of batteries sold of this version. Therefore, this data set is biased and not representative of the operational data of the entire population of this system version. An improved version replaced this battery system type. The battery system manufacturer provided the data set for this study and allowed its open-source release under the condition of anonymity.</div> <br> <div>Each battery system consists of 8 prismatic cells in series. Each system has one load current sensor, and each cell has one voltage sensor. The four temperature sensors are placed between adjacent cells, i.e., each temperature sensor is shared by two cells. Furthermore, the battery systems have active cell balancing. The available measurements vary from a single month to five years. Consequently, the number of data rows per system varies from several thousand to millions, depending on the duration of battery operation. The data set contains a total of 133 million rows of measurements.</div> </div> </div> <div> </div> <div> <div><strong>Associated Python Library</strong></div> <div>BattGP <a href="https://github.com/JoachimSchaeffer/BattGP" target="_blank" rel="noopener">https://github.com/JoachimSchaeffer/BattGP</a></div> <div>This library contains classes and functions to analyze the data set with Gaussian processes.</div> <div>Furthermore, data visualization functions are part of the library.</div> <div><br><strong>Associated Article<br></strong>Gaussian Process-based Online Health Monitoring and Fault Analysis of Lithium-Ion Battery Systems from Field Data</div> <div>Cell Report Physical Science <br> <div><a href="https://doi.org/10.1016/j.xcrp.2024.102258">https://doi.org/10.1016/j.xcrp.2024.102258</a></div> <div> </div> <div> </div> </div> </div>
Supplement to "Rotation and Lithium Confirmation of a 500 Parsec Halo for the Open Cluster NGC2516"
<p>This repository contains supplementary data to the paper "Rotation and Lithium Confirmation of a 500 Parsec Halo for the Open Cluster NGC2516", which will be published in the Astronomical Journal in 2021 (https://arxiv.org/abs/2107.08050). Please see README.txt for a detailed description of the contents. To unzip and decompress the gzipped tarball file, <em>tar -xvzf ngc2516supplementary.tar.gz</em> should work on most computers.</p>
Data Repository - Thermal-electrochemical parametrisation of a lithium-ion battery: mapping Li concentration and temperature dependencies
<p>Datasets from "Thermal-electrochemical parametrisation of a lithium-ion battery: mapping Li concentration and temperature dependencies" - Journal of Electrochemical Society.</p> <p>This repository contains parameter values for the electrode solid-state diffusivity, entropic term, exchange current density, electronic conductivity, specific heat capacity, and thermal conductivity.</p>
Stable and compact RF-to-optical link using lithium niobate on insulator waveguides
<p>Stable and compact RF-to-optical link using lithium niobate on insulator waveguides</p> <p>Optical frequency combs have become a very powerful tool in metrology and beyond thanks to their ability to link radio frequencies with optical frequencies via a process known as self-referencing. Typical self-referencing is accomplished in two steps: the generation of an octave-spanning supercontinuum spectrum and the frequency-doubling of one part of that spectrum. Traditionally, these two steps have been performed by two separate optical components. With the advent of photonic integrated circuits, the combination of these two steps has become possible in a single small and monolithic chip. One photonic integrated<br> circuit platform very well suited for on-chip self-referencing is lithium niobate on insulator - a platform characterised by high second and third order nonlinearities. Here we show that combining a lithium niobate on insulator waveguide with a silicon photodiode results in a very compact and direct low-noise path towards self-referencing of mode-locked lasers. Using digital servo electronics, the resulting frequency comb is fully stabilized. Its high degree of stability is verifed with an independent out-of-loop measurement and is quantifed to be 6.8 mHz. Furthermore, we show that the spectrum generated inside the lithium niobate waveguide remains stable over many hours.</p>
Data from: "Lithium-ion battery degradation: measuring rapid loss of active silicon in silicon-graphite composite electrodes"
<p>Dataset from the publication "Lithium-ion battery degradation: measuring rapid loss of active silicon in silicon-graphite composite electrodes". Full experimental details can be found in the related publication in ACS Applied Energy Materials: <a href="https://doi.org/10.1021/acsaem.2c02047">https://doi.org/10.1021/acsaem.2c02047</a></p> <p>Commercial 21700 cylindrical cells (LG M50T, LG GBM50T2170) were cycle aged under 3 different temperatures [10, 25, 40] °C and 2 SoC ranges [0-30, 0-100]%, with multiple cells tested under each condition. Cells were base-cooled at set temperatures using bespoke test rigs (see pubilcation for details). All electrochemical data were recorded using a Biologic BCS-815 battery cycler.</p> <p> </p> <p><strong>Break-in cycles:</strong></p> <p>Prior to any ageing or performance checks, all cells were subject to 5 full charge-discharge cycles as part of the break-in procedure. This consisted of a 0.2C charge to 4.2 V with CV-hold till C/100, and 0.2C discharge to 2.5 V (repeated for 5 cycles). Cells were rested under open circuit conditions for 2 hours after each charge and 4 hours after each discharge. These break-in cycles were performed at 25°C for all cells.</p> <p> </p> <p><strong>Ageing Conditions:</strong></p> <table align="center"> <caption>Ageing Conditions</caption> <thead> <tr> <th scope="col">Expt</th> <th scope="col">SoC Range</th> <th scope="col">C-rate</th> <th scope="col">Temperature</th> <th scope="col"># of cells</th> <th scope="col">Cell IDs</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>10°C</td> <td>3</td> <td>A, B, J</td> </tr> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>25°C</td> <td>3</td> <td>D, E, F</td> </tr> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>40°C</td> <td>3</td> <td>K, L, M</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>10°C</td> <td>3</td> <td>A, B, C</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>25°C</td> <td>2</td> <td>D, E</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>40°C</td> <td>3</td> <td>F, G, H</td> </tr> </tbody> </table> <p>For cells aged in the 0-30% SoC range, each ageing set consisted of 256 cycles over the 0-30% SoC range (discharge to 2.5 V, charge by passing 1500 mA h (== 0.3*nominal capacity)). C-rates were 0.3C for charge, and 1C for discharge.</p> <p>For cells aged in the 0-100% SoC range, each ageing set consisted of 78 cycles over the full SoC range (discharge to 2.5 V, charge to 4.2 V with CV hold till C/100). C-rates were 0.3C for charge, and 1C for discharge.</p> <p> </p> <p><strong>Reference Performance Tests (RPTs):</strong></p> <p>All cells were characterised at beginning of life (BoL) and after each ageing set using a reference performance test (RPT). The RPT was always performed at 25°C. Two different RPT procedures were used: a longer procedure which was performed after each even-numbered ageing set, and a shorter procedure which was used after each odd-numbered ageing set. Both procedures are detailed below. A CC-CV charge at 0.3C to 4.2 V, 4.2 V till C/100 was performed between each step of the procedures.</p> <p>Long RPT procedure:</p> <ol> <li>C/10 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>C/2 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>GITT discharge at 0.5C; 25 pulses with each pulse passing 200 mA h of charge, with 1 hour rest between pulses; lower cut-off voltage of 2.5 V (but continued test for all pulses).</li> <li>GITT discharge at 0.5C; 5 pulses with each pulse passing 1000 mA h of charge, with 1 hour rest between pulses; lower cut-off voltage of 2.5 V (but continued test for all pulses).</li> </ol> <p>Short RPT procedure:</p> <ol> <li>C/10 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>Hybrid CC-pulse test with average current of C/2. A baseline DC current of C/2 was applied with an HPPC-type profile superimposed on top. This was done for discharge and charge (with voltage limits of 2.5 V and 4.2 V).</li> <li>Hybrid CC-pulse test with average current of 1C. A baseline DC current of 1C was applied with an HPPC-type profile superimposed on top. This was done for discharge only (with a voltage limit of 2.5 V).</li> </ol> <p> </p> <p><strong>Extracted Data - Main </strong></p> <p>One csv file exists for each cell being tested, summarising the important data extracted from the ageing cycles and the RPTs. This includes:</p> <p>Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to.</p> <p>Ageing Cycles: number of ageing cycles the cell has been subject to. *this is <strong>not </strong>equivalent full cycles.</p> <p>Ageing Set Start Date/ End date: The date that each ageing set began/ ended.</p> <p>Days of Degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending.</p> <p>Age Set Average Temperature: average recorded surface temperature of the cell during cycle ageing. Temperature was recorded approximately 1/2 way up the length of the cell (i.e. between positive and negative caps) using a K-type thermocouple. Units: °C.</p> <p>Charge Throughput: total accumulated charge recorded during all cycles during ageing (i.e. sum of charge and discharge). This is the cummulative total since BoL (not including RPTs). Units: Ah.</p> <p>Energy Throughput: as with "charge throughput", but for energy. Units: Wh.</p> <p>C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT. Units: mAh.</p> <p>C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT. Units: mAh.</p> <p>0.1s Resistance: The resistance calculated from the 25-pulse GITT test of each even-numbered RPT. This value is taken from the 12th pulse of the procedure (which corresponds to ~52% SoC at BoL). The resistance is calculated by dividing the voltage drop by the current at a timecale of 0.1 seconds after the current pulse is applied (the fastest timescale possible under the 10 Hz recording condition). Units: Ohms.</p> <p> </p> <p><strong>Extracted Data - Degradation Modes:</strong></p> <p>Degradation Mode Analysis (DMA) was also performed on the C/10 discharge data at each RPT. This analysis uses an optimisation function to determine the capacities and offset of the positive and negative electrodes by calculating a full cell voltage vs capacity curve using 1/2 cell data and comparing against the experimentally measured voltage vs capacity data from the C/10 discharge.</p> <p>The results of this analysis are saved in the DMA folder, with 4 csv files for each cell, which contain data for all RPTs. The 4 files contain:</p> <p>Fitting parameters: output from the DMA optimisation function; 5 parameters which detail the upper/lower lithitation fractions of each electrode and the capacity fraction of graphite in the negative electrode.</p> <p>Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity.</p> <p>DM data: Quantities of LLI, LAM-PE, LAM-NE, LAM-NE-Gr, and LAM-NE-Si calculated from the change in capacities/offset of each electrode since BoL.</p> <p>RMSE data: the root-mean-square error of the optimisation function calculated from the residual between the measured and calculated voltage vs capacity profiles.</p> <p> </p> <p><strong>Timeseries data from RPTs:</strong></p> <p>Timeseries datafiles from the Biologic battery cycler which have been exported to csv and sliced for each step of each RPT procedure to help with future use of the data. Files contain [time, voltage, current, charge, temperature] data.</p> <p> </p> <p><strong>Jupyter Notebook:</strong></p> <p>A jupyter notebook has been included to aid futher use of this data. The notebook shows how to load the data into pandas DataFrame objects and provides a couple of example plots to view the datasets.</p> <p> </p> <p><strong>Notes:</strong></p> <p>A faulty electrical connection to cell A of Expt 5 (i.e. one of the cells being aged at 0-100% SoC at 10°C) during RPT4 led to erroneous results for that performance check (as evidenced in the 0.1s resistance value). The faulty electrical connection was fixed prior to subsequent cycling but the RPT was not repeated. We have kept the data collected during this RPT as part of the dataset, so caution should be used when using this specific portion.</p>
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