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414 results for “Lithium”
Dataset of "Thermal Stability of Valuable Metals in Lithium-Ion Battery Cathode Materials: Temperature Range 100-400 °C"
<p>Lithium is crucial in lithium-ion batteries (LIBs), serving as a main component of the electrolyte and cathode. Elements such as cobalt, nickel, and manganese are also vital for high performance, energy density, and stability. This study aimed to examine the behaviour of end- of-life cathode material (LiNi0.6Mn0.2Co0.2O2) and its valuable metals after exposure to temperatures between 100 and 400 °C, comparing it with untreated material. The lithium content cannot be reliably determined by conventional analytical methods, so inductively coupled plasma optical emission spectroscopy (ICP-OES) was chosen for this purpose. For ICP-OES measurements, samples were dissolved in different solvents for a specified time, and the concentrations of lithium, nickel, manganese, and cobalt were measured. From the measured values, their theoretical yields were calculated. Due to the annealing at given temperatures and subsequent dissolution, this step can be considered as the first stage of the pyrometallurgical- hydrometallurgical process used in battery recycling. The study was complemented by further analyses to monitor the effect of annealing temperatures on the properties of the material. Based on the results, it was found that the highest theoretical yield in this temperature range was for material annealed at 400 °C and dissolved in 20% nitric acid for 4 hours.</p>
Single-cycle, 643-mW average power THz source based on tilted pulse front in lithium niobate
<p>This data set is associated with the aforementioned paper.</p> <p>The data and the Jupyter notebooks (Python) to reproduce the figures in this paper can be downloaded below. To run a Jupyter notebook as a beginner, it is easiest to download and install anaconda, a Python environment that comes with many packages preinstalled and also offers Jupyter lab/notebook. It is available at <a href="https://www.anaconda.com/download" target="_blank" rel="noopener">https://www.anaconda.com/download</a>.</p> <h2>Fig.01</h2> <p><strong>Fig.01_literature_lithium_niobate_sources.csv</strong> contains a summary table of the last decades of published THz power values obtained with lithium niobate in the tilted pulse front geometry. The accompanying jupyter notebook allows to reproduce the figure that was used in the paper.</p> <h2>Fig.03</h2> <p>Each individual data frame (df), which is saved as an HDF file in the .zip file, contains a "power curve" measurement (i.e. measured THz power as a function of the applied pump power). Whenever a parameter is changed, all positions, angles, THz power and cryostat parameters are saved.</p> <ul> <li><strong>x1 </strong>is the position of the last mirror before the transmission grating (parallel to the pump beam direction before the crystal) in [mm]</li> <li><strong>x2 </strong>is the position of the first imaging lens in direction of the pump beam propagation direction before the crystal in [mm]</li> <li><strong>x3 </strong>is the position of the second imaging lens in the direction of the pump beam propagation direction before the crystal in [mm]</li> <li><strong>x4 </strong>is the position of the cryostat in the direction of the pump beam before reaching the crystal in [mm]</li> <li><strong>y0 </strong>is the position of the cryostat in the perpendicular direction of the pump beam before reaching the crystal in [mm]</li> <li><strong>α0 </strong>is the angle of the lambda/2 waveplate that allows the pump power to be varied at the crystal in [°]</li> <li><strong>α1 </strong>is the angle of the last mirror before the grating in [°]</li> <li><strong>α2 </strong>is the angle of the transmission grating in [°]</li> <li><strong>thz_power_W </strong>is the obtained power obtained from the Ophir 3A-P-THz power meter in [W]</li> <li><strong>temperature_setpoint_K </strong>is the LakeShore cryostat controller setpoint in [K]</li> <li><strong>temperature_K </strong>is the temperature read from the sensor on the cooling finger (above the crystal) in [K]</li> <li><strong>heater_output </strong>is the amount of power in [%] delivered to the resistive heating element inside the cryostat. 100% corresponds to about 50 W. Its value is controlled by an internal PID loop of the cryostat controller, which tries to stabilize <strong>temperature_K </strong>to <strong>temperature_setpoint_K</strong></li> <li><strong>pump_power </strong>is the average laser power reaching the crystal in [W]. It was calibrated before obtaining the data set by characterizing the lambda/2 waveplate angle <strong>α0</strong> to the value of an NIR power meter just before the cryostat.</li> <li><strong>repetition_rate</strong> is the repetition rate of the laser in [Hz]</li> </ul> <p>As an example, below is one line (for one pump power) of such a data frame:</p> <table> <tbody> <tr> <td> </td> <th>x1</th> <th>x2</th> <th>x3</th> <th>x4</th> <th>y0</th> <th>α0</th> <th>α1</th> <th>α2</th> <th>thz_power_W</th> <th>temperature_setpoint_K</th> <th>temperature_K</th> <th>heater_output</th> <th>pump_power</th> <th>repetition_rate</th> </tr> <tr> <td>0</td> <td>-12.000005</td> <td>2.500039</td> <td>9.100015</td> <td>-5.0</td> <td>-2.0</td> <td>35.905660</td> <td>25.68</td> <td>-23.3</td> <td>0.006000</td> <td>80.0</td> <td>79.883</td> <td>4.4</td> <td>20.0</td> <td>40000.0</td> </tr> </tbody> </table> <p>10 of such power curves were obtained at 100 kHz and 40 kHz and can be found in the respective zip-file.</p> <p> </p> <p><strong>Literature_Power_Efficiency.zip</strong> contains digitzed power and efficiency values from the following references:</p> <ol> <li>X. Wu, D. Kong, S. Hao, et al., "Generation of 13.9-mJ Terahertz Radiation from Lithium Niobate Materials," Advanced Materials 35, 2208947 (2023).</li> <li> <p>P. L. Kramer, M. K. R. Windeler, K. Mecseki, et al., "Enabling high repetition rate nonlinear THz science with a kilowatt-class sub-100 fs laser source," Opt. Express 28, 16951 (2020).</p> </li> <li> <p>T. Kroh, T. Rohwer, D. Zhang, et al., "Parameter sensitivities in tilted-pulse-front based terahertz setups and their implications for high-energy terahertz source design and optimization," Opt. Express, OE 30, 24186–24206 (2022).</p> </li> <li> <p>B. Zhang, Z. Ma, J. Ma, et al., "1.4-mJ High Energy Terahertz Radiation from Lithium Niobates," Laser & Photonics Reviews 15, 2000295 (2021).</p> </li> </ol> <p> </p> <h2>Fig.04</h2> <p><strong>EOS_dfs.p</strong> is a pickle file, contain electro-optic sampling traces, which are already averaged for various pump powers at 40 kHz repetition rate.</p>
Dataset of "Glue-assisted Exfoliation of Two-dimensional Sulfur-rich Niobium Thiophosphate (Nb4P2S21) for Sulfur-equivalent Electrode Study in Lithium Storage"
<p>Two-dimensional (2D) layered thiophosphates have garnered attention for advanced batteries due to their open ionic diffusion channels, high capacity, and unique catalytic properties. However, their potential in energy storage applications remains largely unexplored. In this study, we report for the first time a 2D transition metal thiophosphate (Nb4P2S21) with high sulfur content. Nb4P2S21, synthesized via chemical vapor transport (CVT), is treated as a sulfur-equivalent material with better conductivity than sulfur, suitable for high-capacity lithium storage. The bulk material can be delaminated into high-quality nanoplates via glue-assisted grinding exfoliation, both displaying a layered quasi-one-dimensional (quasi-1D) morphology, which shortens the ion diffusion path and promises enhanced rate performance compared to larger lateral 2D materials. Density functional theory (DFT) calculations indicate that Nb4P2S21 has a direct bandgap of 1.64 eV (HSE06 method), with exfoliated counterparts showing near-infrared (NIR) photoluminescence at 755 nm, broadening potential applications to NIR-based devices. By tuning the working voltage window for lithium-ion batteries (LIBs) and controlling lithiation product formation, the material exhibits distinct electrochemical characteristics at 0 ~ 2.6 V, 0.5 ~ 2.6 V, 1.0 ~ 2.6 V, and 1.5 ~ 2.6 V. However, sulfur-rich electrodes in carbonate electrolytes demonstrate limited electrochemical potential due to polysulfide formation, leading to detrimental side reactions with carbonate-based electrolytes. Transitioning to ether-based electrolytes improves the initial reversible capacity and Coulombic efficiency of Nb4P2S21 by stabilizing the formed polysulfides. Despite this improvement, the material still mirrors the shuttle effect in lithium-sulfur batteries, diminishing active sulfur and undermining battery integrity. Further EDS and TOF-SIMS analyses of post-cycled electrode materials show significant sulfur loss and precipitation within the electrodes, exacerbating the shuttle effect and causing battery failure. Implementing strategies used in lithium-sulfur batteries, such as introducing polar host catalysts, could enhance the potential of these materials.</p>
Dataset for Gate-to-Gate Life Cycle Assessment of Lithium-Ion Battery Recycling Pre-Treatment
<p>Recycling spent lithium-ion batteries (LIBs) is crucial for improving environmental sustainability and conserving resources. Due to the diversity of LIB applications and recycling technologies, the environmental and energy impacts are not well understood. Comprehensive assessments must consider the distinct operations, methodologies, technology efficiency, and final treatment of materials. This study provides a partial gate-to-gate life cycle analysis (LCA) of a small-scale recycling plant in the Czech Republic, focusing on pre-treatment of spent LIBs from electric vehicles (EVs) and consumer electronics cells (CECs). The study highlights the benefits of recycling pre-treatment for CECs, significantly reducing environmental impact categories (EICs) such as climate change, eutrophication, and resource use. A high secondary use rate of obtained materials is crucial for environmental benefits, with metal reuse from packaging, connectors, and current collectors being especially important.</p>
Dataset of "Selective Precipitation of REE-Rich Aluminum Phosphate with Low Lithium Losses from Lithium Enriched Slag Leachate"
<p>Currently, recycling of spent lithium-ion batteries is carried out using mechanical, pyrometallurgical and hydrometallurgical methods and their combination. The aim of this article is to study a part of pyro-hydrometallurgical processing of spent lithium-ion batteries which includes lithium slag hydrometallurgical treatment and refining obtained leachate. Lithium slag intended for leaching experiments contains 3,68 % of Li; 11,02 % of Al; 1,17 % of Co; 1,71 % of Cu and other metals in minority content. Leaching step was realized via dry digestion that is an effective method capable of transferring over 99% of the present metals such as Li, Al, Co, Cu and others to the leachate. The highest content in leachate reached Al (2666 µg/mL) and Li (2239 µg/mL). Extraction of metals from leachate can be conducted using various methods, with precipitation being the most used. In this work, the influence of two types of precipitation agent (NaOH, Na3PO4) on precipitation efficiency of Al and Li losses was investigated. It was found that the precipitation of aluminium with NaOH can result in the co-precipitation of lithium, causing total lithium losses up to 40 %. As suitable precipitating agent for complete Al removal from Li leachate with a minimal loss of lithium (less than 2 %), crystalline Na3PO4 was determined under following condition: pH = 3, 400 rpm, 10 minutes, room temperature. Analysis confirmed that, in addition to aluminium, the precipitate also contains REE La (3.4%), Ce (2.5%), Y (1.3%), Nd (1%) and Pr (0.3%), which selective recovery will be the subject of further study.</p>
Dataset of "Advanced machine learning techniques for State-of-Health estimation in lithium-ion batteries: A comparative study"
This research focuses on State-of-Health (SOH) estimation of lithium-ion (Li-ion) batteries to enhance lifespan and reliability. Using Samsung INR18650-35E cells, 600 cycles were analyzed with machine learning (ML) techniques, including Gaussian Process Regression (GPR), Support Vector Regression (SVR), Feed-Forward Neural Network (FFNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). Input features from charging and discharging cycles were selected with Pearson Correlation Analysis (PCA) and Exhaustive Search (ES) to optimize inputs for each ML method. Models were tested on datasets of varying sizes to evaluate performance and overfitting, including an experiment where SOH estimation of one battery was performed using training data from another. The findings highlight each model's strengths and limitations, guiding their application in battery health prediction.
Dataset of "Mn-doped WSe2 as an efficient electrocatalyst for hydrogen production and as anode material for lithium-ion batteries"
<p>The ongoing energy crisis has made it imperative to develop low-cost, easily fabricated, yet efficient materials. It is highly desirable for these nanomaterials to function effectively in multiple applications. Among transition metal dichalcogenides, tungsten diselenide (WSe2) shows great promise but remains understudied. In this work, we doped WSe2 with Mn using a simple hydrothermal method. The resulting material exhibited excellent electrocatalytic activity for the hydrogen evolution reaction, achieving a low overpotential of –0.28 V vs RHE at -10 mA/cm2, enhanced conductivity, and high stability and durability. Moreover, as an anode material in in lithium-ion batteries, the Mn-doped WSe2 outperformed pristine WSe2, reaching discharge and charge capacities of 1223 and 922 mAh g−1, respectively. Additionally, the Mn-doped material maintained a significantly higher discharge capacity of 201 mAh g−1 compared to intact WSe2, which had 68 mAh g−1 after 150 cycles. This work offers novel insights into designing efficient bifunctional nanomaterials using transition metal dichalcogenides.</p>
Ionic conductivity, viscosity, and self-diffusion coefficients of novel imidazole salts for lithium-ion battery electrolytes
<p>This entry contains the data related to the publication<br><strong>A. Szczęsna-Chrzan <em>et al.</em>, “Ionic conductivity, viscosity, and self-diffusion coefficients of novel imidazole salts for lithium-ion battery electrolytes,”<em> J. Mater. Chem. A</em>, vol. 11, no. 25, pp. 13483–13492, 2023, doi: 10.1039/D3TA01217D.</strong><br><br>It contains experimentally determined conductivity, viscosity and self-diffusion coefficients of anions of the Hückel-type salts lithium 4,5-dicyano-2-(trifluoromethyl)imidazolide (LiTDI), lithium 4,5-dicyano-2-(pentafluoroethyl)imidazolide (LiPDI) and lithium 4,5-dicyano-2-(n‑heptafluoropropyl)imidazolide (LiHDI) for various concentrations of the conducting salts (0 M - 1.5 M) in a solvent mixture containing ethylene carbonate (EC) and ethyl methyl carbonate (EMC) in a ratio of 3:7 by weight.</p> <p>The Python scripts used for the analysis of the NMR data are also included in the dataset.</p>
Lithium-ion battery charge and discharge testing data - current, voltage, soc, ta - at constant levels of power
<p>This dataset helped in the composition of a battery testing and modelling validation, of a lithium-ion battery. The data has the charge and discharge testing acquisition data - current, voltage, soc, ta - at constant levels of power.</p>
Dataset for: Predicting stable lithium iron oxysulphides for battery cathodes
<p>Cathode materials that have high specific energies and low manufacturing costs are vital for the scaling up of lithium-ion batteries (LIBs) as energy storage solutions. Fe-based intercalation cathodes are highly attractive because of the low-cost and the abundance of the raw materials. However, existing Fe-based materials, such as LiFePO<sub>4</sub> suffer from low capacity due to the large size of the polyanions. Turning to mixed anion systems can be a promising strategy to achieve higher specific capacity. Recently, anti-perovskite structured oxysulphide Li<sub>2</sub>FeSO has been synthesised and reported to be electrochemically active.<br> In this work, we perform an extensive computational search for iron-based oxysulphides using <em>ab initio</em> random structure searching (AIRSS). By performing an unbiased sampling of the Li-Fe-S-O chemical space, several new oxysulphide phases have been discovered which are predicted to be less than 50 meV/atom from the convex hull and potentially accessible for synthesis.<br> Among the predicted phases, two anti-Ruddlesden-Popper structured materials Li<sub>2</sub>Fe<sub>2</sub>S<sub>2</sub>O and Li<sub>4</sub>Fe<sub>3</sub>S<sub>3</sub>O<sub>2</sub><br> have been found to be attractive as they have high theoretical capacities with calculated average voltages 2.9 V and 2.5 V respectively. With band gaps as low as about 2.0 eV, they are expected to exhibit good electronic conductivities.<br> By performing nudged-elastic band calculations, we show that the Li-ion transport in these materials takes place by hopping between the nearest neighbouring sites with low activation barriers between 0.3 eV and 0.5 eV.<br> The richness of new materials yet to be synthesised in the Li-Fe-S-O phase field illustrate the great opportunity in these mixed anion systems for energy storage applications and beyond.</p> <p> </p> <p>The dataset includes the structure searching results and outputs of further property calculations. The analysis codes are also included as Jupyter Notebooks.</p> <p> </p> <p>Also hosted on <a href="https://github.com/SMTG-UCL/Li-Fe-S-O-oxysulphides">GitHub</a>.</p> <p>Preprint hosted on <a href="https://doi.org/10.33774/chemrxiv-2021-fbffd-v2">ChemRxiv</a>.</p>
Structures of conventional and solid state lithium ion batteries
<p>A schematic of a single cell of a conventional, liquid-based lithium-ion battery (LiB) and a solid-state LiB. The conventional LiB comprises an anode composed of a Cu current collector and an active anode material (graphite), a separator soaked in an organic electrolyte, and a cathode composed of a Al current collector and an active cathode material, for example, LiCo<sub>2</sub>, as shown here. The solid-state LiB comprises a similar cathode, a solid electrolyte, and an anode composed of a Li-ion plate and Cu current collector. The anode-electrolyte interphase (SEI) and cathode-electrolyte interphase (CEI) for both LiBs are represented as pink and blue transparent layers, respectively. The tabs are shown protruding from the top of the current collectors. Both LiB cells show all components as fully lithiated, with directional Li<sup>+</sup> movement during (dis)charge indicated with arrows.</p>
Lithium tantalate photonic integrated circuits for volume manufacturing
<p>Dataset for the manuscript "Lithium tantalate photonic integrated circuits for volume manufacturing". </p> <p>DOI: 10.1038/s41586-024-07369-1</p> <p>Contains all raw data and code used to produce the Figures and Extended Data Figures in the manuscript. </p>
Tool for the environmental and economic impact assessment of industrial recycling routes for lithium-ion traction batteries
<p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).</p> <p> </p> <p>Please send your inquiries regarding the tool to s.bloemeke@tu-braunschweig.de.</p>
Datasets: Performance Characterization of Lithium-Ion Battery Cells Within Restricted Operating Range Using an Extended Ragone Plot
<h1>Documentation</h1> <p>This repository contains the measurement data presented in <strong>"Performance Characterization of Lithium-Ion Battery Cells Within Restricted Operating Range Using an Extended Ragone Plot."</strong></p> <p>The performance characterization was conducted on three lithium-ion battery cell types, each with two samples. In the publication only datasets from the following cells are included: #1: SCiB-23Ah-01, #2: SLPB8644143-353, #3: M1B-1223-01. The measurements were performed using a Scienlab SL60/300/18BT2C battery test system in combination with a BINDER type MK 720 temperature chamber. Further details about the battery cells, the experimental setup and procedures are available in the publication. An uncertainty analysis for these measurements is included in the supplementary material of the publication.</p> <blockquote> <p><strong>Note:</strong> Please cite the referenced publication when using these datasets in your work.</p> </blockquote> <h2>Datasets Overview</h2> <p><strong>Toshiba SCiB™ 23 Ah (prismatic)<br></strong></p> <ul> <li>SCiB-23Ah-01<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1<br> <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (2.7 V); 2 (2.55 V); 3 (2.4 V); 4 (2.25 V); 5 (2.1 V).</em></li> </ul> </li> </ul> </li> <li>SCiB-23Ah-02 <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (2.7 V); 2 (2.55 V); 3 (2.4 V); 4 (2.25 V); 5 (2.1 V).</em></li> </ul> </li> </ul> </li> </ul> <p><strong>Shenzen Melasta Battery SLPB8644143 (pouch)<br></strong></p> <ul> <li>SLPB8644143-353<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (4.2 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 2 (4.1 V); 3 (4.0 V); 4 (3.9 V); 5 (3.8 V).</em></li> </ul> </li> </ul> </li> <li>SLPB8644143-45 <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (4.2 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 2 (4.1 V); 3 (4.0 V); 4 (3.9 V); 5 (3.8 V).</em></li> </ul> </li> </ul> </li> </ul> <p><strong>LithiumWerks (A123) ANR26650m1B (cylindrical)<br></strong></p> <ul> <li>M1B-1223-01<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (3.6 V); 2 (3.45 V); 5 (3.3 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2<br> <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 3 (3.4 V); 4 (3.35 V).</em></li> </ul> </li> </ul> </li> <li> M1B-1223-02 <ul> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 3 (3.4 V); 4 (3.35 V).</em></li> </ul> </li> </ul> </li> </ul> <h2>Usage instructions</h2> <ol> <li>Raw test reports are stored in the <code>test reports</code> directory as <code>*.csv</code> and <code>*.info.txt</code> files. These files contain the following measurement signals: <ul> <li>Time, Test step, ind_T, ind_U, ind_xP, E [J], Eneg [J], Epos [J], I [A], P [W], Q [As], Qneg [As], Qpos [As], T_1 [°C], T_2 [°C], T_3 [°C], T_Clima [°C], U [V]</li> </ul> </li> <li>The test reports are structured and consolidated into an HDF5 file (<code>Scienlab.h5</code>) for efficient storage and analysis. This HDF5 file can be processed using the <strong>HDF5 Data Analysis and Visualization Toolkit</strong>, provided in this repository. <ul> <li>The Python script <code>hdf5_main.py</code> is included for processing and visualizing the datasets.</li> <li>PowerTemperatureTest_dis-Umax datasets contain cyclization data from multiple constant power (CP) discharges and standardized constant current constant voltage (CCCV) charges, with varying end-of-charge voltages.</li> <li>OCVTest datasets contain low-current galvanostatic data required for performance characterization via reconstruction-based approaches. The test protocol is extensively described in the publication.</li> </ul> </li> </ol>
Data for "Lithium isotope evidence shows Devonian afforestation may have significantly altered the global silicate weathering regime"
<p>This contains measured data for paper "Lithium isotope evidence shows Devonian afforestation may have significantly altered the global silicate weathering regime", under funding of ERC grant 682760 CONTROLPASTCO2. </p> <p>This consists all the Li isotope data obtained from brachiopods/bulk carbonate samples.</p>
Data-driven model enhancement of late-life lithium-ion batteries
<p>Suplement dataset for the paper "Data-driven model enhancement of late-life lithium-ion batteries"</p>
Dataset of "Comprehensive Machine Learning Approaches for Modelling the State of Charge of Lithium-ion Batteries"
<p>This paper evaluates three ML approaches for SOC modeling in LIBs: the multilayer perceptron (MLP), long short-term memory (LSTM), and the nonlinear autoregressive with exogenous input (NARX) neural network architectures. These models were tested using an experimental dataset with multiple input variables, including electrochemical impedance spectroscopy (EIS) data, voltage, and capacity readings for commercial LIB cells. Results indicate that MLP and LSTM are more adaptable with a smaller training dataset (14 samples), while the NARX model required more than 34 out of 67 samples to achieve reasonable accuracy. Additionally, the NARX model is more sensitive to changes in the learning rate (α) and exhibits larger output error deviations. The MLP and LSTM models consistently performed well across various hidden layer sizes, showing no upper bound constraints, whereas the NARX model’s performance deteriorated with certain hidden layer configurations.</p>
Cryogenically Cooled Periodically Poled Lithium Niobate Wafer Stacks for Multi-Cycle Terahertz Pulses
<p>Dataset used for the figures in the paper: "Cryogenically Cooled Periodically Poled Lithium Niobate Wafer Stacks for Multi-Cycle Terahertz Pulses" by Dalton et al. Accepted for publication in Applied Physics Letters on the 6th September 2024.</p>
Recovery of Lithium Carbonate from Dilute Li-Rich Brine via Homogenous and Heterogeneous Precipitation
<p>An extensive experimental campaign on Li recovery<br> from relatively dilute LiCl solutions (i.e., Li+ ∼ 4000 ppm) is<br> presented to identify the best operating conditions for a Li2CO3<br> crystallization unit. Lithium is currently mainly produced via solar<br> evaporation, purification, and precipitation from highly concentrated<br> Li brines located in a few world areas. The process requires<br> large surfaces and long times (18−24 months) to concentrate Li+<br> up to 20,000 ppm. The present work investigates two separation<br> routes to extract Li+ from synthetic solutions, mimicking those<br> obtained from low-content Li+ sources through selective Li+<br> separation and further concentration steps: (i) addition of<br> Na2CO3 solution and (ii) addition of NaOH solution + CO2<br> insufflation. A Li recovery up to 80% and purities up to 99% at 80<br> °C and with high-ionic strength solutions was achieved employing NaOH solution + CO2 insufflation and an ethanol washing step.</p>
Dataset for Link between Anisotropic Electrochemistry and Surface Transformations at Single Crystal Silicon Electrodes: Implications for Lithium Ion Batteries
<p>This dataset provides the raw data to the manuscript</p> <p>"<strong>Link between Anisotropic Electrochemistry and Surface Transformations at Single Crystal Silicon Electrodes: Implications for Lithium Ion Batteries"</strong></p> <p>Specifically, the following measurements are provided:</p> <ul> <li>Electrochemical measurements as cyclic voltammetry using scanning electrochemical cell microscopy for three different Si crystallographic orientations (100, 110, 311) in 1 M LiPF6 in ethylene carbonate - ethyl methyl carbonate ("SECCM/")</li> <li>Scanning electron microscopy and transmission electron microscopy imaging of pristine and cycled samples ("Images/")</li> </ul>
ScienceDex guides
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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.