Lithium fell to 71,350 CNY/T on August 1, 2025, down 0.90% from the previous day. Over the past month, Lithium's price has risen 15.73%, but it is still 10.25% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lithium - values, historical data, forecasts and news - updated on August of 2025.

involving 5 distinct batches

In 2022, the average price of battery-grade lithium carbonate stood at ****** U.S. dollars per metric ton. This figure is by far the highest price for battery-grade lithium carbonate recorded in the period of consideration. For 2024, lithium carbonate price was estimated at ****** U.S. dollars per metric ton. Lithium is a highly reactive soft and silvery-white alkali metal. As the third element in the periodic table, it cannot be found in its pure form in nature. Lithium is the least dense of solid elements and the lightest out of all metals. Lithium and batteries One of lithium’s most well-known end uses is in lithium-ion batteries. Lithium-ion batteries are rechargeable and mostly used in portable electronics and electronic vehicles. In lithium-ion batteries, the lithium ions move from the negative electrode to positive electrode while in use, and the process is reversed while charging. These batteries are highly flammable but are also low-maintenance. They have a high energy density and a low self-discharge. Some drawbacks include the fact that they are expensive to manufacture, and that they require protection circuits to maintain the voltage safely. Lithium-ion batteries are also the single-largest end use of lithium, amounting to an ** percent share of global lithium consumption in 2024. Lithium demand forecasts Looking to the future, lithium demand is forecast to stand at *** million tons by 2025. This growth will be mainly driven by lithium-ion battery demand for electric vehicles. Demand is expected to remain the highest in China, which will consistently account for half of global lithium-ion battery demand.

Data Description:

This data set has been collected from a custom built battery prognostics testbed at the NASA Ames Prognostics Center of Excellence (PCoE). Li-ion batteries were run through 3 different operational profiles (charge, discharge and Electrochemical Impedance Spectroscopy) at different temperatures. Discharges were carried out at different current load levels until the battery voltage fell to preset voltage thresholds. Some of these thresholds were lower than that recommended by the OEM (2.7 V) in order to induce deep discharge aging effects. Repeated charge and discharge cycles result in accelerated aging of the batteries. The experiments were stopped when the batteries reached the end-of-life (EOL) criteria of 30% fade in rated capacity (from 2 Ah to 1.4 Ah). Data Acquisition: The testbed comprises: * Commercially available Li-ion 18650 sized rechargeable batteries, * Programmable 4-channel DC electronic load, * Programmable 4-channel DC power supply, * Voltmeter, ammeter and thermocouple sensor suite, * Custom EIS equipment, * Environmental chamber to impose various operational conditions, * PXI chassis based DAQ and experiment control, and MATLAB based experiment control, data acquisition and prognostics algorithm evaluation setup (appx. data acquisition rate is 10Hz). Parameter Description: Data Structure: cycle: top level structure array containing the charge, discharge and impedance operations type: operation type, can be charge, discharge or impedance ambient_temperature: ambient temperature (degree C) time: the date and time of the start of the cycle, in MATLAB date vector format data: data structure containing the measurements for charge the fields are: Voltage_measured: Battery terminal voltage (Volts) Current_measured: Battery output current (Amps) Temperature_measured: Battery temperature (degree C) Current_charge: Current measured at charger (Amps) Voltage_charge: Voltage measured at charger (Volts) Time: Time vector for the cycle (secs) for discharge the fields are: Voltage_measured: Battery terminal voltage (Volts) Current_measured: Battery output current (Amps) Temperature_measured: Battery temperature (degree C) Current_charge: Current measured at load (Amps) Voltage_charge: Voltage measured at load (Volts) Time: Time vector for the cycle (secs) Capacity: Battery capacity (Ahr) for discharge till 2.7V for impedance the fields are: Sense_current: Current in sense branch (Amps) Battery_current: Current in battery branch (Amps) Current_ratio: Ratio of the above currents Battery_impedance: Battery impedance (Ohms) computed from raw data Rectified_impedance: Calibrated and smoothed battery impedance (Ohms) Re: Estimated electrolyte resistance (Ohms) Rct: Estimated charge transfer resistance (Ohms) Intended Use: The data sets can serve for a variety of purposes. Because these are essentially a large number of Run-to-Failure time series, the data can be set for development of prognostic algorithms. In particular, due to the differences in depth-of-discharge (DOD), the duration of rest periods and intrinsic variability, no two cells have the same state-of-life (SOL) at the same cycle index. The aim is to be able to manage this uncertainty, which is representative of actual usage, and make reliable predictions of Remaining Useful Life (RUL) in both the End-of-Discharge (EOD) and End-of-Life (EOL) contexts.

discharge and Electrochemical Impedance Spectroscopy) at different temperatures. Discharges were carried out at different current load levels until the battery voltage fell to preset voltage thresholds. Some of these thresholds were lower than that recommended by the OEM (2.7 V) in order to induce deep discharge aging effects.

Please see the file readme.txt for information about the data Lithium-ion (Li-ion) batteries are the most popular energy storage technology in consumer electronics and electric vehicles and are increasingly applied in stationary storage systems. Yet, concerns about safety and reliability remain major obstacles, which must be addressed in order to improve the acceptance of this technology. The gradual degradation of Li-ion cells over time lies at the heart of this problem. Time, usage and environmental conditions lead to performance deterioration and cell failures, which, in rare cases, can be catastrophic due to fires or explosions. The physical and chemical mechanisms responsible for degradation are numerous, complex and interdependent. Our understanding of degradation and failure of Li-ion cells is still very limited and more limited yet are reliable and practical methods for the detection and prediction of these phenomena. This dataset contains the results of long term cycling of 8 lithium-ion cells in our lab in Oxford. The full details are given in the readme.txt file.

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 LiFePO₄ 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 Li₂FeSO has been synthesised and reported to be electrochemically active.
In this work, we perform an extensive computational search for iron-based oxysulphides using ab initio 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.
Among the predicted phases, two anti-Ruddlesden-Popper structured materials Li₂Fe₂S₂O and Li₄Fe₃S₃O₂
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.
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.
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.

The dataset includes the structure searching results and outputs of further property calculations. The analysis codes are also included as Jupyter Notebooks.

Also hosted on GitHub.

Preprint hosted on ChemRxiv.

This record constitutes the raw data underlying the paper "Battery pack degradation - Understanding aging in parallel-connected lithium-ion batteries under thermal gradients" (preprint link) The dataset contains all raw data, processed data and analysis codes used to generate figures in the publication. Abstract is as follows:

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.

This data release provides the descriptions of approximately 20 U.S. sites that include mineral regions, mines, and mineral occurrences (deposits and prospects) that contain enrichments of lithium (Li). This release includes sites that have a contained resource and (or) past production of lithium metal greater than 15,000 metric tons. Sites in this database occur in Arkansas, California, Nevada, North Carolina, and Utah. There are several deposits that were not included in the database because they did not meet the cutoff requirement, and those occur in Arizona, Colorado, the New England area, New Mexico, South Dakota, and Wyoming. In the United States, lithium was first mined from pegmatite orebodies in South Dakota in the late 1800s. The Kings Mountain pegmatite belt of North Carolina also had significant production from pegmatites, and the area may still contain as much as 750 million metric tons (Mt) of ore containing 5 Mt lithium metal (Kesler and others, 2012). In 2018, U.S. production of lithium was restricted to a single lithium-brine mining operation in Nevada. In 2018, the U.S. had a net import reliance as a percentage of apparent consumption of more than 50 percent for lithium (U.S. Geological Survey, 2019). The U.S. is not a significant producer of lithium, so the commodity is mainly imported from Chile and Argentina to meet consumer demand. Lithium is necessary for strategic, consumer, and commercial applications. The primary uses for lithium are in batteries, ceramics, glass, metallurgy, pharmaceuticals, and polymers (U.S. Geological Survey, 2019). Lithium has excellent electrical conductivity and low density (lithium metal will float on water), making it an ideal component for battery manufacturing. Lithium is traded in three primary forms: mineral concentrates, mineral compounds (from brines), and refined metal (electrolysis from lithium chloride). Lithium mineralogy is diverse; it occurs in a variety of pegmatite minerals such as spodumene, lepidolite, amblygonite, and in the clay mineral hectorite. Current global production of lithium is dominated by pegmatite and closed-basin brine deposits, but there are significant resources in lithium-bearing clay minerals, oilfield brines, and geothermal brines (Bradley and others, 2017). The entries and descriptions in the database were derived from published papers, reports, data, and internet documents representing a variety of sources, including geologic and exploration studies described in State, Federal, and industry reports. Resources extracted from older sources might not be compliant with current rules and guidelines in minerals industry standards such as National Instrument 43-101 (NI 43-101) or the Joint Ore Reserves Committee Code (JORC Code). The inclusion of a particular lithium mineral deposit in this database is not meant to imply that the deposit is currently economic. Rather, these deposits were included to capture the characteristics of the larger lithium deposits in the United States, which are diverse in their geology and resource potential. Inclusion of material in the database is for descriptive purposes only and does not imply endorsement by the U.S. Government. The authors welcome additional published information in order to continually update and refine this dataset. Bradley, D.C., Stillings, L.L., Jaskula, B.W., Munk, LeeAnn, and McCauley, A.D., 2017, Lithium, chap. K of Schulz, K.J., DeYoung, J.H., Jr., Seal, R.R., II, and Bradley, D.C., eds., Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply: U.S. Geological Survey Professional Paper 1802, p. K1–K21, https://doi.org/10.3133/pp1802K. Kesler, S.E., Gruber, P.W., Medina, P.A., Keoleian, G.A., Everson, M.P., and Wallington, T.J., 2012, Global lithium resources—relative importance of pegmatite, brine and other deposits: Ore Geology Reviews, v. 48, October ed., p. 55—69. U.S. Geological Survey, 2019, Mineral commodity summaries 2019: U.S. Geological Survey, 200 p., https://doi.org/10.3133/70202434.

📈 Daily Historical Stock Price Data for Atlas Lithium Corporation (2022–2025)

A clean, ready-to-use dataset containing daily stock prices for Atlas Lithium Corporation from 2022-12-23 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Atlas Lithium Corporation Ticker Symbol: ATLX Date Range: 2022-12-23 to 2025-05-28 Frequency: Daily Total Records: 607 rows (one… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-atlas-lithium-corporation-20222025.

This dataset presents information concerning 8- and 10-digit trade codes related to the rechargeable lithium-ion battery (LIB) supply chain for the People's Republic of China (PRC) and the European Union, and the United States as classified by Customs and Border Protection (CBP) rulings. Note that this dataset is not intended to be a complete or comprehensive list of trade codes for the LIB supply chain; rather, it presents trade codes from the PRC and the EU that more granularly classify products related to LIB supply chain in comparison to the Harmonized Tariff Schedule of the United States (HTSUS). CBP rulings are included to indicate existing classification decisions for relevant products related to the LIB supply chain. Disaggregated trade codes offer more detailed insight into trade flows, supply chains, and the state of domestic and international industries. The dataset covers raw materials, refined and processed materials, battery materials, cell components, batteries and ...

The demand for lithium-ion batteries (Li-ionBs) has surged, with a projected market value of $116 billion by 2030. However, only 5% of spent Li-ionBs are currently recycled due to the high costs, energy consumption, and environmental risks of existing recycling methods. This study focuses on developing an efficient, eco-friendly process to recover valuable metals like lithium (Li), cobalt (Co), nickel (Ni), and manganese (Mn) from Li-ionB waste, specifically targeting NMC 532 cathodes.Key innovations include using potentiostatic electrowinning with rotating cathodes and Pt-coated Ti anodes to selectively recover high-purity Ni-Co alloys (99% pure) , avoiding traditional, energy-intensive hydrometallurgical steps. Optimized leaching achieved recovery efficiencies of ~98.9% for Li, ~97.1% for Co, ~96.9% for Ni, and ~95.7% for Mn. Subsequent multi-stage precipitation recovered Ni, Co, Mn, and Li in various forms, lithium carbonate (Li₂CO₃, 99 % pure), manganese hydroxide (Mn(OH)₂, 99 % pure), and 0.6[Ni(OH)2].0.3[Mn(OH)2].0.1Co(OH)2. The spectra and chronoamperometry of the recovered materials are presented in the presented data sets.

Cobalt traded flat at 33,335 USD/T on July 24, 2025. Over the past month, Cobalt's price has remained flat, but it is still 25.20% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Cobalt - values, historical data, forecasts and news - updated on August of 2025.

As of 2024, the world's total lithium resources were estimated at some 115 million metric tons of lithium content. Bolivia and Argentina boasted the largest resources at the time, with some 23 million metric tons each. The United States ranked third that year, at about 19 million metric tons of lithium content.

Echelle observations are presented of lithium in 63 F and G dwarfs of the Praesepe cluster. For stars earlier than about G0V, Praesepe follows the same trends seen in the Hyades, which has approximately the same age and composition. Stars in Praesepe later than about G5V have more Li than their Hyades counterparts, possibly because Praesepe is slightly younger than the Hyades or has slightly lower metallicity. Significant differences in the abundance of Li are seen among stars of the same color, and, as in the Hyades, there is a tendency for the deviant stars to be binaries to the extent that duplicity in Praesepe is known. There are also stars with much less Li than most cluster members yet which appear to be true members of Praesepe. The close binary KW 181 has a normal Li abundance, despite the fact that similar close binaries in the Hyades are Li rich.

Intro

Dataset from the publication "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells", DOI: https://doi.org/10.1016/j.jpowsour.2024.234185

Full details of the study can be found in the publication, including thorough descriptions of the experimental methods and structure. A basic desciption of the experimental procedure and data structure is included here for ease of use.

Commercial 21700 cylindrical cells (LG M50T, LG GBM50T2170) were cycle aged under 3 different temperatures [10, 25, 40] °C and 4 different SoC ranges [0-30, 70-85, 85-100, 0-100]%, as well as a further [0-100]% SoC range experiment which utilised a drive-cycle discharge instead of constant-current. The same C-rates (0.3C / 1 C, for charge / discharge) were used in all tests; multiple cells were tested under each condition. These are listed in the table below.

Cells were base-cooled at set temperatures using bespoke test rigs (see our linked publications for details; the supporting information file contains detailed descriptions and photographs). Cells were subject to break-in cycles prior to beginning of life (BoL) performance tests using the ‘Reference Performance Test’ (RPT) procedures. They were then alternately subject to ageing sets and RPTs until the end of testing. Full details of each of these procedures are described in the linked publication.

The data contained in this repository is then described in the Data section below. This includes a description of the folder structure and naming conventions, file formats, and data analysis methods used for the ‘Processed Data’ which has been calculated from the raw data.

An 'experimental_metadata' .xlsx file is included to aid parsing of data. A jupyter notebook has also been included to demonstate how to access some of the data.

Data

Data are organised according to their parent ‘Experiment’, as defined above, with a folder for each. Within each Experiment folder, there are 3 subfolders: ‘Summary Data’, ‘Processed Timeseries Data’, and ‘Raw Data’.

Summary Data

This folder contains data which has been extracted by processing the raw data in the ‘Degradation Cycling’ and ‘Performance Checks’ folders. In most cases, the data you are looking for will be stored here.

It contains:

Performance Summary

A summary file for each cell which details key ageing metrics such as number of ageing cycles, charge throughput, cell capacity, resistance, and degradation mode analysis results. Each row of data corresponds to a different SoH.

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. See our ACS publication for more details.

Data includes:

· Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to.

· Ageing Cycles: number of ageing cycles the cell has been subject to. *this is not equivalent full cycles.

· Ageing Set Start Date/ End date: The date that each ageing set began/ ended.

· Days of degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending.

· 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).

· Charge throughput: total accumulated charge recorded during all cycles during ageing (i.e. sum of charge and discharge). This is the cumulative total since BoL (not including RPTs, and not including break-in cycles).

· Energy throughput: as with "charge throughput", but for energy.

· C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT.

· C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT.

· 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).

· Fitting parameters: output from the DMA optimisation function; 5 parameters which detail the upper/lower SoCs of each electrode, and the capacity fraction of graphite in the negative electrode.

· Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity.

· 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.

· RMSE data: the root mean squared error of the optimisation function calculated from the residual between the measured and simulated voltage vs capacity profiles.

Ageing Sets Summary

Data from the ageing cycles, summarised on an average per cycle and an average per ageing set basis. Metrics include mean/ max/ min temperatures, voltages etc.

Processed Timeseries data

Timeseries data (voltage, current, temperature, etc.) from each subtest (pOCV, GITT, etc.) of the RPTs, all grouped by subtest-type and by cell ID.

Contains the same data as in the ‘Performance Checks’ subfolder of the 'Raw Data' folder, but has been processed to slice into relevant subtests from the RPT procedure and includes only limited variables (time, voltage, current, charge, temperature). These are all saved as .csv files. In general this data will be easier to access than the raw data, but perhaps not as rich.

Raw Data

These are the raw data from the performance checks and from the degradation cycles themselves. The data from here has already been processed by me to get values of ‘energy throughput’, ‘charge throughput’, ‘average ageing temperature’, etc., which are all saved in the ‘Summary Data’ folder as described in the relevant section above.

The data in the ‘Degradation Cycling’ folder are organised by ageing set (where an ageing set is a defined number of ageing cycles, as described in the paper). In theory, each cell should have one datafile in each ageing set subfolder. However, due to experimental issues, tests can sometimes be interrupted midway though, requiring the test to be subsequently resumed. In this case, there may be

This work aims to investigate the behaviour of the lithium abundance in stars with and without detected planets. Our study is based on a sample of 1332 FGK main-sequence stars with measured lithium abundances, for 257 of which planets were detected. Our method reviews the sample statistics and is addressed specifically to the influence of tides and orbital decay, with special attention to planets on close orbits, whose stellar rotational velocity is higher than the orbital period of the planet. In this case, tidal effects are much more pronounced. The analysis also covers the orbital decay on a short timescale, with planets spiralling into their parent star. Furthermore, the sample allows us to study the relation between the presence of planets and the physical properties of their host stars, such as the chromospheric activity, metallicity, and lithium abundance. In the case of a strong tidal influence, we cannot infer from any of the studies described that the behaviour of Li differs between stars that host planets and those that do not. Our sample includes stars with super-solar metallicity ([Fe/H]>0.15dex) and a low lithium abundance (A(Li)0.2) are closest to the Galactic centre. A dedicated study of a set of high-metallicity low-Li stars is needed to test the migration-depletion scenario. Cone search capability for table J/A+A/684/A28/tablea1 (Parameters of stars and their host planets) Cone search capability for table J/A+A/684/A28/tablea2 ([Ti/H] and [Ti/Fe] values for objects with negative U that are older than 8 Ga)

This dataset consists of part 2 of the data associated with publication "Data-driven Direct Diagnosis of PV Connected Batteries "

The synthetic cycles were generated using the mechanistic modeling approach. See “Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis“ (Journal of Power Sources, Volume 479, 15 December 2020, 228806) and "Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis" (Energies 2021, 14, 2371 ) for more details.

Two sets of data are available, one for training and one for validation Training dataset: MEDB_PI folder, clear-sky irradiance, 0.025 triplet resolution up to 50% degradation with 2% increment. Validation dataset: MEDB_Cloud, 18 different cloudy days, 0.05 triplet resolution up to 50% degradation with 2% increment.

All datasets were generated with slightly different cell parameters to account for cell-to-cell variations. Details are available in publication. For each duty cycle, 3 set of files are provided, the *_V files contains V vs. Q data, the *_t files contains V vs. time data and the *_R files contains rate vs. Q data.

For each file, column in the volt, voltT, ot rate variable corresponds to 1 degradation path, the 1001 lines corresponds to the resolution in variable Q (for the capacity based data) or timenorm (for the time-based data). Details of each duty cycle is provided in the pathinfo variable with headers in pathinfo_index ( 1 - % LLI, 2 - % LAMPE, 3 - % LAMNE, 4 - Capacity, 5 - DOD).

All simulations were performed with the 2022 version of the alawa toolbox. Voltage and kinetics of electrodes from different manufacturers, with different composition, or with different architecture might differ significantly.

MEDB_irradiancedata.mat contains data gathered for 2 years at MEDB site (see publication for details). Data provided courtesy of HNEI’s Severine Busquet, Jonathan Kobayashi, and Richard Rocheleau

This matlab structure contains the following variables: dn: time from Matlab reference time dh: hour of the day doy: day of the year dm: month of the year dy: year ghi: global irradiance (W/m2) class: clear sky yes/no perc_clear: clear sky percentage (%) meta: panel metadata tot_POA: clear sky irradiance at POA (W/m2) inc: Solar angle of incidence relative to the POA (degree) tot_horz: clear sky horizontal irradiance (W/m2)