Lithium fell to 73,600 CNY/T on September 26, 2025, down 0.20% from the previous day. Over the past month, Lithium's price has fallen 9.79%, and is down 2.52% compared to the same time last year, 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 September of 2025.

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 34,550 USD/T on September 25, 2025. Over the past month, Cobalt's price has risen 3.64%, and is up 42.18% compared to the same time last year, 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 September of 2025.

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

We present Li and Fe abundances for 87 stars in the globular cluster M4, obtained by using high-resolution spectra collected with GIRAFFE at the Very Large Telescope. The targets range from the turn-off up to the red giant branch bump. The Li abundance in the turn-off stars is uniform, with an average value equal to A(Li)=2.30+/-0.02dex (sigma=0.10dex), consistent with the upper envelope of Li content measured in other globular clusters and in the halo field stars, confirming also for M4 the discrepancy with the primordial Li abundance predicted by Wilkinson Microwave Anisotropy Probe+ big bang nucleosynthesis (WMAP+BBNS). Cone search capability for table J/MNRAS/412/81/table1 (Identification numbers, coordinates, temperatures, gravities, EW(Li), A(Li) and [Fe/H] abundances, and S/N around the Li line for the observed stars)

Nickel fell to 15,155 USD/T on September 26, 2025, down 0.56% from the previous day. Over the past month, Nickel's price has fallen 0.23%, and is down 10.83% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Nickel - values, historical data, forecasts and news - updated on September of 2025.

Platinum rose to 1,588.80 USD/t.oz on September 26, 2025, up 3.80% from the previous day. Over the past month, Platinum's price has risen 18.70%, and is up 58.72% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Platinum - values, historical data, forecasts and news - updated on September of 2025.

DOI Thanks to the accurate determination of the baryon density of the universe by the recent cosmic microwave background experiments, updated predictions of the standard model of Big Bang nucleosynthesis now yield the initial abundance of the primordial light elements with unprecedented precision. In the case of ^7^Li, the CMB+SBBN value is significantly higher than the generally reported abundances for Pop II stars along the so-called Spite plateau. In view of the crucial importance of this disagreement, which has cosmological, galactic and stellar implications, we decided to tackle the most critical issues of the problem by revisiting a large sample of literature Li data in halo stars that we assembled following some strict selection criteria on the quality of the original analyses. We dissect our sample in search of new constraints on Li depletion in halo stars. By means of the Hipparcos parallaxes, we derive the evolutionary status of each of our sample stars, and re-discuss our derived Li abundances. Cone search capability for table J/A+A/442/961/table3 (The data sample and its main characteristics, as found in the literature)

Enterprise-Value-To-Sales-Ratio Time Series for Far East Smarter Energy Co Ltd. Far East Smarter Energy Co., Ltd. provides smart energy and smart city services in China and internationally. It is involved in the research, development, design, manufacture, market, and service of overhead conductor, power cable, wire and cable for electrical equipment, and special cable used in clean energy, smart grid, smart manufacturing, smart transportation, green building and etc.; low voltage, medium voltage, fireproof, pile cable, wind power, and other cable products; optic fiber preform; and control and ship cables. The company offers lithium-ion batteries and power battery packs; lithium copper foil, which is used in power batteries, energy storage batteries, and small power and digital batteries, etc.; and composite copper and aluminum foils, and composite fluid collectors. In addition, the company provides civil airport engineering services. The company was formerly known as Far East Cable Corp.Ltd. The company was founded in 1985 and is based in Yixing, China.

The surface lithium abundance A(Li) of warm metal-poor dwarf stars exhibits a narrow plateau down to [Fe/H]~-2.8dex, while at lower metallicities the average value drops by 0.3dex with a significant star-by-star scatter (called lithium meltdown). This behaviour is in conflict with predictions of standard stellar evolution models calculated with the initial A(Li) provided by the standard Big Bang nucleosynthesis. The lower red giant branch (LRGB) stars provide a complementary tool to understand the initial A(Li) distribution in metal-poor stars. We have collected a sample of high-resolution spectra of 58 LRGB stars spanning a range of [Fe/H] between ~-7.0dex and ~-1.3dex. The LRGB stars display an A(Li) distribution clearly different from that of the dwarfs, without signatures of a meltdown and with two distinct components: (a) a thin A(Li) plateau with an average A(Li)=~1.09+/-0.01dex ({sigma}=~0.07dex), and (b) a small fraction of Li-poor stars with A(Li) lower than ~0.7dex. The A(Li) distribution observed in LRGB stars can be reconciled with an initial abundance close to the cosmological value, by including an additional chemical element transport in stellar evolution models. The required efficiency of this transport allows us to match also the Spite plateau lithium abundance measured in the dwarfs. The emerging scenario is that all metal-poor stars formed with the same initial A(Li) but those that are likely the product of coalescence or that experienced binary mass transfer and show lower A(Li). We conclude that A(Li) in LRGB stars is qualitatively compatible with the cosmological A(Li) value and that the meltdown observed in dwarf stars does not reflect a real drop of the abundance at birth. Cone search capability for table J/A+A/661/A153/table2 (Main information on the used spectra)

Data Repository Items for our upcoming manuscript on "Lithological influence on Li isotope fractionation during silicate weathering" submitted to Geochimica et Cosmochimica Acta.

2 files are attached in this repository: Manuscript Tables: Table 1, Table 2, Table 3 and Table 4, Table 5, Table S1, and Table S2 located in the manuscript and supplementary material are included in separate sheets. Supplementary Data File 1: The supplementary data file contains the complete dataset for all individual samples and elements measured in this study.

Table captions: Table 1. Information on sampling localities

Table 2. Selected in-situ geochemical data, dissolved load elemental concentrations and Li isotopic ratios from all localities. Full elemental data are in supplementary file 1. a Mean value from all repeated samples from each season is presented. b “W” indicates mean value from wet summer season samples. “D” indicates mean value from dry winter season values. c “n” indicates how many repeated samples obtained to calculate mean values d “ND” indicates this variable was not determined during analyses.

Table 3. Li concentrations and isotopic values from bedload and bedrock from all localities. The lithology of the bedrock digested is also listed. a “ND” indicates this variable was not determined during analyses.

Table 4. Breakdown of bedload clay mineralogical abundances.

Table 5. Rayleigh and batch-reactor model parameters and results. See text for full description and explanation of parameters

Table S1: Certified reference materials (CRM) and their errors for lithium isotopic measurements. Run 1 includes only wet season samples from July-August 2019. Run 2 includes both dry and wet season samples from January 2020 and September 2021 respectively. “n” indicates how many repeated measurements for each CRM during the run.

Table S2: Comparison between charge balance- and titration-derived bicarbonate concentrations for select dry season samples (10 out of 15 possible samples).

Data Repository Items for our upcoming manuscript on "Evaluating the influences of bedrock composition and seasonality on silicate weathering processes and dissolved δ7Li in Hong Kong rivers".

2 files are attached in this repository: Manuscript Tables: Table 1, Table 2 and Table S1 located in the manuscript and supplementary material are included in separate sheets. Supplementary Data File 1: The supplementary data file contains the complete dataset for all individual samples and elements measured in this study.

Table captions: Table 1: Selected in-situ geochemical data, dissolved load elemental concentrations and isotopic ratios from all localities. Full elemental data are in supplementary file 1. a Mean value from all repeated samples from each season is presented. b “W” indicates mean value from wet summer season samples. “D” indicates mean value from dry winter season values. c “n” indicates how many repeated samples obtained to calculate mean values d “ND” indicates this variable was not determined during analyses.

Table 2: Breakdown of clay mineralogical abundances for all 7 sampling localities.

Table S1: Certified reference materials (CRM) and their errors for lithium isotopic measurements. Run 1 includes only wet season samples from July-August 2019. Run 2 includes both dry and wet season samples from January 2020 and September 2021 respectively. “n” indicates how many repeated measurements for each CRM during the run.

The main objective of the work package 2 of the REFLECT project is to characterise relevant fluid properties and their reactions for saline fluids (type C). One of the specific goals was to collect fluid samples from several saline fluids from geothermal sites across Europe, determine their properties, and thus contribute to the Fluid Atlas (WP3). Additionally, the REFLECT team will compare those field data with data from lab experiments performed at near natural conditions. Samples of type C fluids were taken from several sites in Germany, Austria, Belgium and the Netherlands. The samples were analysed for major and minor ions, dissolved gases and isotopes. On 10th of May 2021, two thermal water samples were taken by TNO before and after the heat exchanger at the geothermal site Heemskerk in the Netherlands. The samples sent to Hydroisotop were analysed for their hydrochemical composition, heavy metal and dissolved organic carbon (DOC) content and stable isotopes (18O, 2H, 13C-DIC). It should be noted that the pH measured in the laboratory diverges from previously observed pH values which in the past have not been reported below 5,4. Concentrations of major ions had initially been reported too low but re-measurement of the samples yielded values in ranges that had previously been recorded. However, the concentraton of Lithium is much higher than expected. In order to resolve these uncertainties, the site Heemskerk will be sampled again. The dataset contains analysis results associated with the research project REFLECT. It is a comma separated file (csv) containing the following columns: Location,Country,Description,Laboratory (Lab.),Lab. No.,Sampling date,Spec. electr. conductivity (25 degC) Lab. (muS/cm),pH value Lab.,Temperature Lab. (degC),Alkalinity (pH 4.3) Lab. (mmol/l),Sodium (mg/l),Potassium (mg/l),Calcium (mg/l),Magnesium (mg/l),Ammonium (mg/l),Hydrogen carbonate (mg/l),Chloride (mg/l),Sulphate (mg/l),Nitrate (mg/l),Antimony (mg/l),Barium (mg/l),Fluoride (mg/l),Iodide (mg/l),Lithium (mg/l),Silicon (mg/l),Strontium (mg/l),Aluminium (mg/l),Arsenic (mg/l),Lead (mg/l),Iron total (mg/l),Copper (mg/l),Manganese total (mg/l),Nickel (mg/l),Uranium (mg/l),Zinc (mg/l),DOC (mg/l),Oxygen-18 d18O-H2O (per mille VSMOW),Deuterium d2H-H2O (per mille VSMOW),Deuterium-excess (per mille VSMOW),Carbon-13 d13C-DIC (per mille VPDB). Methods are described in the accompanying deliverable Fluid data of geothermal sites (type C) Project summary: The efficiency of geothermal utilisation largely depends on the behaviour of fluids that transfer heat between the geosphere and the engineered components of a power plant. The Horizon 2020 funded project REFLECT aims to avoid problems related to fluid chemistry rather than treat them. Fluid physical and chemical properties are often poorly defined, as in situ sampling and measurements at extreme conditions have proved difficult to date. Therefore, large uncertainties in current model predictions prevail, which are being tackled in REFLECT by collecting new, high-quality data in critical areas. The data is being implemented in a European geothermal fluid atlas and in predictive models to allow recommendations on how to best operate geothermal sites sustainably and to enhance geothermal technology development. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement nº 850626. Project website: https://www.reflect-h2020.eu/ Cordis website: https://cordis.europa.eu/project/id/850626