Zinc rose to 2,826.70 USD/T on August 29, 2025, up 1.32% from the previous day. Over the past month, Zinc's price has risen 1.09%, but it is still 2.43% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Zinc - values, historical data, forecasts and news - updated on August of 2025.

Global Zinc Prices - Historical chart and current data through 2025.

The zinc ingot prices in the United States for Q3 2024 reached 3450 USD/MT in September. The region saw notable fluctuations in zinc ingot prices throughout the quarter, primarily influenced by the limited availability of zinc ingots and ongoing supply chain challenges. These factors, coupled with consistent demand from critical sectors, have kept prices on an upward trajectory, emphasizing the market's sensitivity to supply-demand dynamics.

Explore IMARC’s newly published report, titled “Zinc Ingot Prices, Trend, Chart, Demand, Market Analysis, News, Historical and Forecast Data Report 2024 Edition,” offers an in-depth analysis of zinc ingot pricing, covering an analysis of global and regional market trends and the critical factors driving these price movements.

Lead rose to 1,997.58 USD/T on August 29, 2025, up 0.68% from the previous day. Over the past month, Lead's price has risen 0.10%, but it is still 2.70% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lead - values, historical data, forecasts and news - updated on August of 2025.

Cost-of-Goods-Sold-Including-Depreciation-and-Amortization Time Series for Korea Zinc Inc. Korea Zinc Company, Ltd. operates as a general non-ferrous metal smelting company primarily in South Korea. The company offers zinc slab ingots, alloy jumbo blocks, anode ingots, and die cast ingots; and lead and lead alloy ingots; and copper cathodes. The company also provides gold and silver; and rare metals, including indium, bismuth, and antimony; and sulfuric acid, semi sulfuric acid, and oleum. In addition, it engages in non-ferrous metals import and export, and recycling; wholesale and product brokerage; provision of logistics warehousing services; shipping; construction equipment operation; waste lubricant refining; electricity, gas, and steam supply; concentrate export; and logistics businesses. Further, the company offers private equity fund services; media content production services; electrolytic copper foil for secondary batteries; and electronic waste collection, dismantling, shredding, and processing services. The company was incorporated in 1974 and is headquartered in Seoul, South Korea.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Will the Zinc Index Dictate the Future of the Market?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Magnesium fell to 18,050 CNY/T on August 29, 2025, down 0.55% from the previous day. Over the past month, Magnesium's price has fallen 0.55%, and is down 4.50% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Magnesium.

Other-Operating-Expenses Time Series for Huludao Zinc Industry Co Ltd. Huludao Zinc Industry Co.,Ltd. engages in the non-ferrous metal zinc and lead smelting products primarily in China. The company is involved in the zinc, copper, and lead smelting and deep processing; and cadmium, indium, sulfuric acid, and copper sulfate utilization processing activities. It also provides zinc ingot, electric lead, high purity, silver bullion, hot dip galvanized, zinc sulfate, gold products, etc. The company's products are used in the metallurgy, machinery, electronics, medicine, chemical, military, and other industries. It also exports its products to approximately 20 countries and regions. The company was founded in 1993 and is based in Huludao, China.

Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build potential energy surfaces and explore chemical space. However, most of this work has focused on organic molecules due to the simplicity of their electronic structures as well as the availability of data sets. In this work, we build a deep learning architecture to model the energetics of zinc organometallic complexes. To achieve this, we have compiled a configurationally and conformationally diverse data set of zinc complexes using metadynamics to overcome the limitations of traditional sampling methods. In terms of the neural network potentials, our results indicate that for zinc complexes, partial charges play an important role in modeling the long-range interactions with a neural network. Our developed model outperforms semiempirical methods in predicting the relative energy of zinc conformers, yielding a mean absolute error (MAE) of 1.32 kcal/mol with reference to the double-hybrid PWPB95 method.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Zinc Price Volatility Expected to Impact DJ Commodity Zinc Index.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Summary

Dataset of imaging data related to the publication Raudenska, M., Kratochvilova, M., Vicar, T., Gumulec, J., Balvan, J., Polanska, H. Pribyl, J. & Masarik, M.:Cisplatin enhances cell stiffness and decreases invasiveness rate in prostate cancer cells by actin accumulation. Scientific Reports 2019, 9, 1660

This dataset includes image data of atomic force microcopy (Young modulus) and confocal microscopy(staining of F-actin and β-tubulin) of prostate cell lines PNT1A, 22Rv1, and PC-3.

Materials and Methods

Cells, cell culture conditions

Cells confluent up to 50–60% were washed with a FBS-free medium and treated with a fresh medium with FBS and required antineoplastic drug concentration (IC50 concentration for the particular cell line). The cells were treated with 93 µM (PC-3), 38 µM (PNT1A), and 24 µM (22Rv1) of cisplatin (Sigma-Aldrich, St. Louis, Missouri), respectively. IC50 concentrations used for treatment with docetaxel (Sigma-Aldrich, St. Louis, Missouri) were 200nM for PC-3, 70nM for PNT1A, and 150nM for 22Rv1.

Long-term zinc (II) treatment of cell cultures

Cells were cultivated in the constant presence of zinc(II) ions. Concentrations of zinc(II) sulphate in the medium were increased gradually by small changes of 25 or 50 µM. The cells were cultivated at each concentration no less than one week before harvesting and their viability was checked before adding more zinc. This process was used to select zinc resistant cells naturally and to ensure better accumulation of zinc within the cells (accumulation of zinc is usually poor during the short-term treatment of prostate cancer cells). Total time of the cultivation of cell lines in the zinc(II)-containing media exceeded one year. Resulting concentrations of zinc(II) in the media (IC50 for the particular cell line) were 50 µM for the PC-3 cell line, 150 µM for the PNT1A cell line, and 400 µM for the 22Rv1 cell line. The concentrations of zinc(II) in the media and FBS were taken into account.

Actin and tubulin staining

β-tubulin was labeled with anti- β tubulin antibody EPR1330 at a working dilution of 1/300. The secondary antibody used was Alexa Fluor® 555 donkey anti-rabbit (ab150074) at a dilution of 1/1000. Actin was labeled with Alexa Fluor™ 488 Phalloidin (A12379, Invitrogen); 1 unit per slide. For mounting Duolink® In Situ Mounting Medium with DAPI (DUO82040) was used. The cells were fixed in 3.7% paraformaldehyde and permeabilized using 0.1% Triton X-100.

Confocal microscopy

The microscopy of samples was performed at the Institute of Biophysics, Czech Academy of Sciences, Brno, Czech Republic. Leica DM RXA microscope (equipped with DMSTC motorized stage, Piezzo z-movement, MicroMax CCD camera, CSU-10 confocal unit and 488, 562, and 714 nm laser diodes with AOTF) was used for acquiring detailed cell images (100× oil immersion Plan Fluotar lens, NA 1.3). Total 50 Z slices was captured with Z step size 0.3 μm.

Atomic force microscopy

We used the bioAFM microscope JPK NanoWizard 3 (JPK, Berlin, Germany) placed on the inverted optical microscope Olympus IX‑81 (Olympus, Tokyo, Japan) equipped with the fluorescence and confocal module, thus allowing a combined experiment (AFM‑optical combined images). The maximal scanning range of the AFM microscope in X‑Y‑Z range was 100‑100‑15 µm. The typical approach/retract settings were identical with a 15 μm extend/retract length, Setpoint value of 1 nN, a pixel rate of 2048 Hz and a speed of 30 µm/s. The system operated under closed-loop control. After reaching the selected contact force, the cantilever was retracted. The retraction length of 15 μm was sufficient to overcome any adhesion between the tip and the sample and to make sure that the cantilever had been completely retracted from the sample surface. Force‑distance (FD) curve was recorded at each point of the cantilever approach/retract movement. AFM measurements were obtained at 37°C (Petri dish heater, JPK) with force measurements recorded at a pulling speed of 30 µm/s (extension time 0.5 sec).

The Young's modulus (E) was calculated by fitting the Hertzian‑Sneddon model on the FD curves measured as force maps (64x64 points) of the region containing either a single cell or multiple cells. JPK data evaluation software was used for the batch processing of measured data. The adjustment of the cantilever position above the sample was carried out under the microscope by controlling the position of the AFM‑head by motorized stage equipped with Petri dish heater (JPK) allowing precise positioning of the sample together with a constant elevated temperature of the sample for the whole period of the experiment. Soft uncoated AFM probes HYDRA-2R-100N (Applied NanoStructures, Mountain View, CA, USA), i.e. silicon nitride cantilevers with silicon tips are used for stiffness studies because they are maximally gentle to living cells (not causing mechanical stimulation). Moreover, as compared with coated cantilevers, these probes are very stable under elevated temperatures in liquids – thus allowing long-time measurements without nonspecific changes in the measured signal.

Image analysis

Fluorescence microscopy data were analyzed in ImageJ 1.52h and Python 3.7.1 as follows: cells were manually segmented using actin fluorescence channel, two regions were created for analysis: whole cell and cell periphery, lining a 4 μm thick region around cell border and including most of periphery actin cytoskeleton. In these two regions following parameters were measured for both actin and tubulin fluorescence: Integrated intensity, median intensity, and following regions were measured to describe cell morphology: Cell area, Maximum caliper (max feret diameter), roundness, and aspect ratio. Moreover, stress fibers were manually segmented in every cell and following parameters were measured: number of fibers per cell, feret angle of fiber, integrated intensity, fiber length, mean intensity. Next, a standard deviation of feret angles of individual fibers was calculated relatively to mean of feret angle using a circstd function from scipy package for Python.

Identification of files

Microscopy data

Files are separated into individual zip files. The dataset of confocal microscopy is separated based on treatments: untreated control, docetaxel-treated cells, cisplatin-treated cells, zinc-treated cells. Filenames actin_tubulin_Zstack_cisplatin.zip, actin_tubulin_Zstack_untreated_control.zip, actin_tubulin_Zstack_zinc.zip, actin_tubulin_Zstack_docetaxel.zip. Files included in these ZIP archives are named as follows: "cellline_treatment_FOV". Files are 3-layer 16bit tiff files with layer sequence as follows: F-Actin (Phalloidin)/b-tubulin/Hoechst 33342. The dataset contains 242 FOVs of three cell line types/three treatments + one control, files are Z-stacks made of 50 slices.

The dataset of atomic force microscopy (AFM) is included in one ZIP archive "AFM_YoungModulus_SetpointHeight.zip", which includes data on Young modulus and Setpoint Height of cell lines 22Rv1, PNT1A and PC-3 and treatments zinc, docetaxel, cisplatin (+control), i.e. identical like for confocal microscopy. The file naming is as follows: "AFM_cellline_treatment_FOV_Youngmodulus.tif" for Young modulus and "AFM_cellline_treatment_FOV_setpointheight.tif" for setpoint height. The data are filtered 32-bit tiff images, where the pixel value correspond to cell stiffness (young modulus) in Pa or setpoint height in m.

Confocal microscopy analysis files

Following files are csv tables including image analysis of actin/tubulin staining captured by confocal microscope:

Cytoskeleton_fluo_analysis_Cell_Cell_periphery_morphology.csv: table includes analyzed data for actin and tubulin staining in following cellular regions: cell, cell periphery. Standard ImageJ parameters regarding intensity and morphology included.

Cytoskeleton_fluo_analysis_Fibers.csv: table includes results of manual segmentation and consequent analysis of actin stres fibers in the cells. Apart from standard ImageJ parameters, also number of stress fibers per cell and standard deviation of fiber angle relative to the cell mean angle (for details see methods) are included.

This dataset contains key characteristics about the data described in the Data Descriptor Discharge profile of a zinc-air flow battery at various electrolyte flow rates and discharge currents. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

Solid-state zinc-ion batteries (SSZIBs) are receiving much attention as low-cost and safe energy storage technology for emerging applications in flexible and wearable devices, and grid storage. However, the development of SSZIBs faces many challenges from key battery materials development to structure design. Herein, we review the most recent progress in the development of polymer electrolytes, cell chemistry and configuration, and demonstration of SSZIBs. In conclusion, perspectives for future research in materials, interface, and assessment of SSZIBs are discussed.

Indonesia Average Rural Consumer Price: Zinc Plate: Central Java data was reported at 43,417.000 IDR/m in Dec 2018. This stayed constant from the previous number of 43,417.000 IDR/m for Nov 2018. Indonesia Average Rural Consumer Price: Zinc Plate: Central Java data is updated monthly, averaging 37,895.500 IDR/m from Jan 2008 (Median) to Dec 2018, with 132 observations. The data reached an all-time high of 44,466.000 IDR/m in Dec 2016 and a record low of 26,449.000 IDR/m in Mar 2008. Indonesia Average Rural Consumer Price: Zinc Plate: Central Java data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Prices – Table ID.PE073: Average Rural Consumer Price: By Province: Housing Product: Zinc Plate.

The Derwent Estuary is highly enriched in potentially toxic elements such as Zn, Pb, Cu, As, Hg and Cd. This occurred due to inputs from historical industrial activity adjacent to the river, predominantly prior to strict environmental protection procedures introduced in the 1970s. Contaminants are now buried at shallow depths within the sediment profile, in one or two highly concentrated layers decreasing in concentration away from an electrolytic zinc refinery, regarded as the main source of the contaminants. Enriched metals (Zn, Pb, Cu, Cd and As) in the estuary were estimated from data collected from 37 sediment cores using a portable X-ray fluorescence spectrometer, validated against mass spectrometer analyses. The thickness of the metal and metalloid enriched layers ranges from 32.5 cm to 107.5 cm, with an average thickness of 63 cm. Sedimentation rates based on this layer and the time since the start of zinc processing are approximately 0.46 cm/year. Recent trends in sedimentation based on the thickness of sediments since maximum metal and metalloid concentrations produced rates between 0.17 – 1.64 cm/year. Based on these sedimentation rates, the average time it will take for surface sediments to return to background metal and metalloid concentrations is approximately 123 years.

Indonesia Average Rural Consumer Price: Zinc Plate: Bengkulu data was reported at 38,514.000 IDR/m in Dec 2018. This stayed constant from the previous number of 38,514.000 IDR/m for Nov 2018. Indonesia Average Rural Consumer Price: Zinc Plate: Bengkulu data is updated monthly, averaging 36,774.500 IDR/m from Jan 2008 (Median) to Dec 2018, with 132 observations. The data reached an all-time high of 44,892.000 IDR/m in Dec 2017 and a record low of 25,333.000 IDR/m in Jun 2009. Indonesia Average Rural Consumer Price: Zinc Plate: Bengkulu data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Prices – Table ID.PE073: Average Rural Consumer Price: By Province: Housing Product: Zinc Plate.

Thermoelectricity offers solutions for the low-carbon economy through the development of energy efficient technology. Modest improvement in thermoelectric performance would allow, for example, waste heat in car exhausts to be converted into useful power, and hot spots on computer chips to be cooled using solid state refrigerators. However, most thermoelectric materials have complicated structures with scarce or harmful elements. Zinc oxide and its alloys are attractive candidates because of their simplicity, high thermal stability, corrosion resistance, non-toxicity and low cost. The electronic transport of zinc oxide is ideal for thermoelectric applications, but its thermal conductivity is too high. However, nanostructuring reduces the thermal conductivity by an order of magnitude. We aim to understand this phenomenon by measuring lattice vibrations using inelastic neutron scattering.

For photoionized plasmas, electron energy loss rates due to radiative recombination (RR) are required for thermal equilibrium calculations, which assume a local balance between the energy gain and loss. While many calculations of total and/or partial RR rates are available from the literature, specific calculations of associated RR electron energy loss rates are lacking. Here we focus on electron energy loss rates due to radiative recombination of H-like to Ne-like ions for all the elements up to and including zinc (Z=30), over a wide temperature range. We used the AUTOSTRUCTURE code to calculate the level-resolved photoionization cross section and modify the ADASRR code so that we can simultaneously obtain level-resolved RR rate coefficients and associated RR electron energy loss rate coefficients. We compared the total RR rates and electron energy loss rates of HI and HeI with those found in the literature. Furthermore, we utilized and parameterized the weighted electron energy loss factors (dimensionless) to characterize total electron energy loss rates due to RR. The RR electron energy loss data are archived according to the Atomic Data and Analysis Structure (ADAS) data class adf48. The RR electron energy loss data are also incorporated into the SPEX code for detailed modeling of photoionized plamsas.

Indonesia Average Urban Consumer Price: Zinc Plate: Yogyakarta Municipality data was reported at 69,250.000 IDR/Sheet in Dec 2018. This stayed constant from the previous number of 69,250.000 IDR/Sheet for Nov 2018. Indonesia Average Urban Consumer Price: Zinc Plate: Yogyakarta Municipality data is updated monthly, averaging 52,167.000 IDR/Sheet from Jan 2006 (Median) to Dec 2018, with 156 observations. The data reached an all-time high of 69,250.000 IDR/Sheet in Dec 2018 and a record low of 20,445.000 IDR/Sheet in Oct 2006. Indonesia Average Urban Consumer Price: Zinc Plate: Yogyakarta Municipality data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Prices – Table ID.PE153: Average Urban Consumer Price: By Cities: Housing Product: Zinc Plate.

Iron (Fe) and zinc (Zn) are essential micronutrients required for proper development in both humans and plants. Rice (Oryza sativa L.) grains are the staple food for nearly half of the world’s population, but a poor source of metals such as Fe and Zn. Populations that rely on milled cereals are especially prone to Fe and Zn deficiencies, the most prevalent nutritional deficiencies in humans. Biofortification is a cost-effective solution for improvement of the nutritional quality of crops. However, a better understanding of the mechanisms underlying grain accumulation of mineral nutrients is required before this approach can achieve its full potential. Characterization of gene function is more time-consuming in crops than in model species such as Arabidopsis thaliana. Aiming to more quickly characterize rice genes related to metal homeostasis, we applied the concept of high throughput elemental profiling (ionomics) to Arabidopsis lines heterologously expressing rice cDNAs driven by the 35S promoter, named FOX (Full Length Over-eXpressor) lines. We screened lines expressing candidate genes that could be used in the development of biofortified grain. Among the most promising candidates, we identified two lines ovexpressing the metal cation transporter OsZIP7. OsZIP7 expression in Arabidopsis resulted in a 25% increase in shoot Zn concentrations compared to non-transformed plants. We further characterized OsZIP7 and showed that it is localized to the plasma membrane and is able to complement Zn transport defective (but not Fe defective) yeast mutants. Interestingly, we showed that OsZIP7 does not transport Cd, which is commonly transported by ZIP proteins. Importantly, OsZIP7-expressing lines have increased Zn concentrations in their seeds. Our results indicate that OsZIP7 is a good candidate for developing Zn biofortified rice. Moreover, we showed the use of heterologous expression of genes from crops in A. thaliana as a fast method for characterization of crop genes related to the ionome and potentially useful in biofortification strategies.