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2,686 results for “iron”
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>
Ultrafast demagnetization of iron induced by optical vs terahertz pulses
<p>Data of longitudinal magneto-optical Kerr effect (LMOKE) probing of demagnetization following optical (Opt) and terahertz (THz) excitations in 4 nm iron film on MgO substrate.</p> <p>Pump and probe incidence angle is 50 deg, fluence is on the order of 0.1 mJ/cm^2, time step is 20 fs. In each case both rotation (Rot) and ellipticity (Elp) variations are recorded.</p> <p>Demagnetization ("Demg") traces are the signals odd in sample magnetization and even in the driving field in case of THz pump. These traces are normalized by the static LMOKE. Zeeman response ("Zeem") is odd in both magnetization and driving THz field. Measured traces are deconvoluted with the pump profile, which in case of THz pump is defined as derivative of the Zeeman response and in case of optical pump is measured through Kerr effect in diamond.</p> <p>"True" M quenching is calculated as linear combination of Rot and Elp demagnetization traces for both optical and THz excitations.</p>
Sri Ksetra, Bago, Myanmar. Iron nail casting waster.
<p>Sri Ksetra, Bago, Myanmar. Iron nail casting waster. Collection of the National Museum of Myanmar, Yangon.</p>
Sri Ksetra, Bago, Myanmar. Female figure cast in iron.
<p>Sri Ksetra, Bago, Myanmar. Female figure cast in iron, possibly a mother goddess, now in the National Museum, Yangon. Perhaps 4th century.</p>
iRON_Soil Moisture_Calibrated
<p>**<strong>Error has been found in Glenwood Springs station data. The 8in depth soil moisture sensor was mislabeled as the 40in depth sensor and vice versa for data sets uploaded prior to February 2024. Datasets uploaded after Feb 22, 2024 have the sensor labels corrected <em>for all dates going back to installation.</em>**</strong><br><br>***<strong>An error has been caught in time codes. The time codes currently read GMT-5:00. The CORRECT time code is GMT-7:00 for all datasets</strong>. <strong>Files with FullRecord in the name are in correct, UTC time codes</strong>***</p> <p>These data are the complete data set from installation through March 15, 2018 for AGCI's interactive Roaring Fork Observation Network (iRON). The network consists of 9 stations in the Southern Rockies (Colorado, USA). Descriptions of soil type and site locations can be found at: www.agci.org/iron/stations</p> <p><strong>Please note:</strong> Measurements taken for rain are recorded via tipping bucket rain gauge and may not be accurate for winter precipitation events (e.g. snow). Error readings are represented by -888.88, -138.25, or -162.469 represents a sensor error. Absent data is represented by N/A.</p> <p>Sensors used for soil moisture readings are Decagon EC-5 Dielectric probe (5cm/2in depth); Decagon 10-HS (20cm/8in; 50cm/20in depths). All other equipment is Onset, and the logger device is the RX3000 model. Data collection is generally set to every 20 minutes (most stations) or every hour (Independence Pass station), but maintenance resets, etc. may occasionally have contributed to other collection intervals, e.g. every 5 minutes. Conversely, on occasions of logger failure, there may be full days of data absent.</p> <p>Snow data are available for Northstar Transition Zone and Independence Pass by request.</p> <p> </p> <p><strong>Updated calibrations (V2) are anticipated in the summer of 2018 for the following stations: Glassier Ranch (3), Glenwood Springs (5), Northstar Transition Zone (7), </strong><strong>Spring Valley (8)</strong></p> <p>Some instruments have been replaced over the life of a station. In 2015, all original U30 logger boxes were replaced with the RX3000 model. For complete metadata or records of instrumentation, please contact project advisor E. Osenga at eliseo at agci.org.</p> <p><strong>**Glassier Ranch site is a wetland and the soils are often completely saturated (leading to sensor difficulties in taking accurate readings) much of the year.</strong></p>
Dataset of "Tracking high-valent surface iron species in the oxygen evolution reaction on cobalt iron (oxy)hydroxides"
<p>Dataset of the paper entitled "Tracking high-valent surface iron species in the oxygen evolution reaction on cobalt iron (oxy)hydroxides"</p>
CCE LTER P1908 Upwelling Filament Ocean Acidification Phytoplankton Iron Incubation Experiments
<p>Metatranscriptome assembly, read counts, and annotations from four sets of trace metal clean ocean acidification experimeints in the California Current Ecosystem. Experiments were conducted during August 2019 as part of the CCE LTER program. Samples were collected at the initial time point (T0), and three pCO<sub>2</sub> treatments (400, 800, and 1200 ppm) with two time points for each experiment. </p> <p>Poly-A selected mRNA was sequenced on an Illumina NovaSeq 6000 and then assembled with Trinity for each separate experiment. Proteins from the assembly were then predicted with Genemark S-T. Taxonomic annotation was performed with DIAMOND BLASTP searches against PhyloDB v1.076 and based on the Lineage Proability Index from the top hits. Functional annotation was similarly performed using KEGG with KEGG Orthology annotation based on KofamKOALA results. Read quantification was conducted with Bowtie2. </p> <p>Predicted proteins from each assembly is provided in fasta format. The read counts and annotations for each experiment are in tab-delimited files. </p>
Femtosecond electron diffraction data of iron and cobalt
<p>Femtosecond electron diffraction data of iron and cobalt measured at the Fritz Haber Institute in Berlin. The excitation wavelength was 2300 nm in all measurements. For iron, data were recorded with four different pump fluences. For cobalt, data were recorded with six different pump fluences. The samples were polycrystalline films with a thickness of 20 nm, sandwiched between two layers of silicon nitride with a thickness of 5 nm each. More information is available here: https://arxiv.org/abs/2110.00525</p>
Example input files and output data for 1D hydrodynamic simulations of shock compressed iron
<p>Example input files and output data for 1D hydrodynamic simulations of shock compressed iron. Input files consists of 3 examples from the SIMEX github wiki page for a 50 micron CH ablator with 5 micro Fe foil (laser pulse is a 6 ns flat top pulse, 1064 nm with 0.3 TW/cm<sup>2</sup>). Output data are from Esther hydrocode in .txt format and the SIMEX opmd.h5 format.</p>
Exploration of historical mining site - Mezzano iron mines (San Bartolomeo, Cavargna Valley, Italy, 06/01/2024)
<p>Exploration of the historical mining site of Mezzano (San Bartolomeo, Cavargna Valley, Italy, 06/01/2024)</p> <p>- Main ore minerals: pyrite, chalcopyrite, siderite, aragonite</p> <p>- Provisional References:</p> <ul> <li>https://www.valcavargna.org/luoghi_di_interesse/miniere-di-mezzano/#:~:text=Le%20miniere%20di%20Mezzano&text=A%20partire%20dagli%20ultimi%20anni,Fratelli%20Campioni%20l'anno%20seguente.</li> <li>https://www.isprambiente.gov.it/it/attivita/museo/regioni/musei/miniera-di-mezzano</li> <li>https://www.valcavargna.org/tradizioni_popolari/vecchi-mestieri/siderurgia/</li> <li>https://www.research.unipd.it/handle/11577/3465257</li> <li>http://www.cmalpilepontine.it/cmvlarcer/zf/index.php/servizi-aggiuntivi/index/index/idtesto/13</li> </ul>
Radiocarbon Dates Iron Production Middle-Northern Sweden
<p>Radiocarbon dates analysed in the MA-thesis of Jonatan Rigvald, Uppsala university. This only represents a selection of the full database of radiocarbon dated iron production sites, to be publised (Hennius et al. in press).</p>
DATASETS - Pervasive iron limitation at subsurface chlorophyll maxima of the California Current
<p>R and Python code for processing these datasets are available from: 10.5281/zenodo.1495504</p> <p><strong>Abstract: </strong>Subsurface chlorophyll maximum layers (SCMLs) are nearly ubiquitous in stratified water columns and exist at horizontal scales ranging from the submesoscale to the extent of oligotrophic gyres. These layers of heightened chlorophyll and/or phytoplankton concentrations are generally thought to be a consequence of a balance between light energy from above and a limiting nutrient flux from below, typically nitrate (NO3). Here we present multiple lines of evidence demonstrating that iron (Fe) limits or with light colimits phytoplankton communities in SCMLs along a primary productivity gradient from coastal to oligotrophic offshore waters in the southern California Current ecosystem. SCML phytoplankton responded markedly to added Fe or Fe/light in experimental incubations and transcripts of diatom and picoeukaryote Fe stress genes were strikingly abundant in SCML metatranscriptomes. Using a biogeochemical proxy with data from a 40-y time series, we find that diatoms growing in California Current SCMLs are persistently Fe deficient during the spring and summer growing season.We also find that the spatial extent of Fe deficiency within California Current SCMLs has significantly increased over the last 25 y in line with a regional climate index. Finally, we show that diatom Fe deficiency may be common in the subsurface of major upwelling zones worldwide. Our results have important implications for our understanding of the biogeochemical consequences of marine SCML formation and maintenance.</p>
Diphosphine ligands in iron
<p>Galaxy RO Crate object containing the workflow and data with the reproduction of the results published in: Antonis M. Messinis, Stephen L. J. Luckham, Peter P. Wells, Diego Gianolio, Emma K. Gibson, Harry M. O’Brien, Hazel A. Sparkes, Sean A. Davis, June Callison, David Elorriaga, Oscar Hernandez-Fajardo & Robin B. Bedford (2019) The highly surprising behaviour of diphosphine ligands in iron-catalysed Negishi cross-coupling Nature Catalysis, 2019, 2, 123-133 DOI: 10.1038/s41929-018-0197-z.</p> <div> <p>This RO is published as part of the research data submitted for the paper <strong>Facilitating Reproducibility in Catalysis Research with Managed Workflows and RO-Crates: A Galaxy Case Study</strong>, ChemCatChem, DOI: 10.1002/cctc.202401676.</p> </div>
FeM dataset – An iron ore labeled images dataset for segmentation training and testing
<p>This dataset is composed of 81 pairs of correlated images. Each pair contains one image of an iron ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255).</p> <p>The sample came from an itabiritic iron ore concentrate from Quadrilátero Ferrífero (Brazil) mainly composed of hematite and quartz, with little magnetite and goethite. It was classified by size and concentrated with a dense liquid. Then, the fraction -149+105 μm with density greater than 3.2 was cold mounted with epoxy resin and subsequently ground and polished.</p> <p>Correlative microscopy was employed for image acquisition. Thus, 81 fields were imaged on a reflected light microscope with a 10× (NA 0.20) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 999×756 pixels with a resolution of 1.05 µm/pixel. Finally, the images from SEM were thresholded to generate the reference images.</p> <p>Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012).</p> <p>This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model that reached mean values of 91.43% and 93.13% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights.</p> <p>For further questions and suggestions, please do not hesitate to contact us.</p> <p> </p> <p><strong>Contact email</strong>: ogomes@gmail.com</p> <p> </p> <p>If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5014700</p> <p> </p> <p>Please also cite this paper, which provides additional details about the dataset:</p> <p>Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. <em>Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images</em>. <strong>Minerals Engineering</strong>, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.</p> <p> </p>
Dataset: Experimental and Modelling Analysis of the Hyperthermia Properties of Iron Oxide Nanocubes
<p>This set of data complements the published article "Experimental and Modelling Analysis of the Hyperthermia Properties of Iron Oxide Nanocubes" published on Nanomaterials <a href="https://doi.org/10.3390/nano11092179">https://doi.org/10.3390/nano11092179</a></p> <p>Ferrero, R.; Barrera, G.; Celegato, F.; Vicentini, M.; Sözeri, H.; Yıldız, N.; Atila Dinçer, C.; Coïsson, M.; Manzin, A.; Tiberto, P. Experimental and Modelling Analysis of the Hyperthermia Properties of Iron Oxide Nanocubes. Nanomaterials 2021, 11, 2179. https://doi.org/10.3390/nano11092179</p>
Raw data from Qin et al. (2018) "Modeling the kinetics of hydrogen formation by zerovalent iron: Effects of sulfidation on micro- and nano-scale particles"
<p>Raw hydrogen concentration vs. time data from Qin, H., X. Guan, J. Z. Bandstra, R. L. Johnson, and P. G. Tratnyek (2018) “Modeling the kinetics of hydrogen formation by zerovalent iron: Effects of sulfidation on micro- and nano-scale particles” Environ. Sci. Technol. 52(23): 13887-13896. [10.1021/acs.est.8b04436]</p> <p>This manuscript reports a large set of new concentration vs. time data for dihydrogen (H2) produced by corrosion of granular zerovalent iron (i.e., the hydrogen evolution reaction, HER) in aqueous media relevant to groundwater remediation. Four alternative kinetic models are evaluated by fitting the data using global non-linear regression. Details are given in the main text and supporting information of the (open access) manuscript. </p> <p>The data provided here are in two formats: (i) a .csv file that contains only data and labels, and (ii) a .pxp file that includes the data and graphs (without fits) in the same layout as figures in the original manuscript. The .pxp file was prepared with Igor Pro 8.02 (https://www.wavemetrics.com).</p>
GAP general purpose interatomic potential for iron
<p>A general purpose Gaussian Approximation Potential (GAP) [1,2] for iron. The training database has been computed at the PBE level of theory [3] using the VASP code [4-7]. Fitting of the potentials was done using QUIP/GAP [1,2,8].</p> <p>The potential uses 2-body (distance_2b), 3-body (angle_3b) and SOAP (soap_turbo) [9,10] descriptors, as implemented in the TurboGAP code [11].</p> <p>All files necessary to use the potential QUIP/GAP (compiled with the TurboGAP libraries) are in <em>QUIP_files.tar.gz</em>, all files to use with TurboGAP in <em>TurboGAP_files.tar.gz</em>. The database used to train the potential is in <em>training_database.tar.gz</em>.</p> <p>More details can be found in this publication:</p> <blockquote> <p>Searching for iron nanoparticles with a general-purpose Gaussian approximation potential</p> <p>Richard Jana, Miguel A. Caro</p> <p>https://doi.org/10.1103/PhysRevB.107.245421</p> </blockquote> <p>The authors are grateful to the Academy of Finland for financial support under projects #321713 (R. J. & M.A. C.) and #330488 (M.A. C.), and CSC -- IT Center for Science as well as Aalto University's Science-IT Project for computational resources.</p>
Iron nanoparticle database
<p>This is a database of Fe nanoparticles generated with a GAP interatomic potential for iron [1]. The files are provided in ASE's extended XYZ format. For each nanoparticle, <em>convex_hull.xyz</em> includes the energies as computed with:</p> <ul> <li>PAW-PBE DFT [1,2]</li> <li>our GAP [3]</li> <li>the GAP by Dragoni et al. [4]</li> <li>the EAM by Mendelevet al. [5]</li> <li>the EAM by Finnis and Sinclair et al. [6]</li> </ul> <p>The movie <em>surface.mp4</em> shows the fractions of surface sites resembling surface motifs from pristine crystal surfaces more than the others, for each particle from <em>convex_hull.xyz</em>. Similarity is measured by comparing SOAP descriptors [7] of the surface environments with those from the reference structures.</p> <p>More details can be found in this arXiv preprint:</p> <blockquote> <p>Searching for iron nanoparticles with a general-purpose Gaussian approximation potential</p> <p>Richard Jana, Miguel A. Caro</p> <p>https://doi.org/10.1103/PhysRevB.107.245421</p> </blockquote> <p>The authors are grateful to the Academy of Finland for financial support under projects #321713 (R. J. & M.A. C.) and #330488 (M.A. C.), and CSC -- IT Center for Science as well as Aalto University's Science-IT Project for computational resources.</p>
Dataset for the publication "Reversal of nanomagnets by propagating magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory"
<p>Raw data associated to the manuscript ‘’Reversal of nanomagnets by propagating<br> magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory‘’, Nature Communications (2023); doi: <a href="https://deref-web.de/mail/client/P1XojCfdiYA/dereferrer/?redirectUrl=https%3A%2F%2Fdoi.org%2F10.1038%2Fs41467-023-37078-8">https://doi.org/10.1038/s41467-023-37078-8</a><br> Information about file formats and measurement parameters are described in text files in the specific folders. For micromagnetic simulations Mumax 3.10 was used. The simulation scripts (*.mx3 files) and exemplary plotting scripts in Python 3.9 (*.py files) are included.</p> <p>Paper abstract:<br> Despite the unprecedented downscaling of CMOS integrated circuits, memory-intensive machine learning and artificial intelligence applications are limited by data conversion between memory and processor. There is a challenging quest for novel approaches to overcome this so-called von Neumann bottleneck. Magnons are the quanta of spin waves. Their angular momentum enables power-efficient computation without charge flow. The conversion problem would be solved if spin wave amplitudes could be stored directly in a<br> magnetic memory. Here, we report the reversal of ferromagnetic nanostripes by spin waves which propagate in an underlying spin-wave bus. Thereby, the charge-free angular momentum flow is stored after transmission over a macroscopic distance. We show that the spin waves can reverse large arrays of ferromagnetic stripes at a strikingly small power level. Combined with the already existing wave logic, our discovery is path-breaking for the new era of magnonics-based in-memory computation and beyond von Neumann computer architectures</p>
Potential of Mean Force (PMFs) for Zerovalent Iron (Fe (100-110-111)) NanoParticles
<p>The data is potential of mean force for three fcc configurations of zero valent iron, (100), (110) and (111) nano particles interacting with side chain analugos of 22 different amino acids that are the building blocks of the proteins. We used adaptive Well-Tempered Metadynamics method to calculate the adsorption free energies that has previously been described for measuring the adsorption of the biomolecules for TiO2 and Ag. GROMACS and plumed softwares were used to carry out the simulations and the CHARMM-GUI/Nanomaterial Modeler was used create the iron slabs. </p> <p>The system was solvated using the original form of TIP3 water model . It was then neutralized by NaCl regarding the charge of the whole system. In the MD calculation the energy of the system has been minimized by Verlet particle-based cut-off scheme using charge groups and steepest gradient method for 1000 steps. The system was equilibrated under constant pressure, particles and temperature (300 K) condition (NPT) using Berendsen weak coupling or 1.0 ns. The component of the pressure tensor were set to 1.0 bar. The thermostat and the barostat for the relaxation time was 1.0 ps. The system underwent another more unbiased equilibrations for 10 ns in the NVT ensemble conditions. The Nose-Hoover thermostat’s relaxation time constant for the NVT ensemble was 5 ps. The cut-off distance was set to 1.0 nm for the VdW short range interactions. In the metadynamics biased simulation, the Surface Separation Distance (SSD) describes the reaction coordinates. This SSD measures the minimum distance of the center-of-mass (COMs) of the SCA Rmol and the surface atoms ri along the z coordinate.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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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.