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2,118 results for “Metal”
Data from: CoZr nanocomposites in a ceramic-metal AlOx(OH)y/Al matrix with different Co/Zr ratio and its potential for syngas processing
<p>Data from article in Dalton Transaction</p>
Combined data file for Jokinen et al. "Terrestrial organic matter input drives sedimentary trace metal sequestration in a human-impacted boreal estuary", Science of the Total Environment 717, 2020
<p>The datafile contains all the new raw data presented in the figures in the publication.</p>
Precision-Engineered Metal-Organic Frameworks (PE-MOFs)
<p>This Zenodo record hosts the computationally predicted structures of 94,823 Precision-Engineered Metal-Organic Frameworks (PE-MOFs), designed using a fine-tuned Reverse Topological Approach (RTA). The structures are provided as part of a large-scale effort to systematically explore the vast combinatorial design space of metal and organic building units (BUs), pairing them based on geometric signatures and topological compatibility.</p> <p>These structures are optimized and curated for applications such as post-combustion CO2 capture.</p> <p>In this repository, you will find:</p> <p>Fully optimized structures of PE-MOFs: cif files provided in standard formats compatible with molecular simulation tools for further analysis and exploration.<br>Note: This Zenodo record only provides the computational structures. For the accompanying code, tools, and data used to generate these structures, please visit the GitHub repository here. https://github.com/xiaoyu961031/Fine-tuned-RTA</p> <p>If you use this dataset in your work, please cite our related publication:<br>Wu, X., Jiang, J. (2024). Precision-engineered metal-organic frameworks: Fine-tuning reverse topological structure prediction and design. Chemical Science, 2024, DOI: 10.1039/D4SC05616G </p>
Metadata - Exploring the Luminescence, Redox, and Magnetic Properties in a Multivariate Metal–Organic Radical Framework
Open the record for dataset details and reuse information.
The largest metallicity difference in twin systems: High-precision abundance analysis of the benchmark pair Krios and Kronos
Open the record for dataset details and reuse information.
High Entropy 2D Metals Sulfides: Fast Synthesis, Exfoliation and Electrochemical Activity in Overall Water Splitting at Alkaline pH
<p>Dataset for the article "High Entropy 2D Metals Sulfides: Fast Synthesis, Exfoliation and Electrochemical Activity in Overall Water Splitting at Alkaline pH"</p>
Reaction-Induced Metal-Metal Oxide Interactions in Pd In2O3/ZrO2 Catalysts Drive Selective and Stable CO2 Hydrogenation to Methanol
<p>Ternary Pd-In<sub>2</sub>O<sub>3</sub>/ZrO<sub>2</sub> systems hold promise as industrial catalysts for CO<sub>2</sub>-based methanol synthesis, but maximization of their productivity requires appropriate structuring of the active phase, promoter, and carrier. Here, we report that Pd-In<sub>2</sub>O<sub>3</sub>/ZrO<sub>2</sub> systems prepared by impregnation evolve into a unique catalyst architecture under CO<sub>2</sub> hydrogenation conditions, leading to selective and stable behavior. Detailed space and time-resolved <em>operando</em> characterization and simulations reveal that the restructuring process, completed within the first 30 min under reaction conditions, is governed by the energetics of metal-metal oxide interactions. The resulting architecture comprises InPd<em><sub>x</sub></em> alloy particles decorated by InO<em><sub>x</sub></em> layers, whose proximity is crucial to avoiding performance losses typically observed when palladium agglomerates. The findings highlight the potential beneficial role of reaction-induced restructuring in advancing catalyst design.</p>
Data for: Anderson Localization of Phonons in Thermally Superinsulating Graphene Aerogels with Metal-Like Electrical Conductivity
<p>The dataset contains the raw data for the graphene aerogel study, which evaluated the thermal conductivity, electrical conductivity, and ampacity of different graphene aerogels as a function of density, compressive strain, flake size, and annealing conditions. The package includes the data and figures for the paper entitled "Anderson Localization of Phonons in Thermally Superinsulating Graphene Aerogels with Metal-Like Electrical Conductivity", <a href="https://doi.org/10.1002/smtd.202301536">https://doi.org/10.1002/smtd.202301536</a>.</p>
Figure 1 in Metal nanoparticles produced by plants with antibacterial properties against Staphylococcus aureus
Figure 1. Synthesis of metal nanoparticles using plant extracts.
Figure 4 in Bioaccumulation of heavy metals in the tissues of Schizothorax plagiostomus at River Swat
Figure 4. Concentration of heavy metals in muscles of Schizothorax plagiostomus
Figure 6 in Bioaccumulation of heavy metals in the tissues of Schizothorax plagiostomus at River Swat
Figure 6. Concentration of Nickel (ppm) in the muscles and gills of S. plagiostomus.
Figure 5 in Bioaccumulation of heavy metals in the tissues of Schizothorax plagiostomus at River Swat
Figure 5. Concentration of heavy metals in gills of Schizothorax plagiostomus
Data for a publication "Polymer-metal bilayer with alkoxy groups for antibacterial improvement"
<p><strong>Abstract:</strong></p> <p>Many bio‐applicable materials, medical devices, and prosthetics combine both polymer and metal components to benefit from their complementary properties. This goal is normally achieved by their mechanical bonding or casting only. Here, we report an alternative easy method for the chemical grafting of a polymer on the surfaces of a metal or metal alloys using alkoxy amine salt as a coupling agent. The surface morphology of the created composites was studied by various<br>microscopy methods, and their surface area and porosity were determined by adsorption/desorption nitrogen isotherms. The surface chemical composition was also examined by various spectroscopy techniques and electrokinetic analysis. The distribution of elements on the surface was determined, and the successful bonding of the metal/alloys on one side with the polymer on the other by alkoxy amine was confirmed. The composites show significantly increased hydrophilicity, reliable chemical stability of the bonding, even interaction with solvent for thirty cycles, and up to 95% less bacterial adhesion for the modified samples in comparison with pristine samples, i.e., characteristics that are promising for their application in the biomedical field, such as for implants, prosthetics, etc.<br>All this uses universal, two-step procedures with minimal use of energy and the possibility of production on a mass scale.</p> <p> </p>
Stellar models for "Realistic Uncertainties for Fundamental Properties of Asteroseismic Red Giants and the Interplay Between Mixing Length, Metallicity and Numax" by Li, Yaguang et al. (2024)
<p>This dataset includes a grid of stellar models from Li, Yaguang et al. (2024), titled "Realistic Uncertainties for Fundamental Properties of Asteroseismic Red Giants and the Interplay Between Mixing Length, Metallicity and Numax".</p> <p>The stellar models are first-ascent red giant branch (RGB) models, sampled using a Sobol sequence within the following six-dimensional initial parameter space: mass (0.7, 2.3) solar mass, Y_init (0.22, 0.37), [M/H] (-0.1.0, 0.60) dex, α_MLT (1.3, 2.7), fov_core (0, 0.02), fov_shell (0, 0.008). The evolutionary tracks are computed up to the point on the RGB where the p-mode large separation (Δν) equals 2 μHz. Approximately 32,768 unique evolutionary tracks are presented, divided into 33 individual chunks.</p> <p>To load the models into a Python Pandas dataframe, use the following command: "import pandas as pd; pd.read_parquet(filepath)". Each model (dataframe row) is provided with global parameters (Teff, radius, luminosity, mass, age, ...) and pure p modes with l=0-2 (frequency, mode inertia, angular degree, ...).</p> <p>For detailed descriptions of the stellar models, please refer to the original paper and the github repository.</p>
Experimental volumetric hydrogen uptake determination at 77 K of commercially available metal-organic framework materials
<p>File: carbon-08-00005-with-cover.pdf</p> <p>This file contains the published paper (PDF) with a cover page.</p> <p> </p> <p>File: carbon-1535765-supplementary.pdf</p> <p>This file (PDF) contains supporting data on the characterization of MOFs and hydrogen adsorption measurements.</p>
Dataset for Paul et al., "Trace metals in coastal marine sediments: Natural and anthropogenic sources, correlation matrices, and proxy potentials", Science of The Total Environment (STOTEN-175789, 2024)
<p>The datafile (.xlsx format) contains the solid phase data for all study sites presented in the associated STOTEN publication <a href="https://doi.org/10.1016/j.scitotenv.2024.175789" target="_blank" rel="noopener">(10.1016/j.scitotenv.2024.175789)</a></p> <ol> <li>specifics on the ICP-MS and ICP-OES analysis</li> <li>solid-phase data (Pb, Cd, Cu, Zn, Sb, Sn, Ni, As, Tl, V, Mo, U, Re, TOC, S, Fe, Mn, and Al)</li> <li>coefficient of variation test results (as a basis for evaluating the applicability of data normalizations to the data; i.e. calculation of Enrichment Factors, cf. method section of the original publication)</li> </ol> <p>Please note that the title of the paper and the publication number has changed upon final publication. The Excel file description still refers to the original manuscript number and title "Trace metals in coastal marine sediments: anthropogenic sources, correlation patterns, and proxy potentials".</p>
Raw data files for the "The Mechanism of Rapid and Green Metal–Organic Framework Synthesis by In Situ Spectroscopy and Diffraction" manuscript
<p>All files are organized by figures in the main text of the original publication: https://doi.org/10.1021/acs.chemmater.4c00879, in the form of .csv files.</p>
Integrating diffusion dialysis for sustainable acid recovery from ion exchange regeneration stages: Characterization of metal and non-metal ions migration
<p>Seawater mining presents a potential option for recovering the European Union’s Critical Raw Materials (CRMs), but direct extraction from seawater is challenging due to their low concentrations, as most of them are Trace Elements (TEs) (at levels of mg/L or μg/L). Saltworks <em>bitterns </em>(ultraconcentrated brines resulting from the sea salt production process) offer an alternative solution, naturally concentrated up to 40 times more than seawater. These bitterns can be further processed with chelating Ion Exchange (IX) sorbents to selectively extract TEs. However, this process requires an acidic elution stage with strong acids, followed by neutralization, to recover TEs through precipitation, demanding extensive chemicals consumption. Diffusion Dialysis (DD) could be used to recover the excess acid without external reagents, using an acid-resistant Anion Exchange Membrane (AEM). This study evaluates DD through batch and once-through tests for acid recovery from simulated IX eluate generated in the elution stage of TEs (B, Ga, Ge, Co, Sr) recovery from saltworks bitterns.<br>Batch tests achieved high recoveries for HCl (45–50 %) and H2SO4 (30–37 %), being the theoretical maximum attainable recovery equal to 50 %. B and Ge only partially permeated through the membrane (82 % rejection) by a diffusion mechanism in their neutral form (H3BO3(aq), H4GeO4(aq)). Ga, Co and Sr, in cationic form, were highly rejected (>96 %). Permeability followed the order Ga < Sr < Co ≪ Ge < B, due to the relevant charge and size. HCl permeability correlated linearly with concentration, while H2SO4 was inversely proportional. Once-through tests showed higher acid (74 % HCl, 62 % H2SO4) and oxoacid (66 % H3BO3(aq), 52 % H4GeO4(aq)) recovery at a low specific flow rate, or apparent flux, (0.38 L/(m^2⋅h)) due to increased residence time. Water to acid flow rate ratios did not affect species transport when an excess of water was guaranteed. Conversely, an influence was observed when the ratio was below 1, with a minimum at 0.18, where a very low passage of species was observed due to the reduced dilution volume of the dialysate solution (water). A 1D transport model, incorporating the solutes permeabilities determined experimentally, effectively described the system performance, especially for HCl and B, albeit slightly overestimating the other TEs’ transport.</p>
Dataset from the paper "The boring history of GAIA BH3 from isolated binary evolution": IIB - simulation outcomes, different metallicities
<p>This repository contains (a part) of the population synthesis outputs analysed in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. In particular, it contains the simulation sets with different metallicities.</p> <h2>Content</h2> <p>Each tar file contains the outputs of a given simulation set from <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. The name of the files corresponds to the name of the set as reported in Table 1 of <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. The details of the sets can be found in Section 2.2 and Section 2.3 of <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>. The outputs stored in this repository contain only the systems that satisfy the following conditions:</p> <ul> <li>The two objects are in a binary system</li> <li>An object is a black hole and the other one is a star in any evolutionary phase prior to the ignition of the helium burning in the core</li> <li>The two objects are not currently interacting except for wind mass transfer (no ongoing Roche-Lobe overflow, common envelope or merger).</li> </ul> <p>The tar files contain three different files in gunzipped parquet format (<a href="https://parquet.apache.org">https://parquet.apache.org</a>):</p> <h3><strong>Python scripts</strong></h3> <ul> <li><em>analyse_sample.py</em>: python script we used to produce the file BHMS.pq.gz and BHMS_ic.pq.gz analysing the SEVN outputs</li> <li><em>create_aux.py</em>: python script we used to produce the file BHMS_VSNCE.pq.gz by combining the SEVN log outputs with the BHMS_ic.pq.gz file</li> </ul> <h3> </h3> <h3>BHMS.pq.gz</h3> <p><strong> </strong>File containing all the systems satisfying the above condition.</p> <p><strong>Columns:</strong></p> <ul> <li>ID: unique id of the binary (Long integer)</li> <li>name: unique name of the binary (Long integer)</li> <li>BWorldtime: simulation time in Myr (all the simulations start at BWorldtime=0) </li> <li>BTimestep: timestep of the simulation corresponding to the current binary properties in Myr. In this time range, the binary properties can be considered constant.</li> <li>ms: mass of the star in Msun</li> <li>ks: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2000MNRAS.315..543H/abstract">Hurley+2000</a> classification (integer)</li> <li>ks_sevn: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..426I/abstract">Iorio+2023</a> classification (integer)</li> <li>rs: stellar radius in Rsun</li> <li>Ts: stellar effective temperature in Kelvin</li> <li>Ls: stellar luminosity in Lsun</li> <li>mbh: mass of the black hole in Msun</li> <li>P: binary period in days</li> <li>BEvent: Event corresponding to the SEVN output (see SEVN user guide)</li> <li>Eccentricity: Eccentricity of the binary</li> <li>Semimajor: Semimajor axis of the binary in Rsun</li> <li>bh3dist: "Relative" Euclidean distance of the system from the Gaia BH3 properties:</li> </ul> <p> bh3dist = sqrt( ((ms-ms_GBH3)/(ms_GBH3))**2 + ((mbh-mbh_GBH3)/(mbh_GBH3))**2 + ((P-P_GBH3)/(P_GBH3))**2 + ((ecc-ecc_GBH3)/(ecc_GBH3))**2 )</p> <p> <strong>Notice-1: </strong>in this file each system (identified by ID and name) can appear more than once. Indeed this file contains all the outputs in which a system satisfy the condition BH-star condition discussed above.</p> <p> <strong>Notice-2: </strong><em>the dataframe is sorted based on the bh3dist column,</em> so the first rows contains the objects that are closer to the properties of Gaia BH3 based on the bh3dist statistic.</p> <p> <strong>Notice-3: </strong>The file has been created by analysing the main SEVN outputs using the script <em>analyse_sample.py </em>included in this repository</p> <h3>BHMS_ic.pq.gz</h3> <p>File containing the initial conditions of all the systems included in the file BHMS.pq.gz</p> <p><strong>Columns:</strong></p> <p><em>Info from BHMS.pq.gz</em>:</p> <p>these columns report the same information as in the BHMS.pq.gz file, but each system (identified by ID and name) appears just once (the occurrence from the BHMS.pq.gz with the minimum bh3dist)</p> <ul> <li>ID: unique id of the binary (long integer)</li> <li>name: unique name of the binary (long integer)</li> <li>BWorldtime: simulation time in Myr (all the simulations start at BWorldtime=0) </li> <li>BTimestep: timestep of the simulation corresponding to the current binary properties in Myr. In this time range, the binary properties can be considered constant.</li> <li>ms: mass of the star in Msun</li> <li>ks: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2000MNRAS.315..543H/abstract">Hurley+2000</a> classification (integer)</li> <li>ks_sevn: stellar evolutionary phase following the <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..426I/abstract">Iorio+2023</a> classification (integer)</li> <li>rs: stellar radius in Rsun</li> <li>Ts: stellar effective temperature in Kelvin</li> <li>Ls: stellar luminoisity in Lsun</li> <li>mbh: mass of the black hole in Msun</li> <li>P: binary period in days</li> <li>BEvent: Event corresponding to the SEVN output (see SEVN user guide)</li> <li>Eccentricity: Eccentricity of the binary</li> <li>Semimajor: Semimajor axis of the binary in Rsun</li> <li>bh3dist: "Relative" Eucledian distance of the system from the Gaia BH3 properties:</li> </ul> <p> bh3dist = sqrt( ((ms-ms_GBH3)/(ms_GBH3))**2 + ((mbh-mbh_GBH3)/(mbh_GBH3))**2 + ((P-P_GBH3)/(P_GBH3))**2 + ((ecc-ecc_GBH3)/(ecc_GBH3))**2 )</p> <p><em>Initial properties</em>:</p> <ul> <li>mzams_0: Initial (ZAMS) mass of the black hole in Msun</li> <li>mzams_1: Initial (ZAMS) mass of the star in Msun</li> <li>Semimajor_ini: Initial semimajor axis of the binary in Rsun </li> <li>Eccentricity_ini: Initial eccentricity axis of the binary </li> <li>seed: Random seed used in the SEVN simulation</li> <li>P_ini: Intial period of the binary in days </li> </ul> <p><strong> Notice: </strong>The file has been created by analysing the main SEVN outputs using the script <em>analyse_sample.py </em>included in this repository</p> <h3>BHMS_VSNCE.pq.gz</h3> <p>A file containing additional information about the evolutionary events of the systems stored in BHMS.pq.gz</p> <ul> <li>ID: unique id of the binary (long integer)</li> <li>name: unique name of the binary (long integer)</li> <li>time_SN: formation time of the black hole (BH) in Myr (all the simulations start from time=0)</li> <li>Vcom: Velocity magnitude of the center of mass after the formation of the BH (before the formation Vcom=0 is assumed)</li> <li>time_CE_preSN: time of the last CE before the BH formation (if empty no CE before BH formation)</li> <li>time_CE_postSN: time of the first CE after the BH formation (if empty no CE after BH formation)</li> <li>time_RL_preSN: time of the last Roche-Lobe overflow before the BH formation (if empty no RLO before BH formation)</li> <li>time_RL_postSN: time of the first Roche-Lobe overflow after the BH formation (if empty no RLO before BH formation)</li> </ul> <p><strong>Note: </strong>The file has been created by combining the file BHMS_ic.pq.gz with the main SEVN outputs using the script <em>create_aux.py </em>included in this repository</p> <h3>How to read the files</h3> <p>The parquet files can be easily read in Python by using the <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_parquet.html">Pandas module</a>:</p> <pre><code>import pandas as pd df_bhms=pd.read_parquet("BHMS.pq.gz") df_bhms_ic=pd.read_parquet("BHMS_ic.pq.gz") df_bhms_vsnce=pd.read_parquet("BHMS_VSNCE.pq.gz")</code></pre> <p> </p> <h2>Notes</h2> <p>The code used in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a> is SEVN (<a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..426I/abstract">Iorio et al., 2023</a>) version 2.10.1 (commit <a href="https://gitlab.com/sevncodes/sevn/-/tree/a4753f1177aab076da2641afc497a06006b30267">a4753f11</a>). </p>
Extremely Metal-Poor Stars used in "A Modified Initial Mass Function of the First Stars with Explodability Theory under Different Enrichment Scenarios"
<p>This is the Extremely Metal-Poor stars used in the paper "A Modified Initial Mass Function of the First Stars with Explodability Theory under Different Enrichment Scenarios" by Jiang, Zhao, Li and Xing 2024. Table "EMPs_list.csv" contains the number of available abundance elements, metallicity and reference. References are also listed as related works in this record, by DOI. </p> <p>If you have any questions about this data, please contact jiangrz@bao.ac.cn for further information. </p>
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