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2,118 results for “Metal”
Multi-element signatures of the selected cobalt-rich metallogenic families. The bold text highlights the most relevant metal occurrences for the selected metallogenic families.
<p>This dataset is table 2 from the article "Predictive assessment of metallogenic signatures using the DataBase Querying (DBQ) method: A European application by Blandine Gourcerol *, Guillaume Bertrand, Laurent Bailly, Pauline Moreau, Isabelle Duhamel-Achin, Maxime Picault, Philippe Négrel</p> <p>DOI : https://doi.org/10.1016/j.gexplo.2022.106966</p>
ER values of the targeted by-products as a function of their metallogenic family. The bold text highlights the most relevant metal association regarding the selected metallogenic families.
<p>This dataset is table 3 from the article "Predictive assessment of metallogenic signatures using the DataBase Querying (DBQ) method: A European application by Blandine Gourcerol *, Guillaume Bertrand, Laurent Bailly, Pauline Moreau, Isabelle Duhamel-Achin, Maxime Picault, Philippe Négrel</p> <p>DOI : https://doi.org/10.1016/j.gexplo.2022.106966</p>
NFFA-Europe|Pilot proposal "Resolving Local Coordination of Doped-Carbon Supported Soft-Landed Metal Nanoparticles For CO2 Electroreduction" (PID: 159). XPS data.
<p>XPS data of copper oxide nanoparticles synthesized withtin the NFFA-Europe|Pilot proposal "Resolving Local Coordination of Doped-Carbon Supported Soft-Landed Metal Nanoparticles For CO2 Electroreduction” (PID: 159).</p>
dataset How does the balance of metal and acid functions on the benchmark Mo/ZSM-5 catalyst drive the Methane dehydroaromatization reaction
<p>dataset How does the balance of metal and acid functions on the benchmark Mo/ZSM-5 catalyst drive the Methane dehydroaromatization reaction</p>
A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2
<p>Data produced and analyzed in the manuscript "A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2" by Pasquadibisceglie et al.</p> <p><br> If you include these data in your manuscript, please cite: Pasquadibisceglie A, Leccese A and Polticelli F (2022) A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2. <em>Front. Chem.</em> 10:1004815. doi: 10.3389/fchem.2022.1004815</p>
supplementary data Chemical and Microbial Leaching of Valuable Metals from PCBs and Tantalum Capacitors of Spent Mobile Phones
<p>Table S1: Chemical composition of waste PCBs and tantalum capacitors (VICs)without HF precious metals; Table S2: Leaching with organic acids; Table S3: Leaching with inorganic acids; Table S4: Bacterial leaching of PCBS varying pulp density; Table S5: Bacterial leaching of PCBS varying ferrous iron concentration; Table S6: Bacterial leaching of PCBS and tantalum capacitor scrap varying particle size; Table S7: Fungal leaching of metals by <em>A</em>. <em>niger</em> spores and filtrate.</p>
Oxidation of L-leucine amino acid initiated by HO● radical - Are transition metal ions an enhancement factor?
<p class="MsoNormal"><span><span>Hydroxyl radical (HO<sup>●</sup>) formation initiated by the Fenton-type reactions of Fe and Cu complexes of L-leucine (Leu) amino acid, as well as its oxidation reaction by HO<sup>●</sup> were computationally investigated by using the density functional theory (DFT) method at the M05-2X/6-311++G(3df,2pd)//M05-2X/6-311++G(d,p) level of theory in the aqueous phase. The results showed that dipole-salt is the main form of Leu in the physiological condition. Leu exhibits high chelating potential towards both Fe(III)/Fe(II) and Cu(II)/Cu(I) ions with the most favorable coordinating positions at two oxygen atoms of the –COO functional groups. Furthermore, the Leu-ions complexes show a high risk of HO<sup>●</sup> formation <em><span>via</span></em> Fenton-like reactions, especially when ascorbate anion exists in the environment as a reducing agent. Finally, the oxidation reaction of L-leucine by HO<sup>●</sup> demonstrated a relatively high overall apparent reaction rate – </span><em><span>k<sub>overall</sub></span></em><span><span> being 1.18 × 10<sup>9</sup> M<sup>-1</sup>s<sup>-1</sup>,</span><span> </span><span>in which</span><span> </span><span>formal hydrogen transfer (FHT) reactions of the dipole-salt form occur as the primary mechanism. Consequently, the Leu oxidation by HO<sup>●</sup> radical can be promoted by the Fenton-reaction enhancement of its transition metal complexes.</span></span></span></p>
Data for the article "Spintronic THz emitters based on transition metals and semi-metals/Pt multilayers"
<p>Data for the article "Spintronic THz emitters based on transition metals and semi-metals/Pt multilayers"</p> <p>(<a href="https://aip.scitation.org/doi/abs/10.1063/5.0079955">https://aip.scitation.org/doi/abs/10.1063/5.0079955</a> and <a href="https://arxiv.org/pdf/2203.08577.pdf">https://arxiv.org/pdf/2203.08577.pdf</a>)</p>
Experimental and modeling study of metal-insulator interfaces to control the electronic transport in single nanowire memristive device - Dataset
<p>This is the dataset of "Experimental and modeling study of metal-insulator interfaces to control the electronic transport in single nanowire memristive device"</p>
Naturally occurring metals in unregulated domestic wells in Nevada, USA
<p>The dominant source of drinking water in rural Nevada, United States, is privately-owned domestic wells. Because the water from these wells is unregulated with respect to government guidelines, it is the owner's responsibility to test their groundwater for heavy metals and other contaminants. Arsenic, lead, cadmium, and uranium have been previously measured at concentrations above Environmental Protection Agency (EPA) guidelines in Nevada groundwater. This is a public health concern because elevated levels of these metals are known to have negative health effects.</p> <p>We recruited individuals through a population health study, the Healthy Nevada Project, to submit drinking water samples from domestic wells for testing. Water samples were returned from 174 households with private wells. We found 22% had arsenic concentrations exceeding the EPA maximum contaminant level (MCL) of 10 mg/L. Additionally, federal, state, or health-based guidelines were exceeded for 8% of the households for uranium and iron, 6% for lithium and manganese, 4% for molybdenum, and 1% for lead. The maximum observed concentrations of arsenic, uranium, and lead were ~80, ~5, and ~1.5 times the EPA guideline values, respectively. 41% of households had a treatment system and submitted both pre- and post-treatment water samples from their well. The household treatments were shown to reduce metal concentrations, but concentrations above guideline values were still observed. Many treatment systems cannot reduce the concentration below guideline values because of water chemistry, treatment failure, or improper treatment techniques. These results show the pressing need for continued education and outreach on regular testing of domestic well waters, proper treatment types, and health effects of metal contamination. These findings are potentially applicable to other arid areas where groundwater contamination of naturally occurring heavy metals occurs.</p>
Dataset to "Sorption kinetics in metal hydrides by leaky coating"
<p>Rawdata to the figures in "Sorption kinetics in metal hydrides by leaky coating" published in the International Journal of Hydrogen Energy</p>
Raw Data for 'Efficient Access of Phenyl-Spaced 5,5´-Bridged Dinuclear Ruthenium Metal Complexes and the Effect of Dynamic Ligand Exchange on Catalysis'
<p>Herein, we provide the raw data for all figures being part of either the mansucript or the supporting information of the publication 'Efficient Access of Phenyl-Spaced 5,5’-Bridged Dinuclear Ruthenium Metal Complexes and the Effect of Dynamic Ligand Exchange on Catalysis'.</p>
Co-occurrence of antimicrobials and metals in swine manure as potential drivers of antimicrobial resistance in the environment
<p>In many regions of the United States (US) and other countries, animal manures are often agronomically applied to cropland to recycle nutrients. However, aggregated manure wastes commonly contain contaminants including veterinary antimicrobials, heavy metals, and antimicrobial resistance genes (ARGs) that can stimulate the development and proliferation of antimicrobial resistance (AMR). While the presence of antimicrobials in manure is well-documented, very little has been reported on the co-occurrence of other chemical stressors in swine manure that may stimulate AMR spread in exposed bacteria. This study quantifies and analyzes correlations between antimicrobials, metals, and certain ARGs present in manure samples from swine farms in Iowa, US. Relationships between contaminants and different stages of swine production or feed composition are also investigated. Results revealed notable levels of tetracyclines (up to 1,260 µg g-1 dry weight (d.w.) of manure for oxytetracycline) detected in all samples. Tiamulin, two ionophores (monensin and lasalocid), and one macrolide (tilmicosin) were detected at maximum class concentrations of 9.4, 0.547, and 0.472 µg g-1 d.w., respectively. The median relative abundances of ErmB and TetM were 0.13 and 0.17 copies g-1 wet weight (w.w.) manure (normalized to 16S gene), respectively. Additionally, high levels of copper (Cu), iron (Fe), and zinc (Zn) were detected in all samples, with maximum concentrations of 887, 1,900, and 2,100 µg g-1 d.w., respectively. Notably, uranium was detected in 11 samples, at concentrations up to 0.77 µg g-1. A global analysis of AMR-stressor relationships using Spearman's rank correlation indicates Cu, and Ba are the most positively and significantly correlated with anhydrotetracycline (ATC) and/or anhydrochlortetracycline (ACTC) concentrations in all tested facilities (Cu-ATC: ρ = 0.67, p = 0.0093; Cu-ACTC: ρ = 0.75, p = 0.0022; Ba-ATC: ρ = 0.84, p = 0.0002). Interestingly, ermB and tetM genes were strongly, positively correlated to each other (ρ=0.92, p= 0.000), suggesting possible co-selection, despite the absence of correlation between ARGs and tetracycline concentrations. This study demonstrates the complexity of interactions between antimicrobials, metals, and ARGs in multiple manure storage pits prior to cropland application.</p>
Decoding the metabolic response of Escherichia coli for sensing trace heavy metals in water
<p>As: Raman spectra from E. coli lysate sample after exposing to As in DI water</p> <p>Cr: Raman spectra from E. coli lysate sample after exposing to Cr in DI water</p> <p>As_TapWater: Raman spectra from E. coli lysate sample after exposing to As in tap water</p> <p>WasteWater_FineTune_Dataset: Raman spectra from E. coli lysate sample after exposing to As in waste water</p> <p>WasteWater 'Unknow' Dataset: Raman spectra from E. coli lysate sample after exposing to waste water</p>
Thermodynamics of the metal-insulator transition in the extended Hubbard model from determinantal quantum Monte Carlo
<p>This zip archive contains the DQMC data sets for the \beta=1/T resolved double<br> occupancy, internal energy per site, antiferromagnetic structure factor and<br> charge density wave structure factor obtained with the ALF Code.<br> The computations have been carried out on a square lattice at half filling for<br> the Hubbard model, the U-V model and the long-range Coulomb(LRC)-Hubbard model.<br> Every model is computed at fixed U/t=1.9.</p> <p>The naming convention of the csv-files is the following:<br> V0.0 = Hubbard model<br> V0.x = U-V model with V/t=0.x<br> Vcx.x = LRC-Hubbard model with V_C/t=x.x<br> <br> - dat_docc_{\Delta\tau}_V{x}.csv = double occupancy at \Delta\tau={0.05, 0.1, 0.2}<br> - dat_docc_{\Delta\tau}_V{x}_err.csv = corresponding statistical error<br> - dat_energy_0.1_V{x}.csv = internal energy at \Delta\tau=0.1<br> - dat_energy_0.1_V{x}_err.csv = corresponding statistical error<br> - dat_saf_0.1_V{x}.csv = antiferromagnetic structure factor at \Delta\tau=0.1<br> - dat_saf_0.1_V{x}_err.csv = corresponding statistical error<br> - dat_scdw_0.1_V{x}.csv = charge density wave structure factor at \Delta\tau=0.1<br> - dat_scdw_0.1_V{x}_err.csv = corresponding statistical error</p> <p>The structure of each file is to be read column-wise:<br> \beta, L=8, L=10, L=12, L=16, L=18, L=20</p> <p> </p>
Graph Neural Network for Metal Organic Framework Potential Energy Approximation
<p>Data set consists of 50,000 different configurations for the Metal Organic Framework (MOF) FIGXAU. Was generated by randomly modifying the positions of the atoms and doing an SCF relaxation on each configuration.</p>
Applying the metallicity-dependent binary fraction to double white dwarf formation: Implications for LISA -- COSMIC + Ananke data
<p>This dataset contains all data required to run the pipeline which produces the results and figures in Thiele+2022 including:</p> <p>- results of all COSMIC simulations for the fiducial, alpha25, alpha5, and q3 models for each double white dwarf type and binary fraction assumption</p> <p>- metallicities, ages, positions and star particle kernel lengths from the Ananke framework of galaxy m12i in the Latte suite of the FIRE-2 simulations.</p>
Are metal-poor stars related to older stars in the solar neighborhood?
<p>Data in a paper submitted to Monthly Notices of the Royal Astronomical Society.</p>
Data for: Can heavy metal pollution induce soil bacterial community resistance to antibiotics in boreal forests?
<p>The emergence of microbial antibiotic resistance is a central threat to global health, food security, and development. It has been shown that heavy metal pollution can give rise to microbial resistance to antibiotics, but how wide-spread this phenomenon is remains an open question that urgently needs filling to enable appropriate environmental risk assessments. Here, we determined whether long-term differences in heavy metal pollution in boreal forests had affected soil microbial communities such that they had increased microbial resistance to antibiotics. First, we assessed variation in metal concentrations in samples collected across a distance trajectory from the pollution source, and also the microbial rates and levels of bacterial community resistance to the heavy metal Cu and the antibiotics tetracycline and vancomycin in those samples. Second, we tested if the exposure to Cu or tetracycline could increase bacterial community resistance to Cu and to antibiotics in soils with high versus low background levels of metal contamination. Metal pollution had affected microbial community structures and suppressed decomposer functioning. Importantly, bacterial community Cu resistance increased with higher metal concentrations, which coincided with an induced bacterial community resistance to tetracycline, but not to vancomycin. Laboratory experiments revealed that bacterial community Cu resistance could be further induced in both the low and high end of the pollution gradient, but also that these short-term inductions of community metal tolerance did not coincide with enhanced antibiotic resistance. This yielded a surprising negative correlation between long-term and short-term effects by metals on microbial metal and antibiotic resistances. One mechanism that could provide protection against both metal cations and tetracycline is the small multidrug resistance (SMR) family, which is an energy demanding physiological mechanism that may take time to confer protection. This may explain the different microbial responses to long-term gradients and metal addition experiments. Policy implications. We show that metal pollution in boreal forests will promote antibiotic resistance in soil bacterial communities, revealing an overlooked reservoir of antibiotic resistance. We recommend that environmental risk assessments for any activity giving rise to increased soil metal concentrations need to also consider the induction of microbial antibiotic resistance.</p>
Isobaric specific heat of metals and metalloids under standard conditions
<p><strong>Isobaric specific heat of metals and metalloids under standard conditions</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>A metal is any of a class of substances characterized by high electrical and thermal conductivity as well as by malleability, ductility, and high reflectivity of light. Approximately three-quarters of all known chemical elements are metals. The most abundant varieties in the Earth's crust are aluminum, iron, calcium, sodium, potassium, and magnesium. The vast majority of metals are found in ores (mineral-bearing substances), but a few such as copper, gold, platinum, and silver frequently occur in the free state because they do not readily react with other elements. Metals are usually crystalline solids. In most cases, they have a relatively simple crystal structure distinguished by a close packing of atoms and a high degree of symmetry. Typically, the atoms of metals contain less than half the full complement of electrons in their outermost shell. Because of this characteristic, metals tend not to form compounds with each other. They do, however, combine more readily with nonmetals (for example, oxygen and sulfur), which generally have more than half the maximum number of valence electrons. Metals differ widely in their chemical reactivity. The most reactive include lithium, potassium, and radium, whereas those of low reactivity are gold, silver, palladium, and platinum. The high electrical and thermal conductivities of the simple metals (namely the non-transition metals of the periodic table) are best explained by reference to the free-electron theory. According to this concept, the individual atoms in such metals have lost their valence electrons to the entire solid, and these free electrons that give rise to conductivity move as a group throughout the solid. In the case of the more complex metals (namely the transition elements), conductivities are better explained by the band theory, which takes into account not only the presence of free electrons but also their interaction with so-called d electrons. The mechanical properties of metals, such as hardness, ability to resist repeated stressing (fatigue strength), ductility, and malleability, are often attributed to defects or imperfections in their crystal structure. The absence of a layer of atoms in its densely packed structure, for example, enables a metal to deform plastically, and prevents it from being brittle. A metalloid, in chemistry, is an imprecise term used to describe a chemical element that forms a simple substance having properties intermediate between those of a typical metal and a typical nonmetal. The term is normally applied to a group of between six and nine elements (boron, silicon, germanium, arsenic, antimony, tellurium, and possibly bismuth, polonium, astatine) found near the center of the P-block or main block of the periodic table. There is no single property which can be used to unambiguously identify an element as a metalloid. Since most metalloids tend to display semiconducting properties in at least one of their allomorphic modifications, the class might reasonably be extended to also include gray silicon (which, unlike white silicon, is a semiconductor rather than a metal) and the graphite form of carbon (which, unlike the diamond form, is a semimetal rather than an insulator). Chemically, metalloids correspond to atoms having intermediate electronegativities and an ability to display a range of both positive and negative oxidation states in their compounds.</p> <p>Metals and metalloids, Isobaric specific heat (kilojoules per kelvin per kilogram)</p> <p>Aluminum 0.91</p> <p>Antimony 0.21</p> <p>Barium 0.20</p> <p>Beryllium 1.83</p> <p>Bismuth 0.13</p> <p>Cadmium 0.23</p> <p>Calsium 0.63</p> <p>Carbon Steel 0.49</p> <p>Cast Iron 0.46</p> <p>Cesium 0.24</p> <p>Chromium 0.46</p> <p>Cobalt 0.42</p> <p>Copper 0.39</p> <p>Gallium 0.37</p> <p>Germanium 0.32</p> <p>Gold 0.13</p> <p>Hafnium 0.14</p> <p>Indium 0.24</p> <p>Iridium 0.13</p> <p>Iron 0.45</p> <p>Lanthanum 0.195</p> <p>Lead 0.13</p> <p>Lithium 3.57</p> <p>Lutetium 0.15</p> <p>Magnesium 1.05</p> <p>Manganese 0.48</p> <p>Mercury 0.14</p> <p>Molybdenum 0.25</p> <p>Nickel 0.44</p> <p>Niobium 0.27</p> <p>Osmium 0.13</p> <p>Palladium 0.24</p> <p>Platinum 0.13</p> <p>Plutonium 0.13</p> <p>Potassium 0.75</p> <p>Rhenium 0.14</p> <p>Rhodium 0.24</p> <p>Rubidium 0.36</p> <p>Ruthenium 0.24</p> <p>Scandium 0.57</p> <p>Selenium 0.32</p> <p>Silicon 0.71</p> <p>Silver 0.23</p> <p>Sodium 1.21</p> <p>Strontium 0.30</p> <p>Tantalum 0.14</p> <p>Thallium 0.13</p> <p>Thorium 0.13</p> <p>Tin 0.21</p> <p>Titanium 0.54</p> <p>Tungsten 0.13</p> <p>Uranium 0.12</p> <p>Vanadium 0.39</p> <p>Yttrium 0.30</p> <p>Zinc 0.39</p> <p>Zirconium 0.27</p> <p>Wrought Iron 0.50</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
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OpenNeuro
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