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67 results for “energy densities”
Density functional theory calculations of coherent bcc Fe-Cu interfacial energy densities
<p>File contains the data required to calculate interfacial energy densities of {100}, {110}, {111}, {210}, {211} and {221} orientated coherence bcc Fe-Cu interfaces.</p> <p>Data produced for the study detailed in: Cu nanoprecipitate morphologies and interfacial energy densities in bcc Fe from density functional theory (DFT) A.M. Garrett and C.P. Race.</p> <p>Submitted to Computational Materials Science.</p> <p>.txt files contain the total energies calculated for relaxed interface-containing and bulk simulation cells at a range of interfacial spacings. This data can be used to calculate the size independent interfacial energy densities for a range of Fe-Cu interface orientations using standard fitting approaches. Columns of the tables in the .txt files are no. atoms, interface-containing simulation cell length, interfacial area, total energy of the relaxed interface-containing simulation cell, total energy of the reference bulk Fe and total energy of the reference bulk Cu. Lengths are in Angstrom and energies are in eV.</p>
Atomic energy densities from the multiple radii functional (MRF)
<p>This dataset contains exchange-correlation energy densities in the gauge of the electrostatic potential for various atomic systems, all in atomic units. The first column represents the distance from the nucleus in bohr, while the remaining columns represent the energy densities. The headings for the columns are specified in the first row of the dataset. The dataset includes the exact energy densities "w_0 [exact]" and "w_1 [exact]" which are taken from reference [Phys.Chem.Chem.Phys., 2017, 19, 6169]. These densities are calculated at the zero and full coupling strengths, respectively. The densities used to calculate the MRF functional are also taken from the same reference, where the computational details can be found. "w_1 [mrf original]" represents the MRF energy densities calculated using the original fluctuation function developed in reference [J. Phys. Chem. Lett. 2017, 8, 2799−2805]. "w_1 [mrf new]" represents the MRF energy densities calculated using a newly developed fluctuation function, specifically designed to satisfy the high-density limit, non-negativity of the correlation part of the energy densities, and the uniform electron gas limit. Files are given in the XLSX format and different file names represent different atoms (ions). Wolfram Mathematica 13.1.0.0. has been used for data curation. </p> <p> </p>
Fig. 6 in Body condition and energy density of juvenile streaked prochilod Prochilodus lineatus (Valenciennes, 1837) in a Neotropical floodplain
Fig. 6. Spearman rank correlations (rs) between the values of energy density (E D) versus relative condition factor (K n) (a), length standard (L s) (b) and weight total (W t) (c) of Prochilodus lineatus in the floodplain of the Upper Paraná River. The line represents the trend of significant Spearman correlations.
Fig. 4 in Body condition and energy density of juvenile streaked prochilod Prochilodus lineatus (Valenciennes, 1837) in a Neotropical floodplain
Fig. 4. Median values of energy density (ED) [(a), (b)] and the relative condition factor (Kn) [(c), (d)], by standard length, in the floodplain of the Upper Paraná River, for each month sampled. Lower bars indicate the 25% percentile, upper bars indicate the 75% percentile. Different upper-case letters (ABC) indicate statistically significant differences (Kruskal-Wallis, P <0.025).
Fig. 1 in Body condition and energy density of juvenile streaked prochilod Prochilodus lineatus (Valenciennes, 1837) in a Neotropical floodplain
Fig. 1. Location of the sampling stations of Prochilodus lineatus on the Upper Paraná River floodplain, associated with the following sub-basins: Baia (5. Guaraná lagoon, 6. Gavião lagoon, 7. Fechada lagoon, 8. Pousada das Garças lagoon, 11. Baia River), Ivinhema (1. Finado Raimundo lagoon, 2. Patos lagoon, 3. Ventura lagoon, 4. Zé do Paco lagoon, 13. Ivinhema River) and Paraná (9. Garças lagoon, 10. Ressaco do Pau Veio, 12. Paraná River).
Fig. 3 in Body condition and energy density of juvenile streaked prochilod Prochilodus lineatus (Valenciennes, 1837) in a Neotropical floodplain
Fig. 3. Linear regression (a) and weight-length relationship (b) of juvenile Prochilodus lineatus in the floodplain of the Upper Paraná River.
Fig. 5 in Body condition and energy density of juvenile streaked prochilod Prochilodus lineatus (Valenciennes, 1837) in a Neotropical floodplain
Fig. 5. Median values of energy density (E D) [(a), (b)] and the relative condition factor (K n) [(c), (d)], by standard length, for each sub-basin of the floodplain of the Upper Paraná River - Baia sub-basins (Bai), Ivinhema (Ivi) and Paraná (Par) - from June 2010 to March 2011. Lower bars indicate the 25% percentile, upper bars indicate the 75% percentile. Different upper-case letters (ABC) indicate statistically significant differences (Kruskal-Wallis, P <0.025).
Fig. 2 in Body condition and energy density of juvenile streaked prochilod Prochilodus lineatus (Valenciennes, 1837) in a Neotropical floodplain
Fig. 2. Daily mean water levels of the Upper Paraná River. Arrows indicate sampling periods. Horizontal lines show the threshold of the water level for river-bank overflow (350 cm = Paraná river, Agostinho et al., 2004b; Thomaz et al., 2004).
Fig. 1 in Variation in energy density of Loricariichthys platymetopon (Siluriformes: Loricariidae) in the upper Paraná River basin
Fig. 1. Study area and location of the sampled points in the upper Paraná River floodplain (1- Patos Lake; 2- Ivinheima River; 3- Baia River; 4- Guaraná Lake; 5- Garças Lake; Patos Lake; 2- Ivinheima River; 3- Baia River; 4- Guaraná Lake; 5- Garças Lake; Rosana Reservoir (6) and Diamante Stream (7, 8 e 9). Number of specimens by sampling site: n = 31, n = 19, n = 15, n = 12, n = 1 2 3 4 5 5, n = 56, n = 35, n = 22 and n = 17.
Fig. 2 in Variation in energy density of Loricariichthys platymetopon (Siluriformes: Loricariidae) in the upper Paraná River basin
Fig. 2. Mean ± standard deviation for muscle energy density of Loricariichthys platymetopon, at different gonadal development stages (immature = IMT; resting = RES; beginning of maturation = BMA; maturing = MAG; mature = MAT; semi-spent = SSP; spent = SPE; recovery = REC), in the upper Paraná River floodplain, Rosana Reservoir and Diamante Stream. Number of specimens between parentheses. One-way ANOVA are presented for each case.
Automated identification of small molecules in cryo-electron microscopy data with density- and energy-guided evaluation
<p>Data and turorials for ligand identification with EMERALD-ID. Each Zip file contains ligand models and data used to generate their respective figures in the EMERALD-ID manuscript. For specific details, consult the README file in each directory.</p>
Dataset for the paper "Ocean wave energy harvesting with high energy density and self-powered monitoring system"
<p>Dataset for the paper "Ocean wave energy harvesting with high energy density and self-powered monitoring system“.</p>
Development of a High-Energy-Density Lithiated Silicon-Sulfur Full Cell with Enhanced Stability and Longevity
<p>The raw materials for the draft: "Development of a High-Energy-Density Lithiated Silicon-Sulfur Full Cell with Enhanced Stability and Longevity"</p>
Proca Energy Density Around Kerr
<p>Proca energy density around a kerr background. alpha value in file name gives the gravitational coupling represented as a fraction (Alpha_x_y is an alpha value of x/y). File type is a numpy archive. Load archive in python using numpy.load.</p>
Field Application of a High-Power Density Electromagnetic Energy Harvester to Power Wireless Sensors in Transportation Infrastructures
<p>Traffic-induced vibration of transportation infrastructures is a reliable source of kinetic energy, which can be harvested to power conventional monitoring sensors and peripherals installed on bridges, thereby reducing some dependence on non-renewable energy. The highway statistics shows that the average daily vehicles miles travelled in the US is more than 5 billion. This is a massive source of kinetic energy that lies unused in the national transportation network. This study focuses on the design and field testing of a high-power density electromagnetic energy harvester (EMEH) to convert such a kinetic energy into electrical energy for powering ubiquitous sensors installed on transportation infrastructures. The principal investigators have been investigating the design of the EMEH using analytical and finite element simulations, as well as, its laboratory prototype fabrication and testing in the first phase. The proposed EMEH utilizes the innovative concept of creating planar array of large number of small permanent magnets through certain optimization criteria to achieve strong and focused magnetic field in a particular orientation. The proposed EMEH has a compact design, such that it can be integrated into the power circuit of wireless sensor nodes (WSNs) and installed at suitable part of a transportation infrastructure without elaborate wiring. It is capable of continuously charging the rechargeable battery of a WSN, thereby extending the lifespan of the monitoring system, almost, indefinitely. For the next phase of this research, the principal investigators propose the development and field implementation of a larger scale and more compact version of the EMEH with a minimum of 500 mW output power to be installed on selected transportation infrastructures for the evaluation of its energy harvesting efficiency and capability to derive different types of monitoring sensors and peripherals. Three different highway bridges with different fundamental frequencies, ideally between 2Hz to 8Hz, will be selected for the field testing of the EMEH. An acceleration sensor will be used to record the traffic-induced vibration of each bridge during a normal daily traffic that after signal processing is used to measure the fundamental frequency of that bridge. The dynamic characteristics of the proposed EMEH (i.e. tip mass and spring stiffness) will be modified to put it into a resonant condition with the bridge by matching their natural frequencies. The output power will be monitored and used to continuously charge a rechargeable battery powering a wireless sensor. The focus is on the feasibility of the proposed EMEH to power sensors that are used to regularly monitor the structural integrity of materials and components of highway bridges such as acceleration and temperature sensors.</p>
Dual Energy Cone-Beam Computed Tomography (DE-CBCT) Assessment of Jaw Bone Density
ClinicalTrials.gov study NCT04686084. IPD Sharing: NO. Countries: 1. Publications: 0.
Effect of Energy Density Over 5 Days in Preschool Children
ClinicalTrials.gov study NCT03010501. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Acoustic propulsion of nano- and microcones: dependence on particle size, acoustic energy density, and sound frequency
<p>Supplementary data for the following manuscript: Johannes Voß, Raphael Wittkowski, "Acoustic propulsion of nano- and microcones: dependence on particle size, acoustic energy density, and sound frequency".</p>
Data used in the publication "High Energy Emissions induced by air density fluctuations of discharges"
<p>This data was used to generate the Figures in the publication "High Energy Emissions induced by air density fluctuations of discharges".</p>
Non-fluorinated electrolytes with micelle-like solvation for ultrahigh energy density lithium metal batteries
<p>Electrolyte engineering plays a critical role in enabling lithium (Li) metal batteries. However, the simultaneous realization of anion-rich solvation structure and high ionic conductivity of electrolytes via solvation structure design remains challenging. Here, we report a low-cost, non-fluorinated electrolyte with a micelle-like solvation structure by introducing amphiphilic n-butyl methyl ether (MNBE) into lithium bis(fluorosulfonyl)imide (LiFSI)/1,2-dimethoxyethane (DME) for stable Li metal batteries. MNBE can effectively promote Li+-FSI- coordination through steric crowding. Meanwhile, the inert alkyl chains of MNBE can mitigate the reaction between electrolyte and Li metal due to their lithiophobicity. Specifically, the micelle-like, non-fluorinated electrolyte exhibits an ionic conductivity as high as 12.55 mS cm-1 and its anion-rich solvation structure promotes the formation of LiF-rich solid-electrolyte-interphase. We constructed a 7.3 Ah Li||NMC811 pouch cell employing this electrolyte under harsh conditions, exhibiting ultrahigh specific energy of 503.7 Wh kg-1 with impressive cycling stability of 84.1% capacity retention after 100 cycles. </p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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