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189 results for “thermodynamics”
Thermodynamic stability of quinary alloys
<p>This dataset describes the thermodynamic stability of over 65000 hypothetical quinary alloys from the combination of 40 metal elements:</p> <pre><code>['Ag', 'Al', 'As', 'Au', 'Bi', 'Cd', 'Co', 'Cr', 'Cu', 'Fe', 'Ga', 'Ge', 'Hf', 'Hg', 'In', 'Ir', 'Mg', 'Mn', 'Mo', 'Nb', 'Ni', 'Os', 'Pb', 'Pd', 'Pt', 'Re', 'Rh', 'Ru', 'Sb', 'Sc', 'Si', 'Sn', 'Ta', 'Te', 'Ti', 'V', 'W', 'Y', 'Zn', 'Zr']</code></pre> <p> </p> <p>The stability is assessed through a binary solid solution model for which the enthalpy of mixing is calculated using density functional theory. The binary interaction parameters used for calculating the enthalpy of mixing are provided as a json file (omegas.json). The highest possible synthesis temperature at 90% of the melting point is considered.</p> <p>In addition to the thermodynamic stability, the dataset provides (inverse) energy above hull, enthalpy of mixing, and structure phase among other properties.</p> <p> </p>
Thermodynamics of single-walled carbon nanotubes
<p><strong>Thermodynamics of single-walled carbon nanotubes</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: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>A system's condition at any given time is called its thermodynamic state. For a gas in a cylinder with a movable piston, the state of the system is identified by the temperature, pressure, and volume of the gas. These properties are characteristic parameters that have definite values at each state and are independent of the way in which the system arrived at that state. In other words, any change in value of a property depends only on the initial and final states of the system, not on the path followed by the system from one state to another. Such properties are called state functions. In contrast, the work done as the piston moves and the gas expands and the heat the gas absorbs from its surroundings depend on the detailed way in which the expansion occurs. The behavior of a complex thermodynamic system can be understood by first applying the principles of states and properties to its component parts, in this case, water, water vapor, and the various gases making up the atmosphere. By isolating samples of material whose states and properties can be controlled and manipulated, properties and their interrelations can be studied as the system changes from state to state. The concept of temperature is fundamental to any discussion of thermodynamics, but its precise definition is not a simple matter. It is necessary to have an objective way of measuring temperature. In general, when two objects are brought into thermal contact, heat will flow between them until they come into equilibrium with each other. When the flow of heat stops, they are said to be at the same temperature. The zeroth law of thermodynamics formalizes this by asserting that if an object A is in simultaneous thermal equilibrium with two other objects B and C, then B and C will be in thermal equilibrium with each other if brought into thermal contact. Object A can then play the role of a thermometer through some change in its physical properties with temperature, such as its volume or its electrical resistance. Energy has a precise meaning in physics that does not always correspond to everyday language, and yet a precise definition is somewhat elusive. The word is derived from the Greek word ergon, meaning work, but the term work itself acquired a technical meaning with the advent of Newtonian mechanics. As the science of physics expanded to cover an ever-wider range of phenomena, it became necessary to include additional forms of energy in order to keep the total amount of energy constant for all closed systems or to account for changes in total energy for open systems. Thermodynamics encompasses all of these forms of energy, with the further addition of heat to the list of different kinds of energy. However, heat is fundamentally different from the others in that the conversion of work or other forms of energy into heat is not completely reversible, even in principle. Although classical thermodynamics deals exclusively with the macroscopic properties of materials, such as temperature, pressure, and volume, thermal energy from the addition of heat can be understood at the microscopic level as an increase in the kinetic energy of motion of the molecules making up a substance. For example, gas molecules have translational kinetic energy that is proportional to the temperature of the gas: the molecules can rotate about their center of mass, and the constituent atoms can vibrate with respect to each other. Additionally, chemical energy is stored in the bonds holding the molecules together, and weaker long-range interactions between the molecules involve yet more energy. The sum total of all these forms of energy constitutes the total internal energy of the substance in a given thermodynamic state. The total energy of a system includes its internal energy plus any other forms of energy, such as kinetic energy due to motion of the system as a whole and gravitational potential energy due to its elevation.</p>
Thermodynamics of double-walled carbon nanotubes
<p><strong>Thermodynamics of double-walled carbon nanotubes</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: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>Thermodynamics deals with the transfer of energy from one place to another and from one form to another. The most important laws of thermodynamics are stated herein. The zeroth law of thermodynamics. When two systems are each in thermal equilibrium with a third system, the first two systems are in thermal equilibrium with each other. This property makes it meaningful to use thermometers as the third system and to define a temperature scale. The first law of thermodynamics. The change in a system's internal energy is equal to the difference between heat added to the system from its surroundings and work done by the system on its surroundings. The second law of thermodynamics. Heat does not flow spontaneously from a colder region to a hotter region, or, equivalently, heat at a given temperature cannot be converted entirely into work. Consequently, the entropy of a closed system, or heat energy per unit temperature, increases over time toward some maximum value. Thus, all closed systems tend toward an equilibrium state in which entropy is at a maximum and no energy is available to do useful work. The third law of thermodynamics. The entropy of a perfect crystal of an element in its most stable form tends to zero as the temperature approaches absolute zero. This allows an absolute scale for entropy to be established that, from a statistical point of view, determines the degree of randomness or disorder in a system. The laws of thermodynamics are deceptively simple to state, but they are far-reaching in their consequences. The first law is put into action by considering the flow of energy across the boundary separating a system from its surroundings. Consider the classic example of a gas enclosed in a cylinder with a movable piston. The walls of the cylinder act as the boundary separating the gas inside from the world outside, and the movable piston provides a mechanism for the gas to do work by expanding against the force holding the piston in place. If the gas does work as it expands, and absorbs heat from its surroundings through the walls of the cylinder, then this corresponds to a net flow of energy across the boundary to the surroundings. In order to conserve the total energy, there must be a counterbalancing change in the internal energy of the gas. From a formal mathematical point of view, the incremental change in the internal energy is an exact differential, while the corresponding incremental changes in heat and work are not, because the definite integrals of these quantities are path-dependent. These concepts can be used to great advantage in a precise mathematical formulation of thermodynamics. The science of thermodynamics provides a rich variety of formulas and techniques that allow the maximum possible amount of information to be extracted from a limited number of laboratory measurements of the properties of materials. However, as the thermodynamic state of a system depends on several variables, such as temperature, pressure, and volume, in practice it is necessary first to decide how many of these are independent and then to specify what variables are allowed to change while others are held constant. For this reason, the mathematical language of partial differential equations is indispensable to the further elucidation of the subject of thermodynamics. Of especially critical importance in the application of thermodynamics are the amounts of work required to make substances expand or contract and the amounts of heat required to change the temperature of substances. The first is determined by the equation of state of the substance and the second by its heat capacity. Once these physical properties have been fully characterized, they can be used to calculate other thermodynamic properties, such as the free energy of the substance under various conditions of temperature and pressure.</p>
Thermodynamic database and calculator of free energies and potentials for redox reactions involving iron minerals in aqueous media (IMTD)
<p>Database of free energies of formation for iron minerals and associated aqueous species, which are used in a tableu style spreadsheet to calculate free energies of redox reactions involving iron minerals, which in turn are used to calculate free energies and formal potentials for these reactions, under specified environmental conditions.</p> <p>The database and calculators were assembled by students and postdocs (Jeff Hudson, Ania Pavitt, Ying Lan, and Miranda Bradley) working under direction of Professor Paul G. Tratnyek at the Oregon Health & Science University, Portland, Oregon, USA. Drew Latta, Thomas Robinson, and Michelle Scherer contributed to the database and extended the calculations.</p> <p>Early versions of this tool were used in several publications, including (i) Fan, D., Y. Lan, P. G. Tratnyek, R. L. Johnson, J. Filip, D. M. O'Carroll, A. N. Garcia, and A. Agrawal. 2017. <em>Environ. Sci. Technol.</em> 51(22): 13070–13085. [DOI: 10.1021/acs.est.7b04177] and (ii) Bradley, M. J., and P. G. Tratnyek. 2019. <em>ACS Earth & Space Chemistry</em> 3(3): 688-699. [DOI: 10.1021/acsearthspacechem.8b00200].</p> <p>This tool is provided as a spreadsheet in .xlsx format. The file includes six sheets. The first contains background, constants, and calculations that apply throughout the remaining tabs. The second contains free energies of formation from various authoritative sources, and a mechanism for designating “recommend values”. The third contains a tableu that calculates free energies of redox reactions using the recommended free energy of formation and user-specified stoichiometries. The fourth calculates free energies and formal potentials of the redox reactions using the standard potentials, and specific solution conditions. The last tab summarizes previous published formal potentials from a variety of sources. </p> <p>While the database was checked thoroughly, it still is unlikely to be completely accurate. For critical applications, we recommend that you track-down the primary sources (listed on the first tab of the spreadsheet) and use them for data, conditions, and other caveats. Obviously, we do not accept any responsibility for what anyone does with information obtained from this document.</p> <p>In the future, if significantly corrections or additions are made to this document, we may publish it here as new versions. If the contributions of others result in major improvements, we are open to adding new authors to those versions. Feel free to contact us with corrections, suggests, or offers to help.</p> <p>The development of this version of the tool was funded through grants from the Strategic Environmental Research and Development Program (SERDP) and the U.S. Department of Energy.</p>
Chemical, thermodynamic and electromagnetic parameters of electrochemically-stimulated flames (numerical data)
<p><span>Key component concentration fields in the stimulated flames</span></p> <p><span> </span><span>Main impact factors of the electrochemical stimulation and their influence on the NOx generation/suppression/destruction</span></p>
Chemical and thermodynamic parameters of thermal flames (numerical and experimental data)
<p><span>Parameters and characteristics of the high temperature flames for the real environment in the single nozzle atmospheric burner, mini engine afterburner and high-pressure facility</span></p>
Data on thermodynamic, gas-dynamic and electromagnetic parameters of gas discharge in poly-phase medium
<p><span>Temperature, concentrations and electromagnetic parameters distribution in the model environment for main poly-phase medium types</span></p>
Fig. 5 in New insight in the determination of thermodynamic equilibrium thickness using heat budget over Barents Sea
Fig. 5 — Retrieved Thermodynamic Equilibrium Thickness (TET) over Barents Sea during (a) November, (b) December, (c) January, (d) February, (e) March, and (f) April for the span of 2002 – 2020
Fig. 2 in New insight in the determination of thermodynamic equilibrium thickness using heat budget over Barents Sea
Fig. 2 — Climatology of Sea Ice Area (SIA) and Oceanic Energy (OE) over Barents Sea for the span 2002 – 2020
Fig. 4 in New insight in the determination of thermodynamic equilibrium thickness using heat budget over Barents Sea
Fig. 4 — Histogram representing percentage contribution of Barents Sea Ice Thickness (SIT) over Arctic Sea Ice Thickness (SIT) during (a) November, (b) December, (c) January, (d) February, (e) March, and (f) April for the span of 2002 – 2020
Dynamic and thermodynamic crossover scenarios in the Kob-Andersen mixture: Insights from multi-CPU and multi-GPU simulations
<p>This dataset is associated with "Dynamic and thermodynamic crossover scenarios in the Kob-Andersen mixture: Insights from multi-CPU and multi-GPU simulations", Daniele Coslovich, Misaki Ozawa, and Walter Kob, Eur. Phys. J. E 62, 41 (2018) [<a href="https://doi.org/10.1140/epje/i2018-11671-2">doi:10.1140/epje/i2018-11671-2</a> <a href="https://arxiv.org/abs/1804.04559">arXiv:1804.04559</a>]</p> <p>It includes scripts and data files to allow for the replication of the figures. EPS figures were generated using gnuplot version 5.0.</p> <p>Notes:</p> <ul> <li>Small differences in the dynamic data for the N=3600 dataset obtained with the MD protocol reflect additional statistics gathered since acceptance of the paper.</li> <li>Figure 6(b) in the published version of the manuscript was obtained using slightly incorrect values of the parameters J, T_0 entering equation 10. This minor issue has been fixed in this dataset.</li> </ul>
Changes in four decades of near-CONUS tropical cyclones in an ensemble of 12km thermodynamic global warming simulations
<p>Snapshot level data of TC extractions from the thermodynamical global warming runs described in "Changes in four decades of near-CONUS tropical cyclones in an ensemble of 12km thermodynamic global warming simulations."</p>
Equation of state tables used in the paper: "Exploring the Catastrophic Regime: Thermodynamics and Disintegration in Head-On Planetary Collisions"
<p>The EoS (Equation of State) tables for iron and forsterite used in the paper "<span>Exploring the Catastrophic Regime: Thermodynamics and Disintegration</span><br><span>in Head-On Planetary Collisions</span>" are available here for use.</p> <p>These tables can be directly integrated into the SPH (Smoothed Particle Hydrodynamics) code <a href="https://swift.strw.leidenuniv.nl/">SWIFT</a> to conduct simulations of planetary giant impacts.</p> <p>The tables were generated using the GitHub repository maintained by Sarah T. Stewart, specifically for <a href="https://github.com/ststewart/aneos-iron-2020">iron</a> and <a href="https://github.com/ststewart/aneos-forsterite-2019">forsterite</a>.</p> <p>All tables were created with the tension regions of the materials removed.</p> <p>Table "ANEOS_iron_S20_100gcc_denseTgrid_NOTension.txt" was generated with denser grid at temperate range 1e5 to 1e6 to using for impacts with target mass above 10 Earth mass.</p> <p>Table "ANEOS_forsterite_S19_80gcc_NOTension.txt" was generated with dense grid in both rho and temperate dimention.</p> <p>"ANEOS_iron_S20_100gcc_denseTgrid_NOTension.txt" and "ANEOS_forsterite_S19_80gcc_NOTension.txt" were generated to deal with extreme cases that impact speeds and target masses are very high. </p> <p>Most of the simulations were run with "ANEOS_forsterite_S19.txt" and "ANEOS_iron_S20.txt".</p> <p> </p>
Supporting Information for Accelerating Combustion Mechanism Discovery with Automated Uncertainty, Sensitivity, Thermodynamics, and Kinetics Calculations
<p>Supplementary material to accompany the manuscript "Accelerating Combustion Mechanism Discovery with Automated Uncertainty, Sensitivity, Thermodynamics, and Kinetics Calculations" by Sevy Harris and Richard H West.</p> <ul> <li>The software (mostly Python scripts) is in autoscience_workflow.zip. </li> <li>DFT results (Gaussian log files, Arkane input files, Arkane output files) for all species and reactions are in dft.zip</li> <li>RMG-built detailed kinetic models are in mechanisms.zip </li> <li>Additional plots and results (as described in the manuscript) are in supporting_information.pdf</li> </ul>
Thermodynamic rarity of metals 2020-2050
<p>Thermodinamic Rarity of metals 2020-2050 of the technologies for the energy and digital transition in Spain. </p> <p>Bulk Metals: Al, Cu, Ni, Mn</p> <p>Technological Metals: Ag, Au, Co, Li, Nd, Dy, Pd, Pt</p>
A novel R744 multi-temperature cycle for refrigerated transport applications with low-temperature ejector: experimental ejector characterization and thermodynamic cycle assessment - Ejector experimental data
<p>Experimental data obtained during the characterization of a R744 ejector in low-temperature suction operating conditions.</p> <p>The complete description of the experimental setup and of the R744 cooling unit concept designed to employ the tested ejector, as well as the discussion on the experimental data, are available in:</p> <p>Fabris, F., Pardiñas, Á. Á., Marinetti, S., Rossetti, A., Hafner, A., Minetto, S. (2023). A novel R744 multi-temperature cycle for refrigerated transport applications with low-temperature ejector: experimental ejector characterization and thermodynamic cycle assessment. International Journal of Refrigeration.</p>
Exact Thermodynamics and Transport in the Classical Sine-Gordon Model
<p>Raw data and Mathematica Notebook for the thermodynamics of the classical sine-Gordon model.</p> <p>It can be found:</p> <ol> <li>A transfer matrix code for equilibrium correlation functions.</li> <li>A solver for the classical Thermodynamic Bethe Ansatz and partitioning protocol.</li> <li>Monte Carlo data of the partitioning protocol.</li> </ol> <p> </p>
ThermoCodegen Example Thermodynamic Databases
<p>Custom Thermodynamic Databases used in the examples for ThermoCodegen. Including</p> <ul> <li>Forsterite-Fayalite Ideal Binary system</li> <li>Forsterite-SiO<sub>2 </sub>Binary with custom assymetric regular solution for liquid phase (Tweed, 2021)</li> <li>Forsterite-H<sub>2</sub>O system for simple olivine hydration to serpentine</li> <li>MgFeSiO<sub>4 </sub>Solid phase system from Stixrude & Lithgow-Berthelloni, 2011</li> </ul> <p>Each database includes autogenerated C and C++ source code for a custom set of endmembers and phases, together with xml description files containing models and parameters for each endmember and phase. </p>
A climatology of thermodynamic vs. dynamic Arctic wintertime sea ice thickness effects during the CryoSat-2 era: Data
<p>Data for:</p> <p>Anheuser, J., Liu, Y., and Key, J.: A climatology of thermodynamic vs. dynamic Arctic wintertime sea ice thickness effects during the CryoSat-2 era, submitted to: The Cryosphere. 2022</p> <p>Code can be found at:</p> <pre>https://doi.org/10.5281/zenodo.7987926</pre>
Tailoring magnetic hysteresis of Fe-Ni additive manufactured permalloy via multiphysics-multiscale simulations: Temperature-dependent parameters, thermodynamic database, results, and utilities
<p>This dataset contains temperature-dependent parameters and thermodynamic database, supplementary data and utilities of the publication "Tailoring magnetic hysteresis of additive manufactured Fe-Ni permalloy via multiphysics-multiscale simulations of process-property relationships" (<a href="http://doi.org/10.1038/s41524-023-01058-9">Yang et al., 2023</a>).</p> <p>We performed non-isothermal phase-field simulations of SLS process of the Fe<sub>21.5</sub>Ni<sub>78.5</sub> permalloy and subsequential mesoscopic thermo-elasto-plastic calculations and nanoscopic chemical order-disorder (<span>\(\gamma/\gamma'\)</span>) transition simulations as well as micromagnetic hysteresis calculations on nanostructures. Temperature-dependent parameters are employed. We then investigate the dependence of the fusion zone size, the residual stress and plastic strain, and the magnetic hysteresis of AM-produced Fe<sub>21.5</sub>Ni<sub>78.5 </sub>on beam power and scan speed.</p> <p>This dataset contains:</p> <ul> <li><em>feni_cac.tdb</em>: Thermodynamic database of the Fe-Ni binary system based on <a href="https://doi.org/10.1016/j.intermet.2010.02.026">Cacciamani et al., 2010</a></li> <li><em>average_values.csv</em>: Average quantities for creating the contours in Fig. 6a, 6b, 7a, 7b, 8a, and Supp. Fig. 10a, 10b.</li> <li><em>mesostructures.zip</em>: Containing resampled mesostructures from SLS single scan simulations (final timestep) with associated temperature, stress, and strain evolution. Nodal values are explained in Table 1. Naming pattern is <ul> <li>SLS-TEP__<power>-<scan_speed>__.e</li> </ul> </li> <li><em>parameters.zip</em>: Containing temperature-dependent parameters for performing SLS simulations and thermo-elasto-plastic calculations with fine (1K) temperature increments. The same temperature-dependent parameters with coarse temperature increments are already listed as Supp. Table 1, 2.</li> <li><em>sampled_point_data.zip</em>: Containing mechanical quantities on sampled points and corresponding results of nanoscopic <span>\(\gamma'\)</span> phase fraction (<span>\(\Psi_{\gamma'}\)</span>) and magnetic coercivity <span>\(H_\mathrm{c}\)</span>. Naming pattern is <ul> <li>mech__<power>-<scan_speed>__.csv</li> <li>Psi__<power>-<scan_speed>__.csv</li> <li>Hc__<power>-<scan_speed>__.csv</li> </ul> </li> <li><em>utilities.zip</em>: Containing Python utilities to perform calculations of free energy density and related thermodynamic quantities, extracting parameters from <em>feni_cac.tdb. </em><br><strong>Notice: </strong><a href="https://pycalphad.org/docs/latest/">pyCALPHAD</a> (ver 0.8.4) is requested for performing the Python utilities.</li> </ul> <p>Table 1. Nodal values in an exodus file Nodal value name Symbol Meaning Unit T <span>\(T\)</span> Normalized Temperature by <span>\(T_\mathrm{M}\)</span> - c <span>\(\rho\)</span> Substance order parameter - pb <span>\(\xi\)</span> Fusion zone indicator - eps (eps_11, eps_12, eps_13, eps_22, eps_23, eps_33) <span>\({\varepsilon}\)</span> Strain - epsp (epsp_11, epsp_12, epsp_13, epsp_22, epsp_23, epsp_33) <span>\({\varepsilon}_\mathrm{pl}\)</span> Plastic Strain - peeq <span>\(p_\mathrm{e}\)</span> Accumulated plastic strain - sigma (sigma_11, sigma_12, sigma_13, sigma_22, sigma_23, sigma_33) <span>\({\sigma}\)</span> Stress MPa vonmises <span>\(\sigma_\mathrm{e}\)</span> von Mises stress MPa u (u_X, u_Y, u_Z) <span>\(\mathbf{u}\)</span> Displacement μm</p> <p> </p> <p><strong>Notice</strong>: The raw transient outputs are not cured in this dataset due to the vast file size. Please contact the authors to acquire related files/utilities.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.