Solid Structure Properties Research Articles

Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids

Nanofluids are extensively applied in various heat transfer mediums for improving their heat transfer characteristics and hence their performance. Specific heat capacity of nanofluids, as one of the thermophysical properties, performs principal role in heat transfer of thermal mediums utilizing nanofluids. In this regard, different studies have been carried out to investigate the influential factors on nanofluids specific heat. Moreover, several regression models based on correlations or artificial intelligence have been developed for forecasting this property of nanofluids. In the current review paper, influential parameters on the specific heat capacity of nanofluids are introduced. Afterwards, the proposed models for their forecasting and modeling are proposed. According to the reviewed works, concentration and properties of solid structures in addition to temperature affect specific heat capacity to large extent and must be considered as inputs for the models. Moreover, by using other effective factors, the accuracy and comprehensive of the models can be modified. Finally, some suggestions are offered for the upcoming works in the relevant topics.

Laser-Induced Nanocluster Thin-Film Systems with Controlled Topology and Composition: The Possibility of Creating Superconducting Structures Based on New Physical Principles

The physical properties of solid structures depending on the boundary conditions in topologically ordered nanocluster systems are discussed. These systems are synthesized using various laser methods in the form of metal/semiconductor nanostructures/nanoclusters and thin films on solid substrates. A particular attention is paid to the possible mechanisms and tendency to high-temperature superconductivity in these thin-film nanocluster structures.

DIELECTRIC CONSTANT AND SEEBECK COEFFICIENT FOR SEMICONDUCTORS: THERMODYNAMIC AND DFT STUDIES

Dielectric constants and Seebeck coefficients for semiconductor materials are studied by thermodynamic method plus ab initio quantum density functional theory (DFT). A single molecule which is formed in semiconductor material is treated in gas phase with molecular boundary condition and then electronic polarizability is directly calculated through Mulliken and atomic polar tensor (APT) density charges in the presence of the external electric field. This electronic polarizability can be converted to dielectric constant for solid material through the Clausius–Mossotti formula. Seebeck coefficient is first simulated in gas phase by thermodynamic method and then its value divided by its dielectric constant is regarded as Seebeck coefficient for solid materials. Furthermore, unit cell of semiconductor material is calculated with periodic boundary condition and its solid structure properties such as lattice constant and band gap are obtained. In this way, proper DFT function and basis set are selected to simulate electronic polarizability directly and Seebeck coefficient through chemical potential. Three semiconductor materials Mg 2 Si , β- FeSi 2 and SiGe are extensively tested by DFT method with B3LYP, BLYP and M05 functionals, and dielectric constants simulated by the present method are in good agreement with experimental values. Seebeck coefficients simulated by the present method are in reasonable good agreement with experiments and temperature dependence of Seebeck coefficients basically follows experimental results as well. The present method works much better than the conventional energy band structure theory for Seebeck coefficients of three semiconductors mentioned above. Simulation with periodic boundary condition can be generalized directly to treat with doped semiconductor in near future.

Density-functional theory for structure and freezing of star polymer solutions

We use the soft fundamental measure theory (SFMT) to investigate a system of classical particles interacting with the pair potential of star polymers in solution. To that end we calculate liquid and solid structural properties, as well as freezing, solid-to-solid, and remelting phase transitions. Even subtle physical effects, like deviations from Gaussian crystal peaks and an anomalous peak broadening upon increasing density as well as a reasonable vacancy concentration are captured correctly. Good overall quantitative agreement with simulation data is found, however, with a tendency to overestimate the structural correlations. Furthermore, we demonstrate that all recent developments of its hard core counterpart can be incorporated systematically into SFMT.