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Symmetry
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Materials Science: Synthesis, Structure, Properties
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Materials Science: Synthesis, Structure, Properties

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Chemistry: Symmetry/Asymmetry".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 20277

Special Issue Editor

Dr. Denis A. Vinnik

E-Mail Website1 Website2
Guest Editor
South Ural State University (national research university)
Interests: single crystal growth; thermodynamic modeling; ceramics; functional magnetic oxides
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Functional materials with a strong correlation between synthesis features, chemical composition, crystal structure, and physical properties attract much attention due to the large number of fundamental phenomena and prospects for practical applications. The features of chemical processes critically influence crystal structure and physical properties in functional materials. That is why it is so important to investigate correlations between synthesis, structure, and properties for new materials development. New theoretical and experimental data lead to the emergence of new technologies that will make our world a better place. I kindly invite you to make a contribution to the Special Issue of Symmetry titled as “Materials Science: Synthesis, Structure, and Properties”.

Dr. Denis A. Vinnik
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • single crystal growth
  • thermodynamic modeling
  • ceramics
  • crystal structure
  • properties investigation
  • synthesis methods
  • functional materials
  • microstructure
  • physical properties
  • chemical interactions

Published Papers (7 papers)

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Research

19 pages, 5042 KiB  
Article
Nanofluids Characterization for Spray Cooling Applications
by Miguel Sanches, Guido Marseglia, Ana P. C. Ribeiro, António L. N. Moreira and Ana S. Moita
Symmetry 2021, 13(5), 788; https://doi.org/10.3390/sym13050788 - 02 May 2021
Cited by 15 | Viewed by 2341
Abstract
In this paper the mathematical and physical correlation between fundamental thermophysical properties of materials, with their structure, for nanofluid thermal performance in spray cooling applications is presented. The present work aims at clarifying the nanofluid characteristics, especially the geometry of their nanoparticles, leading [...] Read more.
In this paper the mathematical and physical correlation between fundamental thermophysical properties of materials, with their structure, for nanofluid thermal performance in spray cooling applications is presented. The present work aims at clarifying the nanofluid characteristics, especially the geometry of their nanoparticles, leading to heat transfer enhancement at low particle concentration. The base fluid considered is distilled water with the surfactant cetyltrimethylammonium bromide (CTAB). Alumina and silver are used as nanoparticles. A systematic analysis addresses the effect of nanoparticles concentration and shape in spray hydrodynamics and heat transfer. Spray dynamics is mainly characterized using phase Doppler interferometry. Then, an extensive processing procedure is performed to thermal and spacetime symmetry images obtained with a high-speed thermographic camera to analyze the spray impact on a heated, smooth stainless-steel foil. There is some effect on the nanoparticles’ shape, which is nevertheless minor when compared to the effect of the nanoparticles concentration and to the change in the fluid properties caused by the addition of the surfactant. Hence, increasing the nanoparticles concentration results in lower surface temperatures and high removed heat fluxes. In terms of the effect of the resulting thermophysical properties, increasing the nanofluids concentration resulted in the increase in the thermal conductivity and dynamic viscosity of the nanofluids, which in turn led to a decrease in the heat transfer coefficients. On the other hand, nanofluids specific heat capacity is increased which correlates positively with the spray cooling capacity. The analysis of the parameters that determine the structure, evolution, physics and both spatial and temporal symmetry of the spray is interesting and fundamental to shed light to the fact that only knowledge based in experimental data can guarantee a correct setting of the model numbers. Full article
(This article belongs to the Special Issue Materials Science: Synthesis, Structure, Properties)
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38 pages, 8346 KiB  
Article
Mechanical and Corrosion Studies of Friction Stir Welded Nano Al2O3 Reinforced Al-Mg Matrix Composites: RSM-ANN Modelling Approach
by Chandrashekar A., B. V. Chaluvaraju, Asif Afzal, Denis A. Vinnik, Abdul Razak Kaladgi, Sagr Alamri, Ahamed Saleel C. and Vineet Tirth
Symmetry 2021, 13(4), 537; https://doi.org/10.3390/sym13040537 - 25 Mar 2021
Cited by 43 | Viewed by 3326
Abstract
Nano aluminum oxide was prepared by the combustion method using aluminum nitrate as the oxidizer and urea as a fuel. Characterization of synthesized materials was performed using SEM (scanning electron microscope), powder XRD (X-ray diffraction), FTIR (Fourier transform infrared spectroscopy), and TEM (transmission [...] Read more.
Nano aluminum oxide was prepared by the combustion method using aluminum nitrate as the oxidizer and urea as a fuel. Characterization of synthesized materials was performed using SEM (scanning electron microscope), powder XRD (X-ray diffraction), FTIR (Fourier transform infrared spectroscopy), and TEM (transmission electron microscope). Al-Mg/Al2O3 (2, 4, 6, and 8 wt%) metal matrix nanocomposites were prepared by liquid metallurgy route-vertex technique. The homogeneous dispersion of nano Al2O3 particles in Al-Mg/Al2O3 metal matrix nanocomposites (MMNCs) was revealed from the field emission SEM analysis. The reinforcement particles present in the matrix were analyzed through energy-dispersive X-ray spectroscopy method. The properties (corrosion and mechanical) of the fabricated composites were evaluated. The mechanical and corrosion properties of the prepared nanocomposites initially increased and then decreased with the addition of nano Al2O3 particles in Al-Mg Matrix. The studies show that, the presence of 6 wt% of nano Al2O3 particles in the matrix improved the properties of other combinations of nano Al2O3 in the Al-Mg matrix material. Further, the Al-Mg/Al2O3 (6 wt%) MMNCs are joined by friction stir welding and evaluated for microstructural, mechanical, and corrosion properties. Al-Mg/Al2O3 MMNCs may find applications in the marine field. The response surface method (RSM) was used for the optimization of tensile strength, Young’s modulus, and microhardness of the synthesized material which resulted in a 95% of statistical confidence level. Artificial neural network (ANN) analysis was also carried out which perfectly predicted these two properties. The ANN model is optimized to obtain 99.9% accurate predictions by changing the number of neurons in the hidden layer. Full article
(This article belongs to the Special Issue Materials Science: Synthesis, Structure, Properties)
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17 pages, 7695 KiB  
Article
Prediction of Band Gap Energy of Doped Graphitic Carbon Nitride Using Genetic Algorithm-Based Support Vector Regression and Extreme Learning Machine
by Taoreed O. Owolabi and Mohd Amiruddin Abd Rahman
Symmetry 2021, 13(3), 411; https://doi.org/10.3390/sym13030411 - 03 Mar 2021
Cited by 23 | Viewed by 2938
Abstract
Graphitic carbon nitride is a stable and distinct two dimensional carbon-based polymeric semiconductor with remarkable potentials in organic pollutants degradation, chemical sensors, the reduction of CO2, water splitting and other photocatalytic applications. Efficient utilization of this material is hampered by the [...] Read more.
Graphitic carbon nitride is a stable and distinct two dimensional carbon-based polymeric semiconductor with remarkable potentials in organic pollutants degradation, chemical sensors, the reduction of CO2, water splitting and other photocatalytic applications. Efficient utilization of this material is hampered by the nature of its band gap and the rapid recombination of electron-hole pairs. Heteroatom incorporation due to doping alters the symmetry of the semiconductor and has been among the adopted strategies to tailor the band gap for enhancing the visible-light harvesting capacity of the material. Electron modulation and enhancement of reaction active sites due to doping as evident from the change in specific surface area of doped graphitic carbon nitride is employed in this work for modeling the associated band gap using hybrid genetic algorithm-based support vector regression (GSVR) and extreme learning machine (ELM). The developed GSVR performs better than ELM-SINE (with sine activation function), ELM-TRANBAS (with triangular basis activation function) and ELM-SIG (with sigmoid activation function) model with performance enhancement of 69.92%, 73.59% and 73.67%, respectively, on the basis of root mean square error as a measure of performance. The four developed models are also compared using correlation coefficient and mean absolute error while the developed GSVR demonstrates a high degree of precision and robustness. The excellent generalization and predictive strength of the developed models would ultimately facilitate quick determination of the band gap of doped graphitic carbon nitride and enhance its visible-light harvesting capacity for various photocatalytic applications. Full article
(This article belongs to the Special Issue Materials Science: Synthesis, Structure, Properties)
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18 pages, 8457 KiB  
Article
Determining the Impact of High Temperature Fire Conditions on Fibre Cement Boards Using Thermogravimetric Analysis
by Tomas Veliseicik, Ramune Zurauskiene and Marina Valentukeviciene
Symmetry 2020, 12(10), 1717; https://doi.org/10.3390/sym12101717 - 18 Oct 2020
Cited by 7 | Viewed by 2600
Abstract
When exposed to temperatures that are progressively and rapidly raised, large dimension fibre cement boards tend to crack. This occurrence is analysed and explained for a specific issue of asymmetric growth of a curvilinear crack in high temperatures. This phenomenon occurred while performing [...] Read more.
When exposed to temperatures that are progressively and rapidly raised, large dimension fibre cement boards tend to crack. This occurrence is analysed and explained for a specific issue of asymmetric growth of a curvilinear crack in high temperatures. This phenomenon occurred while performing Single Burning Item (SBI) experiments at fire loads which are higher than those used in countries of the European Union, which better reflect fire events that may occur in high-rise buildings. In such conditions, fibre cement boards crack, allowing the fire to reach the thermal insulating material which then combusts, thereby helping to spread the conflagration to upper floors. This experiment investigated the temperatures at which fibre cement boards crack, and why. Thermal analysis methods and thermogravimetric experiments were conducted on the fibre boards, followed by X-ray phase analysis investigations. During this phase, X-ray structural analysis was performed while the fibre cement was exposed to temperatures of 1000 °C. The article also presents ongoing change results when heating only composite fibre-cement board materials; phase changes that take place in high temperatures are discussed. Full article
(This article belongs to the Special Issue Materials Science: Synthesis, Structure, Properties)
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15 pages, 4061 KiB  
Article
Theoretical Insights into the Structure of the Aminotris(Methylenephosphonic Acid) (ATMP) Anion: A Possible Partner for Conducting Ionic Media
by Henry Adenusi, Gregory Chass and Enrico Bodo
Symmetry 2020, 12(6), 920; https://doi.org/10.3390/sym12060920 - 02 Jun 2020
Cited by 3 | Viewed by 2170
Abstract
We present a computational characterisation of Aminotris(methylenephosphonic acid) (ATMP) and its potential use as an anionic partner for conductive ionic liquids (ILs). We argue that for an IL to be a good candidate for a conducting medium, two conditions must be fulfilled: (i) [...] Read more.
We present a computational characterisation of Aminotris(methylenephosphonic acid) (ATMP) and its potential use as an anionic partner for conductive ionic liquids (ILs). We argue that for an IL to be a good candidate for a conducting medium, two conditions must be fulfilled: (i) the charge must be transported by light carriers; and (ii) the system must maintain a high degree of ionisation. The result trends presented herein show that there are molecular ion combinations that do comply with these two criteria, regardless of the specific system used. ATMP is a symmetric molecule with a total of six protons. In the bulk phase, breaking the symmetry of the fully protonated state and creating singly and doubly charged anions induces proton transfer mechanisms. To demonstrate this, we used molecular dynamics (MD) simulations employing a variable topology approach based on the reasonably reliable semiempirical density functional tight binding (DFTB) evaluation of the atomic forces. We show that, by choosing common and economical starting compounds, we can devise a viable prototype for a highly conductive medium where charge transfer is achieved by proton motion. Full article
(This article belongs to the Special Issue Materials Science: Synthesis, Structure, Properties)
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12 pages, 4976 KiB  
Article
A New Experimental Method for Determining the Thickness of Thin Surface Layers of Intensive Plastic Deformation Using Electron Backscatter Diffraction Data
by Alexander Smirnov, Evgeniya Smirnova and Sergey Alexandrov
Symmetry 2020, 12(4), 677; https://doi.org/10.3390/sym12040677 - 24 Apr 2020
Cited by 4 | Viewed by 2438
Abstract
It is, in general, essential to investigate correlations between the microstructure and properties of materials. Plastic deformation often localizes within thin layers. As a result, many material properties within such layers are very different from the properties in bulk. The present paper proposes [...] Read more.
It is, in general, essential to investigate correlations between the microstructure and properties of materials. Plastic deformation often localizes within thin layers. As a result, many material properties within such layers are very different from the properties in bulk. The present paper proposes a new method for determining the thickness of a thin surface layer of intensive plastic deformation in metallic materials. For various types of materials, such layers are often generated near frictional interfaces. The method is based on data obtained by Electron Backscatter Diffraction. The results obtained are compared with those obtained by an alternative method based on microhardness measurements. The new method allows for determining the layer thickness of several microns in specimens after grinding. In contrast, the measurement of microhardness does not reveal the presence of this layer. The grain-based and kernel-based types of algorithms are also adopted for determining the thickness of the layer. Data processed by the strain contouring and kernel average misorientation algorithms are given to illustrate this method. It is shown that these algorithms do not clearly detect the boundary between the layer of intensive plastic deformation and the bulk. As a result, these algorithms are unable to determine the thickness of the layer with high accuracy. Full article
(This article belongs to the Special Issue Materials Science: Synthesis, Structure, Properties)
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13 pages, 1987 KiB  
Article
Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning
by Shaobo Li, Yabo Dan, Xiang Li, Tiantian Hu, Rongzhi Dong, Zhuo Cao and Jianjun Hu
Symmetry 2020, 12(2), 262; https://doi.org/10.3390/sym12020262 - 08 Feb 2020
Cited by 19 | Viewed by 3601
Abstract
In this paper, a hybrid neural network (HNN) that combines a convolutional neural network (CNN) and long short-term memory neural network (LSTM) is proposed to extract the high-level characteristics of materials for critical temperature (Tc) prediction of superconductors. Firstly, by obtaining [...] Read more.
In this paper, a hybrid neural network (HNN) that combines a convolutional neural network (CNN) and long short-term memory neural network (LSTM) is proposed to extract the high-level characteristics of materials for critical temperature (Tc) prediction of superconductors. Firstly, by obtaining 73,452 inorganic compounds from the Materials Project (MP) database and building an atomic environment matrix, we obtained a vector representation (atomic vector) of 87 atoms by singular value decomposition (SVD) of the atomic environment matrix. Then, the obtained atom vector was used to implement the coded representation of the superconductors in the order of the atoms in the chemical formula of the superconductor. The experimental results of the HNN model trained with 12,413 superconductors were compared with three benchmark neural network algorithms and multiple machine learning algorithms using two commonly used material characterization methods. The experimental results show that the HNN method proposed in this paper can effectively extract the characteristic relationships between the atoms of superconductors, and it has high accuracy in predicting the Tc. Full article
(This article belongs to the Special Issue Materials Science: Synthesis, Structure, Properties)
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