• Advances in nano research
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• v.12 no.1
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• pp.81-99
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• 2022

The aim of current work is to evaluate thermo-electrical characteristics of graphene nanoplatelets Reinforced Composite (GNPRC) coupled with magneto-electro-elastic (MEE) face sheet. In this regard, a cylindrical smart nanocomposite made of GNPRC with an external MEE layer is considered. The bonding between the layers are assumed to be perfect. Because of the layer nature of the structure, the material characteristics of the whole structure is regarded as graded. Both mechanical and thermal boundary conditions are applied to this structure. The main objective of this work is to determine critical temperature and critical voltage as a function of thermal condition, support type, GNP weight fraction, and MEE thickness. The governing equation of the multilayer nanocomposites cylindrical shell is derived. The generalized differential quadrature method (GDQM) is employed to numerically solve the differential equations. This method is integrated with Deep Learning Network (DNN) with ADADELTA optimizer to determine the critical conditions of the current sandwich structure. This the first time that effects of several conditions including surrounding temperature, MEE layer thickness, and pattern of the layers of the GNPRC is investigated on two main parameters critical temperature and critical voltage of the nanostructure. Furthermore, Maxwell equation is derived for modeling of the MEE. The outcome reveals that MEE layer, temperature change, GNP weight function, and GNP distribution patterns GNP weight function have significant influence on the critical temperature and voltage of cylindrical shell made from GNP nanocomposites core with MEE face sheet on outer of the shell.

• Advances in nano research
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• v.17 no.2
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• pp.181-195
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• 2024

This study introduces a novel functionally graded material model, termed the "Coated Functionally Graded Graphene-Reinforced Composite (FG GRC)" model, for investigating the free vibration response of plates, highlighting its potential to advance the understanding and application of material property variations in structural engineering. Two types of coated FG GRC plates are examined: Hardcore and Softcore, and five distribution patterns are proposed, namely FG-A, FG-B, FG-C, FG-D, and FG-E. A modified displacement field is proposed based on the higher-order shear deformation theory, effectively reducing the number of variables from five to four while accurately accounting for shear deformation effects. To solve the equations of motion, an analytical solution based on the Galerkin approach was developed for FG GRC plates resting on a viscoelastic Winkler/Pasternak foundation, applicable to various boundary conditions. A comprehensive parametric analysis elucidates the impact of multiple factors on the fundamental frequencies. These factors encompass the types and distribution patterns of the coated FG GRC plates, gradient material distribution, porosities, nonlocal length scale parameter, gradient material scale parameter, nanoplate geometry, and variations in the elastic foundation. Our theoretical research aims to overcome the inherent challenges in modeling structures, providing a robust alternative to experimental analyses of the mechanical behavior of complex structures.

• Textile Coloration and Finishing
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• v.30 no.2
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• pp.98-106
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• 2018

In this study, graphene oxide(GO) was synthesized by using Hummer's method. Then, GO was used as a additive for epoxy resin nanocomposites that were prepared by mixing Tetraglycidyl diamino diphenyl methane(TGDDM) and hardner(MDEA+M-MIPA). Thermal and mechanical properties of epoxy resin nanocomposites were confirmed by analytical methods such as TG-DTA, DMA, fracture toughness, tensile strength, and flexural strength. The fracture surfaces of epoxy resin nanocomposites with different content of the GO were observed by a Scanning Electron Microscope(SEM). The mechanism for mechanical properties of epoxy resin nanocomposites was analyzed by modeling of nanocomposites with different GO weight. Due to the GO, both the heat resistance and the glass transition temperature of the epoxy resin nanocomposites were improved. Interestingly, when 0.1wt.% of GO was added to the epoxy resin/hardner mixture, the properties of mechanical increased compared with the neat epoxy resin. This results were caused by an aggregation between the GO.

• Journal of Welding and Joining
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• v.31 no.2
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• pp.26-29
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• 2013

The latest research & development trend on friction stir welding and friction stir processing technologies presented in the international symposium, 'Friction Stir Welding & Processing VII'. Papers and presentations about high temperature materials such as advanced high strength steel, stainless steel and titanum alloy shoot up this year. Papers on modeling of metal flow and control of process parameters also increased. The FSP technologies for manufacturing of carbon materials reinforced metal matrix composites were reported, too.

• Advances in nano research
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• v.16 no.6
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• pp.623-638
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• 2024

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

• Membrane Journal
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• v.31 no.4
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• pp.253-261
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• 2021

Solutions to water pollution, global warming, and climate change have been currently discussed. Pressure retarded osmosis (PRO) using a difference in salt concentration between two fluids is proposed to meet the demand for clean water and produce eco-friendly energy. Although PRO has been researched continuously, it has not been commercialized yet due to limitations such as lack of technology and the high price of membranes. Meanwhile, the membrane is one of the most significant parts of the PRO engine and salinity gradient power (SGP) technology. Research continues to technologically develop graphene oxide membranes and nanocomposite membranes used in salinity gradient power generation. Studies on efficient membranes, solvents, and solutes are active to enable high energy efficiency of the osmotic heat engine even at low temperatures of waste. Studies have been conducted on reducing internal concentration polarization and increasing power density by using membranes with balanced permeability and selectivity. In this review, dealing with these studies, we discuss the types of PRO membranes, theoretical modeling of technologies through efficient membranes, and other technologies to develop the process efficiency.

• Advances in nano research
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• v.12 no.1
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• pp.73-79
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• 2022

Due to the extensive use of concrete structures in various applications, the improvement of their strength and quality has become of great importance. A new way of achieving this purpose is to add different types of nanoparticles to concrete admixtures. In this work, a mathematical model has been employed to analyze the vibration of concrete beams reinforced by graphene oxide (GO) nanoparticles. To verify the accuracy of the presented model, an experimental study has been conducted to compare the compressive strengths of these beams. Since GO nanoparticles are not readily dissolved in water, before producing the concrete samples, the GO nanoparticles are dispersed in the mixture by using a shaker, magnetic striker, ultrasonic devices, and finally, by means of a mechanical mixer. The sinusoidal shear deformation beam theory (SSDBT) is employed to model the concrete beams. The Mori-Tanaka model is used to determine the effective properties of the structure, including the agglomeration influences. The motion equations are calculated by applying the energy method and Hamilton's principle. The vibration frequencies of the concrete beam samples are obtained by an analytical method. Three samples containing 0.02% GO nanoparticles are made and their compressive strengths are measured and compared. There is a good agreement between our results and those of the mathematical model and other papers, with a maximum difference of 1.29% between them. The aim of this work is to investigate the effects of nanoparticle volume fraction and agglomeration and the influences of beam length and thickness on the vibration frequency of concrete structures. The results show that by adding the GO nanoparticles, the vibration frequency of the beams is increased.

• Steel and Composite Structures
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• v.43 no.5
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• pp.581-601
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• 2022

The main objective of this research work is to investigate the free vibration behavior of annular sandwich plates resting on the Kerr foundation at thermal conditions. This sandwich configuration is composed of two FGM face sheets as coating layer and a porous GPLRC (GPL reinforced composite) core. It is supposed that the GPL nanofillers and the porosity coefficient vary continuously along the core thickness direction. To model closed-cell FG porous material reinforced with GPLs, Halpin-Tsai micromechanical modeling in conjunction with Gaussian-Random field scheme is used, while the Poisson's ratio and density are computed by the rule of mixtures. Besides, the material properties of two FGM face sheets change continuously through the thickness according to the power-law distribution. To capture fundamental frequencies of the annular sandwich plate resting on the Kerr foundation in a thermal environment, the analysis procedure is with the aid of Reddy's shear-deformation plate theory based high-order shear deformation plate theory (HSDT) to derive and solve the equations of motion and boundary conditions. The governing equations together with related boundary conditions are discretized using the generalized differential quadrature (GDQ) method in the spatial domain. Numerical results are compared with those published in the literature to examine the accuracy and validity of the present approach. A parametric solution for temperature variation across the thickness of the sandwich plate is employed taking into account the thermal conductivity, the inhomogeneity parameter, and the sandwich schemes. The numerical results indicate the influence of volume fraction index, GPLs volume fraction, porosity coefficient, three independent coefficients of Kerr elastic foundation, and temperature difference on the free vibration behavior of annular sandwich plate. This study provides essential information to engineers seeking innovative ways to promote composite structures in a practical way.

• Clean Technology
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• v.28 no.3
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• pp.210-217
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• 2022

Boron(B) is a rare resource used for various purposes such as glass, semiconductor materials, gunpowder, rocket fuel, etc. However, Korea depends entirely on imports for boron. Considering the global boron reserves and its current production rate, boron will be depleted on earth in 50 years. Thus, a process including proper adsorbent materials recovering boron from seawater is demanded. This research proposed carbonized nanofibers prepared from electrospun PAN(polyacrylonitrile) mats as promising materials to adsorb boron in aqueous solution. First, the mechanism of boron adsorption on carbonized nanofibers was investigated by DFT(density functional method)-based molecular modeling and the calculated energetics demonstrated that the boron chemisorption on the nitrogen-doped graphene surface by a two-step dehydration is possible with viable activation energies. Then, the electrospun PAN mats were stabilized in air and then carbonized in an argon atmosphere before being immersed in the boric acid aqueous solution. Analytically, SEM(scanning electron microscopy) and Raman measurements were employed to confirm whether the electrospinning and carbonization of PAN mats proceeded successfully. Then, XPS(X-ray photoelectron spectroscopy) peak analysis showed whether the intended nitrogen-doped carbon nanofiber surface was formed and boron was properly adsorbed on nanofibers. Those results demonstrated that the carbonized nanofibers prepared from electrospun PAN mats could be feasible adsorbents for boron recovery in seawater.