In the present work, a thermal treatment technique is applied for the synthesis of Ce_xSn_1−xO₂ nanoparticles. Using this method has developed understanding of how lower and higher precursor values affect the morphology, structure, and optical properties of Ce_xSn_1−xO₂ nanoparticles. Ce_xSn_1−xO₂ nanoparticle synthesis involves a reaction between cerium and tin sources, namely, cerium nitrate hexahydrate and tin (II) chloride dihydrate, respectively, and the capping agent, polyvinylpyrrolidone (PVP). The findings indicate that lower x values yield smaller particle size with a higher energy band gap, while higher x values yield a larger particle size with a smaller energy band gap. Thus, products with lower x values may be suitable for antibacterial activity applications as smaller particles can diffuse through the cell wall faster, while products with higher x values may be suitable for solar cell energy applications as more electrons can be generated at larger particle sizes. The synthesized samples were profiled via a number of methods, such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FT-IR). As revealed by the XRD pattern analysis, the Ce_xSn_1−xO₂ nanoparticles formed after calcination reflect the cubic fluorite structure and cassiterite-type tetragonal structure of Ce_xSn_1−xO₂ nanoparticles. Meanwhile, using FT-IR analysis, Ce-O and Sn-O were confirmed as the primary bonds of ready Ce_xSn_1−xO₂ nanoparticle samples, whilst TEM analysis highlighted that the average particle size was in the range 6−21 nm as the precursor concentration (Ce(NO₃)₃·6H₂O) increased from 0.00 to 1.00. Moreover, the diffuse UV-visible reflectance spectra used to determine the optical band gap based on the Kubelka–Munk equation showed that an increase in x value has caused a decrease in the energy band gap and vice versa.

This paper presents an enhanced artificial neural network (ANN) to predict the displacement in composite pipes impacted by a drop weight having different velocities. The impact response of fiber-reinforced polymer composite pipes depends on several factors including thickness, stacking sequence, and the number of layers. These factors were investigated in an earlier study using sensitivity analysis, and it was found that they had the most prominent effect on the impact resistance of the composite pipes. In this present study, composite pipes with a diameter of 54 mm are considered to explore the damages induced by low-velocity impact and the influence of these damages on their strength. To evaluate the effect of low-velocity, the pipes were exposed to impacts at different velocities of 1.5, 2, 2.5, and 3 m/s, and preliminary damage was initiated. Next, we used Jaya and E-Jaya algorithms to enhance the ANN algorithm for good training and prediction. The Jaya algorithm has a basic structure and needs only two requirements, namely, population size and terminal condition. Recently, Jaya algorithm has been widely utilized to solve various problems. Due to its single learning technique and limited population information, Jaya algorithm may quickly be trapped in local optima while addressing complicated optimization problems. For better prediction, an enhanced Jaya (E-Jaya) algorithm has been presented to enhance global searchability. In this study, ANN is enhanced based on the influential parameters using E-Jaya to test its effectiveness. The results showed the effectiveness of the E-Jaya algorithm for best training and prediction compared with the original algorithm.

To improve the structural stiffness, strength and reduce the weight of nanoplate structure, functionally graded (FG) graphene-reinforced nanocomposite (GRNC) laminated plates are exploited in this paper. The bending and buckling behaviors of FG-GRNC laminated nanoplates are investigated by using novel quasi-3D hyperbolic higher order shear deformation plate theory in conjunction with modified continuum nonlocal strain gradient theory, which considered both length and material scale parameters. The modified model of Halpin–Tsai is employed to calculate the effective Young’s modulus of the GRNC plate along the thickness direction, and Poisson’s ratio and mass density are computed by using the rule of mixture. An analytical approach of the Galerkin method is developed to solve governing equilibrium equations of the GRNC nanoplate and obtain closed-form solutions for bending deflection, stress distributions and critical buckling loads. A detailed parametric analysis is carried out to highlight influences of length scale parameter (nonlocal), material scale parameter (gradient), distribution pattern, the GPL weight fraction, thickness stretching, geometry and size of GPLs, geometry of the plate and the total number of layers on the stresses, deformation and critical buckling loads. Some details are studied exclusively for the first time, such as stresses and nonlocality effect.

Theoretical research has numerous challenges, particularly about modeling structures, unlike experimental analysis, which explores the mechanical behavior of complex structures. Therefore, this study suggests a new model for functionally graded shell structures called “Tri-coated FGM” using a spatial variation of material properties to investigate the free vibration response incorporating the porosities and microstructure-dependent effects. Two types of tri-coated FG shells are investigated, hardcore and softcore FG shells, and five distribution patterns are proposed. A novel modified field of displacement is proposed by reducing the number of variables from five to four by considering the shear deformation effect. The shell is rested on a viscoelastic Winkler/Pasternak foundation. An analytical solution based on the Galerkin approach is developed to solve the equations of motion derived by applying the principle of Hamilton. The proposed solution is addressed to study different boundary conditions. The current study conducts an extensive parametric analysis to investigate the influence of several factors, including coated FG nanoshell types and distribution patterns, gradient material distribution, porosities, length scale parameter (nonlocal), material scale parameter (gradient), nanoshell geometry, and elastic foundation variation on the fundamental frequencies. The provided results show the accuracy of the developed technique using different boundary conditions.

This study examined the impact of hybrid reinforcement particles, specifically zirconium carbide (ZrC) and tungsten carbide (WC), as well as the parameters of friction stir processing (FSP), on the microstructure, mechanical properties, and dynamic behavior of aluminum alloys. The hybrid particles were integrated into the aluminum alloy using friction stir processing (FSP). The fabricated metal matrix composites (MMCs) were characterized using optical microscopy, scanning electron microscopy (SEM), and dynamic mechanical analysis (DMA). The results showed that the FSP parameters and reinforcement particles played an important role in improving the grain refinement of the MMCs. This study’s results suggest that the FSP samples’ UTS can be maximized using a tool rotation speed of 600 rpm and a traverse speed of 30 mm/min. The grain refinement in the composite surface was attributed to the dynamic recrystallization during the friction stir processing (FSP) process. The reinforcement particles also acted as grain growth restrictors, further refining the grain size. This resulted in a 34% increase in ultimate tensile strength compared to AA2024 alloys and a 12% increase compared to AA7075 alloys. The composite surface also exhibited enhanced dynamic properties, with an increase in impact energy of 26%. The free vibration test showed that the hybrid reinforcement particles significantly improved the strength and damping capacity of the aluminum alloys, resulting in a high resonant frequency. This is important for applications such as vibration damping and noise reduction.

This study investigated the effects of reinforcing pure copper with hybrid B₄C and Si₃N₄ nanoparticles on the mechanical and physical properties of the nanocomposite matrix. The composite matrix was prepared using the powder metallurgy (PM) method, allowing uniform nanoparticle dispersion within the copper matrix. The PM method was a practical approach for achieving a homogeneous and good dispersion of the reinforcing particles in the matrix while controlling the porosity and improving the microstructure of the fabricated composite matrix. The addition of B₄C and Si₃N₄ are both very hard and dense materials. When added to a material, they can fill voids and reduce porosity. This can lead to significant improvements in the material’s mechanical properties. The study found that adding hybrid B₄C and Si₃N₄ nanoparticles enhanced the microhardness and mechanical properties of the nanocomposites. The improvements in the mechanical and physical properties of such composites containing 5% B₄C were 21.6% and 18.4% higher than the copper base alloy. The findings suggest that including ceramic particles is a viable strategy for enhancing the mechanical characteristics of copper in its pure form. For example, adding 5% B₄C particles to copper resulted in a 23% increase in Young’s modulus of the material while reducing electrical conductivity by 4.6%. On the other hand, the hybrid composite Cu/5%B₄C + 2.5%Si₃N₄ showed a 32% improvement in Young’s modulus and 71% in the microhardness value compared to the base metal. This makes it a promising option for various engineering applications, such as high-performance electrical contacts and bearings.