Bisphenol A (BPA) is one of the chemicals used in monomer epoxy resins and polycarbonate plastics. The surface-enhanced Raman spectroscopy (SERS) method is precise for identifying biological materials and chemicals at considerably low concentrations. In the present article, the substrates coated with gold nanoparticles have been studied to identify BPA and control the diseases caused by this chemical. Gold nanoparticles were made by a simple chemical method and by applying gold salt and trisodium citrate dihydrate reductant and were coated on glass substrates by a spin-coat approach. Finally, using these SERS substrates as plasmonic sensors and Raman spectroscopy, the Raman signal enhancement of molecular vibrations of BPA was investigated. Then, the molecular vibrations of BPA in some consumer goods were identified by applying SERS substrates as plasmonic sensors and Raman spectroscopy. The fabricated gold nanoparticles are spherical and quasi-spherical nanoparticles that confirm the formation of gold nanoparticles by observing the plasmon resonance peak at 517 nm. Active SERS substrates have been coated with nanoparticles, which improve the Raman signal. The enhancement of the Raman signal is due to the resonance of the surface plasmons of the nanoparticles. Active SERS substrates, gold nanoparticles deposited on a glass substrate, were fabricated for the detection of BPA; a detection limit of 10-9 M and a relative standard deviation (RSD) equal to 4.17% were obtained for ten repeated measurements in the concentration of 10-9 M. Hence, the Raman results indicate that the active SERS substrates, gold nanoparticles for the detection of BPA along with the developed methods, show promising results for SERS-based studies and can lead to the development of microsensors. In Raman spectroscopy, SERS active substrate coated with gold nanoparticles are of interest, which is larger than gold particles due to the resonance of the surface plasmons of gold nanoparticles and the scattering of light from gold particles since the Raman signal amplifies the molecular vibrations of BPA. By decreasing the concentration of BPA deposited on the active SERS substrates, the Raman signal is also weakened due to the reduction of molecular vibrations. By increasing the surface roughness of the active SERS substrates, the Raman signal can be enhanced due to increased light scattering from rough centers, which are the same as the larger particles created throughout the deposition by the spin-coat method, and as a result, they enhance the signal by increasing the scattering of light. Then, the molecular vibrations of BPA were identified in some consumer goods by SERS substrates as plasmonic sensors and Raman spectroscopy.

Static stability behaviors of annular sandwich plates constructed from two layers of particle-reinforced nanocomposites have been investigated in the present article. The type of nanoscale particles has been considered to be graphene oxide powders (GOPs). The particles are assumed to have uniform and graded dispersions inside the matrix and the material properties have been defined according to Halpin-Tsai micromechanical model. The core layer is assumed to have honeycomb configuration. Annular plate has been formulated according to thin shell assumptions considering geometrical nonlinearities. After solving the governing equations via Galerkin's technique, it is showed that the post-buckling curves of annular sandwich plates rely on the core wall thickness, amount of GOP particles, sector radius, and thickness of layers.

Synthesis approaches usually affect the physical and chemical properties of ferrites. This helps ferrite materials to design them for desired applications. Some of these methods are mechanical milling, ultrasonic method, micro-emulsion, co-precipitation, thermal decomposition, hydrothermal, microwave-assisted, sol-gel, etc. These methods are extensively reviewed by taking example of ZnFe₂O₄. These methods also affect the microstructure and local structure of ferrite which ultimately affect the physical and chemical properties of ferrites. Various spectroscopic techniques such as Raman spectroscopy, Fourier Transform Infrared spectroscopy, Ultra Violet-Visible spectroscopy, Mossbauer spectroscopy, extended x-ray absorption fine structure, and electron paramagnetic resonance are found helpful to reveal this information. Hence, the basic principle and the usefulness of these techniques to find out appropriate information in ZnFe₂O₄ nanoparticles is elaborated in this review.

This paper aims to investigate the vibration analysis of functionally graded porous (FGP) beams using State-space approach with several classical and non-classical boundary conditions. The materials properties of the porous FG beams are considered to have even and uneven distributions profiles along the thickness direction. The equation of motion for FGP beams with various boundary conditions is obtained through Hamilton's principle. State-space approach is used to obtain the governing equation of porous FG beam. The comparison of the results of this study with those in the literature validates the present analysis. The effects of span-to-depth ratio (L/h), of distribution shape of porosity and others parameters on the dynamic behavior of the beams are described. The results show that the boundary conditions, the geometry of the beams and the distribution shape of porosity affect the fundamental frequencies of the beams.

Based on the nonlocal strain gradient (NSG) theory and considering the influence of moment of inertia, the governing equations of motion of porous functionally graded (FG) nanoplates with four edges clamped are established; The Galerkin method is applied to eliminate the spatial variables of the partial differential equation, and the partial differential governing equation is transformed into an ordinary differential equation with time variables. By satisfying the boundary conditions and solving the characteristic equation, the dispersion relations of the porous FG strain gradient nanoplates with four edges fixed are obtained. It is found that when the wave number is very small, the influences of nonlocal parameters and strain gradient parameters on the dispersion relation is very small. However, when the wave number is large, it has a great influence on the group velocity and phase velocity. The nonlocal parameter represents the effect of stiffness softening, and the strain gradient parameter represents the effect of stiffness strengthening. In addition, we also study the influence of power law index parameter and porosity on guided wave propagation.

Crystalline silicon photovoltaic cells have advantages of zero pollution, large scale and high reliability. A major challenge is that sunlight wavelength with photon energy lower than semiconductor band gap is converted into heat and increase its temperature and reduce its conversion efficiency. Traditional cooling PV method is using water flowing below the modules to cool down PV temperature. In this paper, the idea is proposed to reduce the temperature of the module and improve the energy conversion efficiency of the module through the modulation of the solar spectrum. A spectrally selective nanofilm reflector located directly on the surface of PV is designed, which can reflect sunlight wavelength with low photon energy, and even enhance absorption of sunlight wavelength with high photon energy. The results indicate that nanofilm reflector can reduce spectral reflectivity integral from 9.0% to 6.93% in 400~1100 nm wavelength range, and improve spectral reflectivity integral from 23.1% to 78.34% in long wavelength range. The nanofilm reflector can reduce temperature of PV by 4.51℃ and relatively improved energy conversion efficiency of PV by 1.25% when solar irradiance is 1000 W/m². Furthermore, the nanofilm reflector is insensitive in sunlight's angle and polarization state, and be suitable for high irradiance environment.

This study investigates the stability of protein tissues regarding the vibration analysis based on the classical beam theory coupled with the nonlocal elasticity theory concerning the exercise impact. As reported in the previous research, four different types of protein tissues are supposed, and the influence of sports training is investigated. The protein tissues are made of protein fibers surrounded by an elastic foundation. The exercise enhances the muscle area and plays an essential role in the stability and strength of protein and muscle tissues. The results are examined in detail to examine the impact of different parameters on the stability of nano protein fibers.

This study investigates the efficiency of ensemble machine learning for predicting the lightweight-aggregate concrete (LWC) characteristics. A stacking ensemble (STEN) approach was proposed to estimate the dry density (DD) and 28 days compressive strength (Fc-28) of LWC using two meta-models called random forest regressor (RFR) and extra tree regressor (ETR), and two novel ensemble models called STEN-RFR and STEN-ETR, were constructed. Four standalone machine learning models including artificial neural network, gradient boosting regression, K neighbor regression, and support vector regression were used to compare the performance of the proposed models. For this purpose, a sum of 140 LWC mixtures with 21 influencing parameters for producing LWC with a density less than 1000 kg/m³, were used. Based on the experimental results with multiple performance criteria, it can be concluded that the proposed STEN-ETR model can be used to estimate the DD and Fc-28 of LWC. Moreover, the STEN-ETR approach was found to be a significant technique in prediction DD and Fc-28 of LWC with minimal prediction error. In the validation phase, the accuracy of the proposed STEN-ETR model in predicting DD and Fc-28 was found to be 96.79% and 81.50%, respectively. In addition, the significance of cement, water-cement ratio, silica fume, and aggregate with expanded glass variables is efficient in modeling DD and Fc-28 of LWC.