Adsorption Thermodynamics, Modeling, and Kinetics Studies for the Removal of Lead Ions Using ZnO Nanorods

In the present investigation, zinc oxide nanorods (ZnO-NR) were synthesized via the hydrothermal method using ZnCl$_2$ as a zinc ion precursor in the presence of cetyltrimethylammonium bromide. Synthesized ZnO-NR was featured using advanced techniques including XRD, PL, SEM, and UV-visible spectroscopy. The role of these assynthesized ZnO-NR was evaluated for the sequestration of lead ions in batch mode. The elimination of lead ions was achieved at pH 6-7 using a 0.06 g adsorbent dose, 25 min contact time, 25 mg/L initial lead ion concentration, 323 K temperature, and 200 rpm agitation speed. A thermodynamic study revealed the endothermic nature of lead ion sequestration onto ZnO-NR. The lead ion sequestration followed kinetic (pseudo-second-order) and isotherm (Langmuir) models. The lead ions were eliminated up to 142 mg/g at the optimum level of affecting variables. The ZnO-NR might be a potential adsorbent for lead ion removal from industrial effluents

Infrared Efficiency and Ultraviolet Management of Red-Pigmented Polymethylmethacrylate Photoselective Greenhouse Films

Red-pigmented photoselective polymethylmethacrylate (PMMA) films were prepared by casting from polymer/chloroform solution. The films were doped with efficient red fluorescent perylene dyes specialized for plastic coloration, namely KREMER 94720 and KREMER 94739, which have excellent weathering stability and a high fluorescence quantum yield. The effect of the doping concentration was studied using the atomic force microscope (AFM), optical transmission, color measurement, time-resolved fluorescence, and Fourier transform infrared spectroscopy (FTIR). The obtained results suggested the potential usefulness for photoselective greenhouse cladding applications as the lowest doping concentration (10−5 wt%) displaying the UV-open effect, whereas the best UV-blocking and thermic effects were obtained for the highest doping concentration (10−1 wt)

Multiple slot nano-jet impingement cooling of a sinusoidal hot surface by using active rotating cylinders under magnetic field

In this study, cooling performance of a multi-slot jet impingement system for a wavy surface are explored under the triple combined effects of using magnetic field (MG-F), double active rotating cylinders and nanofluid. Double rotating cylinders which provide additional cooling are used while Galerkin weighed residual finite element method is used for the solution of the governing equations. Effects of Rew (rotational Reynolds number, between −1000 and 1000), Ha (MG-F strength between 0 and 30), MG-F inclination (between 0 and 90) and sub-cooling temperature of the active cylinders (dT between 0 and 10) on the cooling performance are assessed. Rotations of the double cylinders generally provide higher Nusselt number (Nu) while 41% and 18.9% increment in the Nu is obtained when using pure fluid and nanofluid. The average Nu behavior is different when using MG-F depending upon the rotations are active or not. Average Nu is sharply reduced by about 25.1% without rotations but it rises by about 89% at Ha = 10 by using rotations. The impacts of sub-cooling is very effective when rotations are active while up to 37.9% rise of Nu is obtained at Rew = −1000. When no cylinders are used, using MG-F reduced the average Nu by about 15.4%. The best cooling performance case in the absence of MG-F with cylinders is obtained at Rew = −1000 and dT = 10

Machine Learning Assisted Prediction of Power Conversion Efficiency of All-Small Molecule Organic Solar Cells: A Data Visualization and Statistical Analysis

Organic solar cells are famous for their cheap solution processing. Their industrialization needs fast designing of efficient materials. For this purpose, testing of large number of materials is necessary. Machine learning is a better option due to cheaper prediction of power conversion efficiencies. In the present work, machine learning was used to predict power conversion efficiencies. Experimental data were collected from the literature to feed the machine learning models. A detailed data visualization analysis was performed to study the trends of the dataset. The relationship between descriptors and power conversion efficiency was quantitatively determined by Pearson correlations. The importance of features was also determined using feature importance analysis. More than 10 machine learning models were tried to find better models. Only the two best models (random forest regressor and bagging regressor) were selected for further analysis. The prediction ability of these models was high. The coefficient of determination (R2) values for the random forest regressor and bagging regressor models were 0.892 and 0.887, respectively. The Shapley additive explanation (SHAP) method was used to identify the impact of descriptors on the output of models

Photonics with Gallium Nitride Nanowires

The surface plasmon resonance in low-dimensional semiconducting materials is a source of valuable scientific phenomenon which opens widespread prospects for novel applications. A systematic study to shed light on the propagation of plasmons at the interface of GaN nanowire is reported. A comprehensive analysis of the interaction of light with GaN nanowires and the propagation of plasmons is carried out to uncover further potentials of the material. The results obtained on the basis of calculations designate the interaction of light with nanowires, which produced plasmons at the interface that propagate along the designed geometry starting from the center of the nanowire towards its periphery, having more flux density at the center of the nanowire. The wavelength of light does not affect the propagation of plasmons but the flux density of plasmons appeared to increase with the wavelength. Similarly, an increment in the flux density of plasmons occurs even in the case of coupled and uncoupled nanowires with wavelength, but more increment occurs in the case of coupling. Further, it was found that an increase in the number of nanowires increases the flux density of plasmons at all wavelengths irrespective of uniformity in the propagation of plasmons. The findings point to the possibility of tuning the plasmonics by using a suitable number of coupled nanowires in assembly

Mechanical and Gamma Ray Absorption Behavior of PbO-WO3-Na2O-MgO-B2O3 Glasses in the Low Energy Range

The Makishima and Mackenzie model has been used to determine the mechanical properties of the PbO-WO3-Na2O-MgO-B2O3 glass system. The number of bonds per unit volume of the glasses (nb) increases from 9.40 × 1022 to 10.09 × 1022 cm−3 as the PbO content increases from 30 to 50 mol%. The Poisson’s ratio (σ) for the examined glasses falls between 0.174 and 0.210. The value of the fractal bond connectivity (d) for the present glasses ranges from 3.08 to 3.59. Gamma photon and fast neutron shielding parameters were evaluated via Phy-X/PSD, while that of electrons were calculated via the ESTAR platform. Analysis of the parameters showed that both photon and electron attenuation ability improve with the PbO content. The fast neutron removal cross section of the glasses varies from 0.094–0.102 cm−1 as PbO molar content reduced from 50–30 mol%. Further analysis of shielding parameters of the investigated glass system showed that they possess good potential to function in radiation protection applications

Parameter Identification of Switched Reluctance Motor SRM Using Exponential Swept-Sine Signal

Switched reluctance motors (SRMs) received major interest in several domains, e.g., in electric vehicles. This interest is due to the many advantages of SRMs, including operation at a wide range of speeds, high performances, low cost, robustness to run under degraded conditions, and controllability. One of the major aspects in the design and implementation of controllers for SRMs is the estimation of the motor parameters. An accurate estimate of these parameters is a challenge due to the highly nonlinear behavior of SRMs in addition to their magnetic saturated operating mode to maximize the energy transfer. This paper aims at estimating the parameters of SRM by developing a new SRM model using an analytical technique. The proposed technique is based on a parallel connection of several Hammerstein models that have polynomial nonlinearity. The model is driven by a swept-sine signal, and then finite element method analysis is performed to estimate the SRM parameters. The effectiveness of the proposed method is highlighted by numerical simulation. All these simulations were performed using MATLAB/SIMULINK

Adsorption Thermodynamics, Modeling, and Kinetics Studies for the Removal of Lead Ions Using ZnO Nanorods

In the present investigation, zinc oxide nanorods (ZnO-NR) were synthesized via the hydrothermal method using ZnCl2 as a zinc ion precursor in the presence of cetyltrimethylammonium bromide. Synthesized ZnO-NR was featured using advanced techniques including XRD, PL, SEM, and UV-visible spectroscopy. The role of these assynthesized ZnO-NR was evaluated for the sequestration of lead ions in batch mode. The elimination of lead ions was achieved at pH 6-7 using a 0.06 g adsorbent dose, 25 min contact time, 25 mg/L initial lead ion concentration, 323 K temperature, and 200 rpm agitation speed. A thermodynamic study revealed the endothermic nature of lead ion sequestration onto ZnO-NR. The lead ion sequestration followed kinetic (pseudo-second-order) and isotherm (Langmuir) models. The lead ions were eliminated up to 142 mg/g at the optimum level of affecting variables. The ZnO-NR might be a potential adsorbent for lead ion removal from industrial effluents