Dispersion of polymeric-coated–silica aerogel particles in unsaturated polyester composites: Effects on thermal-mechanical properties

The thermal-mechanical properties of unsaturated polyester (UP) composite were enhanced by the dispersion of silica aerogel (SA) with preserved pores. Low-cost SA was prepared from rice husk via the sol-gel process and ambient pressure drying. A new method was proposed to encapsulate the hydrophobic aerogel surface pores with hydrophilic polyvinyl alcohol (PVA) film using the fluidized-bed coating process. The dispersion of PVA-coated aerogel with preserved pores in the polyester matrix resulted in an increase of specific compressive strength (44.1?MPa?·?cm³?g^?1), thermal insulation (0.23?W?m^?1?K^?1), and thermal stability (T_onset?=?310°C), but decreased the glass transition temperature (T_g?=?260°C).     

Catalytic Activity and Morphological Properties of Ni/MgO Catalysts for the Oxidative Coupling of Methane

The catalytic activity of Ni/MgO catalysts was studied for the oxidative coupling of methane (OCM). The catalysts were characterized using transmission electron microscope (TEM) and XRD. The increase in C2+ selectivity of Ni/MgO was attributed to the presence of bulk dislocations and MgNiO₂ phase. This revised version was published online in June 2006 with corrections to the Cover Date.     

Isolation,characterization, and quantification of curcuminoids and their comparative effects in cerebral ischemia

The study reports a rapid and short analytical technique for separation, characterization, and quantitation along with comparative pharmacological effect of curcuminoids in cerebral ischemia. Flash chromatography, using silica and diol columns along with gradient mobile phase, was utilized to separate three curcuminoids, i.e., curcumin (Cur), demethoxycurcumin (DMC), and bisdemethoxycurcumin (BDMC) for the first time. The separated peaks were monitored at 200–360?nm, whereas the purity of compounds (96.2–97.6%) was determined through qualitative analysis such as infrared and ¹H and ¹³C-nuclear magnetic resonance spectroscopy, mass spectrometry (MS), and differential scanning calorimetry. Furthermore, chitosan nanoparticles (CS-NPs) for curcuminoids were prepared and characterized through zeta sizer, zeta potential, scanning electron microscopy, and transmission electron microscopy. The developed ultra performance of liquid chromatography-electrospray ionization-quadrupole-time of flight-mass spectrometry/mass spectrometry (UPLC-ESI-Q-TOF-MS/MS) method showed simplified automation and shorter run time for Combi flash over conventional separation techniques. The CS-NPs for all the three curcuminoids and combined-curcuminoids (CCr) (combined and administered together for a synergistic effect), following intranasal administration in middle cerebral artery–occluded rats were evaluated for grip strength, locomotor activity, and histopathological examination where the anti-ischemic activity was observed, in terms of potency, for all three CS-NPs and CCr as CCr>Cur>DMC>>BDMC. Cur-CS-NPs exhibited more potency among Cur, DMC, and BDMC, whereas CCr was the more potent anti-ischemic drug compared to Cur, DMC, and BDMC. For Cur the characteristic activity is proposed because of the presence of methoxy group on the phenyl ring whereas for CCr it is synergistic effect of curcuminoids.     

Ternary and binary copper(II) complexes: synthesis,characterization, ROS-inductive,proteasome inhibitory,and anticancer properties

Three ternary copper(II) complexes, [Cu(phen)(L-phe)Cl]·2H₂O, [Cu(phen)(L-leu)Cl]·4½H₂O, and [Cu(phen)(L-tyr)Cl]·3H₂O, and four binary copper(II) complexes, [Cu(phen)Cl₂]_, Cu(L-phe)₂·½H₂O, Cu(L-leu)₂·½H₂O, and Cu(L-tyr)₂·H₂O (where phen = 110-phenanthroline, L-phe = L-phenylalanine, L-tyr = L-tyrosine, L-leu = L-leucine and Cl^- = chloride), were synthesized and characterized by elemental analysis, spectroscopic techniques (FTIR, UV–visible, fluorescence spectroscopy), magnetic susceptibility, molar conductivity, and lipophilicity measurement. X-ray diffraction determination of a single crystal of [Cu(phen)(L-tyr)Cl] showed two independent molecules in the asymmetric unit, each with the same distorted square pyramidal geometry about copper(II). p-Nitrosodimethylaniline assay revealed that the three ternary complexes were better inducers of reactive oxygen species over time than binary complexes, CuCl₂, and free ligands. All the copper(II) complexes in this series inhibited the three proteolytic activities in the order Trypsin-like > Caspase-like > Chymotrypsin-like. In terms of anticancer properties, the copper(II)-phen complexes had GI50 values of less than 4 μM against MCF-7, HepG2, CNE1 and A549 cancer cell lines, more potent than cisplatin.     

Regression and ANN models for estimating minimum value of machining performance

Surface roughness is one of the most common performance measurements in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measurement such as surface roughness (R_a) must be formulated in the standard mathematical model. To predict the minimum R_a value, the process of modeling is taken in this study. The developed model deals with real experimental data of the R_a in the end milling machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum R_a value. The results show that regression and ANN models have reduced the minimum R_a value of real experimental data by about 1.57% and 1.05%, respectively.     

Optimization of neural network for ionic conductivity of nanocomposite solid polymer electrolyte system (PEO-LiPF6-EC-CNT)

In this study, the ionic conductivity of a nanocomposite polymer electrolyte system (PEO-LiPF₆-EC-CNT), which has been produced using solution cast technique, is obtained using artificial neural networks approach. Several results have been recorded from experiments in preparation for the training and testing of the network. In the experiments, polyethylene oxide (PEO), lithium hexafluorophosphate (LiPF₆), ethylene carbonate (EC) and carbon nanotubes (CNT) are mixed at various ratios to obtain the highest ionic conductivity. The effects of chemical composition and temperature on the ionic conductivity of the polymer electrolyte system are investigated. Electrical tests reveal that the ionic conductivity of the polymer electrolyte system varies with different chemical compositions and temperatures. In neural networks training, different chemical compositions and temperatures are used as inputs and the ionic conductivities of the resultant polymer electrolytes are used as outputs. The experimental data is used to check the system’s accuracy following the training process. The neural network is found to be successful for the prediction of ionic conductivity of nanocomposite polymer electrolyte system.     

Penalty Method for Variational Inequalities

In this paper we deal with the existence theory for a problem and give the proof of the existence by a penalty argument. We shall treat the problem for a variational inequality by introducing the penalized differential equation and then taking the limits of the equation resulting from the penalized approximation. We also discuss the error estimate for the difference of the two solutions in an appropriate norm.     

Hybrid GR-SVM for prediction of surface roughness in abrasive water jet machining

This paper presents a hybridization model of support vector machine (SVM) and grey relational analysis (GRA) in predicting surface roughness value of abrasive water jet (AWJ) machining process. The influential factors of five process parameters in AWJ, namely traverse speed, water jet pressure, standoff distance, abrasive grit size and abrasive flow rate, need to be analyzed using GRA approach. Then, the irrelevance factors of process parameters are eliminated. There is a need of determining the influential factors of process parameters to the surface roughness as to develop a robust prediction model. GRA acts as feature selection method in preprocessing process of hybrid grey relational-support vector machine (GR-SVM) prediction model. Efficiency of the proposed model is demonstrated. GR-SVM presents more accurate result than conventional SVM as it removes the redundant features and irrelevant element from the experimental datasets.     

Experimental investigation of long-wavelength optical lattice vibrations in quaternary AlxInyGa1−x−yN alloys and comparison with results from the pseudo-unit cell model

Raman and Fourier transform infrared (FTIR) spectroscopies have been utilized to measure long-wavelength optical lattice vibrations of high-quality quaternary Al_xIn_yGa_1−x−yN thin films at room temperature. The Al_xIn_yGa_1−x−yN films were grown on c-plane (0 0 0 1) sapphire substrates with AlN as buffer layers using plasma assisted molecular beam epitaxy (PA-MBE) technique with aluminum (Al) mole fraction x ranging from 0.0 to 0.2 and constant indium (In) mole fraction y=0.1. Pseudo unit cell (PUC) model was applied to investigate the phonons frequency, mode number, static dielectric constant, and high frequency dielectric constant of the Al_xIn_yGa_1−x−yN mixed crystals. The theoretical results were compared with the experimental results obtained from the quaternary samples by using Raman and FTIR spectroscopies. The experimental results indicated that the Al_xIn_yGa_1−x−yN alloy had two-mode behavior, which includes A₁(LO), E₁(TO), and E₂(H). Thus, these results are in agreement with the theoretical results of PUC model, which also revealed a two-mode behavior for the quaternary nitride. We also obtained new values of E₁(TO) and E₂(H) for the quaternary nitride samples that have not yet been reported in the literature.