Phase Diagram,Equilibrium Data,and Correlation for Aqueous Two-Phase System Formed by Tetrahydrofuran and Sorbitol

Theoretical Foundations of Chemical Engineering - The importance of aqueous two-phase systems has been increased recently as a method with greater performance capability in separation and...     

Effect of apple cider vinegar agent on the microstructure,phase evolution,and magnetic properties of CoFe2O4 magnetic nanoparticles

In the present study, spinel structure CoFe₂O₄ nanoparticles were successfully synthesized by the sol-gel auto-combustion technique. The effect of apple cider vinegar (ACV) addition as an organic biocompatible agent on the size, morphology, and magnetic properties of CoFe₂O₄ nanoparticles was investigated in detail. The phase evolution, particle size, and lattice parameter changes of the synthesized phase have been estimated by using Rietveld structure refinement analysis of X-ray powder diffraction data. Also, Fourier transform infrared spectra (FT-IR) of the samples verified the presence of two expected bands correspond to tetrahedral and octahedral metal-oxygen complexes within the spinel structure. Furthermore, microstructural observations revealed that ultrafine particles have a semi-spherical morphology. It was shown that the particles size decreased from ~45 to ~17 nm with an increase in the amount of ACV. Magnetic properties were carried out by vibrating sample magnetometer (VSM) at room temperature. Both the saturation magnetization (M_s) and coercivity (H_c) were found to be significantly dependent on the crystallite size and the amount of ACV.     

Compressive strength and wear properties of SiC/Al6061 composites reinforced with high contents of SiC fabricated by pressure-assisted infiltration

Pressure-assisted infiltration was used to synthesize SiC/Al 6061 composites containing high weight percentages of SiC. A combination of PEG and glass water was used to fabricate SiC preforms and the effect of the presence of glass water on the microstructure and mechanical properties of the preforms was evaluated by performing compression tests on the preforms. Also, the compressive strength and the hardness of the SiC/Al composites were investigated. The results revealed that the glass water improved the compressive strength of the preforms by about five times. The microstructural characterization of the composites showed that the penetration of the aluminum melt into the preforms was completed and almost no porosity could be seen in the microstructures of the composites. Moreover, the composite containing 75 wt% SiC exhibited the highest compressive strength as well as the maximum hardness. The results of the wear tests showed that increasing the SiC content reduces the wear rate so that the Al-75 wt% SiC composite has a lower wear rate and a lower coefficient of friction than those of Al-67 wt% SiC composite. This indicated higher wear resistance in these composites than the Al alloy due to the formation of a tribological layer on the surface of the composites.     

Aqueous solution of Oxone as new oxidizing agent for multi-walled carbon nanotube functionalization

In this work, multi-wall carbon nanotube (MWCNT) was successfully modified using aqueous solution of Oxone as a new oxidant. The effect of oxidation temperature on various characteristics of the treated MWCNTs was also investigated. FTIR and titration analysis proved the formation of carboxyl, carbonyl and epoxide groups at the surface of MWCNTs. The concentration of the functional groups increased as the modification temperature increased. The presence of such oxygen containing groups at the surface of MWCNTs justified the long time stability of the treated MWCNTs suspensions in water and methanol. The modified MWCNTs showed higher entanglement compared to row MWCNT due to the cross-links adjacent effect of pendant functional groups. Finally, it was concluded that Oxone oxidation process destroys the structure of the MWCNTs, but not severe enough to unzip the MWCNTs.     

Medium access control layer management for saving energy in wireless sensor networks routing algorithms

Wireless sensor networks (WSNs) consist of small nodes that are capable of sensing, computing, and communication. One of the greatest challenges in WSNs is the limitation of energy resources in nodes. This limitation applies to all of the protocols and algorithms that are used in these networks. Routing protocols in these networks should be designed considering this limitation. Many papers have been published examining low energy consumption networks. One of the techniques that has been used in this context is cross-layering. In this technique, to reduce the energy consumption, layers are not independent but they are related to each other and exchange information with each other. In this paper, a cross-layer design is presented to reduce the energy consumption in WSNs. In this design, the communication between the network layer and medium access layer has been established to help the control of efforts to access the line to reduce the number of failed attempts. In order to evaluate our proposed design, we used the NS2 software for simulation. Then, we compared our method with a cross-layer design based on an Ad-hoc On-demand Distance Vector routing algorithm. Simulation results show that our proposed idea reduces energy consumption and it also improves the packet delivery ratio and decreases the end-to-end delay in WSNs.     

An efficient classifier to diagnose of schizophrenia based on the EEG signals

In this paper, electroencephalogram (EEG) signals of 13 schizophrenic patients and 18 age-matched control participants are analyzed with the objective of classifying the two groups. For each case, multi-channels (22 electrodes) scalp EEG is recorded. Several features including autoregressive (AR) model parameters, band power and fractal dimension are extracted from the recorded signals. Leave-one (participant)-out cross validation is used to have an accurate estimation for the separability of the two groups. Boosted version of Direct Linear Discriminant Analysis (BDLDA) is selected as an efficient classifier which applied on the extracted features. To have comparison, classifiers such as standard LDA, Adaboost, support vector machine (SVM), and fuzzy SVM (FSVM) are applied on the features. Results show that the BDLDA is more discriminative than others such that their classification rates are reported 87.51%, 85.36% and 85.41% for the BDLDA, LDA, Adaboost, respectively. Results of SVM and FSVM classifiers were lower than 50% accuracy because they are more sensitive to outlier instances. In order to determine robustness of the suggested classifier, noises with different amplitudes are added to the test feature vectors and robustness of the BDLDA was higher than the other compared classifiers.     

A novel stimuli-responsive magnetic hydrogel based on nature-inspired tragacanth gum for chemo/hyperthermia treatment of cancerous cells

Journal of Polymer Research - Derivatives of formyl pyrazole were synthesized by the reaction of acetophenone, 4-methyl acetophenone, 3-acetyl furan, 3-acetyl thiophen and phenyl hydrazine...     

Highly efficient photocatalytic degradation of methylene blue using carbonaceous WO<Subscript>3</Subscript>/TiO<Subscript>2</Subscript> composites

Recent improvements in the performance of photocatalysts made it possible to tackle pollution through environment friendly methods. This study investigates the modification of the photocatalytic activity of TiO₂ by employing WO₃ and conductive polymers, namely, polyaniline (Pani) and polypyrrole (Ppy). Basing on our previous improvement of TiO₂ using a conductive polymer and activated carbon (AC), this study determines the activated carbon forms of TiO₂. The prepared composites are characterized using X-ray powder diffraction, transmission electron microscopy, Fourier transform infrared, thermogravimetric analysis, Brunauer–Emmet–Teller, and UV–Vis spectroscopy. The specific surface area of the mesoporous composites is as follows: WO₃/TiO₂·AC (Pani) > WO₃/TiO₂·AC (Ppy) > WO₃/TiO₂·Pani > WO₃/TiO₂·Ppy (127 > 98 > 68 > 44 m² g^?1), which exhibited a similar trend to the photocatalytic performances (100 > 95 > 91 > 72 % conversion rate). This result could be attributed to higher porosity, surge of charge separation, and photo-responding range extension induced by the synergistic effect of WO₃, conducting polymers, and TiO₂ in the samples.     

Solving the stochastic support vector regression with probabilistic constraints by a high-performance neural network model

This paper offers a recurrent neural network to support vector machine (SVM) learning in stochastic support vector regression with probabilistic constraints. The SVM is first converted into an equivalent quadratic programming (QP) formulation in linear and nonlinear cases. An artificial neural network for SVM learning is then proposed. The presented neural network framework guarantees obtaining the optimal solution of the SVM problem. The existence and convergence of the trajectories of the network are studied. The Lyapunov stability for the considered neural network is also shown. The efficiency of the proposed method is shown by three illustrative examples.