Synthesis of Fe<Subscript>3</Subscript>O<Subscript>4</Subscript>@PPy–MWCNT nanocomposite and its application for extraction of ultra-trace amounts of PAHs from various samples

In the present study, multi-walled carbon nanotube oxide was immobilized on the pyrrole magnetic nanoparticles. Application of the synthesized material was investigated for the magnetic solid-phase extraction (MSPE) of polycyclic aromatic hydrocarbons (PAHs), from the environmental samples. Determinations of the analytes were performed with gas chromatography–mass spectrometry. The structure and morphology of Fe₃O₄@PPy–MWCNT were characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, thermal gravimetric analysis, and vibrating sample magnetometer. Performance of MSPE is mainly affected by extraction time, sorbent amount, sample solution volume, and eluent type and volume. In this study, the best possible performance of MSPE has been achieved using a combination of central composite design and Bayesian regularized artificial neural network technique. Under the optimum extraction conditions, linear range between 0.5 and 250 µg L^?1 (R ² > 0.994), preconcentration factors from 232 to 403 and limits of detection ranging from 0.1 to 0.3 µg L^?1 were obtained. Relative standard deviations for intra-day and inter-day precision were 3.3–5.1% and 3.7–5.6%, respectively. In addition, feasibility of the method was demonstrated by extraction and determination of PAHs from some real samples containing tap water, hookah water as well as soil samples, and relative recovery in the range of 85.4–106.8% was obtained. This MSPE method provides several advantages, such as high extraction efficiency, minimum sorbent for extraction of the analytes from high sample volumes, convenient extraction procedure, and short analysis times.     

LDPE/EVA/graphene nanocomposites with enhanced mechanical and gas permeability properties

In the present work, graphene oxide (GO) and reduced graphene oxide (RGO) were incorporated at low‐density polyethylene (LDPE)/ethylene vinyl acetate (EVA) copolymer blend using solution casting method. Monolayer GO with 1‐nm thickness and good transparency was synthesized using the well‐known Hummers's method. Fourier transform infrared and X‐ray photoelectron spectroscopy data exhibited efficient reduction of GO with almost high C/O ratio of RGO. Scanning electron microscopy showed the well distribution of GO and RGO within LDPE/EVA polymer matrix. The integrating effects of GO and RGO on mechanical and gas permeability of prepared films were examined. Young's modulus of nanocomposites are improved 65% and 92% by adding 7 wt% of GO and RGO, respectively. The tensile measurements showed that maximum tensile strength emerged in 3 wt% of loading for RGO and 5 wt% for GO. The measured oxygen and carbon dioxide permeability represented noticeably the attenuation of gas permeability in composite films compared with pristine LDPE/EVA blend. Copyright © 2015 John Wiley & Sons, Ltd.     

Correction to: 1H-imidazol-3-ium tricyanomethanide {[HIM]C(CN)3} as a nanostructured molten salt catalyst: application to the synthesis of pyrano[4,3‐b]pyrans

Research on Chemical Intermediates - Since it has been cleared that a fake compound has been sold to us as cyanoform.     

Dynamic Bio-Barcode Assay Enables Electrochemical Detection of a Cancer Biomarker in Undiluted Human Plasma: A Sample-In-Answer-Out Approach

There is a need for biosensing systems that can be operated at the point-of-care (POC) for disease screening and diagnostics and health monitoring. In spite of this, simple to operate systems with the required analytical sensitivity and specificity in clinical samples, using a sample-in-answer-out approach, remain elusive. Reported here is an electrochemical bio-barcode assay (e-biobarcode assay) that integrates biorecognition with signal transduction using molecular (DNA/protein) machines and signal readout using nanostructured electrodes. The e-biobarcode assay eliminates multistep processing and uses a single step for analysis following sample collection into the reagent tube. A clinically relevant performance for the analysis of prostate specific antigen (PSA) in undiluted and unprocessed human plasma: a log-linear range of 1 ng mL⁻¹–200 ng mL⁻¹ and a LOD of 0.4 ng mL⁻¹, was achieved. The e-biobarcode assay offers a realistic solution for biomarker analysis at the POC.     

Intra-group and inter-group trading differentiation analysis: A new approach for detecting the effect of trading volume on stock market fluctuations

Analyzing statistical properties of stock market data using statistical physics has received much attention from physicists and economists in recent years. Although some statistical characteristics of stock market data such as power-low tails of stock returns have become established fact, behavior of other related variables such as trading volume are less studied. In this paper, in order to examine the impact of trading volume on statistical properties of stock market returns, different trading behavior of different traders in Tehran Stock Exchange is analyzed. We define a new coefficient which measures the equilibrium between these different forces affecting the market at any given trading day. By adjusting market returns by this coefficient, we also assessed the impact of these forces on the statistical properties of stock market returns.     

Numerical study on the effect of the injection pressure on spray penetration length

This paper investigates the fuel spray behavior and variation of the spray characteristics under different injection pressures in internal combustion engines. In diesel engines the fuel spray is affected by the cavitation phenomenon which occurs in the injector orifice. The cavitation is one of the important phenomena which has a significant effect on the fuel spray characteristics. In this paper, for a specified geometry of the nozzle and the combustion chamber, the effect of the cavitation phenomenon on the spray characteristics, i.e. spray penetration length, the Sauter main diameter and evaporation are studied numerically for different values of the injection pressures. High injection pressure causes high velocity of the fuel in the injector orifice which leads to an effective atomization process with small and dispersed fuel droplets. The fluid flow equations are calculated in the combustion chamber to obtain the spray model. Since it is known that, high injection pressure together with low discharge pressure leads to creation of cavitation phenomenon inside the injector orifice, then for having cavitation phenomenon inside the injector orifice and consequently for investigating the cavitation phenomenon effects on the spray characteristics, the injection pressure values of 10–150 MPa are considered while the discharge pressure remains constant. The injector and combustion chamber are simulated in separated regions and the results of the outlet of the nozzle are used as the boundary conditions for solving the fuel flow inside the combustion chamber to achieve the spray simulation. The results of this study show that by increasing the injection pressure, the value of the spray penetration length increases and the Sauter main diameter decreases for constant discharge pressure. The Hydraulic Flip phenomenon occurs after the injection pressure of 120 MPa on the base of the results of this work.     

On finding robust approximate inverses for large sparse matrices

This paper presents a method based on matrix-matrix multiplication concepts for determining the approximate (sparse) inverses of sparse matrices. The suggested method is a development on the well-known Schulz iteration and it can successfully be combined with iterative solvers and sparse approximation techniques as well. A detailed discussion on the convergence rate of this scheme is furnished. Results of numerical experiments are also reported to illustrate the performance of the proposed method.     

Two new classes of optimal Jarratt-type fourth-order methods

In this paper, we investigate the construction of some two-step without memory iterative classes of methods for finding simple roots of nonlinear scalar equations. The classes are built through the approach of weight functions and these obtained classes reach the optimal order four using one function and two first derivative evaluations per full cycle. This shows that our classes can be considered as Jarratt-type schemes. The accuracy of the classes is tested on a number of numerical examples. And eventually, it is observed that our contributions take less number of iterations than the compared existing methods of the same type to find more accurate approximate solutions of the nonlinear equations.     

A locally linear neuro-fuzzy model for supplier selection in cosmetics industry

Supplier selection and evaluation is a complicated and disputed issue in supply chain network management, by virtue of the variety of intellectual property of the suppliers, the several variables involved in supply demand relationship, the complex interactions and the inadequate information of suppliers. The recent literature confirms that neural networks achieve better performance than conventional methods in this area. Hence, in this paper, an effective artificial intelligence (AI) approach is presented to improve the decision making for a supply chain which is successfully utilized for long-term prediction of the performance data in cosmetics industry. A computationally efficient model known as locally linear neuro-fuzzy (LLNF) is introduced to predict the performance rating of suppliers. The proposed model is trained by a locally linear model tree (LOLIMOT) learning algorithm. To demonstrate the performance of the proposed model, three intelligent techniques, multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network and least square-support vector machine (LS-SVM) are considered. Their results are compared by using an available dataset in cosmetics industry. The computational results show that the presented model performs better than three foregoing techniques.