Thermal,mechanical, and barrier properties of polyethylene/surlyn/organoclay nanocomposites blown films prepared by different mixing methods

(Low‐density polyethylene) (LDPE)/clay nanocomposites were prepared by melt blending in a twin‐screw extruder by using different mixing methods. Zinc‐neutralized carboxylate ionomer was used as a compatibilizer. Blown films of the nanocomposites were then prepared. The effect of mixing method on the clay dispersion and properties of the nanocomposites was evaluated by wide‐angle X‐ray diffraction analysis, mechanical properties, thermal properties, and barrier properties. The structure and properties of nanocomposites containing different amounts of nanoclay prepared by selected mixing techniques were also investigated. It was found that melt compounding of Surlyn/clay masterbatch with pure LDPE and Surlyn (two‐step‐a method) results in better dispersion and intercalation of the nanofillers than melt mixing of LDPE/Surlyn/clay masterbatch with pure LDPE and surlyn (two‐step‐b method) and direct mixing of LDPE with clay. The films containing ionomer have good barrier properties. A wide‐angle X‐ray diffraction pattern indicates that intercalation of polymer chains into the clay galleries decreases by increasing the clay content. Barrier properties and tensile modulus of the films were improved by increasing the clay content. In addition, tensile strength increased in the machine direction, but it decreased in the transverse direction by increasing the clay content. DSC results showed that increasing the clay content does not show significant change in the melting and crystallization temperatures. The results of thermogravimetric analysis showed that the thermal stability of the nanocomposites decreased by increasing the clay content more than 1 wt%. J. VINYL ADDIT. TECHNOL., 21:60–69, 2015. © 2014 Society of Plastics Engineers     

Nafion/histidine functionalized carbon nanotube: High-performance fuel cell membranes

Here we show preparation and characterization of a new type of composite membrane based on Nafion^®/histidine modified carbon nanotube by imidazole groups (Im-CNT), for direct methanol fuel cell (DMFC) applications. Due to the presence of this imidazole-based amino acid on the surface of CNT, new electrostatic interactions can be formed in the interface of Nafion^® and Im-CNT. Physical characteristics of these nanocomposite membranes are investigated by water uptake, methanol permeability, ion exchange capacity, proton conductivity, as well as fuel cell performance results.     

Numerical simulation of two-phase mass transport in three-dimensional naturally fractured reservoirs using discrete streamlines

Simulation of multiphase mass transport in large field-scale heterogeneous, naturally fractured reservoirs with common grid-based methods is very time consuming and costly. An in-house streamline-based simulator is extended to simulate two-phase flow of oil and water in 3D heterogeneous, naturally fractured reservoirs. Different waterflooding problems have been simulated in 2D and 3D fractured reservoirs, and their results are compared with those of the grid-based commercial software. It has been shown that the streamline-based simulator can predict the results of the grid-based software with an acceptable accuracy, while its computational time and required memory is exceptionally less than the grid-based method.     

Toward prediction of surface tension of branched n-alkanes using ANN technique

This study features the application of artificial neural network for prediction of surface tension of branched alkanes. Surface and interfacial tensions of alkanes show a specific interaction between adjacent molecules of the higher n-alkanes which results in an anisotropic dispersion force component of the surface energy. The surface tension of branched alkanes was studied for temperatures between 283.15 and 448.15 K. Two intelligent models named multilayer perceptron model (MLP) and radial basis function (RBF) model were developed and the accuracy of two models was examined by different graphical and statistical methods. Results showed that the both models are accurate and effective in prediction of surface tension of branched alkanes. However, the results were compared with experimental data and it was found that the estimated surface tension by multi-layer perceptron neural network is more accurate than radial basis function network.     

An integrated approach for aircraft turbofan engine fault detection based on data mining techniques

The present study proposes an algorithm for fault detection in terms of condition‐based maintenance with data mining techniques. The proposed algorithm is applied on an aircraft turbofan engine using flight data and consists of two main sections. In the first section, the relationship between engine exhaust gas temperature (EGT) as the main engine health monitoring criterion and other operational and environmental parameters of the engine was modelled using the data‐driven models. In the second section, a data set including EGT residuals, that is, the difference between the actual EGT of the system and the EGT estimated by the developed model in the health conditions of the engine, was created. Finally, faults occurring in each flight were detected based on the identification of abnormal events by a one‐class support vector machine trained by the health condition EGT residual data set. The results indicated that the proposed algorithm was an effective approach for inspecting aircraft engine conditions and detecting faults, with no need for technical knowledge on the interior characteristics of the aircraft engine.     

A survey and taxonomy on virtual data center embedding

The Journal of Supercomputing - Data center network virtualization is being considered as a promising technology to provide a performance guarantee for cloud computing applications. One important...     

An optimized skin texture model using gray-level co-occurrence matrix

Neural Computing and Applications - Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group...     

ERES: an extended regular expression signature for polymorphic worm detection

Journal of Computer Virology and Hacking Techniques - The quick spreading of modern sophisticated polymorphic worms poses a serious threat to the internet security. So far, several signature...     

Integrated Fuzzy DEA-ANFIS to Measure the Success Effect of Human Resource Spirituality

Workplace spirituality has gained attention as it is proven to be a contributor to organizational performance improvement. This paper aims to assess the impact of human resource spirituality on the success of organizational strategic change projects. The success of the projects is measured by the well-known criterion of deviation from the planned budget cost. Data collection is based on a questionnaire survey of 252 personnel in 36 large and medium-scale organizational change projects in power industry. The paper proposes an integrated algorithm of fuzzy data envelopment analysis (FDEA) and adaptive network-based fuzzy inference system (ANFIS) for measuring the pure effect of human resource spirituality on the success of organizational change projects in the power industry. It also achieves a verified tool capable of addressing complexity, nonlinearity, ambiguity, and fuzziness for measuring spirituality of human resources in the projects. Results show that spirituality of the project team has a significant effect on project success.