Melt processing and properties of linear low density polyethylene-graphene nanoplatelet composites

Composites of Linear Low Density Polyethylene (LLDPE) and Graphene Nanoplatelets (GNPs) were processed using a twin screw extruder under different extrusion conditions. The effects of screw speed, feeder speed and GNP content on the electrical, thermal and mechanical properties of composites were investigated. The inclusion of GNPs in the matrix improved the thermal stability and conductivity by 2.7% and 43%, respectively. The electrical conductivity improved from 10-11 to 10-5 S/m at 150 rpm due to the high thermal stability of the GNPs and the formation of phonon and charge carrier networks in the polymer matrix. Higher extruder speeds result in a better distribution of the GNPs in the matrix and a significant increase in thermal stability and thermal conductivity. However, this effect is not significant for the electrical conductivity and tensile strength. The addition of GNPs increased the viscosity of the polymer, which will lead to higher processing power requirements. Increasing the extruder speed led to a reduction in viscosity, which is due to thermal degradation and/or chain scission. Thus, while high speeds result in better dispersions, the speed needs to be optimized to prevent detrimental impacts on the properties.</p

Review of natural fibre-reinforced hybrid composites

Natural fibre-reinforced hybrid composites which contain one or more types of natural reinforcement are gaining increasing research interest. This paper presents a review of natural fibre-reinforced hybrid composites. Both thermoplastic and thermoset composites reinforced by hybrid/synthetic fibres or hybrid/hybrid fibres are reviewed. The properties of natural fibres, the properties and processing of composites are summarised

Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks

The focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP) were processed in a twin screw extruder with three different screw speeds and feeder speeds (50, 100, and 150 rpm). These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. The tensile strength reached a maximum value at 4 wt% C-GNP and a speed of 150 rpm as this represented the optimum condition for the stress transfer through the amorphous chains of the matrix to the C-GNP. ANN can be confidently used as a tool to predict the above material properties before investing in development programs and actual manufacturing, thus significantly saving money, time, and effort

‘Containers’ for self-healing epoxy composites and coating: Trends and advances

The introduction of self-healing functionality into epoxy matrix is an important and challenging topic. Various micro/nano containers loaded self-healing agents are developed and incorporated into epoxy matrix to impart self-healing ability. The current report reviews the major findings in the area of self-healing epoxy composites and coatings with special emphasis on these containers. The preparation and use of polymer micro/nano capsules, polymer fibers, hollow glass fibers/bubbles, inorganic nanotubes, inorganic meso- and nano-porous materials, carbon nanotubes etc. as self-healing containers are outlined. The nature of the container and its response to the external stimulations greatly influence the self-healing performance. The self-healing mechanism associated with each type of container and the role of container parameters on self-healing performance of self-healing epoxy systems are reviewed. Comparison of the efficiency offered by different types of containers is introduced. Finally, the selection of containers to develop cost effective and green self-healing systems are mentioned

Towards the design of smart video-surveillance system

Security and monitoring systems are increasingly demanding in terms of quality, reliability and flexibility especially those dedicated to video surveillance. The aim of this study is to identify some limiting factors in the existing video-surveillance systems and to propose a set of best practices for developing a smart platform for a security monitoring system incorporating advanced techniques for video processing and analysis. In this work, we focus on the effect of the video quality on the biometric part of the video-surveillance systems for public security. In such systems, face detection and recognition from video sequences acquired from surveillance cameras, are challenging tasks, due to presence of strong illumination variations, noise, and changes in facial expressions. In this paper, we mainly focus on the illumination issue occurred in video surveillance. The low light video data is processed using a perceptual based approach, namely multi-scale Retinex method, to improve the video quality, followed by face detection. The experimental results demonstrate significant performance improvement in face detection and recognition, by improving the illumination of video sequences over the unprocessed video data. � 2018 IEEE.This publication was made possible by NPRP grant # NPRP8-140-2-065 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

The generalized trust-region sub-problem with additional linear inequality constraints-Two convex quadratic relaxations and strong duality

In this paper, we study the problem of minimizing a general quadratic function subject to a quadratic inequality constraint with a fixed number of additional linear inequality constraints. Under a regularity condition, we first introduce two convex quadratic relaxations (CQRs), under two different conditions, that are minimizing a linear objective function over two convex quadratic constraints with additional linear inequality constraints. Then, we discuss cases where the CQRs return the optimal solution of the problem, revealing new conditions under which the underlying problem admits strong Lagrangian duality and enjoys exact semidefinite optimization relaxation. Finally, under the given sufficient conditions, we present necessary and sufficient conditions for global optimality of the problem and obtain a form of S-lemma for a system of two quadratic and a fixed number of linear inequalities. 2020 by the authors.Funding: The authors would like to express their thanks to Qatar University for supporting their project under Grant NCBP-QUCP-CAS-2020-1.Scopu

Penalty ADM Algorithm for Cardinality Constrained Mean-Absolute Deviation Portfolio Optimization

In this paper, we study the cardinality constrained mean-absolute deviation portfolio optimization problem with risk-neutral interest rate and short-selling. We enhance the model by adding extra constraints to avoid investing in those stocks without short-selling positions. Also, we further enhance the model by determining the short rebate based on the return. The penalty alternating direction method is used to solve the mixed integer linear model. Finally, numerical experiments are provided to compare all models in terms of Sharpe ratios and CPU times using the data set of the NASDAQ and S&P indexes. Copyright 2022 International Academic PressScopu

Preparation and characterization of urea-formaldehyde microcapsules filled with paraffin oil

Paraffin oil was encapsulated in a urea–formaldehyde polymer shell by in situ polymerization. The effect of modifying the fabrication parameters, specifically the emulsifier, the core material concentration, the stirring rate, and the pH, on the resulting microcapsules was characterized by FTIR, SEM, particle size analysis and TGA. The stiffness and the mechanical stability during mixing of the microcapsules were also evaluated. It was found that the ethylene maleic anhydride copolymer (EMA)-based microcapsules are smaller, harder and have an increase in yield of 15 % or more compared to the polyvinyl alcohol (PVA)-based microcapsules. Both EMA- and PVA-based microcapsules have good thermal stability up to 400 °C. Smaller EMA-based microcapsules require a higher force, up to 0.96 N, to be 80 % deformed.Scopu

Characterizing Biaxiallly Stretched Polypropylene / Graphene Nanoplatelet Composites

In this work, polypropylene (PP) nanocomposites containing different weight concentration of graphene nanoplatelets (GNP) were prepared by melt-mixing using an industrial-scale, co-rotating, intermeshing, twin-screw extruder. The materials were then compression moulded into sheets, and biaxially stretched at different stretching ratios (SRs) below the PP melting temperature. The effects of GNP content and biaxial stretching on the bulk properties of unfilled PP and PP/GNP nanocomposites have been investigated in details. Results show that the addition of GNP (&gt;5wt%) can lead to electrically conductive composites due to the formation of percolation network. The GNP have led to increased polymer crystallinity and enhanced materials stiffness and strength. Biaxial stretching process further enhances the materials mechanical properties but has slightly decreased the composites electrical conductivity. The PP/GNP nanocomposites were also processed into 3D demonstrator parts using vacuum forming, and the properties of which were comparable with biaxially stretched composites