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.

Radiological and material characterization of high volume fly ash concrete

The main goal of research presented in this paper was the material and radiological characterization of high volume fly ash concrete (HVFAC) in terms of determination of natural radionuclide content and radon emanation and exhalation coefficients. All concrete samples were made with a fly ash content between 50% and 70% of the total amount of cementitious materials from one coal burning power plant in Serbia. Physical (fresh and hardened concrete density) and mechanical properties (compressive strength, splitting tensile strength and modulus of elasticity) of concrete were tested. The radionuclide content (226Ra, 232Th and 40K) and radon massic exhalation of HVFAC samples were determined using gamma spectrometry. Determination of massic exhalation rates of HVFAC and its components using radon accumulation chamber techniques combined with a radon monitor was performed. The results show a beneficial effect of pozzolanic activity since the increase in fly ash content resulted in an increase in compressive strength of HVFAC by approximately 20% for the same mass of cement used in the mixtures. On the basis of the obtained radionuclide content of concrete components the I -indices of different HVFAC samples were calculated and compared with measured values (0.27e0.32), which were significantly below the recommended 1.0 index value. The prediction was relatively close to the measured values as the ratio between the calculated and measured I-index ranged between 0.89 and 1.14. Collected results of mechanical and radiological properties and performed calculations clearly prove that all 10 designed concretes with a certain type of fly ash are suitable for structural and non-structural applications both from a material and radiological point of view.

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.