Porous Materials with Tunable Structure and Mechanical Properties via Templated Layer-by-Layer Assembly

The deposition of stiff and strong coatings onto porous templates offers a novel strategy for fabricating macroscale materials with controlled architectures at the micro- and nanoscale. Here, layer-by-layer assembly is utilized to fabricate nanocomposite-coated foams with highly customizable properties by depositing polymer–nanoclay coatings onto open-cell foam templates. The compressive mechanical behavior of these materials evolves in a predictable manner that is qualitatively captured by scaling laws for the mechanical properties of cellular materials. The observed and predicted properties span a remarkable range of density-stiffness space, extending from regions of very soft elastomer foams to very stiff, lightweight honeycomb and lattice materials.

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.