Prediction of mechanical properties of graphite nanoflake/ polydimethylsiloxane nanocomposites as affected by processing method

Porosity is suppressible but unavoidable in manufacturing nanocomposites, therefore affecting their mechanical properties like intrinsic properties and other filler-related factors. Despite all the efforts, it is still regarded as challenging to develop theoretical models for mechanical properties of filler-reinforced nanocomposites which consider void defects created during manufacturing process. Here, graphite nanoflake/polydimethylsiloxane (GnF/PDMS) nanocomposites for paint-on applications are manufactured with different processing methods (i.e., solvent casting, hand lay-up, and spray lay-up) to address imperfections involved in each process and their implication to mechanical properties. The manufacturing processes were found to have almost no alternation on filler-related features (i.e., size, orientation, and distribution) except porosity. The evolution of a porosity is best modeled with the logistic function. We establish a set of prediction models for elastic modulus, fracture strength, and elongation at break by combining the previous models (i.e., Tsai-Hahn, Piggott-Leidner, and Landel-Nielsen models) with the logistic function on porosity. The proposed models are in good agreement with the experimental data on GnF/PDMS nanocomposites. The effect of process method on surface morphology is also discussed.

Automated summary

This study investigates the impact of manufacturing processes on porosity and mechanical properties of nanocomposites. It is found that the evolution of porosity is best modeled with the logistic function, and prediction models for mechanical properties are established based on this. The proposed models are in good agreement with experimental data.