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The development of nanocomposites relies on structure-property relations,which necessitate multiscale modeling approaches. This study presents amodelling framework that exploits mesoscopic models to predict the thermal andmechanical properties of nanocomposites starting from their molecularstructure. In detail, mesoscopic models of polypropylene (PP) and graphenebased nanofillers (Graphene (Gr), Graphene Oxide (GO), and reduced GrapheneOxide (rGO)) are considered. The newly developed mesoscopic model for the PP/Grnanocomposite provides mechanistic information on the thermal and mechanicalproperties at the filler-matrix interface, which can be then exploited toenhance the prediction accuracy of traditional continuum simulations bycalibrating the thermal and mechanical properties of the filler-matrixinterface. Once validated through a dedicated experimental campaign, thismultiscale model demonstrates that with the modest addition of nanofillers (upto 2 wt.%), the Young's modulus and thermal conductivity show up to 35% and 25%enhancement, respectively, while the Poisson's ratio slightly decreases. Amongthe different combinations tested, PP/Gr nanocomposite shows the bestmechanical properties, whereas PP/rGO demonstrates the best thermalconductivity. This validated mesoscopic model can contribute to the developmentof smart materials with enhanced mechanical and thermal properties based onpolypropylene, especially for mechanical, energy storage, and sensingapplications.

Mesoscopic modeling and experimental validation of thermal and  mechanical properties of polypropylene nanocomposites reinforced by  graphene-based fillers