The development of nanocomposites relies on structure–property
relations, which necessitate multiscale modeling approaches. This
study presents a modeling framework that exploits mesoscopic models
to predict the thermal and mechanical properties of nanocomposites
starting from their molecular structure. In detail, mesoscopic models
of polypropylene (PP)- and graphene-based nanofillers (graphene (Gr),
graphene oxide (GO), and reduced graphene oxide (rGO)) are considered.
The newly developed mesoscopic model for the PP/Gr nanocomposite provides
mechanistic information on the thermal and mechanical properties at
the filler–matrix interface, which can then be exploited to
enhance the prediction accuracy of traditional continuum simulations
by calibrating the thermal and mechanical properties of the filler–matrix
interface. Once validated through a dedicated experimental campaign,
this multiscale model demonstrates that with the modest addition of
nanofillers (up to 2 wt %), the Young’s modulus and thermal
conductivity show up to 35 and 25% enhancement, respectively, whereas
the Poisson’s ratio slightly decreases. Among the different
combinations tested, the PP/Gr nanocomposite shows the best mechanical
properties, whereas PP/rGO demonstrates the best thermal conductivity.
This validated mesoscopic model can contribute to the development
of smart materials with enhanced mechanical and thermal properties
based on polypropylene, especially for mechanical, energy storage,
and sensing applications.