Effects of stacking-up and aggregation of graphene nanoplatelet on the polymer nanocomposite effective properties

The aim of the present work is to investigate and model the influencing factors including graphene nanoplatelet (GNP) size, aggregation state, stacking-up and random distribution of GNPs within the polymer nanocomposite. A high-fidelity generalized method of cell (HFGMC) analytical micromechanical model based on a unit cell is extended to estimate the elastic modulus of GNP-reinforced polymer nanocomposite. For reliable modelling and estimating the elastic modulus close to the experimental data, it is assumed that the GNPs inside the nanocomposite are randomly distributed. GNP has linear and transversely isotropic elastic behavior and polymer matrix has linear and isotropic elastic behavior. By considering the important factors involved in the construction of nanocomposite in the current micromechanical modeling, including (i) the GNP agglomeration dependent on the volume fraction, (ii) the GNP size, and (iii) stacking of GNP inside the nanocomposite, the current model can estimate the nanocomposite elastic modulus in a way that is very close to the experimental data. The agglomeration and stacking of GNPs are modelled through a representative volume element (RVE) of nanocomposite containing two phases including GNP and polymer matrix. Considering the agglomeration state dependent on the volume fraction of GNP leads to a very accurate estimation of the elastic modulus of the nanocomposite compared to the case where the agglomeration is independent of the volume fraction. It is shown that the increase of GNP stacking and agglomeration state inside the nanocomposite has a detrimental effect on the effective properties of the nanocomposite.