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This study presents an experimental design approach for optimization and prediction of specific total work of fracture (w f ) in polypropylene/ethylenepropylene diene monomer (EPDM) nanocomposites based on disparate graphene derivatives including multilayer graphene, few-layer graphene and graphene oxide. Box-Behnken design and response surface methodology (RSM) were exploited at three levels and four factors to construct a predictive mathematical model for the value of w f . The model predicted that the maximum value of w f (136.7 J/mm 2 ) is achieved by applying dynamically vulcanized system at EPDM content and few-layer graphene content of 20 wt% and of 0.5 wt%, respectively. In addition, by observing the fracture surfaces of specimens, a direct correlation between RSM predictions and morphology of nanocomposites was established. In this regard, transmission electron microscopy and wide-angle X-ray diffraction analyses showed that by using few-layer graphene, thanks to formation of an exfoliated structure, more energy for the failure of nanocomposites is required which substantiates the model predictions. Scanning electron microscopy observations depicted that the droplets diameter of homogeneous EPDM particle size plays a pivotal role in number of microvoids and nano-voids, which consequently promotes the creation of fibril structure and eventually heightens nanocomposite resistance against the applied load. The optimum status of this phenomenon happens at 20 wt% of EPDM and as the model showed, the value of w f reaches the highest point at this EPDM content. Likewise, by applying dynamic vulcanization, as truly predicted by the model, the formation of multi fibrillar structure led to dissipation of extra energy in nanocomposites inducing a remarkable improvement in the value of w f .

Polypropylene (PP)/ethylene propylene diene monomer (EPDM)/ graphene nanosheets (GNs) were compounded by a two-step melt mixing process via an internal mixer (brabender plasticorder). The effect of GNs, graphene oxide (GOSs) and graphene oxide functionalized with PP chains (PP-g-GOSs) on various blend properties were investigated. Wide X-ray diffraction (WAXD) patterns and transmission electron microscopy (TEM) images of the prepared nanocomposites revealed that the nanofillers were mostly dispersed into the PP phase and the dispersion state of GNs was improved with functionalization of graphene. SEM photomicrographs indicated that rubber droplets were distributed in the PP phase and a reduction of the dispersed EPDM droplet size was observed most likely due to increase in the viscosity of the PP-phase during melt mixing. The effects of nanofillers on thermal, mechanical and rheological properties were reported, and the obtained results were discussed in terms of morphology, state of dispersion and distribution of the nanofillers within the PP matrix. As for the mechanical properties, an improvement of 56% in tensile modulus and 48% in tensile strength, while 72% reduction in elongation at break was observed. The DMTA results revealed that the nanocomposites based on PP-g-GOSs had lower damping behavior and the intensity of the loss factor decreased by increasing the GNs content. These results indicate the presence of a strong interfacial interaction between the nanoplatelets and the polymer matrix.

In this study, the effects of graphene network formation on stress–strain behavior and different mechanical properties in PP/EPDM nanocomposites were investigated. TEM observation clarified that up to the optimum content of nanoparticles, the efficient networks of graphene formed which heightened the nanocomposites tolerance against different mechanical loads. The efficiency of graphene networks in arresting crack along with the mechanisms through which they dissipate energy were extensively examined. SEM observations from the fracture surface of impact test indicated that as a result of these networks, the cracks perpendicular to the load directions were formed which efficiently assisted the nanocomposite in dissipating energy and/or hindering further development of cracks and the premature failure in PP/EPDM samples. The stress–strain behavior of resultant nanocomposites was also studied by Arruda–Boyce hyperelastic model which can predict the behavior of nanocomposites with a high degree of accuracy that is rooted in the increased interaction between graphene and polymer matrix by graphene network formation. Exceeding the optimum content, the agglomerations form and results in deviation of experimental results with the model predictions. The findings of this model also substantiated the efficiency of graphene networks in developing a load‐resistant the microstructure of PP/EPDM nanocomposites.