Study on the morphology development and dispersion mechanism of polypropylene/graphene nanoplatelets composites for different shear field
Source abstract
Abstract
Abstract Four different screw configurations were designed to prepare polypropylene (PP) nanocomposites reinforced with graphene nanoplatelets (GNPs). The relationship between the shear field, the morphology development of GNPs in PP and the properties of PP/GNPs nanocomposites were investigated. Finite element method software, POLYFLOW, was used to quantify the shear flow field in the four screw configurations. The morphology development was analyzed by field-emission SEM and optical transmission microscopy. The dispersion mechanism of GNPs in the PP matrix under the effect of a shear field was simulated showing that left-handed kneading elements resulted into the longest residence time and the addition of mixing elements resulted in the highest shear stress. Calculation results showed that during the extrusion process, large GNPs agglomerates (above 40 μm) tend to be exfoliated into smaller ones by two dispersion mechanisms corresponding to rupture and erosion. Since the shear stress provided by the twin-screw extruder configuration was limited, small agglomerates (below 40 μm) would exfoliate to thin layers only by following an erosion mechanism. The erosion mechanism and GNPs migration resulted to be mainly affected by the residence time. By increasing the residence time, nanocomposites resulted to be better dispersed and the particle size distribution was more homogeneous. Moreover, we found that the more the GNPs agglomerates thickness can be reduced by exfoliation, the more connection can established within the GNPs network, resulting in higher electrical and thermal conductivities.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Graphene research and applications · High voltage insulation and dielectric phenomena · Membrane Separation Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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