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Effect of morphology on the behaviour of ternary composites of polypropylene with inorganic fillers and elastomer inclusions
Authors:J Jancar  A T Dibenedetto
Affiliation:(1) Faculty of Chemistry, Technical University Brno, Veslarska 230, 637 11 Brno, Czech Republic;(2) Institute of Materials Science, University of Connecticut, 97 North Eagleville Road, 06269-3136 Storrs, CT, USA
Abstract:The effects of phase morphology, interfacial adhesion, rigid filler particle shape and elastomer volume fraction on the tensile yield strength of polypropylene (PP) filled with inorganic filler (CaCO3 or Mg(OH)2) and ethylene-propylene elastomer (EPR) were investigated. Separation of the filler and elastomer particles was achieved using maleic-anhydride-grafted PP (MPP) to enhance the filler-matrix adhesion. Encapsulation of the rigid filler by the elastomer was achieved using maleic-anhydride-grafted EPR (MEPR) to increase the filler-elastomer adhesion. The two limiting morphologies differ significantly in mechanical properties under tensile loading at the same material composition. Elastomer particles separately dispersed in the matrix enhance the shear banding in the bulk matrix which prevents the crazes growing from the filler surface from becoming unstable and, thus, increases the ductility of the material. Encapsulation by an elastomer layer on the filler surface relieves triaxial stresses at the filler surface, changing the major local failure mechanism from crazing to shear yielding and, hence, increasing the ductility of the material. Increase of the elastomer volume fraction also causes, in both cases, an increase in matrix ductility. Composite models are used to predict upper and lower limits of yield strength (sgry) for the two limiting morphologies over an interval of elastomer volume fractions (V e) from 0 to 0.2 at a constant filler loading of 30 vol.% and over a filler volume fraction from 0 to 0.4 at a constant EPR content in the matrix. Satisfactory agreement was found between the experimental data and theoretical predictions.
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