INVESTIGADORES
FAILLA Marcelo Daniel
congresos y reuniones científicas
Título:
MATHEMATICAL MODEL FOR THE GRAFTING OF MALEIC ANHYDRIDE ONTO POLYPROPYLENE
Autor/es:
MARÍA FERNANDA GONZALEZ FRAGUAS; A. BRANDOLIN; M. D. FAILLA
Lugar:
Córdoba
Reunión:
Simposio; Archipol 2009- V Simposio Binacional de Polímeros Argentina-Chile; 2009
Resumen:
The functionalization of polypropylene (PP) with polar monomers, such as maleic anhydride (MA), is used to improve their physico-chemical properties. This process increases the compatibility of PP with others polymers and improves adhesion to different materials. The functionalization can be initiated by a organic peroxide at the usual processing temperature of the polymer. There exist various processing parameters that affect the grafting efficiency and the reaction kinetics. A theoretical understanding of the kinetic mechanism would help to optimize the process by the rational used of a grafting agent and the appropriate selection of the graft conditions. Few mathematical models that describe the grafting process have been reported in the literature. For example, Zhu et al. used a monte carlo simulation to predict the grafting of MA onto PP using dicumyl peroxide as initiator, while Giudici et al  proposed a kinetic model that can predict roughly the main trend of experimentally measured value of the grafting degree, as well the changes in the polymer molecular weight distribution, as a function of MA and initiator concentrations. In the present work we proposed a kinetic model for the free radical grafting of MA onto PP.  The model takes into account MA grafting, either as monomer or as oligomer, and homopolymerization of MA. The model aim is to predict the number- and weight-average molecular weights and the degree of grafting as function of reaction time. The resulting infinite system of mass balance equations is solved using the moment technique. Initial conditions include concentrations of initiator and MA, Mn and Mw of virgin PP, mass of PP and temperature. The kinetic constants were estimated by comparing the predicted results with the experimental data obtained.