Modification of Theoretical models to predict mechanical behavior of PVC/NBR/organoclay nanocomposites


Correspondence to: G. Naderi (E-mail:


Organo-modified nanoclay (Cloisite 30B) was added via direct melt mixing to the acrylonitrile butadiene rubber/poly(vinyl chloride) (PVC/NBR) to fabricate polymer blend/clay nanocomposites. The states of nano-fillers dispersion were investigated by transmission electron microscopy (TEM) and X-ray diffraction (XRD). From the morphological study of nanocomposites, it is concluded that exfoliated morphology is obtainable by introduction of 2.5 vol % of nanoclay. The effect of nano-filler volume content on the mechanical properties of PVC/NBR matrix reinforced by Cloisite 30B was investigated by tensile test. Experimental results show that the Young's modulus and tensile strength of composites can significantly improved with a small amount of nanofiller. Moreover, to investigate the stress–strain behavior of NBR/PVC nanocomposites, seven constitutive models such as Arruda–Boyce, Mooney–Rivilen, Marlow, second order of polynomial, Van der Waals, and third order Odgen were studied and compared with experimental data. Results showed that Malow and second order polynomial model can be used for nanoclay-filled compound whereas the other models show more deviation from experimental data. Three micromechanical models named liner rule of mixtures (LROM) and the inverse rule of mixtures (IROM). Halpin–Tsai theory was applied to evaluate the dependence of Young modulus of nanocomposites on volume fraction of nanofiller. Two modifying factors were proposed to evaluate the Young's modulus of nanocomposites which could greatly improve the theoretical prediction obtained from inverse rule of mixtures (IROM) and Halpin–Tsai equation. The modifying factors were introduced by adopting an exponential, power-law and linear factors in the equation. In order to verify the suitability of the modified models, the ensuing theoretical predictions are compared to the other experimental data available in the literature. Good predictability of the modified models is demonstrated in the results. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 130: 3229–3239, 2013