Thermal degradation of Pb(Zr0.53Ti0.47)O3/poly(vinylidene fluoride) composites as a function of ceramic grain size and concentration

Poly(vinylidene fluoride)/Pb(Zr0.53Ti0.47)O3,([PVDF]1−x/[PZT]x) composites of volume fractions x and (0–3) type connectivity were prepared in the form of thin films. PZT powders with average grain sizes of 0.2, 0.84, and 2.35 μm in different volume fraction of PZT up to 40 % were mixed with the polymeric matrix. The influence of the inorganic particle size and its content on the thermal degradation properties of the composites was then investigated by means of thermo-gravimetric analysis. It is observed that filler size affects more than filler concentration the degradation temperature and activation energy of the polymer. In the same way and due to their larger specific area, smaller particles leave larger solid residuals after the polymer degradation. The polymer degradation mechanism is not significantly modified by the presence of the inorganic fillers. On the other hand, an inhibition effect occurs due to the presence of the fillers, affecting particularly the activation energy of the process.

Effect of cylindrical filler aggregation on the electrical conductivity of composites

This work reports on the effect of carbon nanotube aggregation on the electrical conductivity and other network properties of polymer/carbon nanotube composites by modeling the carbon nanotubes as hard-core cylinders. It is shown that the conductivity decreases for increasing filler aggregation, and that this effect is more significant for higher cylinder volume fractions. It is also demonstrated, for volume fractions at which the giant component is present, that increasing the fraction of cylinders within clusters leads to a break of the giant component and the formation of a set of finite clusters. The decrease of the giant component with the increase of the fraction of cylinders within the cluster can be related to a decrease of the spanning probability due to a decrease of the number of cylinders between the clusters. Finally, it is demonstrated that the effect of aggregation can be understood by employing the network theory.