Development of a predictive model for choosing cure agents for elastomer blends

Abstract

In elastomer blends with either polarity differences or unbalanced double bond concentration, cure agents have a preferential partition to one of the phases, with a tendency of having overcure in one phase and undercure in the other. To achieve proper vulcanization and improved performance in the final product, it is essential to have a correct balance between solubility and miscibility of each of the various cure agents in the elastomer blend composite. Cure agents with varying polarities and various reactivities have different crosslinking densities in the rubber compound. A homogeneous crosslinking density leads to superior mechanical properties. The method presented in this work includes a new tool based on a genetic optimization algorithm for assessing the partitioning of cure agents in different elastomers and their blends. The quantitative data allowed for a series of analyses of the solubility of the cure agents in the elastomer mixtures of different phases and was validated by correlation with their physico-chemical and mechanical properties of the resulting blend. This is an important tool for planning a cure system in rubber blends and to avoid incorrect partitioning of cure agents and consequent uneven curing of the final compound.