The chemistry of accelerated sulfur vulcanization is reviewed and a fundamental kinetic model for the vulcanization process is developed. The vulcanization of natural rubber by the benzothiazolesulfenamide class of accelerators is studied, where 2-(morpholinothio) benzothiazole (MBS) has been chosen as the representative accelerator. The reaction mechanisms that have been proposed for the different steps in vulcanization chemistry are critically evaluated with the objective of developing a holistic description of the governing chemistry, where the mechanisms are consistent for all reaction steps in the vulcanization process. A fundamental kinetic model has been developed for accelerated sulfur vulcanization, using population balance methods that explicitly acknowledge the polysulfidic nature of the crosslinks and various reactive intermediates. The kinetic model can accurately describe the complete cure response including the scorch delay, curing and the reversion for a wide range of compositions, using a single set of rate constants. In addition, the concentration profiles of all the reaction intermediates as a function of polysulfidic lengths are predicted. This detailed information obtained from the population balance model is used to critically examine various mechanisms that have been proposed to describe accelerated sulfur vulcanization. The population balance model provides a quantitative framework for explicitly incorporating mechanistically reasonable chemistry of the vulcanization process.

No abstract

No abstract

The continuing development of high throughput experiments (HTEs) in catalysis has dramatically increased the amount of data that can be collected in relatively short periods of time. Even when HTEs can afford “Edisonian” discovery, how can the increasing amounts of data be converted to knowledge to guide the next search in the vast design space of catalytic materials? To address this question, we recently proposed a catalyst design architecture that uses detailed kinetic models. In this paper, we describe Reaction Modeling Suitea rational, automated, and intelligent environment, based on systems, artificial intelligence, and optimization techniques that aid the development of kinetic models. We demonstrate its utility in developing a kinetic model for propane aromatization on zeolite. We also show the proof-of-concept of how a genetic algorithm-based search strategy can be used to search for kinetic parameters that correspond to an improved catalyst.

No abstract