Vulcanizate Structures of SBR Compounds with Silica and Carbon Black Binary Filler Systems at Different Curing Temperatures.

The tire industry has shown an increasing demand for the reduction in rolling resistance. Efforts have been made to improve the viscoelastic properties of tire compounds and reduce the weight of tires through optimization of the vulcanizate structure, which has become extremely complex. In this study, vulcanizates using carbon black and silica as binary fillers were prepared at various curing temperatures. Vulcanizate structures with respect to curing temperature were classified according to the chemical crosslink density by sulfur, carbon black bound rubber (i.e., physical crosslink due to carbon black), and silica-silane-rubber network. All properties exhibited a decreasing trend under the application of high curing temperatures, and the decrease in the crosslink density per unit content of filler with an increase in curing temperature was shown to be greater in carbon black than in silica. Mechanical and viscoelastic properties were also measured to evaluate the impact that the compound variates have on tire tread performance. These results serve as a guideline for determining the content and filler type and for setting the cure condition during the design of actual compound formulations to increase the crosslink density of rubber while retaining the necessary mechanical and viscoelastic properties for practical application.

Inhibition of geranylgeranyl diphosphate synthase by bisphosphonates and diphosphates: a potential route to new bone antiresorption and antiparasitic agents.

We report the inhibition of a human recombinant geranylgeranyl diphosphate synthase (GGPPSase) by 23 bisphosphonates and six azaprenyl diphosphates. The IC50 values range from 140 nM to 690 microM. None of the nitrogen-containing bisphosphonates that inhibit farnesyl diphosphate synthase were effective in inhibiting the GGPPSase enzyme. Using three-dimensional quantitative structure-activity relationship/comparative molecular field analysis (CoMFA) methods, we find a good correlation between experimental and predicted activity: R2 = 0.938, R(cv)2 = 0.900, R(bs)2 = 0.938, and F-test = 86.8. To test the predictive utility of the CoMFA approach, we used three training sets of 25 compounds each to generate models to predict three test sets of three compounds. The rms pIC50 error for the nine predictions was 0.39. We also investigated the pharmacophore of these GGPPSase inhibitors using the Catalyst method. The results demonstrated that Catalyst predicted the pIC50 values for the nine test set compounds with an rms error of 0.28 (R2 between experimental and predicted activity of 0.948).