Silica-filled styrene butadiene rubber (SBR)/butadiene rubber (BR) compounds plasticized with mineral oils are mainly used to produce green tire treads. Previous works have demonstrated that the partial replacement of naphthenic oil (ONAF) by bio-based oils can provide processing and performance improvements for rubber compounds, along with environmental benefits. In this study, two modified soybean oils (esterified, OEST or esterified and epoxidized, OEPX) were investigated with the aim of evaluating the complete replacement of ONAF and determining whether the chemical properties of the oils affect the performance of silica-filled E-SBR/BR compounds, using the compound with ONAF as a reference. The physical properties, curing characteristics, morphology, and dynamic mechanical behavior were evaluated. The use of the modified soybean oils decreased the optimal cure time while increasing the crosslink density and the abrasive wear resistance. Further, the compounds with both modified soybean oils showed a good balance of mechanical properties. The modified soybean oils decreased the glass transition temperature of the rubber compounds, thus acting as true plasticizers. At 0°C, the tan δ value of E-SBR/BR/OEPX increased relative to that of E-SBR/BR/ONAF, whereas at 60°C, the values of the compounds with both modified soybean oils showed slight increases. The tan δ values reveal that compared with E-SBR/BR/ONAF, E-SBR/BR/OEPX has better wet grip and a similar rolling resistance, whereas E-SBR/BR/OEST has a higher rolling resistance. Thus, both modified soybean oils can fully replace ONAF and appear to be extremely attractive plasticizers for use in silica-filled E-SBR/BR compounds employed as green tire treads for passenger cars.

Predicting the curing behaviour of industrially employed elastomeric compounds under typical processing conditions in a reliable and scientifically driven way is important for rubber processing simulation routines, such as injection moulding. Herein, a rubber process analyser was employed to study the crosslinking kinetics of solid silicone rubber based on the concentration of dicumylperoxide. A model was proposed to describe the optimal cure time variation with peroxide concentration and temperature, based on the analysis of processing parameters applying kinetic and thermodynamic judgments. Additionally, the conversion rate was described with the aid of a phenomenological model, and the effect of dicumylperoxide concentration on the final crosslink state was investigated using kinetic and thermodynamic explanations. Optimal curing time was affected both by temperature and dicumylperoxide concentration. However, the effects were less pronounced for high temperatures (>170 ${}^{\circ }$C) and high concentrations (>0.70 phr). A limit on the crosslink state was detected, meaning that the dicumylperoxide capacity to crosslink the silicone network is restricted by the curing mechanism. Curing restrictions were presumed to be primarily thermodynamic, based on the proton abstraction mechanism that drives the crosslinking reaction. In addition to providing more realistic crosslinking models for rubber injection moulding simulation routines, the results of this study may also explain the chemical behaviour of organic peroxides widely used for silicone crosslinking.

Application of elastomers in general demands the conversion of their soluble networks into crosslinked structures. This abrupt change causes several modifications, both in the atomic/molecular level and at the macro‐scale. In this study, solid silicone rubber (high molecular weight poly(dimethylsiloxane)), was crosslinked with dicumylperoxide (DCP), a widely used crosslinking agent by the rubber industry. The changes caused by different DCP concentrations were investigated, aiming to bring attention to the molecular transformations, usually neglected when processing‐oriented studies are conducted. DCP concentration showed a limited contribution to the network's molecular dynamics, which was found to be mainly dominated by entanglements. The dominance of entanglements over other molecular constraints, like crosslink points, justifies the threshold and counter‐intuitive behavior of tensile and hardness properties. However, differences were found in the crystallization ability after crosslinking, when the more crosslink points were introduced, the lower the crystallinity was and the less stable the PDMS crystallites were. In addition to providing a deeper understanding of an industrially applied rubber system n terms of the effective concentration of DCP, and the reasoning behind such concentration, the findings of this study add to the state‐of‐the‐art comprehension of elastomeric networks, and how they behave on a molecular level.

Selecting the optimal settings for the production of rubber goods can be a very time-consuming and resource-intensive process. A promising method for optimizing rubber processing in a short period of time is the use of simulation routines. However, process simulations have only recently enabled meaningful predictions of not only the part’s state of cure but also its mechanical characteristics. As a first approach, second-order polynomials were considered suitable for describing the properties of compression-molded parts. However, more precision is required for injection molding due to the narrower distribution of mechanical characteristics of parts produced at different vulcanization temperatures. This became evident when the approximation of mechanical data with second order models partly revealed significant failures of part behavior prediction. To tackle this issue, a combined approach for approximation is proposed in this contribution by means of logistic growth function in addition to second order polynomials. To feed the model, an experimental plan was designed for producing injection-molded parts from an SBR compound at various temperatures and to different degrees of cure. The parts obtained were then characterized mechanically, and the results were opposed to varying degrees of cure and extents of reaction to calculate the model coefficients. Once available, a simulation-based calculation of the mechanical part quality is possible. The comparison of test results from the simulation and the real process has shown a reliable prediction, as simulation results were found within the natural deviation of the real measurements.