Optimization of the Rubber Formulation for Footwear Applications from the Response Surface Method

Enhanced Control of Isoprene Polymerization with Trialkyl Rare Earth Metal Complexes through Neutral Donor Support

The development of catalysts for stereospecific polymerization of 1,3-dienes is an area of interest due to the robust nature of poly(1,3-diene)s' physical and mechanical properties, as well as the material's versatility in many applications. Dialkyl rare earth metal complexes supported by a diverse cast of ligand frameworks are selective for the polymerization of 1,3-dienes and are an exciting option for examination. However, development in this area has been hampered by the focus on complex catalyst systems that are costly to make. In this study, we synthesize a series of simple homoleptic trialkyl rare earth metal precatalysts and highlight their efficacy for isoprene polymerization using 1 or 2 equiv of [Ph3C][B(C6F5)4] activator. We investigated the addition of commercially available in situ donors, leading to the identification of triphenylphosphine as an ideal support to enhance the dispersity control and prevent loss of catalyst activity. We demonstrated how the activation and reaction conditions, including the order/time of reagent addition and donor electronics, had a major impact on the rate, control, and selectivity for the polymerization of 1,3-dienes. Further interrogation of the catalyst system signals the crucial role of triphenylphosphine in providing enhanced stability and control in this living catalyst system.

Determination of the most significant rubber components influencing the hardness of natural rubber (NR) using various statistical methods

Manufacturers use a large number of components in the production of modern rubber products. The selection of the constituents of the rubber recipe is primarily determined by the purpose of use. The different fields of applications of rubbers require the presence of appropriate mechanical properties. In this respect, it can be useful to know which substances forming the rubber recipe have significant influence on the different mechanical properties. In this study, the statistical analysis of the influence of rubber components on the hardness of natural rubber (NR) is proposed based on literature review. Based on the literature data, various statistical analyses, like linear regression, constrained linear regression, Ridge regression, Ridge sparse regression and binary classification decision trees were performed to determine which rubber components have the most significant effect on the hardness. In the statistical analyses, the effect of a total of 42 constituents of rubber compound on hardness was investigated. Most of the applied statistical methods confirmed that the traditional frequently used rubber components, such as carbon black and sulfur, have a primary effect on the hardness. However, the substances forming the rubber compound that are not widely used in practice or newly developed components appear differently in the lists of significant additives obtained by the different statistical methods.

3,4-Enhanced Polymerization of Isoprene Catalyzed by Side-Arm Tridentate Iminopyridine Iron Complex with High Activity: Optimization via Response Surface Methodology

3,4-Enhanced polymerization of isoprene catalyzed by late transition metal with high activity remains one of the great challenges in synthetic rubber chemistry. Herein, a library of [N, N, X] tridentate iminopyridine iron chloride pre-catalysts (Fe 1-4) with the side arm were synthesized and confirmed by the element analysis and HRMS. All the iron compounds served as highly efficient pre-catalysts for 3,4-enhanced (up to 62%) isoprene polymerization when 500 equivalent MAOs were utilized as co-catalysts, delivering the corresponding high-performance polyisoprenes. Furthermore, optimization via single factor and response surface method, it was observed that the highest activity was obtained by complex Fe 2 with 4.0889 × 10⁷ g·mol(Fe)^-1·h^-1 under the following conditions: Al/Fe = 683; IP/Fe = 7095; t = 0.52 min.

Natural Rubber Blend Optimization via Data-Driven Modeling: The Implementation for Reverse Engineering

Natural rubber formulation methodologies implemented within industry primarily implicate a high dependence on the formulator’s experience as it involves an educated guess-and-check process. The formulator must leverage their experience to ensure that the number of iterations to the final blend composition is minimized. The study presented in this paper includes the implementation of blend formulation methodology that targets material properties relevant to the application in which the product will be used by incorporating predictive models, including linear regression, response surface method (RSM), artificial neural networks (ANNs), and Gaussian process regression (GPR). Training of such models requires data, which is equal to financial resources in industry. To ensure minimum experimental effort, the dataset is kept small, and the model complexity is kept simple, and as a proof of concept, the predictive models are used to reverse engineer a current material used in the footwear industry based on target viscoelastic properties (relaxation behavior, tanδ, and hardness), which all depend on the amount of crosslinker, plasticizer, and the quantity of voids used to create the lightweight high-performance material. RSM, ANN, and GPR result in prediction accuracy of 90%, 97%, and 100%, respectively. It is evident that the testing accuracy increases with algorithm complexity; therefore, these methodologies provide a wide range of tools capable of predicting compound formulation based on specified target properties, and with a wide range of complexity.