Reinforcement Mechanism of Carbon Black-Filled Rubber Nanocomposite as Revealed by Atomic Force Microscopy Nanomechanics

In this study, atomic force microscopy (AFM) nanomechanics were used to visualize the nanoscale stress distribution in carbon black (CB)-reinforced isoprene rubber (IR) vulcanizates at different elongations and quantitatively evaluate their volume fractions for the first time. The stress concentrations in the protofibrous structure (stress chains) that formed around the CB filler in CB-reinforced IR vulcanizates were directly observed at the nanoscale. The relationship between the local nanoscale stress distribution and macroscopic tensile properties was revealed based on the microscopic stress distribution and microscopic spatial structure. This study can help us gain insight into the microscopic reinforcement mechanism of carbon black-containing rubber composites.

Curing characteristics and rheological properties of bentonite- filled rubber blends

The study deals with the examination of the rheological behaviour of rubber blends which were filled with bentonite. The filler - polymer as well as the filler - filler interactions were studied and determined from the frequency sweep and strain sweep rheological measurements. The used natural bentonite was extracted from the locality called Jelsovy Potok. The natural bentonite had a fine fraction with a particle size of 15μm a 45 μm and it was added into rubber blends as a partial replacement of commonly used filler. The rubber blends were characterised on the basis of curing characteristics (minimum torque ML, maximum torque MH, optimum time of cure t(c90), processing safety of blend ts,). Moreover, the complex viscosity and Payne effect were also specified. The required measurements were done by using PRPA 2000.

Dynamics of Toxorhynchites splendens population in the Larval phase at a rubber plantation in Banjarbaru, South Kalimantan, Indonesia

Muhamat, Hadisusanto S, Umniyati SR, Soesilohadi RCH. 2021. Dynamics of Toxorhynchites splendens population in the Larval phase at a rubber plantation in Banjarbaru, South Kalimantan, Indonesia. Biodiversitas 22: 4915-4922. This study aims to describe a water-filled rubber sap bowl as a habitat for larval Toxorhynchites splendens. The research used a quota drive count method, taking the first 10 rubber sap bowls found in the study area: (i) with larval Tx. splendens, (ii) with other mosquito larvae, (iii) with water but without mosquito larvae, and (iv) without water. The number of larval Tx. splendens was calculated based on the developmental phase and other mosquito larvae present in each bowl. Environmental factors, such as temperature and humidity, rainfall, wind velocity, and duration of irradiation were the additional data. The results of this study showed that the average frequency of On average, over 20% of bowls contained larval Tx. splendens, and the percentage increased in high rainfall. During low rainfall in August and September, Tx. splendens used water-filled rubber sap bowls as breeding places. This study concludes that Tx. splendens can make use of water-filled rubber sap bowls as places for breeding. Each rubber sap bowl contained one or more individuals of instar larvae 2, and the number decreased as the developmental phase continued because of the limited volume of water in the bowl, cannibalism, and the number of other mosquito larvae as prey.

Comparison of discontinuous damage models of Mullins-type

The Mullins effect is a characteristic property of filled rubber materials whose accurate and efficient modelling is still a challenging task. Innumerable constitutive models for elastomers are described in the literature. Therefore, this contribution gives a review on some widely used approaches, presents a classification, proves their thermodynamic consistency, and discusses reasonable modifications. To reduce the wide range of models, the choice is restricted to those which reproduce the idealised, discontinuous Mullins effect. Apart from the theoretical considerations, two compounds were produced and tested under cyclic uniaxial and equibiaxial tension as well as pure shear. Based on this experimental data, a benchmark that compares the fitting quality of the discussed models is compiled and favourable approaches are identified. The results are a sound basis for establishing novel or improving existing rubber models.

Effect of the Functional Group Position in Functionalized Liquid Butadiene Rubbers Used as Processing Aids on the Properties of Silica-Filled Rubber Compounds

Recently, research conducted on tread compounds with liquid butadiene rubber (LqBR) have been conducted in the tire industry. In particular, the introduction of functional groups into LqBRs is expected to lower hysteresis loss caused by the free chain ends of LqBR. To study this, LqBRs with functional groups at different positions were synthesized. The occurrences of in-chain and chain-end functionalization of functionalized LqBRs (F-LqBRs) were confirmed, the microstructure and functionalization efficiency of F-LqBRs were calculated through the characterizations. This novel functionalization technology was beneficial not only to immobilizing the free chain ends of LqBRs to the surfaces of silica to decrease the number of free chain ends, but also chemically bonding the LqBR chains on the base polymer through a crosslinking reaction to enhance the filler-rubber interaction. The effects of the functional group position and number of the free chain ends on the physical properties and hysteresis of the compounds were investigated by partially replacing the treated distillate aromatic extract (TDAE) oil with LqBR in silica-filled rubber compounds. The results showed that compounds that had applied DF-LqBR with both end functionalization performed better, including improving the silica dispersion, higher extraction resistance, and lower rolling resistance, than other F-LqBRs compounds.

Analysis on Microstructure–Property Linkages of Filled Rubber Using Machine Learning and Molecular Dynamics Simulations

A better understanding of the microstructure–property relationship can be achieved by sampling and analyzing a microstructure leading to a desired material property. During the simulation of filled rubber, this approach includes extracting common aggregates from a complex filler morphology consisting of hundreds of filler particles. However, a method for extracting a core structure that determines the rubber mechanical properties has not been established yet. In this study, we analyzed complex filler morphologies that generated extremely high stress using two machine learning techniques. First, filler morphology was quantified by persistent homology and then vectorized using persistence image as the input data. After that, a binary classification model involving logistic regression analysis was developed by training a dataset consisting of the vectorized morphology and stress-based class. The filler aggregates contributing to the desired mechanical properties were extracted based on the trained regression coefficients. Second, a convolutional neural network was employed to establish a classification model by training a dataset containing the imaged filler morphology and class. The aggregates strongly contributing to stress generation were extracted by a kernel. The aggregates extracted by both models were compared, and their shapes and distributions producing high stress levels were discussed. Finally, we confirmed the effects of the extracted aggregates on the mechanical property, namely the validity of the proposed method for extracting stress-contributing fillers, by performing coarse-grained molecular dynamics simulations.