Nanoclay Reinforcement of Liquid Silicone Rubber

A Strengthened and Sensorised Custom Silicone Glove for use with an Intelligent Prosthetic Hand

External gloves for anthropomorphic prosthetic hands protect the mechanisms from damage and ingress of contaminants and can be used to create a pleasing, life-like appearance. The properties of the glove material are the result of a compromise between the resistance to damage and flexibility. Silicone gloves are easier to flex and keep clean, but also more easily damaged. This paper details the use of nanoclay fillers to enhance the properties of silicone, successfully increasing strength whilst maintaining flexibility. The performance of the enhanced silicone is as robust and resistant to tear and puncture as commercial gloves, while being more flexible. This flexibility makes the incorporation of a piezo-electric pressure sensor based on the EEonyx conductive fabric, practical. A sandwich of the cloth and copper fabric creates the sensor, which decreases in resistance with increasing pressure. The sensors are characterised and production variability within the silicone are tested. Three sensors are incorporated into a glove made to fit around a Southampton Intelligent Hand. The hand adapts its grip shape and force depending on the object held. The technology is adaptable and it can be incorporated in a glove produced to fit any prosthetic hand.

Effect of Quartz Nano-Particles on the Performance Characteristics of Asphalt Mixture

Nano-technology has played a vital role in upgrading the durability and sustainability of asphalt pavements during the last decade. Conventional bitumen does not provide adequate performance against permanent deformation in severe weather conditions. Quartz nano-size particles (QNPs) (1, 3, and 5% by weight of the bitumen) were employed in this research to modify the conventional base bitumen PG 58–16. Conventional physical (penetration, softening, ductility, and flash and fire point), rheological, frequency sweep, high-performance grading, and moisture susceptibility tests were performed to investigate the enhancement of the base bitumen performance. Marshall mix design was conducted on each asphalt mixture to determine the optimum bitumen content (OBC) percentage for the preparation of wheel tracker samples to evaluate the effect of QNPs on asphalt mixtures. Laboratory test findings showed that the optimum concentration of QNPs is 5% by weight of base bitumen. The homogeneity and proper stable dispersion of QNPs in the bitumen were validated via scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), and storage stability tests. At higher temperatures, the QNP-modified asphalt mixtures exhibited higher stiffness, stripping resistance, and rutting potential. It was concluded that QNPs effectively upgrade durability and permanent deformation resistance at high temperatures and, therefore, the sustainability of asphalt roads.

Modelling of Asphalt's Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis

The modification by polymers and nanomaterials can significantly improve different properties of asphalt. However, during the service life, the oxidation affects the constituents of modified asphalt and subsequently results in deviation from the desired properties. One of the important properties affected due to oxidation is the adhesive properties of modified asphalt. In this study, the adhesive properties of asphalt modified with the polymers (styrene-butadiene-styrene and styrene-butadiene) and carbon nanotubes were investigated. Asphalt samples were aged in the laboratory by simulating the field conditions, and then adhesive properties were evaluated by different tips of atomic force microscopy (AFM) following the existing functional group in asphalt. Finally, a predictive modelling and machine learning technique called the classification and regression tree (CART) was used to predict the adhesive properties of modified asphalt subjected to oxidation. The parameters that affect the behaviour of asphalt have been used to predict the results using the CART. The results obtained from CART analysis were also compared with those from the regression model. It was observed that the CART analysis shows more explanatory relationships between different variables. The model can predict accurately the adhesive properties of modified asphalts considering the real field oxidation and chemistry of asphalt at a nanoscale.

How I met your elastomers: from network topology to mechanical behaviours of conventional silicone materials

Main silicone elastomer formulations with different crosslinking chemistries are compared in terms of network structure versus mechanical properties.

Looking over liquid silicone rubbers: (2) mechanical properties vs network topology

In the previous paper of this series, eight formulations were analyzed under their uncross-linked forms to relate liquid silicone rubber (LSR) chemical compositions to material network topologies. Such topologies were confirmed by swelling measurements and hardness evaluation on vulcanized samples. In this article, characterization of cross-linked materials is further done using different mechanical measurements on final materials, including dynamic mechanical analysis, compression set, stress-strain behavior and tear resistance. It was shown that the compression set value is mainly related to the chains motion: increasing the filler-polymer interactions and/or decreasing the dangling/untethered chains content positively impact the compression resistance. Elongation at break depends on the molar mass between cross-linking points, showing an optimum value set at around 20 000 g mol(-1), i.e., the critical mass between entanglements. The distribution of elastic strands into the network has strong implications on the stress-strain curves profiles. By generating bimodal networks, the ultimate properties are enhanced. The materials cured by hydride addition on vinyl groups catalyzed by peroxide exhibit poorer compression set and tensile strength values, respectively, because of post-cross-linking reaction and broad polydispersity index of elastic network chains.