ZnO as a cheap and effective filler for high breakdown strength elastomers

Cheap, high-performance dielectric elastomers are in high demand from industry concerning new products based on dielectric elastomer transducers. However, formulating an elastomer that fulfils all the requirements for dielectric elastomers is difficult and, first and foremost, not cheap. In this article, we explore the use of a cheap and abundant metal oxide filler, namely ZnO, as a filler in silicone-based dielectric elastomers. The electro-mechanical properties of the elastomer composites are investigated, and their performance is evaluated by means of figures of merit. Various commercial silicone elastomers and a self-formulated silicone elastomer are utilised as elastomer matrices, the effects of which on the final properties of the elastomer composite are investigate

Self-Healing, High-Permittivity Silicone Dielectric Elastomer

Currently used dielectric elastomers do not have the ability to self-heal after detrimental events such as tearing or electrical breakdown, which are critical issues in relation to product reliability and lifetime. In this paper, we present a self-healing dielectric elastomer that additionally possesses high dielectric permittivity and consists of an interpenetrating polymer network of silicone elastomer and ionic silicone species that are cross-linked through proton exchange between amines and acids. The ionically cross-linked silicone provides self-healing properties after electrical breakdown or cuts made directly to the material due to the reassembly of the ionic bonds that are broken during damage. The dielectric elastomers presented in this paper pave the way to increased lifetimes and the ability of dielectric elastomers to survive millions of cycles in high-voltage conditions

Regression analysis for paths inference in a novel Proton CT system

In this work, we analyse the proton paths inference for the construction of CT imagery based on a new proton CT proton system, which can record multiple proton paths/residual energies. Based on the recorded paths of multiple protons, every proton path is inferred. The inferred proton paths can then be used for the residual energies detection and CT imagery construction for analyzing a specific tissue. Different regression methods (linear regression and Gaussian process regression models) are exploited for the path inference of every proton in this work. The studies on a recorded proton trajectories dataset show that the Gaussian process regression method achieves better accuracies for the path inference, from both path assignment accuracy and root mean square errors (RMSEs) studies

Degradation patterns of silicone-based dielectric elastomers in electrical fields

Silicone elastomers have been heavily investigated as candidates for the flexible insulator material in dielectric elastomer transducers and are as such almost ideal candidates because of their inherent softness and compliance. However, silicone elastomers suffer from low dielectric permittivity. This shortcoming has been attempted optimized through different approaches during recent years. Material optimization with the sole purpose of increasing the dielectric permittivity may lead to the introduction of problematic phenomena such as premature electrical breakdown due to high leakage currents of the thin elastomer film. Within this work, electrical breakdown phenomena of various types of permittivity-enhanced silicone elastomers are investigated. Results showed that different types of polymer backbone chemistries lead to differences in electrical breakdown patterns, which were revealed through SEM imaging. This may pave the way towards a better understanding of electrical breakdown mechanisms of dielectric elastomers and potentially lead to materials with increased electrical breakdown strengths