Magnetic double-network composite capable of large recoverable deformation.

This paper presents the design and fabrication of a magnetic double network (DN) composite, which consists of permanent magnet chains embedded in an elastomer matrix, and was capable of large yet fully recoverable deformation. The initially connected magnets served as reusable sacrificial components in the composite. The strong magnetic attraction between neighboring magnetics endowed the composite with the high strength while the compliance of the elastomer matrix provided the high extensibility. Having a similar mechanism as DN gels, the composite was found to be significantly tougher than either of the constituents. The nonlinear behavior in the composite separated it into two coexisting phases - a softer phase with separated magnet links and a stiffer phase with connected magnet links - which led to the stress plateau on the tensile curve. Further stretching was manifested by the growth of the disconnected softer phase at the expense of the linked stiffer phase, until all magnets were separated. The unloading curves appeared drastically different from the loading curves, as the force needed to separate two magnets was much higher than the force at which two separated magnets snapped back. Such asymmetry between loading and unloading was the main cause of the hysteresis in the stress-strain curve and the energy dissipation. To further understand the physical mechanism and the damage process of the magnetic DN composite, a simple model was developed to examine the deformation and damage dissipation process of composite. With very few parameters, the model predictions agree qualitatively with the measured properties of the material, and the difference can be further reduced by accounting for the interfacial friction/adhesion, a second means of energy dissipation. With a combination of desired properties including high stretchability, self-healing, and high toughness, the magnetic DN composite is a viable candidate for various applications.