Filled Elastomers: Mechanistic and Physics-Driven Modeling and Applications as Smart Materials.

Elastomers are made of chain-like molecules to form networks that can sustain large deformation. Rubbers are thermosetting elastomers that are obtained from irreversible curing reactions. Curing reactions create permanent bonds between the molecular chains. On the other hand, thermoplastic elastomers do not need curing reactions. Incorporation of appropriated filler particles, as has been practiced for decades, can significantly enhance mechanical properties of elastomers. However, there are fundamental questions about polymer matrix composites (PMCs) that still elude complete understanding. This is because the macroscopic properties of PMCs depend not only on the overall volume fraction (ϕ) of the filler particles, but also on their spatial distribution (i.e., primary, secondary, and tertiary structure). This work aims at reviewing how the mechanical properties of PMCs are related to the microstructure of filler particles and to the interaction between filler particles and polymer matrices. Overall, soft rubbery matrices dictate the elasticity/hyperelasticity of the PMCs while the reinforcement involves polymer-particle interactions that can significantly influence the mechanical properties of the polymer matrix interface. For ϕ values higher than a threshold, percolation of the filler particles can lead to significant reinforcement. While viscoelastic behavior may be attributed to the soft rubbery component, inelastic behaviors like the Mullins and Payne effects are highly correlated to the microstructures of the polymer matrix and the filler particles, as well as that of the polymer-particle interface. Additionally, the incorporation of specific filler particles within intelligently designed polymer systems has been shown to yield a variety of functional and responsive materials, commonly termed smart materials. We review three types of smart PMCs, i.e., magnetoelastic (M-), shape-memory (SM-), and self-healing (SH-) PMCs, and discuss the constitutive models for these smart materials.

Constitutive Modeling of Oriented and Non-oriented Magnetostrictive Particulate Composites.

This study presents a mathematical framework for two-phase magnetostrictive composites composed of oriented and non-oriented magnetostrictive Terfenol-D particles embedded in passive polymer matrices. The phase constitutive behavior of the monolithic Terfenol-D with arbitrary crystal orientations is represented by a recently developed discrete energy averaged model. This unique Terfenol-D constitutive model results in close-form and linear algebraic equations accurately describing the nonlinear magnetostriction and magnetization in magnetostrictive composites subjected to a given loading or magnetic field increment. The effectiveness of this new mathematical framework in capturing magnetostrictive particle size orientation, phase volume fractions, mechanical loading conditions, and magnetic field excitations are validated using a series of experimental data available in literature. Compared to existing models that prevalently addressed particle orientation in composite constitutive level, the model framework in this study directly handles particle orientation in the phase constitutive level, and therefore achieves enhanced efficiency while maintaining comparable accuracy.