Chain breaking in the statistical mechanical constitutive theory of polymer networks

Elastomers are used in a wide range of applications because of their large strain to failure, low density, and tailorable stiffness and toughness. The mechanical behavior of elastomers derives mainly from the entropic elasticity of the underlying network of polymer chains. Elastomers under large deformation experience bonds breaking within the backbone chains that constitute the polymer network. This breaking of chains damages the network, can lead to material failure, and can be utilized as an energy dissipation mechanism. In the case of reversible bonds, broken chains may reform and heal the damage in the network. If the reversible bonds are dynamic, chains constantly break and reform and create a transient network. A fundamental constitutive theory is developed to model the mechanics of these polymer networks. A statistical mechanical derivation is conducted to yield a framework that takes in an arbitrary single-chain model (a Hamiltonian) and outputs the following: the single-chain mechanical response, the breaking and reforming kinetics, the equilibrium distribution of chains in the network, and the partial differential equations governing the deformation-coupled network evolution. This statistical mechanical framework is then brought into the continuum scale by using macroscopic thermodynamic constitutive theory to obtain a constitutive relation for the Cauchy stress. The potential-supplemented freely jointed chain (uFJC) model is introduced, and a parametric study of its mechanical response and breaking kinetics is provided. This single-chain model is then implemented within the constitutive framework, which we specialize and apply in two exemplary cases: the mechanical response and irreversible breakdown of a multinetwork elastomer, and the mechanical response of a dual crosslink gel. After providing a parametric study of the general constitutive model, we apply it to a hydrogel with reversible metal coordination crosslinks. In several cases, we find that the breakdown of the network causes secondary physical mechanisms to become important and inhibit the accuracy of our model. We then discuss these mechanisms and indicate how our existing framework can be adjusted to incorporate them in the future.