Brief Description
In this work, we develop a statistical theory of damage for transient networks that can directly bridge the molecular mechanisms and macroscopic response. The final model being able to capture the evolution of rate-dependent and anisotropic damage in transient networks.
Abstract
Dynamic polymer networks utilize weak bonding interactions to dissipate the stored energy and provide a source of self-healing for the material. Due to this, tracking the progression of damage in these networks is poorly understood as it becomes necessary to distinguish between reversible and irreversible bond detachment (by kinetic bond exchange or chain rupture, respectively). In this work, we present a statistical formulation based on the transient network theory to track the chain conformation space of a dynamic polymer network whose chains rupture after being pulled past a critical stretch. We explain the predictions of this model by the observable material timescales of relaxation and self-healing, which are related to the kinetic rates of attachment and detachment. We demonstrate our model to match experimental data of cyclic loading and self-healing experiments, providing physical interpretation for these complex behaviors in dynamic polymer networks.
Figures
Top: Distinction between chain rupture and chain detachment in a transient network. In this network, a chain can be found in three distinct states: attached, detached, and ruptured. The ruptured chains are unable to create new network connections and are at the origin of irreversible damage.
Bottom: Cyclic loading experiment performed at a constant strain rate λ̇. (a) High Weissenberg loading. Energy dissipation is primarily a result of chain rupture. (b) Low Weissenberg loading. Energy dissipation is primarily due to reversible bond kinetics. Contour plots indicate the distribution ϕ at the respective stage of loading.
Citation
Lamont, S. C.; Mulderrig, J.; Bouklas, N.; Vernerey, F. J. Rate-Dependent Damage Mechanics of Polymer Networks with Reversible Bonds. Macromolecules 2021, 54 (23), 10801–10813. https://doi.org/10.1021/acs.macromol.1c01943.