Rethinking failure in polymer networks: a probabilistic view on progressive damage

TL;DR

This paper introduces a probabilistic, statistical mechanics-based model to analyze chain failure in polymer networks, aiding the design of tougher soft materials and understanding damage mechanisms.

Contribution

The work develops a simple, adaptable model linking force distribution and bond energy to failure probability, applicable to damage analysis in polymer networks.

Findings

Quantifies force distribution along polymer chains.

Calculates activation energy for bond dissociation.

Integrates local damage models into 3D network simulations.

Abstract

The mechanics of single-chain stretching and rupture are central to understanding the resilience of biological polymers and designing strong and tough soft materials such as double-network gels and multi-network elastomers. In this work, we develop a statistical mechanics based model that enables one to determine the distribution of forces along the chain segments. By combining the force distribution with a tilted bond potential that captures the stretch energy stored in these bonds, we calculate the corresponding activation energy required for bond dissociation. This allows us to determine the probability of bond (and consequently chain) failure. The proposed approach is simple, direct, and readily adaptable for constructing higher-level coarse-grained descriptions of damage and fracture in polymer networks. We demonstrate this by applying the theory to two problems of practical interest: (1) toughening networks via sacrificial bond rupture in polymer chains and (2) incorporation of the local chain model into a 3-dimensional constitutive relation that captures damage in elastomers. The latter was implemented through the micro-sphere framework, which accounts for different chain orientations, as well as the computationally inexpensive eight chain model. The findings from this work provide a physically-based model to quantify the stretching and failure of a single chain and pave the way to the integration of local damage models into 3-dimensional networks.