Dynamical Networking of Polymer Networks with Dedicated Cross-linker Particles

TL;DR

This paper develops a theoretical and simulation framework for understanding how dedicated cross-linker particles influence the dynamics of polymer networks, revealing effects on structure and flexibility.

Contribution

It introduces a field-theoretical formalism incorporating cross-linkers explicitly, enabling analysis of their impact on polymer dynamics and structure.

Findings

Cross-linking decreases polymer persistence lengths.

Cross-linking broadens diffusive peaks in structure factors.

Theoretical predictions align with molecular dynamics simulations.

Abstract

This paper extends a field-theoretical dynamical networking formalism for mesoscopic polymer dynamics to explicitly include dedicated cross-linker particles. Cross-linkers are represented within a Martin-Siggia-Rose generating functional and reversibly coupled to polymers through Gaussian networking fields, enabling an approximation scheme that reduces their degrees of freedom while remaining compatible with polymer dynamics. The framework is applied to a two-species polymer system in which intra- and inter-species cross-linking are assigned different statistical advantages. Effective networking potentials are derived and used to calculate correlation functions and dynamic structure factors. To validate these results, molecular dynamics simulations of semi-flexible polymers with reversible intra- and inter-species cross-linking are performed. Simulations show that cross-linking decreases polymer persistence lengths and local alignment, and the resulting trajectories yield dynamic structure factors consistent with theoretical predictions. In both approaches, cross-linking broadens the diffusive peaks and enhances the high-frequency tails of the structure factors. Together, theory and simulation provide complementary insights into the dynamics of cross-linked polymers, establishing a tractable framework that captures essential features observed in experiments and offering a basis for exploring more complex synthetic and biological networks.