The existence of the reversible noncovalent bonds (the green stickers shown in Fig. 1), such as hydrogen bonds, - stacking, metal-ligand and ionic interactions
in the polymers makes it possible to form transient networks. On the other hand, the presence of covalent bonds (the red stickers shown in Fig. 1) can lead to the
formulation of permanent networks. While the transient networks are reversible, easily processable and recyclable, the permanent networks are able to resist flow
and creep, and to swell without losing their coherence. The co-existence of these two kinds of networks in polymers makes them ideal for applications, such as high
mechanical strength, large reversible deformability in shear and extension, substantial reversible swelling, and self-healing properties. We are aiming at unveiling the
mechanisms behind these unique properties by modeling so that they can be fully exploited.
Since Rubinstein et al. firstly proposed the sticky Rouse model, many improvements have been done to this model. However, most of the models are single chain
model and it has inborn shortcomings. For one thing, single chain model can not predict the structure of associating polymers. For another, in single chain model
system, the interactions between different chains are simulated through virtual (not real) coupling. Differently, we adopt multi-chain sticky Rouse model to simulate
the associating polymers. Additionally, periodic boundary conditions are utilized, which allows us to simulate the macroscopic phenomena from the mesoscopic scale (Fig. 2).
The expected accomplishments are: simulating multiple dynamics in reversible DDNs, modelling the nonlinear viscoelastic properties of sticky DDNs, study the influence of chain stretching on the lifetime of reversible properties and relating chain stretching and strain hardening to adhesive properties.
