Covalent adaptable networks (CANs) can be classified into dissociative (Diss-CANs) and associative (Asso-CANs) networks according to the exchange mechanism of covalent bonds. We simulate the exchange reaction by the hybrid Monte Carlo–molecular dynamics (hybrid MC/MD) algorithm, aiming to discover the connection and difference between Diss-CANs and Asso-CANs in viscoelasticity behavior. In the linear regime, a major difference originating from the cross-linking density is reflected in the pre-exponential factor τs 0 of the characteristic relaxation time τs. For nonlinear rheology, Diss-CANs show a faster shear thinning behavior under steady shear, while Asso-CANs have a stronger strain hardening under the shear rate start-up. The physics behind the phenomenon results from the different chain conformations and configurations related to the exchange mechanism. Compared with Diss-CANs, the inability for sticker dissociation of Asso-CANs generates a slower relaxation under shear, leading to less chain orientation and tumbling. Meanwhile, we find that multiscale relaxation times obtained from linear viscoelasticity (LVE) can be crucial limits in nonlinear applications for associative polymers (APs). Our work strongly deepens the understanding of APs in terms of both linear and nonlinear viscoelasticities.

Biomaterials for soft tissue engineering scaffolds require a combination of multiple properties including suitable mechanical properties, biodegradability, and biocompatibility. In this work, a series of light-crosslinking waterborne polyurethanes (LWPUs) were prepared using l-lysine ethyl ester diisocyanate (LDI), 1,3-propanediol (PDO) and l-lysine as hard segments and poly(ε-caprolactone) (PCL) and poly(ethylene glycol) (PEG) as soft segments. The obtained LWPUs exhibited appropriate stretchability with a break elongation of 1400-2500% and an excellent strength of 12-18 MPa, which could admirably meet the requirements for soft tissue engineering scaffolds. In addition, the hydrophilic surfaces of LWPUs could effectively reduce protein adsorption and platelet adhesion and favor cell proliferation compared with traditional biomedical polyurethanes. The ultimate degradation products of LWPUs were proven to be nontoxic in a cytotoxicity test. More interestingly, a cytokine release test of macrophages adherent to the LWPU film surfaces shows that these macrophages secreted less pro-inflammation cytokine TNF-α and more anti-inflammation cytokine IL-10 after 3 days' culture, indicating that LWPUs possess the potential ability to aid in the transition of macrophages toward a wound healing phenotype. Furthermore, the LWPU films could support the adhesion and proliferation of endothelial cells. Thus, the obtained LWPUs have great potential for applications in soft tissue engineering scaffolds for tissue repair and wound healing.

Dual polymer networks with stickers have a reputation for enhanced modulus and toughness. We propose a modified sticky Rouse model (SRM) from the single-chain perspective for permanent and transient dual networks, aiming to find a universal description of associative polymer dynamics. The computational complexity of obtaining the analytical relaxation spectrum is simplified by graph theory, implementing matrix reduction of the Rouse–Zimm matrix based on the symmetry. The analytical relaxation spectrum can also return to the case of linear polymers and permanent networks. The modified SRM for dual polymer networks predicts a Rouse-like scale of the linear relaxation modulus G(t) ∝ t –1/2 in sticker relaxation, consistent with the existing experimental results. In particular, the key parameter in the SRM, namely, the effective friction coefficient, can be extracted from the lifetime of sticky bonds and diffusion of chains, obtained by molecular dynamics simulations (MD). Based on that, the SRM model can predict the linear viscoelasticity of dual polymer networks, quantitatively in agreement with our MD results. Our work strongly supports the applicability of the single-chain molecular model SRM for polymer complex networks with reversible associative interactions.