Multi-Scale Simulation of Hyperbranched Polymers

Abstract

In a previous work, we described a multi-scale protocol for the simulation of the conformation and dynamics of macromolecules that was applied to dendrimer molecules proving its predictive capability by comparison with experimental data. That scheme is now employed in order to predict conformational properties (radius of gyration) and overall hydrodynamic properties (translational diffusion and intrinsic viscosity) of hyperbranched molecules in dilute solution. For that purpose, we use a very simple coarse-grained bead-and-spring model whose parameters are not adjusted against experimental properties but they are obtained from previous atomic-level (Langevin) simulations of small fragments of real hyperbranched polymers. In addition, we devise a method to generate structures with different degree of branching. The Monte Carlo simulation technique was used to generate the set conformations of the coarse-grained model. In spite of the difficulties of reproducing experimental data of highly polydisperse entities (in terms of both molecular weight and topology) without using adjustable parameters, the results of this paper show that the proposed methodology allows for qualitative predictions of the behavior of such complex systems and lead us to conclude that, after some improvement, acceptable quantitative predictions can be achieved.

pH-Dependent Conformations for Hyperbranched Poly(ethylenimine) from All-Atom Molecular Dynamics

“…Without imposing the attachment probability parameter (meaning equal probability with p = 0), hyperbranched polymers built with the SG model have DB = 0.6 to 0.8, whereas polymers built with QG lead to DB ∼ 0.5, indicating these methods induce different classes of hyperbranched structures. We observed that the DB value changes if the probability changes, and the DB converges to a specific value as the number of monomers N increases (Figure S2), similar to the formula suggested by Schmidt and co-workers . However, the behavior of the DB values does not agree with the formula for small N values.…”

“…Here T , L , and D specify the number of terminal, linear, and dendritic units, respectively . In order to obtain structures with the target degree of branching (0.5), we applied an additional constraint to the attachment probability parameter p = 0.2, which indicates the ratio of the probability of introducing a new node to linear nodes (i.e., secondary amine) to terminal nodes (i.e., primary amine). In both cases we prohibit formation of cyclic rings.…”

Gelation Suppression in RAFT Polymerization

“…For example, at [DM]/[M] = 0.02, α = 0.55, indicative of flexible chains similar in character to perfectly linear PMMA. The impact of branch length on the scaling behavior was recently demonstrated by Schmidt et al by multiscale simulation . In their work, four monomers with varied Kuhn lengths are used to calculate polymer properties as a function of molecular weight using atomistic Langevin calculations with a coarse-grained bead-and-spring model.…”

“…The impact of branch length on the scaling behavior was recently demonstrated by Schmidt et al by multiscale simulation. 42 In their work, four monomers with varied Kuhn lengths are used to calculate polymer properties as a function of molecular weight using atomistic Langevin calculations with a coarsegrained bead-and-spring model. They predict that polymers with shorter branches have a smaller α value (more globular) in agreement with our findings.…”

HBP Builder: A Tool to Generate Hyperbranched Polymers and Hyperbranched Multi-Arm Copolymers for Coarse-grained and Fully Atomistic Molecular Simulations

“…For example, HBPs under shear flow 44 45 and elongational flow 46 , the conformational behavior of charged/uncharged HBPs in solution 47 and the influence of the Wiener index 48 on the intrinsic viscosity and radius of gyration 49 were investigated by Adolf and Karatasos et al Nevertheless, these models are still deviated from the actual polydisperse HBPs. The third type of models has sincerely considered the polydispersity of HBPs, and was developed by Ricardo Rodríguez Schmidt and coworkers 50 51 . In this model, they firstly obtained the coarse-grained model parameters from atomic-level simulations of small chains fragments, and then they used the Monte Carlo technique to generate a set of polydisperse HBPs with required DBs .…”