Macromol. Theory Simul. 6/2017

Abstract

Front Cover: The image depicts samples of cross‐linked molecules as obtained from the random graph model for diacrylate polymer networks shortly after the onset of the topological phase transition. The vast variability in molecular sizes, topologies, and shapes that can be seen in the image is completely defined by a relatively small amount of information – the degree distribution of the underlying random graph. More information can be found in article number https://doi.org/10.1002/mats.201700047 by Verena Schamboeck,* Ivan Kryven, and Pieter D. Iedema.

Dynamic Networks that Drive the Process of Irreversible Step-Growth Polymerization

“…4. a, Comparison of an 13 C NMR spectrum (HPE in DMF [45]) and the predicted degree distribution for the system with A 2 :B 3 = 1:1 at p A = 0.93. b, Comparison of the phenolic unit degree distribution (only out-edges) in a phenol-methylene system predicted by MD (dots) simulation [30] to the theory (solid lines). The gel point is predicted at p A = 0.58 by the MD simulation, the theory predicts it at p A = 0.5.…”

“…In fact, currently existing theories of hyperbranched polymers are largely based on the early developments of Flory: the modern viewpoint of network theory is only starting to diffuse back into polymer chemistry where this theory has arguably originated [10][11][12][13][14][15][16].…”

Computational Design of Photocured Polymers Using Stochastic Reaction–Diffusion Simulation

“…The numerical studies show that this method can correlate fabrication parameters to polymer properties like crosslink heterogeneity, relaxation time, phonon density of states, and bulk modulus, in silico. [18,19] Herein, we modeled polymerization using reaction-diffusion master equation (RDME) as a stochastic process consisting of initiation, propagation, crosslinking, and fluctuation events. [5] Two sets of parameters control the kinetics and properties of photopolymerization: 1) chemical composition of the resin (i.e., rate constants, interaction energies, of monomers and initiators), and 2) curing conditions (e.g., light source, curing time, temperature).…”

“…Previous efforts at stochastic simulation of polymerization omitted either the fluctuation of the network or the diminishing diffusivity. [18,19] Herein, we modeled polymerization using reaction-diffusion master equation (RDME) as a stochastic process consisting of initiation, propagation, crosslinking, and fluctuation events. Other methods include: simulation of crosslinking epoxies using molecular dynamics that involves relaxation of the network after new bond formations [16,17] and random graph models that stochastically update network connectivity but the network does not represent a spatial configuration.…”

“…Neglecting these details fail to simulate autoacceleration, post‐cure polymerization, and the network structure as a function of polymerization kinetics. Other methods include: simulation of crosslinking epoxies using molecular dynamics that involves relaxation of the network after new bond formations and random graph models that stochastically update network connectivity but the network does not represent a spatial configuration …”