The Rouse Dynamic Properties of Dendritic Chains: A Graph Theoretical Method

The branching angle effect on the properties of rigid dendrimers studied by Monte Carlo simulation

We studied the properties of rigid dendrimers with different branching angles by means of Monte Carlo simulations on a coarse-grained level. It was found that the terminal groups of dendrimers with both rigid and flexible spacers could locate near the center of the molecule. In flexible dendrimers, the wide distribution is attributed to the back folding of flexible spacers, while in rigid dendrimers, it is caused by the branching angle effect that a branch will grow laterally due to the restriction of a non-zero branching angle. It has been established that the branching angle is a key parameter for rigid dendrimers, which can be applied to tune the properties of rigid dendrimers: decreasing branching angle is helpful to obtain dendrimers with a larger size, lower density, and more terminal groups locating at periphery.

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

Many research fields, reaching from social networks and epidemiology to biology and physics, have experienced great advance from recent developments in random graphs and network theory. In this paper we propose a generic model of step-growth polymerisation as a promising application of the percolation on a directed random graph. This polymerisation process is used to manufacture a broad range of polymeric materials, including: polyesters, polyurethanes, polyamides, and many others. We link features of step-growth polymerisation to the properties of the directed configuration model. In this way, we obtain new analytical expressions describing the polymeric microstructure and compare them to data from experiments and computer simulations. The molecular weight distribution is related to the sizes of connected components, gelation to the emergence of the giant component, and the molecular gyration radii to the Wiener index of these components. A model on this level of generality is instrumental in accelerating the design of new materials and optimizing their properties, as well as it provides a vital link between network science and experimentally observable physics of polymers.