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

TL;DR

This paper models irreversible step-growth polymerization using directed random graphs, providing analytical tools to predict polymer microstructure and properties, aiding in material design and optimization.

Contribution

It introduces a novel network-based model linking polymerization processes to directed configuration graphs, offering new analytical expressions for microstructure characteristics.

Findings

Molecular weight distribution linked to component sizes

Gelation corresponds to giant component emergence

Gyration radii related to Wiener index of components

Abstract

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 to view percolation on a directed random graph as a generic model for step-growth polymerisation. 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, and in this way, obtain new analytical expressions describing the polymeric microstructure. Thus, 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.