Random graph theory of gelation
Random graph theory of gelation is a mathematical theory for sol–gel processes. The theory is a collection of results that generalise the Flory–Stockmayer theory, and allow identification of the gel point, gel fraction, size distribution of polymers, molar mass distribution and other characteristics for a set of multiple polymerising monomers carrying arbitrary numbers and types of reactive functional groups. The theory builds upon the notion of the random graph, introduced by mathematicians Paul Erdős and Alfréd Rényi, and independently by Edgar Gilbert in late 1950s', as well as on the generalisation of this concept known as the random graph with a fixed degree sequence[1]. The theory has been originally developed[2] to explain step-growth polymerisation, and adaptations to other types of polymerisation now exist. Along with providing theoretical results the theory is also constructive. It indicates that the graph-like structures resulting from polymerisation can be sampled with an algorithm using the configuration model, which makes these structures available for further examination with computer experiments.

Premises and degree distribution
At a given point of time, degree distribution , is the probability that a randomly chosen monomer has connected neighbours. The central idea of the random graph theory of gelation is that a cross-linked or branched polymer can be studied separately at two levels: 1) monomer reaction kinetics that predicts and 2) random graph with a given degree distribution. The advantage of such a decoupling is that the approach allows one to study the monomer kinetics with relatively simple linear rate equations, and then deduce the degree distribution serving as input for the random graph model. In several cases the aforementioned rate equations have a known analytical solution.
One type of functional groups
In the case of step-growth polymerisation of monomers carrying functional groups of the same type (so called polymerisation) the degree distribution is given by: where is bond conversion, is the average functionality, and is the initial fractions of monomers of functionality . In the later expression unit rate is assumed without loss of generality. According to the theory[3], the system is in the gel state when , where the gelation conversion is . Analytical expression for average molecular weight and molar mass distribution are known too[3]. When more complex reaction kinetics is involved, for example chemical substitution, side reactions or degradation, one may still apply the theory by computing using numerical integration[3]. In which case, signifies that the system is in the gel state (or the sol state when the inequality sign is flipped).
Two types of functional groups
When monomers with two types of functional groups A and B undergo step growth polymerisation by virtue of a reaction between the A and B groups, a similar analytical result are known[4]. Several examples are listed in the figure. In this case, is the fraction of initial monomers with groups A and groups B. Suppose that A is the groups that is depleted first. Random graph theory states that gelation takes place when , where gelation conversion is and . Molecular size distribution, the molecular weight averages, and the distribution of gyration radii have known formal analytical expressions[5].
When degree distribution , giving the fraction of monomers with neighbours connected via A group and monomers connected via B group at time is solved numerically, the gel state is detected[2] when , where and .
Generalisations
Known generalisations include monomers with an arbitrary number of functional group types[6], cross-liking polymerisation[7], and complex reaction networks[8].
- ^ Molloy, Michael; Reed, Bruce (1995). "A critical point for random graphs with a given degree sequence". Random Structures & Algorithms. 6 (2–3): 161–180. doi:10.1002/rsa.3240060204. ISSN 1042-9832.
- ^ a b Kryven, Ivan (2016-07-27). "Emergence of the giant weak component in directed random graphs with arbitrary degree distributions". Physical Review E. 94 (1): 012315. doi:10.1103/PhysRevE.94.012315.
- ^ a b c Kryven, Ivan (2018-01-01). "Analytic results on the polymerisation random graph model". Journal of Mathematical Chemistry. 56 (1): 140–157. doi:10.1007/s10910-017-0785-1. ISSN 1572-8897.
- ^ Kryven, Ivan (2016-07-27). "Emergence of the giant weak component in directed random graphs with arbitrary degree distributions". Physical Review E. 94 (1): 012315. doi:10.1103/PhysRevE.94.012315.
- ^ Schamboeck, Verena; Iedema, Piet D.; Kryven, Ivan. "Dynamic Networks that Drive the Process of Irreversible Step-Growth Polymerization". Scientific Reports. 9 (1): 2276. doi:10.1038/s41598-018-37942-4. ISSN 2045-2322. PMC 6381213. PMID 30783151.
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: CS1 maint: PMC format (link) - ^ Kryven, Ivan. "Bond percolation in coloured and multiplex networks". Nature Communications. 10 (1): 404. doi:10.1038/s41467-018-08009-9. ISSN 2041-1723. PMC 6345799. PMID 30679430.
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: CS1 maint: PMC format (link) - ^ Schamboeck, Verena; Iedema, Piet D.; Kryven, Ivan. "Coloured random graphs explain the structure and dynamics of cross-linked polymer networks". Scientific Reports. 10 (1): 14627. doi:10.1038/s41598-020-71417-9. ISSN 2045-2322. PMC 7471966. PMID 32884043.
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: CS1 maint: PMC format (link) - ^ Orlova, Yuliia; Kryven, Ivan; Iedema, Piet D. "Automated reaction generation for polymer networks". Computers & Chemical Engineering. 112: 37–47. doi:10.1016/j.compchemeng.2018.01.022.
, although, generalisations to radical polymerisation also exist.