Driven Brownian coagulation of polymers

TL;DR

This paper analyzes the long-term behavior of polymer cluster size distributions in driven Brownian coagulation, revealing distinct regimes based on the fractal dimension and characterizing stationary and non-stationary states.

Contribution

It provides a mean-field analysis of the coagulation kinetics, identifying conditions for stationary and bimodal distributions depending on the fractal dimension.

Findings

For 0 ≤ a < 1/2, the size distribution reaches a stationary power-law state with exponent 3/2.

For 1/2 < a ≤ 1, the distribution becomes bimodal with growing large clusters and a gap.

At a=1/2, the distribution is likely stationary with a logarithmic correction.

Abstract

We present an analysis of the mean-field kinetics of Brownian coagulation of droplets and polymers driven by input of monomers which aims to characterize the long time behavior of the cluster size distribution as a function of the inverse fractal dimension, $a$, of the aggregates. We find that two types of long time behavior are possible. For $0\leq a < 1/2$ the size distribution reaches a stationary state with a power law distribution of cluster sizes having exponent 3/2. The amplitude of this stationary state is determined exactly as a function of $a$. For $1/2 < a \leq 1$, the cluster size distribution never reaches a stationary state. Instead a bimodal distribution is formed in which a narrow population of small clusters near the monomer scale is separated by a gap (where the cluster size distribution is effectively zero) from a population of large clusters which continue to grow for all time by absorbing small clusters. The marginal case, $a=1/2$, is difficult to analyze definitively, but we argue that the cluster size distribution becomes stationary and there is a logarithmic correction to the algebraic tail.