Linear Polymers in Disordered Media - the shortest, the longest and the mean(est) SAW on percolation clusters

TL;DR

This paper investigates the scaling behavior of linear polymers modeled as self-avoiding walks on percolation clusters in disordered media, revealing that kinetic averaging ensures renormalizability and well-defined scaling limits, unlike static averaging.

Contribution

It provides a 2-loop order calculation of scaling exponents for shortest, longest, and average SAWs on percolation clusters, and shows kinetic averaging's renormalizability.

Findings

Kinetic averaging leads to renormalizable models.

Static averaging does not yield a well-defined scaling limit.

Calculated multifractal exponents for SAWs on percolation clusters.

Abstract

Long linear polymers in strongly disordered media are well described by self-avoiding walks (SAWs) on percolation clusters. The length-distribution of these SAWs encompasses to distinct averages, viz. the averages over cluster- and SAW-conformations. For the latter average, there are two basic options, one being static and one being kinetic. It is well known for static averaging that if the disorder of the underlying medium is weak, differences to the ordered case appear merely in non-universal quantities. Using dynamical field theory, we show that the same holds true for kinetic averaging. For strong disorder, i.e., the medium being close to the percolation point, we employ a field theory for the nonlinear random resistor network in conjunction with a real-world interpretation of Feynman diagrams, and we calculate the scaling exponents for the shortest, the longest and the mean or average SAW to 2-loop order. In addition, we calculate to 2-loop order the entire family of multifractal exponents that governs the moments of the the statistical weights of the elementary constituents (bonds or sites of the underlying fractal cluster) contributing to the SAWs. Our RG analysis reveals that kinetic averaging leads to renormalizability whereas static averaging does not, and hence, we argue that the latter does not lead to a well-defined scaling limit. We discuss the possible implications of this finding for experiments and numerical simulations which have produced wide-spread results for the exponent of the average SAW. To corroborate our results, we also study the well-known Meir-Harris model for SAWs on percolation clusters. We demonstrate that this model leads back to 2-loop order to the renormalizable real world formulation with kinetic averaging if the replica limit is consistently performed at the first possible instant of the calculation.