The Jamming Perspective on Wet Foams

TL;DR

This paper explores the jamming transition in wet foams, linking geometric and flow properties to the broader jamming framework, and discusses experimental validation of theoretical models for disordered soft materials.

Contribution

It provides a comprehensive review of how jamming theory applies to wet foams, highlighting experimental observations and proposing a scaling rheology model for disordered media.

Findings

Square root scaling of contact number with packing fraction observed in experiments

Disorder and fluctuations significantly influence foam flow behavior

A scaling model captures key rheological features of foams and similar materials

Abstract

Amorphous materials as diverse as foams, emulsions, colloidal suspensions and granular media can {\em jam} into a rigid, disordered state where they withstand finite shear stresses before yielding. The jamming transition has been studied extensively, in particular in computer simulations of frictionless, soft, purely repulsive spheres. Foams and emulsions are the closest realizations of this model, and in foams, the (un)jamming point corresponds to the wet limit, where the bubbles become spherical and just form contacts. Here we sketch the relevance of the jamming perspective for the geometry and flow of foams --- and also discuss the impact that foams studies may have on theoretical studies on jamming. We first briefly review insights into the crucial role of disorder in these systems, culminating in the breakdown of the affine assumption that underlies the rich mechanics near jamming. Second, we discuss how crucial theoretical predictions, such as the square root scaling of contact number with packing fraction, and the nontrivial role of disorder and fluctuations for flow have been observed in experiments on 2D foams. Third, we discuss a scaling model for the rheology of disordered media that appears to capture the key features of the flow of foams, emulsions and soft colloidal suspensions. Finally, we discuss how best to confront predictions of this model with experimental data.