Tuning the properties of colloidal gels using shear history
Thibaut Divoux, Researcher Scientist, Massachusetts Institute of Technology
Gelation of colloidal suspensions plays a crucial role in the formation of numerous solids. Examples range from cement to yogurt, which result respectively from the aggregation of CSH nanoparticles and casein micelles. In both systems, short-range attractive interactions between the particles lead to the formation of a percolated network that is responsible for the solid-like behavior of the material. Generated by a kinetic arrest, these solids are metastable out-of-equilibrium structures, whose properties are sensitive to the route followed during gelation. For instance, external shear often comes to compete with the attractive interactions that drives the gelation, affecting the gel’s microstructure, which encodes the gel’s shear history. This “memory effect” is, for instance, easily visualized in the various morphologies of crack patterns that form when the gel is left to dry.
In this talk, we will discuss various aspects of shear-induced memory effect in colloidal gels. First, I will illustrate a way to quantify the gel’s memory through the so called rheological hysteresis. Indeed, the constitutive equation of colloidal gels, i.e. shear stress versus shear rate, is generally obtained by sweeping up or down the shear rate over a finite temporal window. The up and down sweeps do not superimpose and define a rheological hysteresis loop, which can be used to define a single material-dependent time scale. Such timescale spans over a wide range of values, from vanishingly small values in simple (memory-less) yield stress fluids to large values for strongly time-dependent materials.
Second, limiting the previous protocol to a single down sweep that brings the gel to a complete stop, I will show that flow cessation can be used to tune the microstructural and mechanical properties of (conductive) gels. Experiments performed with various ramp durations allow us to prepare a gel in different states of elasticity and conductivity linked by a remarkable power-law scaling. Abrupt flow cessation leads to strong and connected gels, whereas slow ramps lead to softer and less-conductive gels, a signature of lower connectivity in the sample-spanning network. This scenario is robust and data obtained with different particle concentrations collapse on a master curve that should be useful for the design of colloidal-based materials.