Shear-driven memory effects in carbon black gels

TL;DR

This study reveals how shear flow induces and retains structural memory in carbon black gels, with microstructure dictating rheological behavior, offering insights for designing adaptive smart materials.

Contribution

The paper uncovers the mechanisms of shear-induced memory in colloidal gels using USAXS and fractal analysis, linking microstructure to rheology and identifying two distinct memory regimes.

Findings

Microstructure under shear is retained after flow cessation.

Two mechanisms of structural memory governed by Mason number.

Rheology is determined by the gel's double-fractal architecture.

Abstract

In recent years, significant effort has been devoted to developing smart materials whose mechanical properties can adapt under physical stimuli. Particulate colloidal gels, which behave as solids but can also flow under stress, have emerged as promising candidates. Resulting from the attractive interaction between their constituents, their network architecture exhibit solid-like properties even at very low volume fractions. This structural flexibility allows them to adopt various configurations and store structural information making them highly susceptible to memory effects. Shear flow, applied through rheometry, offers a simple and effective way to tune their properties and imprint a ``rheological memory'' of the flow history. However, the precise relationship between flow history and viscoelastic response remains elusive, largely due to the limited structural characterization of these systems during flow and after flow cessation. Here, we use ultra-small angle X-ray scattering (USAXS) to reveal a strong structural memory in the solid state, where the microstructure formed under shear is retained after flow cessation. We identify two distinct mechanisms of structural memory, as governed by the ratio of viscous to attractive forces, namely, the Mason number. Using recently developed fractal scaling laws, we show that the rheology is fully determined by the gel microstructure. Notably, these gels exhibit a double-fractal architecture, highlighting the remarkably broad range of length scales over which these disordered materials are structured. By clarifying how memory is encoded, our results offer strategies to tune shear sensitivity of colloidal gels and design smart materials.