In-situ and quantitative imaging of evaporation-induced stratification in binary suspensions

The drying of a multi-component dispersion, such as water-based paint, ink and sunscreen to form a solid film, is a widespread process. Binary colloidal suspensions have proven capable of spontaneous layer for-mation through size segregation during drying. To design bespoke stratification patterns, a deeper under-standing of how these emerge is crucial. Here, we visualize and quantify the spatiotemporally evolving concentration profiles in situ and with high resolution using confocal fluorescence microscopy of custom-designed binary dispersions in a well-defined geometry. Our results conclusively establish two dis-tinct stratification routes, which give rise to three layered structures. A first thin layer develops directly underneath the evaporation front in which large particles are kinetically trapped. At later times, asymmet-rical particle interactions lead to the formation of two subsequent layers enriched in small and large parti-cles, respectively. The spatial extent and magnitude of demixing strongly depend on the initial volume fraction. We explain and reproduce the experimental concentration profiles using a theoretical model based on dynamic arrest and higher-order thermodynamic and hydrodynamic interactions. These insights unravel the key mechanisms underlying colloidal auto-stratification in multi-component suspensions, and allow preprogramming of stratification patterns in single-deposition formulations for future applications.(c) 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

Automated summary

This study visualizes and quantifies the evolving concentration profiles of binary colloidal dispersions during drying. Two distinct stratification routes are conclusively established, resulting in three layered structures. These insights are important for understanding the mechanisms of colloidal auto-stratification and preprogramming stratification patterns for future applications.