(338a) Using First-Principles Multi-Scale Modeling to Design Rheology Modifiers for Waterborne Paints

Modern paints are complex mixtures comprised of multiple components such as water, pigments, latex binders, dispersants, rheology modifiers or thickeners, and others. Today, Dow is a premiere supplier of binders, dispersants, thickeners, and other additives, using high-throughput experimentation and computational modeling for new formulation and process design. This presentation is specifically devoted to the use of modeling to understand the rheology and colloidal stability of a specific system -- hydrophobically ethoxylated urethanes (HEUR) thickeners in waterborne paints. Since their invention in 1970s, HEURs have been actively used as rheology modifiers for paints. Thermodynamic and rheological behavior of HEUR molecules in aqueous solutions is now very well understood and is based on the concept of transient network (TN), where the association of hydrophobic groups into networks of flower micelles causes viscosity to increase dramatically as function of polymer concentration. The behavior of complex mixtures containing water, HEUR, and latex (“binder”) particles, however, is understood less well, even though it has utmost importance in the paint formulation design. To model the colloidal stability and rheology of these systems, Dow partnered with the University of Michigan research team led by Prof. Ronald Larson. We developed a multi-scale modeling framework in which the molecular details of the HEUR polymers are used to describe their adsorption onto the binder particles and ultimately the viscoelastic behavior of the overall binder/HEUR aqueous dispersion. The model is able to qualitatively describe many important features of the water/latex/HEUR mixtures. The proposed approach could potentially lead to the design of new HEUR structures with improved rheological performance. Collaboration: Antony van Dyk, Alan Nakatani, Tirtha Chatterjee, Susan Fitzwater, Alex Kalos (Dow); Ronald Larson, Fang Yuan, Shihu Wang, Hussein Rezvantalab, Elnaz Hajizadeh (University of Michigan).