Unfolding the elementary building blocks of dynamics and rheology of soft glassy materials

University Of Akron, Akron OH

Investigators

Abstract

Microgels are polymer networks swollen by the solvent (e.g., water) in which they are dissolved. Soft solids are highly concentrated suspensions of such microgels which are nontoxic, have high water content, and enjoy a reasonably low cost of production. As such they have great potential for several applications in drug delivery, tissue engineering, and wound dressing. The production efficiency as well as the end-use properties (e.g., shelf-life) of soft solids depend on microscopic-level understanding of mechanical properties and their flow behavior. This award supports a joint computational, theoretical, and experimental investigation to provide a fundamental understanding of the properties of suspensions of soft solids. The outcome of this award will provide engineering tools to design soft solids with targeted properties. This award will provide resources to train graduate students in computational physics, engineering, and experimentation. The educational activities will provide professional training to undergraduate students from underrepresented groups to work on an interdisciplinary research framework. Soft particle glasses are yield stress fluids that are jammed beyond the random close packing of equivalent hard spheres. Experimental studies show that the flow behavior of these suspensions can be controlled by tuning the extent of interparticle adhesion and repulsion. This award investigates the fundamental connection between the microscopic dynamics at the individual particle level and the macroscopic properties and builds an efficient computational tool for predicting nonlinear rheological properties of soft particle glasses with different interparticle interactions in bulk and at the interface. The effect of contact forces, volume fractions, and flow strength on the yielding, flow curves, and flow-induced dynamical heterogeneities will be determined. The role of shear-driven localized dynamical events on the flow at low and high deformation rates and their connection with the stress distribution will be established. Scaling relationships between the length scale of the domain with dynamical heterogeneities and shear rate will be provided. The effect of mechanical history and residual stress on the start-up flow behavior will be determined. Machine learning models will be developed to predict the yield behavior from the microstructure of suspensions at rest. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.