Granular hydrogels as brittle yield stress fluids.

While granular hydrogels are increasingly used in biomedical applications, methods to capture their rheological behavior generally consider shear-thinning and self-healing properties or produce ensemble metrics such as the dynamic moduli. Analytical approaches paired with common oscillatory shear tests can describe not only solid-like and fluid-like behavior of granular hydrogels but also transient characteristics inherent in yielding and unyielding processes. Combining oscillatory shear testing with consideration of Brittility (Bt) via the Kamani-Donley-Rogers (KDR) model, we show granular hydrogels behave as brittle yield stress fluids with complex transient rheology.

Crosslink strength governs yielding behavior in dynamically crosslinked hydrogels.

Yielding of dynamically crosslinked hydrogels, or the transition between a solid-like and liquid-like state, allows facile injection and utility in translational biomedical applications including delivery of therapeutic cells. Unfortunately, the time-varying nature of the transition is not well understood, nor are there design rules for understanding the effects of yielding on encapsulated cells. Here, we unveil underlying molecular mechanisms governing the yielding transition of dynamically crosslinked gels currently being researched for use in cell therapy.

Linking structural and rheological memory in disordered soft materials.

Linking the macroscopic flow properties and nanoscopic structure is a fundamental challenge to understanding, predicting, and designing disordered soft materials. Under small stresses, these materials are soft solids, while larger loads can lead to yielding and the acquisition of plastic strain, which adds complexity to the task. In this work, we connect the transient structure and rheological memory of a colloidal gel under cyclic shearing across a range of amplitudes a generalized memory function using rheo-X-ray photon correlation spectroscopy (rheo-XPCS).

Brittle and ductile yielding in soft materials.

Many soft materials yield under mechanical loading, but how this transition from solid-like behavior to liquid-like behavior occurs can vary significantly. Understanding the physics of yielding is of great interest for the behavior of biological, environmental, and industrial materials, including those used as inks in additive manufacturing and muds and soils. For some materials, the yielding transition is gradual, while others yield abruptly.

Unbiased construction of constitutive relations for soft materials from experiments via rheology-informed neural networks.

The ability to concisely describe the dynamical behavior of soft materials through closed-form constitutive relations holds the key to accelerated and informed design of materials and processes. The conventional approach is to construct constitutive relations through simplifying assumptions and approximating the time- and rate-dependent stress response of a complex fluid to an imposed deformation. While traditional frameworks have been foundational to our current understanding of soft materials, they often face a twofold existential limitation: i) Constructed on ideal and generalized assumptions, precise recovery of material-specific details is usually serendipitous, if possible, and ii) inherent biases that are involved by making those assumptions commonly come at the cost of new physical insight.