(317d) Tuning Mechanical Failure in Multiphase Soft Particulate Systems
AIChE Annual Meeting
2024
2024 AIChE Annual Meeting
Engineering Sciences and Fundamentals
Particulate and Multiphase Flows: Colloids and Polymers
Tuesday, October 29, 2024 - 1:15pm to 1:30pm
Soft particulate suspensions are ubiquitous in nature, with their flow properties influencing a wide array of materials, from the efficiency of electrolyte flow in Li-ion batteries to the propensity of landslides on hillslopes. However, most research in suspension rheology has focused on two-phase mixtures, where one phase consists of particles/gels and the other constitutes the background solvent. Pure two-phase mixtures are rarely found in nature due to the variability in material type, size polydispersity, and diverse interparticle interactions. Here, we investigate the mechanical failure of a multiphase particulate system, using model granular particles dispersed in a fluorescent gel, by adjusting the balance between frictional and cohesive interactions in the soft material mixture. Using in-house assembled equipments, including a confocal rheoscope and a granular rheometer with imaging capability, we extract microscopic signatures accompanying these yielding transitions. Our key finding is that we can adjust the failure mode from ductile to brittle by varying the ratio of cohesive and frictional elements in the multiphase system. We propose a 3D rheological phase space that delineates three identified flow regimes: rate-independent plastic, rate-dependent plastic, and quasi-Newtonian, as a function of deformation rate, shear stress, and effective particle pressure. We demonstrate that the mechanical framework proposed here is applicable to a model soil material: a multiphase suspension mixture of non-swelling kaolin clay and silica sand particles. The proposed phase space accurately captures the boundaries of flow and yielding regimes of our model soil material and those documented in the literature. Our approach provides a novel method to enhance hazard prediction models in multiphase soft particulate systems.