Search for: All records

Metamaterials are composite structures whose extraordinary properties arise from a mesoscale organization of their constituents. Here, we introduce a different material class—viscosity metafluids. Specifically, we demonstrate that we can rapidly drive large viscosity oscillations in shear-thickened fluids using acoustic perturbations with kHz to MHz frequencies. Because the timescale for these oscillations can be orders of magnitude smaller than the timescales associated with the global material flow, we can construct metafluids whose resulting time-averaged viscosity is a composite of the thickened, high-viscosity and dethickened, low-viscosity states. We show that viscosity metafluids can be used to engineer a variety of unique properties including zero, infinite, and negative viscosities. The high degree of control over the resulting viscosity, the ease with which they can be accessed, and the variety of exotic properties achievable make viscosity metafluids attractive for uses in technologies ranging from coatings to cloaking to 3D printing.

An electronically actuated artificial hinged ciliary platform capable of generating efficient bidirectional pumping at the microscale.

Articular joints facilitate motion and transfer loads to underlying bone through a combination of cartilage tissue and synovial fluid, which together generate a low‐friction contact surface. Traumatic injury delivered to cartilage and the surrounding joint capsule causes secretion of proinflammatory cytokines by chondrocytes and the synovium, triggering cartilage matrix breakdown and impairing the ability of synovial fluid to lubricate the joint. Once these inflammatory processes become chronic, posttraumatic osteoarthritis (PTOA) development begins. However, the exact mechanism by which negative alterations to synovial fluid leads to PTOA pathogenesis is not fully understood. We hypothesize that removing the lubricating macromolecules from synovial fluid alters the relationship between mechanical loads and subsequent chondrocyte behavior in injured cartilage. To test this hypothesis, we utilized an ex vivo model of PTOA that involves subjecting cartilage explants to a single rapid impact followed by continuous articulation within a lubricating bath of either healthy synovial fluid, phosphate‐buffered saline (PBS), synovial fluid treated with hyaluronidase, or synovial fluid treated with trypsin. These treatments degrade the main macromolecules attributed with providing synovial fluid with its lubricating properties; hyaluronic acid and lubricin. Explants were then bisected and fluorescently stained to assess global and depth‐dependent cell death, caspase activity, and mitochondrial depolarization. Explants were tested via confocal elastography to determine the local shear strain profile generated in each lubricant. These results show that degrading hyaluronic acid or lubricin in synovial fluid significantly increases middle zone chondrocyte damage and shear strain loading magnitudes, while also altering chondrocyte sensitivity to loading.

In various biological systems, analyzing how cell behaviors are coordinated over time would enable a deeper understanding of tissue-scale response to physiologic or superphysiologic stimuli. Such data is necessary for establishing both normal tissue function and the sequence of events after injury that lead to chronic disease. However, collecting and analyzing these large datasets presents a challenge—such systems are time-consuming to process, and the overwhelming scale of data makes it difficult to parse overall behaviors. This problem calls for an analysis technique that can quickly provide an overview of the groups present in the entire system and also produce meaningful categorization of cell behaviors. Here, we demonstrate the application of an unsupervised method—the Variational Autoencoder (VAE)—to learn the features of cells in cartilage tissue after impact-induced injury and identify meaningful clusters of chondrocyte behavior. This technique quickly generated new insights into the spatial distribution of specific cell behavior phenotypes and connected specific peracute calcium signaling timeseries with long term cellular outcomes, demonstrating the value of the VAE technique.

Systems driven far from equilibrium often retain structural memories of their processing history. This memory has, in some cases, been shown to dramatically alter the material response. For example, work hardening in crystalline metals can alter the hardness, yield strength, and tensile strength to prevent catastrophic failure. Whether memory of processing history can be similarly exploited in flowing systems, where significantly larger changes in structure should be possible, remains poorly understood. Here, we demonstrate a promising route to embedding such useful memories. We build on work showing that exposing a sheared dense suspension to acoustic perturbations of different power allows for dramatically tuning the sheared suspension viscosity and underlying structure. We find that, for sufficiently dense suspensions, upon removing the acoustic perturbations, the suspension shear jams with shear stress contributions from the maximum compressive and maximum extensive axes that reflect or “remember” the acoustic training. Because the contributions from these two orthogonal axes to the total shear stress are antagonistic, it is possible to tune the resulting suspension response in surprising ways. For example, we show that differently trained sheared suspensions exhibit (1) different susceptibility to the same acoustic perturbation, (2) orders of magnitude changes in their instantaneous viscosities upon shear reversal, and (3) even a shear stress that increases in magnitude upon shear cessation. We work through these examples to explain the underlying mechanisms governing each behavior. Then, to illustrate the power of this approach for controlling suspension properties, we demonstrate that flowing states well below the shear jamming threshold can be shear jammed via acoustic training. Collectively, our work paves the way for using acoustically induced memory in dense suspensions to generate rapidly and widely tunable materials.