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Jamming Memory into Acoustically Trained Dense Suspensions under Shear

Edward Y. X. Ong1,*, Anna R. Barth2, Navneet Singh2, Meera Ramaswamy2,†, Abhishek Shetty3, Bulbul Chakraborty4, James P. Sethna2, and Itai Cohen2,5

  • 1Department of Applied Engineering and Physics, Cornell University, Ithaca, New York 14850, USA
  • 2Department of Physics, Cornell University, Ithaca, New York 14850, USA
  • 3Department of Rheology, Anton Paar, Ashland, Virginia 23005, USA
  • 4Department of Physics, Brandeis University, Waltham, Massachusetts 02453, USA
  • 5Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, New York, USA

  • *Corresponding author: eo263@cornell.edu Present address: Department of Heterogeneous Integration, Institute of Microelectronics, Singapore 117685.
  • †Present address: Princeton Center for Complex Materials, Princeton University, Princeton, New Jersey 08544, USA.
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Phys. Rev. X 14, 021027 – Published 14 May, 2024

DOI: https://doi.org/10.1103/PhysRevX.14.021027

Abstract

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.

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Popular Summary

The microstructure of a material is a key determinant of its bulk properties. Solid materials often retain structural memories of their processing history, enabling these processes to endow sometimes dramatic changes to the material bulk responses. In flowing systems, significantly larger changes in structure—and consequently bulk properties—should be possible also. But whether memory of processing history can be similarly exploited in these systems remains poorly understood. Here, we demonstrate a promising route to embedding such useful memories.

Our strategy involves modifying the microstructure in the flowing state via a training protocol that uses a combination of shear and acoustic perturbation. Signatures of the training are then preserved via shear jamming, a rapid solidification of the flowing state driven by shear. We demonstrate this strategy on a dense suspension and find that the bulk suspension response is largely determined by orthogonal force networks that reflect, or “remember,” the training.

The antagonistic nature of the force networks can lead to unusual and dramatic changes to the bulk behavior. For example, the shear-jammed state can be tuned to resist acoustic unjamming, flow with a hundredfold increase in viscosity when the shear is reversed, and even exhibit an increase in the shear stress during relaxation. Surprisingly, our training strategy can also be applied to sufficiently dense non-shear-jamming suspensions, inducing shear jamming in these otherwise flowing states.

Collectively, our work paves the way for harnessing memory in dense suspensions to generate rapidly and widely tunable materials, with potential applications to other systems such as polymers undergoing photopolymerization and alloys undergoing rapid solidification.

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Phys. Rev. X 14, 021027– Published 14 May, 2024
  • Received 10 June 2023
  • Revised 31 December 2023
  • Accepted 11 April 2024
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