Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2024Jamming Memory into Acoustically Trained Dense Suspensions under Shearcitations
  • 2023Characterization of the temperature and frequency dependency of the complex Poisson’s ratio using a novel combined torsional-axial rheometer7citations

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Chart of shared publication
Barth, Anna R.
1 / 1 shared
Ramaswamy, Meera
1 / 1 shared
Ong, Edward Yong Xi
1 / 1 shared
Singh, Navneet
1 / 2 shared
Sethna, James
1 / 1 shared
Chakraborty, Bulbul
1 / 1 shared
Troiss, Alexander
1 / 1 shared
Rodríguez Agudo, José Alberto
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Kaschta, Joachim
1 / 5 shared
Haeberle, Jan
1 / 2 shared
Müller-Pabel, Michael
1 / 34 shared
Giehl, Christopher
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Chart of publication period
2024
2023

Co-Authors (by relevance)

  • Barth, Anna R.
  • Ramaswamy, Meera
  • Ong, Edward Yong Xi
  • Singh, Navneet
  • Sethna, James
  • Chakraborty, Bulbul
  • Troiss, Alexander
  • Rodríguez Agudo, José Alberto
  • Kaschta, Joachim
  • Haeberle, Jan
  • Müller-Pabel, Michael
  • Giehl, Christopher
OrganizationsLocationPeople

article

Jamming Memory into Acoustically Trained Dense Suspensions under Shear

  • Barth, Anna R.
  • Ramaswamy, Meera
  • Ong, Edward Yong Xi
  • Singh, Navneet
  • Sethna, James
  • Chakraborty, Bulbul
  • Shetty, Abhishek
Abstract

<jats:p>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.</jats:p><jats:sec><jats:title/><jats:supplementary-material><jats:permissions><jats:copyright-statement>Published by the American Physical Society</jats:copyright-statement><jats:copyright-year>2024</jats:copyright-year></jats:permissions></jats:supplementary-material></jats:sec>

Topics
  • impedance spectroscopy
  • strength
  • viscosity
  • hardness
  • yield strength
  • tensile strength
  • size-exclusion chromatography
  • susceptibility