3.8 Proceedings Paper

From Nanoscale Cohesion To Macroscale Entanglement: Opportunities For Designing Granular Aggregate Behavior By Tailoring Grain Shape And Interactions

Journal

POWDERS AND GRAINS 2013
Volume 1542, Issue -, Pages 3-6

Publisher

AMER INST PHYSICS
DOI: 10.1063/1.4811858

Keywords

granular materials; clustering; particle shape; packing; evolutionary computation; 3d printing

Funding

  1. NSF MRSEC [DMR-0820054]
  2. US Army Research Office [W911NF-12-1-0182]

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The packing arrangement of individual particles inside a granular material and the resulting response to applied stresses depend critically on particle-particle interactions. One aspect that recently received attention are nanoscale surface features of particles, which play an important role in determining the strength of cohesive van der Waals and capillary interactions and also affect tribo-charging of grains. We describe experiments on freely falling granular streams that can detect the contributions from all three of these forces. We show that it is possible to measure the charge of individual grains and build up distributions that are detailed enough to provide stringent tests of tribo-charging models currently available. A second aspect concerns particle shape. In this case steric interactions become important and new types of aggregate behavior can be expected when non-convex particle shapes are considered that can interlock or entangle. However, a general connection between the mechanical response of a granular material and the constituents' shape remains unknown. This has made it infeasible to tackle the inverse packing problem, namely to start from a given, desired behavior for the aggregate as a whole and then find the particle shape the produces it. We discuss a new approach, using concepts rooted in artificial evolution that provides a way to solve this inverse problem. This approach facilitates exploring the role of arbitrary particle geometry in jammed systems and invites the discovery and design of granular matter with optimized properties.

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