Non-Eulerian behavior of graphitic materials under compression

TL;DR

This study reveals that multi-layer graphitic materials exhibit non-Eulerian compression behavior, with critical strain decreasing as thickness increases, modeled by interlayer springs, and showing negative stiffness post-failure, indicating metamaterial properties.

Contribution

It provides a comprehensive experimental and theoretical analysis of the compression behavior of layered graphitic materials, highlighting their deviation from classical Eulerian mechanics.

Findings

Critical strain decreases with increasing flake thickness.

Interlayer van der Waals forces modeled as springs in series match experimental results.

Multi-layer graphene exhibits negative stiffness after failure.

Abstract

The mechanical behavior of graphitic materials is greatly affected by the weak interlayer bonding with van der Waals forces for a range of thickness from nano to macroscale. Herein, we present a comprehensive study of the effect of layer thickness on the compression behavior of graphitic materials such as graphene which are fully embedded in polymer matrices. Raman Spectroscopy was employed to identify experimentally the critical strain to failure of the graphitic specimens. The most striking finding is that, contrary to what would be expected from Eulerian mechanics, the critical compressive strain to failure decreases with increase of flake thickness. This is due to the layered structure of the material and in particular the weak cohesive forces that hold the layers together. The plate phenomenology breaks down for the case of multi-layer graphene, which can be approached as discrete single layers weakly bonded to each other. This behavior is modelled here by considering the interlayer bonding (van der Waals forces) as springs in series, and very good agreement was found between theory and experiment. Finally, it will be shown that in the post failure regime multi-layer graphenes exhibit negative stiffness and thus behave as mechanical metamaterials.