Nonlinear elasticity of wrinkled atomically thin membranes

Journal Article (2021)
Author(s)

A. Sarafraz (TU Delft - Dynamics of Micro and Nano Systems)

Hadi Arjmandi Tash (TU Delft - Dynamics of Micro and Nano Systems)

Laura Dijkink (Student TU Delft)

B. Sajadi (TU Delft - Dynamics of Micro and Nano Systems)

Mohsen Moeini (Amirkabir University of Technology)

Peter Steeneken (TU Delft - Dynamics of Micro and Nano Systems, TU Delft - QN/Steeneken Lab, Kavli institute of nanoscience Delft)

F. Alijani (TU Delft - Dynamics of Micro and Nano Systems)

Research Group
Dynamics of Micro and Nano Systems
Copyright
© 2021 A. Sarafraz, H. Arjmandi Tash, Laura Dijkink, B. Sajadi, Mohsen Moeini, P.G. Steeneken, F. Alijani
DOI related publication
https://doi.org/10.1063/5.0061822
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 A. Sarafraz, H. Arjmandi Tash, Laura Dijkink, B. Sajadi, Mohsen Moeini, P.G. Steeneken, F. Alijani
Research Group
Dynamics of Micro and Nano Systems
Issue number
18
Volume number
130
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Abstract

Owing to their atomic thickness and low bending rigidity, suspended two-dimensional (2D) materials are prone to wrinkle formation. Here, we use molecular dynamics (MD) simulations to probe the effect of these wrinkles on the nonlinear elasticity of atomically thin graphene membranes. We observe a stress-strain response that consists of two linear regions that are separated by a transition. It is found that this transition is sharp in membranes where wrinkles are formed by uneven stresses at the boundaries. However, when wrinkles are formed by crystal defects, this nonlinear transition is seen to be more gradual. To capture these effects, we use a phenomenological model based on experimentally measurable quantities. We demonstrate the model's fidelity by fitting it to the MD simulated nonlinear response of many graphene membranes providing evidence that the sharpness of the transition between the linear regions in the stress-strain response is a measure of the type of wrinkles and can be quantified by our model.

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