The University of Southampton
University of Southampton Institutional Repository

High-velocity ballistics of twisted bilayer graphene under stochastic disorder

High-velocity ballistics of twisted bilayer graphene under stochastic disorder
High-velocity ballistics of twisted bilayer graphene under stochastic disorder

Graphene is one of the strongest, stiffest, and lightest nanoscale materials known to date, making it a potentially viable and attractive candidate for developing lightweight structural composites to prevent high-velocity ballistic impact, as commonly encountered in defense and space sectors. In-plane twist in bilayer graphene has recently revealed unprecedented electronic properties like superconductivity, which has now started attracting the attention for other multi-physical properties of such twisted structures. For example, the latest studies show that twisting can enhance the strength and stiffness of graphene by many folds, which in turn creates a strong rationale for their prospective exploitation in high-velocity impact. The present article investigates the ballistic performance of twisted bilayer graphene (tBLG) nanostructures. We have employed molecular dynamics (MD) simulations, augmented further by coupling gaussian process-based machine learning, for the nanoscale characterization of various tBLG structures with varying relative rotation angle (RRA). Spherical diamond impactors (with a diameter of 25Å) are enforced with high initial velocity (Vi) in the range of 1 km/s to 6.5 km/s to observe the ballistic performance of tBLG nanostructures. The specific penetration energy (Ep*) of the impacted nanostructures and residual velocity (Vr) of the impactor are considered as the quantities of interest, wherein the effect of stochastic system parameters is computationally captured based on an efficient Gaussian process regression (GPR) based Monte Carlo simulation approach. A data-driven sensitivity analysis is carried out to quantify the relative importance of different critical system parameters. As an integral part of this study, we have deterministically investigated the resonant behaviour of graphene nanostructures, wherein the high-velocity impact is used as the initial actuation mechanism. The comprehensive dynamic investigation of bilayer graphene under the ballistic impact, as presented in this paper including the effect of twisting and random disorder for their prospective exploitation, would lead to the development of improved impact-resistant lightweight materials.

Ballistic performance, Coupled molecular dynamics simulation, Gaussian process regression, Monte carlo simulation, Twisted bilayer graphene
2287-237X
529-547
Gupta, K. K.
52bd46e7-a3fb-4b61-8ef2-73a1d57fe2b4
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Roy, L.
51d8e699-f9c0-4735-a3d1-405361ecab2a
Dey, S.
f5f9f67a-a250-4f67-aa68-935e5faf4f3a
Gupta, K. K.
52bd46e7-a3fb-4b61-8ef2-73a1d57fe2b4
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Roy, L.
51d8e699-f9c0-4735-a3d1-405361ecab2a
Dey, S.
f5f9f67a-a250-4f67-aa68-935e5faf4f3a

Gupta, K. K., Mukhopadhyay, T., Roy, L. and Dey, S. (2022) High-velocity ballistics of twisted bilayer graphene under stochastic disorder. Advances in Nano Research, 12 (5), 529-547. (doi:10.12989/anr.2022.12.5.529).

Record type: Article

Abstract

Graphene is one of the strongest, stiffest, and lightest nanoscale materials known to date, making it a potentially viable and attractive candidate for developing lightweight structural composites to prevent high-velocity ballistic impact, as commonly encountered in defense and space sectors. In-plane twist in bilayer graphene has recently revealed unprecedented electronic properties like superconductivity, which has now started attracting the attention for other multi-physical properties of such twisted structures. For example, the latest studies show that twisting can enhance the strength and stiffness of graphene by many folds, which in turn creates a strong rationale for their prospective exploitation in high-velocity impact. The present article investigates the ballistic performance of twisted bilayer graphene (tBLG) nanostructures. We have employed molecular dynamics (MD) simulations, augmented further by coupling gaussian process-based machine learning, for the nanoscale characterization of various tBLG structures with varying relative rotation angle (RRA). Spherical diamond impactors (with a diameter of 25Å) are enforced with high initial velocity (Vi) in the range of 1 km/s to 6.5 km/s to observe the ballistic performance of tBLG nanostructures. The specific penetration energy (Ep*) of the impacted nanostructures and residual velocity (Vr) of the impactor are considered as the quantities of interest, wherein the effect of stochastic system parameters is computationally captured based on an efficient Gaussian process regression (GPR) based Monte Carlo simulation approach. A data-driven sensitivity analysis is carried out to quantify the relative importance of different critical system parameters. As an integral part of this study, we have deterministically investigated the resonant behaviour of graphene nanostructures, wherein the high-velocity impact is used as the initial actuation mechanism. The comprehensive dynamic investigation of bilayer graphene under the ballistic impact, as presented in this paper including the effect of twisting and random disorder for their prospective exploitation, would lead to the development of improved impact-resistant lightweight materials.

This record has no associated files available for download.

More information

Published date: 1 May 2022
Additional Information: Funding Information: KKG is grateful for the financial support from MoE, India during the research work. TM acknowledges the initiation grants received from IIT Kanpur. Publisher Copyright: © 2022. Techno-Press, Ltd.
Keywords: Ballistic performance, Coupled molecular dynamics simulation, Gaussian process regression, Monte carlo simulation, Twisted bilayer graphene

Identifiers

Local EPrints ID: 483917
URI: http://eprints.soton.ac.uk/id/eprint/483917
ISSN: 2287-237X
PURE UUID: 3fd849c1-5f80-435b-9689-839f7c66f2bb
ORCID for T. Mukhopadhyay: ORCID iD orcid.org/0000-0002-0778-6515

Catalogue record

Date deposited: 07 Nov 2023 18:27
Last modified: 18 Mar 2024 04:10

Export record

Altmetrics

Contributors

Author: K. K. Gupta
Author: T. Mukhopadhyay ORCID iD
Author: L. Roy
Author: S. Dey

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×