Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/161785
Title: Thermal-fluctuation gradient induced tangential entropic forces in layered two-dimensional materials
Authors: Zhu, Fangyan
Leng, Jiantao
Jiang, Jin-Wu
Chang, Tienchong
Zhang, Tongyi
Gao, Huajian
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Zhu, F., Leng, J., Jiang, J., Chang, T., Zhang, T. & Gao, H. (2022). Thermal-fluctuation gradient induced tangential entropic forces in layered two-dimensional materials. Journal of the Mechanics and Physics of Solids, 163, 104871-. https://dx.doi.org/10.1016/j.jmps.2022.104871
Project: 002479-00001 
Journal: Journal of the Mechanics and Physics of Solids 
Abstract: Recent studies on nanomechanical devices based on low-dimensional nanomaterials have revealed several different types of thermal fluctuation gradient induced tangential entropic forces (TEFs), including expulsion force, edge force, thermophoretic force, nanodurotaxis force, etc. While all these forces originate from thermal fluctuation gradients, they can take different forms for different problems and have been treated case-by-case in the literature. Here, we develop a unified theoretical framework for TEFs in layered low-dimensional materials. In particular, we derive explicit analytical solutions for TEFs in layered two-dimensional materials and validate them with molecular dynamics simulations for various bilayers composed of graphene, graphyne, hexagonal-boron nitride (h-BN), boron-carbon-nitride (BCN), and double walled nanotubes. We present also approximate solutions to TEFs in hetero- or substrate-supported-bilayers based on a solution-guided machine learning (SGML) technique. The developed concept for TEFs is unique to nanomechanical systems and may serve as one of the founding pillars of nanomechanics.
URI: https://hdl.handle.net/10356/161785
ISSN: 0022-5096
DOI: 10.1016/j.jmps.2022.104871
Schools: School of Mechanical and Aerospace Engineering 
Organisations: Institute of High Performance Computing, A*STAR
Rights: © 2022 Published by Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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