Atomistic simulation study of mechanical properties of periodic graphene nanobuds

Fereidoon, Abdolhossein; Khorasani, Media Ghasem Zadeh; Ganji, Masoud Darvish; Memarian, Farzaneh

doi:10.1016/j.commatsci.2015.05.004

Cited by 7 publications

(1 citation statement)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[32][33][34][35][36][37][38][39] Development of a conducting network by GS helps in improving thermal and electrical conductivity of many matrices; and it has the capability to serve as a thermal interface material in high power microelectronic devices. 40 The major graphene structures used in nanocomposites fabrication are planar nanosheets either undoped or doped, 41 nanoribbons, [42][43][44][45] nanopores, 46 nanobuds 47 and nanoplatelets. 48 These shapes and morphologies play an important role in determining the strength and damage resistance of these nanoscale carbon reinforcements in polymers.…”

Section: Introductionmentioning

confidence: 99%

Atomistic modeling of graphene/hexagonal boron nitride polymer nanocomposites: a review

Verma

Parashar

Packirisamy

2017

WIREs Comput Mol Sci

112

View full text Add to dashboard Cite

Due to their exceptional properties, graphene and hexagonal boron nitride (h‐BN) nanofillers are emerging as potential candidates for reinforcing the polymer‐based nanocomposites. Graphene and h‐BN have comparable mechanical and thermal properties, whereas due to high band gap in h‐BN (~5 eV), have contrasting electrical conductivities. Atomistic modeling techniques are viable alternatives to the costly and time‐consuming experimental techniques, and are accurate enough to predict the mechanical properties, fracture toughness, and thermal conductivities of graphene and h‐BN‐based nanocomposites. Success of any atomistic model entirely depends on the type of interatomic potential used in simulations. This review article encompasses different types of interatomic potentials that can be used for the modeling of graphene, h‐BN, and corresponding nanocomposites, and further elaborates on developments and challenges associated with the classical mechanics‐based approach along with synergic effects of these nano reinforcements on host polymer matrix. This article is categorized under: Molecular and Statistical Mechanics > Molecular Mechanics Structure and Mechanism > Computational Materials Science Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

show abstract