Literature DB >> 29436032

Graphene-Graphene Interactions: Friction, Superlubricity, and Exfoliation.

Robert C Sinclair1, James L Suter1, Peter V Coveney1.   

Abstract

Graphite's lubricating properties due to the "weak" interactions between individual layers have long been known. However, these interactions are not weak enough to allow graphite to readily exfoliate into graphene on a large scale. Separating graphite layers down to a single sheet is an intense area of research as scientists attempt to utilize graphene's superlative properties. The exfoliation and processing of layered materials is governed by the friction between layers. Friction on the macroscale can be intuitively understood, but there is little understanding of the mechanisms involved in nanolayered materials. Using molecular dynamics and a new forcefield, graphene's unusual behavior in a superlubric state is examined, and the energy dissipated between two such surfaces sliding past each other is shown. The dependence of friction on temperature and surface roughness is described, and agreement with experiment is reported. The accuracy of the simulated behavior enables the processes that drive exfoliation of graphite into individual graphene sheets to be described. Taking into account the friction between layers, a peeling mechanism of exfoliation is predicted to be of lower energy cost than shearing.
© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  2D-materials exfoliation; atomistic simulations; graphene; superlubricity

Year:  2018        PMID: 29436032     DOI: 10.1002/adma.201705791

Source DB:  PubMed          Journal:  Adv Mater        ISSN: 0935-9648            Impact factor:   30.849


  9 in total

1.  100 km wear-free sliding achieved by microscale superlubric graphite/DLC heterojunctions under ambient conditions.

Authors:  Deli Peng; Jin Wang; Haiyang Jiang; Shuji Zhao; Zhanghui Wu; Kaiwen Tian; Ming Ma; Quanshui Zheng
Journal:  Natl Sci Rev       Date:  2021-06-24       Impact factor: 17.275

2.  A novel route to the synthesis of an Fe3O4/h-BN 2D nanocomposite as a lubricant additive.

Authors:  Jun Zhao; Guangyan Chen; Yongyong He; Shuangxi Li; Zhiqiang Duan; Yingru Li; Jianbin Luo
Journal:  RSC Adv       Date:  2019-02-25       Impact factor: 4.036

3.  Uncertainty Quantification in Alchemical Free Energy Methods.

Authors:  Agastya P Bhati; Shunzhou Wan; Yuan Hu; Brad Sherborne; Peter V Coveney
Journal:  J Chem Theory Comput       Date:  2018-05-02       Impact factor: 6.006

4.  Synergistic Tribo-Activity of Nanohybrids of Zirconia/Cerium-Doped Zirconia Nanoparticles with Nano Lamellar Reduced Graphene Oxide and Molybdenum Disulfide.

Authors:  Dinesh Kumar Verma; Nivedita Shukla; Bharat Kumar; Alok Kumar Singh; Kavita Shahu; Mithilesh Yadav; Kyong Yop Rhee; Rashmi Bala Rastogi
Journal:  Nanomaterials (Basel)       Date:  2020-04-08       Impact factor: 5.076

5.  Dependence of the fluorination intercalation of graphene toward high-quality fluorinated graphene formation.

Authors:  Kun Fan; Jiemin Fu; Xikui Liu; Yang Liu; Wenchuan Lai; Xiangyang Liu; Xu Wang
Journal:  Chem Sci       Date:  2019-04-30       Impact factor: 9.825

6.  Uncertainty quantification in classical molecular dynamics.

Authors:  Shunzhou Wan; Robert C Sinclair; Peter V Coveney
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2021-03-29       Impact factor: 4.226

7.  Large Scale Study of Ligand-Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols.

Authors:  Agastya P Bhati; Peter V Coveney
Journal:  J Chem Theory Comput       Date:  2022-03-16       Impact factor: 6.578

8.  Viscous peeling of a nanosheet.

Authors:  Adyant Agrawal; Simon Gravelle; Catherine Kamal; Lorenzo Botto
Journal:  Soft Matter       Date:  2022-05-25       Impact factor: 4.046

Review 9.  Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction.

Authors:  Shunzhou Wan; Agastya P Bhati; Stefan J Zasada; Peter V Coveney
Journal:  Interface Focus       Date:  2020-10-16       Impact factor: 3.906

  9 in total

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