Graphene–Titanium Interfaces from Molecular Dynamics Simulations

Fonseca, Alexandre F.; Liang, Tao; Zhang, Difan; Choudhary, Kamal; Phillpot, Simon R.; Sinnott, Susan B.

doi:10.1021/acsami.7b09469

Cited by 39 publications

(21 citation statements)

References 70 publications

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“…Performances of several reactive and non-reactive potentials in graphene-based materials are compared in ref. 95 . These potentials allow the realization of the thermodynamic stability of graphene-based materials under different conditions, environmental chemistry, substrate, interlayer interactions, and structural defects.…”

Section: Molecular Dynamicsmentioning

confidence: 99%

Multiscale computational understanding and growth of 2D materials: a review

et al. 2020

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The successful discovery and isolation of graphene in 2004, and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional (2D) materials that explore their growth, new atomic-scale physics, and potential device applications. This review aims to provide an overview of theoretical, computational, and machine learning methods and tools at multiple length and time scales, and discuss how they can be utilized to assist/guide the design and synthesis of 2D materials beyond graphene. We focus on three methods at different length and time scales as follows: (i) nanoscale atomistic simulations including density functional theory (DFT) calculations and molecular dynamics simulations employing empirical and reactive interatomic potentials; (ii) mesoscale methods such as phase-field method; and (iii) macroscale continuum approaches by coupling thermal and chemical transport equations. We discuss how machine learning can be combined with computation and experiments to understand the correlations between structures and properties of 2D materials, and to guide the discovery of new 2D materials. We will also provide an outlook for the applications of computational approaches to 2D materials synthesis and growth in general.npj Computational Materials (2020) 6:22 ; https://doi.

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Section: Molecular Dynamicsmentioning

confidence: 99%

Multiscale computational understanding and growth of 2D materials: a review

et al. 2020

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“…Graphene/metal composites inspire broad interests in investigating the structure and properties of graphene/metal interface because they are a key to the development of catalysis, sensors, hydrogen storage, and nanoelectronic devices 7 . Furthermore, incorporating graphene is proved to improve not only the designed functions but also the mechanical performances of the metal matrix phases, showing great prospects in engineering applications 8 – 11 .…”

Section: Introductionmentioning

confidence: 99%

Molecular dynamics study of strengthening mechanism of nanolaminated graphene/Cu composites under compression

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Ning

et al. 2018

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116

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Molecular dynamics simulations of nanolaminated graphene/Cu (NGCu) and pure Cu under compression are conducted to investigate the underlying strengthening mechanism of graphene and the effect of lamella thickness. It is found that the stress-strain curves of NGCu undergo 3 regimes i.e. the elastic regime I, plastic strengthening regime II and plastic flow regime III. Incorporating graphene monolayer is proved to simultaneously contribute to the strength and ductility of the composites and the lamella thickness has a great effect on the mechanical properties of NGCu composites. Different strengthening mechanisms play main role in different regimes, the transition of mechanisms is found to be related to the deformation behavior. Graphene affected zone is developed and integrated with rule of mixtures and confined layer slip model to describe the elastic properties of NGCu and the strengthening effect of the incorporated graphene.

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“…Then, a quenching process was applied to minimize the total potential energy of the whole system. Afterward, the adhesion energy E, defined as the energy per unit area to connect graphene nanosheet with iron substrate, was calculated by [17,23,35]:…”

Section: Interatomic Potentials For Graphene and Iron Systemmentioning

confidence: 99%

“…Many attempts have been made to explore the interfacial characteristics of graphene and metals, such as quantification of adhesion energy of graphene to metallic substrate. Among differently scaled simulation methods [17,[19][20][21], first principle (FP) and molecular dynamic (MD) methods are commonly used to calculate adhesive energy, for example graphene on Ni, Cu and other seven metallic substrates by FP [17], on Fe by FP [22] and on Ti by MD [23]. There are also a few experimental works which directly measured the adhesion energy of graphene to metallic substrates, such as Cu and Ni [24,25].…”

Section: Introductionmentioning

confidence: 99%

Graphene Adhesion Mechanics on Iron Substrates: Insight from Molecular Dynamic Simulations

et al. 2019

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The adhesion feature of graphene on metal substrates is important in graphene synthesis, transfer and applications, as well as for graphene-reinforced metal matrix composites. We investigate the adhesion energy of graphene nanosheets (GNs) on iron substrate using molecular dynamic (MD) simulations. Two Fe–C potentials are examined as Lennard–Jones (LJ) pair potential and embedded-atom method (EAM) potential. For LJ potential, the adhesion energies of monolayer GN are 0.47, 0.62, 0.70 and 0.74 J/m2 on the iron {110}, {111}, {112} and {100} surfaces, respectively, compared to the values of 26.83, 24.87, 25.13 and 25.01 J/m2 from EAM potential. When the number of GN layers increases from one to three, the adhesion energy from EAM potential increases. Such a trend is not captured by LJ potential. The iron {110} surface is the most adhesive surface for monolayer, bilayer and trilayer GNs from EAM potential. The results suggest that the LJ potential describes a weak bond of Fe–C, opposed to a hybrid chemical and strong bond from EAM potential. The average vertical distances between monolayer GN and four iron surfaces are 2.0–2.2 Å from LJ potential and 1.3–1.4 Å from EAM potential. These separations are nearly unchanged with an increasing number of layers. The ABA-stacked GN is likely to form on lower-index {110} and {100} surfaces, while the ABC-stacked GN is preferred on higher-index {111} surface. Our insights of the graphene adhesion mechanics might be beneficial in graphene growing, surface engineering and enhancement of iron using graphene sheets.

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Graphene–Titanium Interfaces from Molecular Dynamics Simulations

Cited by 39 publications

References 70 publications

Multiscale computational understanding and growth of 2D materials: a review

Multiscale computational understanding and growth of 2D materials: a review

Molecular dynamics study of strengthening mechanism of nanolaminated graphene/Cu composites under compression

Graphene Adhesion Mechanics on Iron Substrates: Insight from Molecular Dynamic Simulations

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