Snap-Through Instability of Graphene on Substrates

Nanoscale Research Letters20095:169

DOI: 10.1007/s11671-009-9460-1

Received: 6 August 2009

Accepted: 2 October 2009

Published: 17 October 2009

Abstract

We determine the graphene morphology regulated by substrates with herringbone and checkerboard surface corrugations. As the graphene–substrate interfacial bonding energy and the substrate surface roughness vary, the graphene morphology snaps between two distinct states: (1) closely conforming to the substrate and (2) remaining nearly flat on the substrate. Since the graphene morphology is strongly tied to the electronic properties of graphene, such a snap-through instability of graphene morphology can lead to desirable graphene electronic properties that could potentially enable graphene-based functional electronic components (e.g. nano-switches).

Keywords

Graphene Nanopatterns Morphology Instability Substrate regulation

Introduction

Graphene is a monolayer of carbon atoms densely packed in a honeycomb crystal lattice. It exhibits extraordinary electrical and mechanical properties [15], and has inspired an array of tantalizing potential applications (e.g., transparent flexible displays and biochemical sensor arrays) [610]. Graphene is intrinsically non-flat and tends to be randomly corrugated [11, 12]. The random graphene morphology can lead to unstable performance of graphene devices as the corrugating physics of graphene is closely tied to its electronic properties [13, 14]. Future success of graphene-based applications hinges upon precise control of the graphene morphology over large areas, a significant challenge largely unexplored so far. Recent experiments show that, however, the morphology of graphene can be regulated by the surface of an underlying substrate [1519]. In this paper, we quantitatively determine the regulated graphene morphology on substrates with various engineered surface patterns, using energy minimization. The results reveal the snap-through instability of graphene on substrates, a promising mechanism to enable functional components for graphene devices.

Recent experiments show that monolayer and few-layer graphene can partially follow the rough surface of the underlying substrates [1519]. The resulting graphene morphology is regulated, rather than the intrinsic random corrugations in freestanding graphene. The substrate-regulated graphene morphology results from the interplay between the interfacial bonding energy and the strain energy of the graphene-substrate system [15, 17], which can be explained as follows.

When graphene is fabricated on a substrate surface via mechanical exfoliation [3] or transfer printing [10, 20], the graphene–substrate interfacial bonding energy is usually weak (e.g., van der Waals interaction). As the graphene corrugates to follow the substrate surface, the graphene–substrate interaction energy decreases due to the nature of van der Waals interaction; on the other hand, the strain energy in the system increases due to the intrinsic bending rigidity of graphene. At the equilibrium graphene morphology on the substrate, the sum of the interaction energy and the system strain energy reaches its minimum.

The above energetic consideration can be used to quantitatively determine the regulated graphene morphology on a rough substrate surface. Furthermore, with a systematic understanding of the governing mechanisms of substrate-regulated graphene morphology, we envision a promising strategy to precisely pattern graphene into desired morphology on engineered substrate surfaces. In this paper, we illustrate this strategy by determining the regulated graphene morphology on two types of engineered substrate surfaces: herringbone corrugations and checkerboard corrugations (Fig. 1). These substrate surface features can be fabricated via approaches combining lithography [21, 22] and strain engineering [23, 24].
http://static-content.springer.com/image/art%3A10.1007%2Fs11671-009-9460-1/MediaObjects/11671_2009_Article_9460_Fig1_HTML.jpg
Figure 1

Schematics of substrate surface corrugations: a herringbone and b checkerboard

Computational Model

The graphene–substrate interaction energy can be determined by summing up all van der Waals forces between the graphene carbon atoms and the substrate atoms. The van der Waals force between a graphene–substrate atomic pair of distance r can be characterized by a Lennard–Jones pair potential, V LJ(r) = 4ɛ(σ12 / r 12 − σ6/r 6), where http://static-content.springer.com/image/art%3A10.1007%2Fs11671-009-9460-1/MediaObjects/11671_2009_Article_9460_IEq1_HTML.gif is the equilibrium distance of the atomic pair and ɛ is the bonding energy at the equilibrium distance. The number of atoms over an area d S on the graphene and a volume d V s in the substrate are ρc d S and ρs d V s , respectively, where ρc is the homogenized carbon atom area density of graphene that is related to the equilibrium carbon–carbon bond length l by http://static-content.springer.com/image/art%3A10.1007%2Fs11671-009-9460-1/MediaObjects/11671_2009_Article_9460_IEq2_HTML.gif and ρs is the molecular density of substrate that can be derived from the molecular mass and mass density of substrate. The interaction energy, denoted by E int, between a graphene of area S and a substrate of volume V s is then given by
http://static-content.springer.com/image/art%3A10.1007%2Fs11671-009-9460-1/MediaObjects/11671_2009_Article_9460_Equa_HTML.gif
(1)

Since Lennard–Jones potential decays rapidly beyond equilibrium atomic pair distance, E int can be estimated by adding up the van der Waals forces between each graphene carbon atom and the substrate portion within a cut-off distance from this carbon atom. If the cut-off distance is large enough, such an estimate of interaction energy converges to the theoretical value of E int. In all simulations reported in this paper, a cut-off distance of 3 nm was used and shown to lead to variations in the estimated value of E int less than 1%.

We have developed a Monte Carlo numerical scheme to compute the multiple integrals in Eq. 1, as summarized below [25]. For the i th graphene carbon atom, n random locations are generated in the substrate portion within the cut-off distance from this carbon atom. The interaction energy between this carbon atom and the substrate is estimated by
http://static-content.springer.com/image/art%3A10.1007%2Fs11671-009-9460-1/MediaObjects/11671_2009_Article_9460_Equb_HTML.gif
(2)
where r ij is the distance between the i th graphene carbon atom and the j th random substrate location. Equation 2 is evaluated at m equally spaced locations over the graphene of area S. The graphene–substrate interaction energy over this area can then be estimated by
http://static-content.springer.com/image/art%3A10.1007%2Fs11671-009-9460-1/MediaObjects/11671_2009_Article_9460_Equc_HTML.gif
(3)

As n and m become large enough, Eq. 3 converges to the theoretical value of E int. In all simulations in this paper, n = 106, m = 400.

The strain energy in the graphene–substrate system results from the corrugating deformation of the graphene and the interaction-induced deformation of the substrate. When an ultrathin monolayer graphene partially conforms to a rigid substrate (e.g., SiO2), the substrate deformation due to the weak graphene–substrate interaction is expected to be negligible. Also, when the graphene spontaneously follows the substrate surface under weak interaction (imagine a fabric naturally conforming to a rough surface) and is not subject to any mechanical constraints (e.g., pinning [26]), the in-plane stretching of the graphene is also expected to be negligible. Under the above assumptions, the strain energy in the graphene–substrate system can be reasonably estimated by the graphene strain energy due to out-of-plane bending, denoted by E g. Effect of the above assumptions on results is to be further elaborated later in this paper. Denoting the out-of-plane displacement of the graphene by w g(x y), the graphene strain energy over an area S can be given by
http://static-content.springer.com/image/art%3A10.1007%2Fs11671-009-9460-1/MediaObjects/11671_2009_Article_9460_Equd_HTML.gif
(4)

where D and ν are the bending rigidity and the Poisson’s ratio of graphene, respectively.

The out-of-plane herringbone corrugations of the substrate surface (Fig. 1a) and the out-of-plane corrugations of the graphene regulated by such a substrate surface are described by
http://static-content.springer.com/image/art%3A10.1007%2Fs11671-009-9460-1/MediaObjects/11671_2009_Article_9460_Eque_HTML.gif
(5)
respectively, where A s and A g are the amplitudes of the substrate surface corrugations and the graphene corrugations, respectively; for both the graphene and the substrate, λ x is the wavelength of the out-of-plane corrugations, λ y and A y are the wavelength and the amplitude of in-plane jogs, respectively; and h is the distance between the middle planes of the graphene and the substrate surface. Given the symmetry of the herringbone pattern, we only need to consider a graphene segment over an area of λ x /2 by λ y /2, and its interaction with the substrate. By substituting Eq. 5 into Eq. 4, the strain energy of such a graphene segment is given by
http://static-content.springer.com/image/art%3A10.1007%2Fs11671-009-9460-1/MediaObjects/11671_2009_Article_9460_Equf_HTML.gif
(6)

As shown in Eq. 6, for a given substrate surface corrugation (i.e., A s, A y , λ x , and λ y ), E g increases monotonically as A g increases. On the other hand, the graphene–substrate interaction energy, E int, minimizes at finite values of A g and h, due to the nature of van der Waals interaction. As a result, there exists a minimum of (E g + E int) where A g and h reach their equilibrium values. The energy minimization was carried out by running a customized code on a high performance computation cluster. In all computations, D = 1.41 eV,l = 0.142 nm, ρs = 2.20 × 1028/m3,σ = 0.353 nm and A s = 0.5 nm, which are representative of a graphene-on-SiO2 structure [27, 28]. Various values of ɛ λ x , λ y , and A y were used to study the effects of interfacial bonding energy and substrate surface roughness on the regulated graphene morphology.

Results and Discussion

Figure 2a plots the normalized amplitude of the regulated graphene corrugation, A g / A s, as a function of D/ε for various λ x . Here λ y = 2 λ x and A y = λ y /4. Thus, various λ x define a family of substrate surfaces with self-similar in-plane herringbone patterns and the same out-of-plane amplitude (i.e., A s). For a given substrate surface pattern, if the interfacial bonding energy is strong (i.e., small D/ε), A g tends to A s. In other words, the graphene closely follows the substrate surface (Fig. 2b). In contrast, if the interfacial bonding is weak (i.e., large D/ε), A g approaches zero. That is, the graphene is nearly flat and does not conform to the substrate surface (Fig. 2c). Interestingly, there exists a threshold value of D/ε, below and above which a sharp transition occurs between the above two distinct states of the graphene morphology. We call such a sharp transition the snap-through instability of graphene. The threshold value of D/ε increases as λ x increases. For a given interfacial bonding energy, A g increases as λ x increases. That is, graphene tends to conform more to a substrate surface with smaller out-of-plane waviness.
http://static-content.springer.com/image/art%3A10.1007%2Fs11671-009-9460-1/MediaObjects/11671_2009_Article_9460_Fig2_HTML.jpg
Figure 2

a A g / A s on substrates with herringbone surface corrugations as a function of D/ε for various λ x . At a threshold value of D/ε, the graphene morphology snaps between two distinct states: b closely conforming to the substrate surface and c remaining nearly flat on the substrate surface

Figure 3 shows the effect of in-plane waviness of the substrate surface on graphene morphology. Figure 3a plots A g/A s as a function of D/ε for various λ y . Here λ x = 6 nm and A y = λ y /4. For a given substrate surface pattern, if the interfacial bonding energy is strong (i.e., small D/ε), A g tends to A s. For a given interfacial bonding energy, A g increases as λ y /λ x increases. That is, graphene tends to conform more to a substrate surface with smaller in-plane waviness. In particular, when λ y /λ x is large (e.g., 100), the predicted graphene corrugation amplitude converges to that of graphene regulated by straight substrate surface grooves with the same λ x and A s[25]. Figure 3b further plots A g/A s as a function of D/ε for various A y with fixed λ x and λ y . Similar effect of in-plane waviness of the substrate surface on graphene morphology emerges from Fig. 3b. Moreover, the snap-through instability of graphene, similar to that illustrated in Fig. 2, is also evident in the results shown in Fig. 3.
http://static-content.springer.com/image/art%3A10.1007%2Fs11671-009-9460-1/MediaObjects/11671_2009_Article_9460_Fig3_HTML.jpg
Figure 3

Effects of in-plane waviness of the substrate surface on graphene morphology. a A g/A s as a function of D/ε for various λ y . b A g/A s as a function of D/ε for various A y . The snap-through instability of graphene morphology, similar to that shown in Fig. 2, is evident in both cases

The snap-through instability of graphene on a substrate surface can be explained as follows. Figure 4 plots the normalized total system energy as a function of A g/A s for various D/ε. Here λ x = 9 nm, λ y = 2 λ x and A y = λ y /4. If the interfacial bonding energy is weaker (D/ε = 575) than a threshold value, the total energy profile reaches its minimum at a small graphene corrugation amplitude A g/A s = 0.14. If the interfacial bonding energy (D/ε = 750) is stronger than the threshold value, the total energy profile reaches its minimum at a large graphene corrugation amplitude A g/A s = 0.93. At the threshold value of D/ε = 650, the total energy profile assumes a double-well shape, whose two minima (A g/A s = 0.20 and 0.91) correspond to the two distinct states of the graphene morphology on the substrate surface.
http://static-content.springer.com/image/art%3A10.1007%2Fs11671-009-9460-1/MediaObjects/11671_2009_Article_9460_Fig4_HTML.jpg
Figure 4

The normalized total system energy as a function of A g/A s for various D/ε. At a threshold value of D/ε = 650, the total system energy minimizes at two points, corresponding to the two distinct states of graphene morphology. Here λ x = 9 nm, λ y = 2 λ x and A y = λ y /4

In the case of graphene regulated by a substrate surface with checkerboard pattern (Fig. 1b), the substrate surface corrugations and the regulated graphene corrugations are described by
http://static-content.springer.com/image/art%3A10.1007%2Fs11671-009-9460-1/MediaObjects/11671_2009_Article_9460_Equg_HTML.gif
(7)

respectively, where λ is the wavelength of the out-of-plane corrugations for both the graphene and the substrate surface. The numerical strategy similar to that aforementioned was implemented to determine the equilibrium amplitude of the regulated graphene morphology.

Figure 5 plots A g/A s regulated by the checkerboard substrate surface as a function of D/ε for various λ. For a given substrate surface roughness, A g/A s decreases as D/ε increases. For a given interfacial bonding energy, A g/A s increases as λ increases. On a substrate surface with checkerboard corrugations, graphene exhibits the snap-through instability as well, which also results from the double-well shape of the system energy profile at the threshold value of D/ε, similar to that shown in Fig. 4. The threshold value of D/ε at the graphene snap-through instability increases as λ increases.
http://static-content.springer.com/image/art%3A10.1007%2Fs11671-009-9460-1/MediaObjects/11671_2009_Article_9460_Fig5_HTML.jpg
Figure 5

A g/A s on substrates with checkerboard surface corrugations as a function of D/ε for various λ. The insets illustrate the two distinct states of graphene morphology at the snap-through instability

In this paper we focus on graphene morphology spontaneously regulated by substrate surfaces via weak interaction. When a graphene/substrate structure is subject to external loading, the graphene strain energy due to stretching and the substrate strain energy may also need to be considered. In this sense, the present model overestimates the graphene corrugation amplitude. Also the graphene/substrate interaction can be enhanced by the possible chemical bondings or pinnings at the interface [26, 29, 30]. In this sense, the present model underestimates the graphene corrugation amplitude.

Concluding Remarks

In summary, we investigate the graphene morphology regulated by substrates with herringbone and checkerboard surface corrugations. Depending on interfacial bonding energy and substrate surface roughness, the graphene morphology exhibits a sharp transition between two distinct states: (1) closely conforming to the substrate surface and (2) remaining nearly flat on the substrate surface. The quantitative results suggest a promising strategy to control the graphene morphology through substrate regulation. While it is difficult to directly manipulate freestanding graphene [31], it is feasible to pattern the substrate surface via lithography [21, 22] and strain engineering [23, 24]. The regulated graphene morphology on such engineered substrate surfaces may lead to new pathways to control the graphene electronic properties, introducing desirable properties such as band-gap, or p/n junction behavior. In particular, the results shown in this paper (e.g., Figs. 2 3 5) reveal a wide range of design tunability of the graphene snap-through instability on substrates through substrate surface patterning and interfacial adhesion tailoring, which offers abundant unexplored potential toward the design of functional graphene device components (e.g., nano-switches, nano-resonators). We then call for experimental demonstration of these envisioned concepts.

Declarations

Acknowledgment

This work is supported by the Minta-Martin Foundation, a UMD General Research Board summer research award to T. L., and NSF CMMI 0856540. Z.Z. also thanks the support of the A. J. Clark Fellowship.

Authors’ Affiliations

(1)
Department of Mechanical Engineering, University of Maryland
(2)
Maryland NanoCenter, University of Maryland

References

  1. Novoselov KS, Geim AK, Morozov SV, Jiang D, Zhang Y, Dubonos SV, Grigorieva IV, Firsov AA: Science. 2004, 306: 666. COI number [1:CAS:528:DC%2BD2cXos1Kqt70%3D]; Bibcode number [2004Sci...306..666N] 10.1126/science.1102896View Article
  2. Geim AK, Novoselov KS: Nat. Mater.. 2007, 6: 183. COI number [1:CAS:528:DC%2BD2sXit1Khtrg%3D]; Bibcode number [2007NatMa...6..183G] 10.1038/nmat1849View Article
  3. Novoselov KS, Jiang D, Schedin F, Booth TJ, Khotkevich VV, Morozov SV, Geim AK: Proc. Natl Acad. Sci. USA. 2005, 102: 10451. COI number [1:CAS:528:DC%2BD2MXntVSit7g%3D]; Bibcode number [2005PNAS..10210451N] 10.1073/pnas.0502848102View Article
  4. Katsnelson MI: Mater. Today. 2007, 10: 20. COI number [1:CAS:528:DC%2BD2sXht1arsL4%3D] 10.1016/S1369-7021(06)71788-6View Article
  5. Geim AK: Science. 2009, 324: 1530. COI number [1:CAS:528:DC%2BD1MXnsFOrsLk%3D]; Bibcode number [2009Sci...324.1530G] 10.1126/science.1158877View Article
  6. Wang X, Zhi L, Mullen K: Nano Lett.. 2008, 8: 323. COI number [1:CAS:528:DC%2BD2sXhsVejtbnK]; Bibcode number [2008NanoL...8..323W] 10.1021/nl072838rView Article
  7. Eda G, Fanchini G, Chhowalla M: Nat. Nanotechnol.. 2008, 3: 270. COI number [1:CAS:528:DC%2BD1cXlsFSltrk%3D] 10.1038/nnano.2008.83View Article
  8. Schedin F, Geim AK, Morozov SV, Hill EW, Blake P, Katsnelson MI, Novoselov KS: Nat. Mater.. 2007, 6: 652. COI number [1:CAS:528:DC%2BD2sXpvFKjsrs%3D]; Bibcode number [2007NatMa...6..652S] 10.1038/nmat1967View Article
  9. Stoller MD, Park SJ, Zhu YW, An JH, Ruoff RS: Nano Lett.. 2008, 8: 3498. COI number [1:CAS:528:DC%2BD1cXhtFaitLjE]; Bibcode number [2008NanoL...8.3498S] 10.1021/nl802558yView Article
  10. Kim KS, Zhao Y, Jang H, Lee SY, Kim JM, Ahn JH, Kim P, Choi JY, Hong BH: Nature. 2009, 457: 706. COI number [1:CAS:528:DC%2BD1MXht1ehtL4%3D]; Bibcode number [2009Natur.457..706K] 10.1038/nature07719View Article
  11. Fasolino A, Los JH, Katsnelson MI: Nat. Mater.. 2007, 6: 858. COI number [1:CAS:528:DC%2BD2sXht1CgtLfP]; Bibcode number [2007NatMa...6..858F] 10.1038/nmat2011View Article
  12. Meyer JC, Geim AK, Katsnelson MI, Novoselov KS, Booth TJ, Roth S: Nature. 2007, 446: 60. COI number [1:CAS:528:DC%2BD2sXit1arsLs%3D]; Bibcode number [2007Natur.446...60M] 10.1038/nature05545View Article
  13. Morozov SV, Novoselov KS, Katsnelson MI, Schedin F, Ponomarenko LA, Jiang D, Geim AK: Phys. Rev. Lett.. 2006, 97: 016801. COI number [1:STN:280:DC%2BD28vosV2ktQ%3D%3D]; Bibcode number [2006PhRvL..97a6801M] 10.1103/PhysRevLett.97.016801View Article
  14. Neto AHC, Guinea F, Peres NMR, Novoselov KS, Geim AK: Rev. Mod. Phys.. 2009, 81: 109. Bibcode number [2009RvMP...81..109C] Bibcode number [2009RvMP...81..109C] 10.1103/RevModPhys.81.109View Article
  15. Ishigami M, Chen JH, Cullen WG, Fuhrer MS, Williams ED: Nano Lett.. 2007, 7: 1643. COI number [1:CAS:528:DC%2BD2sXltV2ju7c%3D]; Bibcode number [2007NanoL...7.1643I] 10.1021/nl070613aView Article
  16. Stolyarova E, Rim KT, Ryu S, Maultzsch J, Kim P, Brus LE, Heinz TF, Hybertsen MS, Flynn GW: Proc. Natl Acad. Sci. USA. 2007, 104: 9209. COI number [1:CAS:528:DC%2BD2sXmtlarsLk%3D]; Bibcode number [2007PNAS..104.9209S] 10.1073/pnas.0703337104View Article
  17. Stoberl U, Wurstbauer U, Wegscheider W, Weiss D, Eroms J: Appl. Phys. Lett.. 2008, 93: 051906. Bibcode number [2008ApPhL..93e1906S] Bibcode number [2008ApPhL..93e1906S] 10.1063/1.2968310View Article
  18. Tikhonenko FV, Horsell DW, Gorbachev RV, Savchenko AK: Phys. Rev. Lett.. 2008, 100: 056802. COI number [1:STN:280:DC%2BD1c7psVGisA%3D%3D]; Bibcode number [2008PhRvL.100e6802T] 10.1103/PhysRevLett.100.056802View Article
  19. Geringer V, Liebmann M, Echtermeyer T, Runte S, Schmidt M, Ruckamp R, Lemme MC, Morgenstern M: Phys. Rev. Lett.. 2009, 102: 4. 10.1103/PhysRevLett.102.076102View Article
  20. Chen JH, Ishigami M, Jang C, Hines DR, Fuhrer MS, Williams ED: Adv. Mater.. 2007, 19: 3623. COI number [1:CAS:528:DC%2BD2sXhtlKktbfE] 10.1002/adma.200701059View Article
  21. Xia Y, Rogers JA, Paul KE, Whitesides GM: Chem. Rev.. 1999, 99: 1823. COI number [1:CAS:528:DyaK1MXjtVyisr8%3D] 10.1021/cr980002qView Article
  22. Chou SY, Krauss PR, Renstrom PJ: Science. 1996, 272: 85. COI number [1:CAS:528:DyaK28XitVKkt7Y%3D]; Bibcode number [1996Sci...272...85C] 10.1126/science.272.5258.85View Article
  23. Choi WM, Song J, Khang D-Y, Jiang H, Huang YY, Rogers JA: Nano Lett.. 2007, 7: 1655. COI number [1:CAS:528:DC%2BD2sXkvFKlsLc%3D]; Bibcode number [2007NanoL...7.1655C] 10.1021/nl0706244View Article
  24. Chen X, Hutchinson JW: J. Appl. Mech.. 2004, 71: 597. 10.1115/1.1756141View Article
  25. Li T, Zhang Z2009. [http://​arxiv.​org/​abs/​0907.​1639] submitted, Preprint:
  26. Feibelman PJ: Phys. Rev. B. 2008, 77: 165419. Bibcode number [2008PhRvB..77p5419F] Bibcode number [2008PhRvB..77p5419F] 10.1103/PhysRevB.77.165419View Article
  27. Tomnek D, Zhong W, Krastev E: Phys. Rev. B. 1993, 48: 15461. Bibcode number [1993PhRvB..4815461T] Bibcode number [1993PhRvB..4815461T] 10.1103/PhysRevB.48.15461View Article
  28. Israelachvili JN: Intermolecular and Surface Forces. Academic Press, London; San Diego; 1991.
  29. Namilae S, Chandra N: J. Eng. Mater. Technol. Trans. ASME. 2005, 127: 222. COI number [1:CAS:528:DC%2BD2MXjt1Srs74%3D] 10.1115/1.1857940View Article
  30. Sabio J, Seoanez C, Fratini S, Guinea F, Neto AHC, Sols F: Phys. Rev. B. 2008, 77: 195409. Bibcode number [2008PhRvB..77s5409S] Bibcode number [2008PhRvB..77s5409S] 10.1103/PhysRevB.77.195409View Article
  31. Tapaszto L, Dobrik G, Lambin P, Biro LP: Nat. Nanotechnol.. 2008, 3: 397. COI number [1:CAS:528:DC%2BD1cXotFehs7c%3D] 10.1038/nnano.2008.149View Article

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