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The influence of the number of graphene layers on the atomic resolution images obtained

from aberration-corrected high resolution transmission electron microscopy

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2010 Nanotechnology 21 255707

(http://iopscience.iop.org/0957-4484/21/25/255707)

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Nanotechnology 21 (2010) 255707 (5pp) doi:10.1088/0957-4484/21/25/255707

The influence of the number of graphenelayers on the atomic resolution imagesobtained from aberration-corrected highresolution transmission electronmicroscopyJamie H Warner

Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, UK

E-mail: Jamie.warner@materials.ox.ac.uk

Received 25 March 2010, in final form 28 April 2010Published 2 June 2010Online at stacks.iop.org/Nano/21/255707

AbstractImage patterns formed by low voltage aberration-corrected high resolution transmissionelectron microscopy (HRTEM) of few layer graphene provide insight into the number of layerspresent. For odd numbers of graphene layers (three layers or more) and even numbers ofgraphene layers, distinctly different image patterns are produced. A sheet with step edges ofbetween zero, one, two and three layers of graphene is fabricated using in situ electron beamsputtering at 80 kV and atomic resolution images are obtained. A correlation between thetheoretically generated image simulations and the real HRTEM images is found and thisenables us to distinguish between monolayer, bilayer, and trilayer graphene using image patternrecognition.

S Online supplementary data available from stacks.iop.org/Nano/21/255707/mmedia

(Some figures in this article are in colour only in the electronic version)

Graphene and few layer graphene exhibit outstandingelectronic properties that make them highly attractive forapplications such as nanoelectronic field effect transistors,sensors and spintronic devices [1]. Extracting properties thatare different to the bulk form of graphite requires the number ofAB Bernal stacked layers to be reduced to less than ten. Bilayergraphene can be tuned to an insulating state by adjustmentof gate voltage in transistors [2]. Further methods to modifythe electronic transport properties are to either reduce the areaof the sheet to form a nanoribbon [3] or quantum dot [4], orto introduce chemical dopants [5]. Since the properties ofgraphene and few layer graphene are inherently linked to thenumber of layers present, it is extremely vital that techniquesthat can precisely evaluate this are available, continuallydeveloped and well understood.

Raman spectroscopy is now one of the routine tools usedto characterize the number of graphene layers in exfoliatedsheets [6]. It probes micron sized areas and the profile

of the G ′ spectra (i.e. number of peaks present and shape)is used to determine the number of layers of graphene [6].The confidence in using Raman spectroscopy to identifygraphene layers is strong due to the cross-correlation withselected area electron diffraction (SAED) measurements andatomic force microscopy (AFM). In SAED, the change in theintensity of the diffraction spots is examined as a function oftilt angle [7]. Monolayer graphene should not display tilt-dependent intensity fluctuations of the 001 and 022 peaks,where as few layer graphene sheets do [7]. AFM enables adirect measurement of the thickness which is related to thenumber of graphene layers [8, 9]. In situ electron beam inducedsputtering at 80 kV using a transmission electron microscopecan also be used to count the number of graphene sheets [10].At this low accelerating voltage the electron beam selectivelysputters a monolayer at a time to create holes in the sheet [10].Gradually, sheet by sheet, the sample is thinned out untileventually a hole to vacuum is produced [10]. This method is

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Nanotechnology 21 (2010) 255707 J H Warner

more reliable that counting the contrast produced at the edgesof the sheet, but is destructive to the sample. The number oflines of contrast at the edge of a few layer graphene sheet canprovide insights into the number of layers present [8]. But theproblem is that some layers do not extend the entirety of thesample.

Advances in aberration-corrected low voltage highresolution electron microscopy now enable atomic resolutionimages of graphitic carbon structures to be obtained.This has been demonstrated with graphene [11, 12], fewlayer graphene [10, 13], nanotubes [14] and peapods(i.e. fullerenes inside nanotubes) [15]. The atomic resolutionimaging of graphene and few layer graphene has beenprimarily focused on defect dynamics [10–12, 14], edgestructure [10, 16, 17], rotational stacking faults [13] andelectron beam sputtering [10, 17]. In order to gain deeperinsights into the interpretation of HRTEM images, simulationscan be conducted with parameters that represent the settingsof the electron microscope used to acquire the images [18].Correlating the theoretically generated image simulationwith the real HRTEM micrograph is essential for correctinterpretation.

Here, we perform detailed image simulations for graphenelayers with increasing number from 1 to 9 sheets. The resultingchanges in the image pattern with layer number are examined.The effect of tilt angles between 0◦ and 7.6◦ is investigated forlayer numbers up to 4. Finally, the predicted image patternsare compared with real atomic resolution HRTEM images todemonstrate the ability to evaluate the number of layers basedpurely on HRTEM image pattern recognition. The techniqueonly requires a resolution sufficient to resolve the (110) latticeof 0.21 nm of graphite, which is achievable on most aberration-corrected microscopes at 80 kV.

Graphene and few layer graphene sheets were preparedby sonicating flake graphite in a suitable solvent such as 1,2-dichloroethane or N-methylpyrrolidone for 2 h, then restingfor 1 h to allow the thicker sheets to sediment to the bottom ofthe vial [10, 19]. The top 1/3 of the solution was extracted witha pipette and placed in a clean vial. A drop of the solution wasdeposited onto a lacey carbon coated TEM grid and allowed todry. HRTEM was performed using Oxford’s JEOL 2200MCOfield-emission transmission electron microscope, fitted withprobe and image aberration correctors and operated at anaccelerating voltage of 80 kV.

High resolution electron microscopy (HREM) imagesimulations were performed using the multi-slice method(JEMS software). In the multi-slice approach the specimenis divided into many thin slices. Each slice is considered sothin that it can be approximated as a phase shift of the electronwave. The electron wave is transmitted through a slice and thenpropagated to the next slice as a free space wave. Supercellsof 2 nm × 2 nm × 2 nm were constructed using DS ViewerPro. The simulation parameters were Cs = −0.05 mm, Cc =1.22 mm, defocus spread = 4.9 nm, energy spread = 0.32 eV,defocus = −5 nm. These parameters are sufficient to resolvethe (110) spacings of 0.21 nm, but not the (210) spacingsof 0.12 nm. This simplifies the image pattern recognitiontechnique presented.

Figure 1. Image simulations of graphene with increasing number oflayers (a) 1, (b) 2, (c) 3, (d) 4, (e) 5, (f) 6, (g) 7, (h) 8 and (i) 9.

Figure 1 presents a series of multi-slice image simulationsfor increasing number of graphene sheets, with AB Bernalstacking. A defocus value of −5 nm was used in all cases. Thefull set of image simulations with the defocus varied from −20to 0 nm, in 5 nm steps is included in the supporting information(available at stacks.iop.org/Nano/21/255707/mmedia). Wefound that for a Cs = −0.05 mm, a defocus of −5 nmproduced images that were easiest for comparative analysis forall layer numbers. In figure 1(a), the contrast from grapheneis black, whereas for the bilayer in figure 1(b) the contrastis opposite and white. This is the major difference betweenmono and bilayer graphene. Figure 1(c) shows a distinctlydifferent pattern from the trilayer graphene with the hexagonalpattern observed for 1 and 2 layers of graphene replaced withalternating black and white triangles. This unique patternshould make it possible to distinguish 3-layered graphene from1-and 2-layered graphene. The pattern from 4 layer graphenein figure 1(d) is similar to the case of 2 layer graphene infigure 1(b) and we see that for all even numbers of layers,i.e. 6 and 8 layers in figures 1(f) and (h), the resulting imagepatterns are nearly identical. However, for the case of oddnumbers of layers the image pattern continues to change. Asthe layer number increases from 3, 5, 7 to 9 layers the uniquetriangular pattern merges with the hexagonal pattern observedfor the even layer numbers. These distinct pattern profilesallow the discrimination between even and odd number ofgraphene layers, in particular the ability to distinguish 3 layergraphene from 1 and 2 layers.

A quantitative description can be gained by examining theline profiles taken from the simulated images in figure 1, asindicated with the red line in figure 1(i). Figure 2 presentsthe line profiles taken from the respective image simulations infigure 1. The y-axis plots the greyscale intensity, normalizedwith white = 1 and black = 0, and the x-axis as distance.Peaks and dips are labelled in figure 2(b) with A, B, C and D.In figure 2(a), the black contrast from 1 layer of graphene leads

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Nanotechnology 21 (2010) 255707 J H Warner

Figure 2. Line profiles for (a) 1, (b) 2, (c) 3, (d) 4, (e) 5, (f) 6, (g) 7,(h) 8 and (i) 9 layers of graphene obtained from the respective imagesimulations in figure 1. (j) Plot of the ratio of peak height (A–D) to(C–D) (as labelled in (b)) for odd numbers of graphene layers of 3, 5,7, and 9.

to a dip in the line profile, which is split in two by the presenceof two carbon atoms. For 2 layer graphene, the line profileis reversed due to the contrast from the carbon now beingwhite. The even numbered graphene layers, figures 2(b), (d),(f), and (h) show identical normalized line profiles, which areinverted compared to 1 layer graphene. For the odd numberedlayers of 3, 5, 7 and 9, in figures 2(c), (e), (g), and (i), thesplit in the main peak, labelled A and C in figure 2(b), fromthe two carbon atom columns is asymmetric. The asymmetrywas determined by calculating (A–D) and (C–D) from the lineprofiles and then the ratio of these two values. Figure 2(j) plotsthe ratio (A–D)/(C–D) for 3, 5, 7 and 9 layers of graphene.For 3 layers of graphene a ratio of 2.18 was determined andthis rapidly decreases to 1.6 for 5 layers, and then levels out to1.4 for 7 layers, and 1.33 for 9 layers.

The image simulations presented in figure 1 are for 0◦ tilt.In figure 3, the effect of small tilt angles up to 7.6◦ on the

Figure 3. Image simulations examining the effect of tilt at (i) 7.6◦,(ii) 4.8◦, (iii) 3.0◦ and (iv) 0.0◦ for increasing number of graphenelayers (a) 1, (b) 2, (c) 3, and (d) 4.

image pattern is evaluated for 1, 2, 3 and 4 graphene layers.The 1 layer graphene shows minimal changes as a functionof tilt, whereas all others show significant changes. 2 and 4layers of graphene show similar tilt dependence on their imagepatterns, but the 3 layer graphene shows unique behaviour. Avariation in the tilt angle from 4.8◦ to 7.6◦ results in the imagepattern changing from triangular to hexagonal. These resultsindicate that graphene can be identified from all other multi-layered sheets by comparing the image patterns for differenttilt angles and that the triangular patterning observed for 3 layergraphene is not observed for 1-layer or even-layered graphenesheets when tilted under the conditions presented in figure 3.

Confidence in the validity of the image simulationspresented in figure 1 was obtained by comparison withreal HRTEM images of few layer graphene sheets acquiredusing low voltage aberration-corrected high resolution electronmicroscopy at 80 kV. A high beam current density of∼0.1 pA nm−2 was used to sputter a few layer graphene sheetand selectively remove one layer at a time [10]. This opens upregions that contain step edges between 0, 1, 2, 3 and 4 layers.Figure 4(a) shows such an area with resolved atomic structureand figure 4(b) includes labelling for the respective number oflayers as determined by examining the increased contrast thatoccurs at the step edge between increasing number of layers.A defocus value of approximately −5 nm and negative Cs wasused to acquire the image in figure 4(a) and this leads to blackatomic contrast from the atoms in the 1 layer region. In the2 layer region, the atomic contrast has flipped to white, aspredicted in our image simulations. The 3 layer region containstriangular patterning with alternating white and black contrast.

Figure 4(c) shows a HRTEM image from the region of the3 layer graphene in figure 4(a) indicated with a red box, rotated10◦ to enable a direct comparison to the image simulation infigure 1(c). Alternating black and white triangular patterningis observed, which matches well with the image simulation

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Nanotechnology 21 (2010) 255707 J H Warner

Figure 4. (a) HRTEM image of a region containing 0, 1, 2, and 3 layers of graphene formed by electron beam sputtering. (b) Colour codedHRTEM image with layer number indicated. (c) HRTEM image taken from the red box in (a), rotated 10◦ for presentation. (d) Line profiletaken from 3 layer region in (a).

confirming the unique pattern profile of 3 layer graphene.Figure 4(d) shows the line profile taken from the HRTEMimage of the 3 layer graphene in (a). The average asymmetry(i.e. (A–D)/(C–D) as in figure 2) from figure 4(d) wasmeasured to be 2.11, which agrees well with the value of 2.13determined from the line profile of the image simulations infigure 2(c).

These results demonstrate that low voltage aberration-corrected HRTEM imaging can indentify monolayer grapheneby observing minimal change to the image pattern as afunction of small angle tilt, whereas all multi-layered graphenestructures exhibit tilt-dependent image patterns. Odd numbersof graphene layers (apart from the monolayer) produce patternsin their HRTEM images that enable their identification anddifferentiate them from even numbered graphene layers. Multi-slice image simulations of graphene structures are a power toolfor rapidly evaluating their HRTEM image patterns and bycorrelating these with real HRTEM images, strong confidencein the technique is achieved.

Acknowledgments

JHW thanks the Glasstone Fund and Brasenose College,Oxford for support.

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