Online Determination of Graphene Lattice Orientation Through Lateral Forces
© The Author(s). 2016
Received: 17 December 2015
Accepted: 8 July 2016
Published: 2 August 2016
Rapid progress in graphene engineering has called for a simple and effective method to determine the lattice orientation on graphene before tailoring graphene to the desired edge structures and shapes. In this work, a wavelet transform-based frequency identification method is developed to distinguish the lattice orientation of graphene. The lattice orientation is determined through the different distribution of the frequency power spectrum just from a single scan line. This method is proven both theoretically and experimentally to be useful and controllable. The results at the atomic scale show that the frequencies vary with the lattice orientation of graphene. Thus, an adjusted angle to the desired lattice orientation (zigzag or armchair) can easily be calculated based on the frequency obtained from the single scan line. Ultimately, these results will play a critical role in wafer-size graphene engineering and in the manufacturing of graphene-based nanodevices.
Graphene has been known as the replacement for silicon due to its unique electronic, physical, and mechanical properties as well as its wide range of applications [1, 2]. Although graphene shows extraordinary performance in fast transistors [3–7], super-capacitor , and highly sensitive sensors [9–11], the absence of an energy bandgap is still a grand challenge for applications in semiconductor nanodevices. Fortunately, previous studies have shown that graphene nanoribbons can display metallic or semiconducting properties due to different edges (zigzag or armchair) with the bandgap tunable by the width [12–15].
To date, several different graphene patterning methods such as Catalytic Cutting Technique [16–19], SPM-based Electric Field Tailoring Technique [20–22], AFM Scratching Technique [23, 24], Photocatalytic Patterning Approach , and Energy Beam Cutting method [26–28] have been developed. But whatever the cutting technique is, one of the prerequisites for tailoring graphene into desired nanodevice is to know its original lattice orientation, based on which the desired geometry configuration can be designed. Moreover, recent progress has been made in making large area graphene, with the largest reports on 4-in. wafers (http://www.electronicsweekly.com/Articles/2010/02/03/47937/100mm-graphene-wafer-grown.htm). Therefore, it becomes absolutely necessary to develop a simple, fast, flexible, and controllable method to determine the lattice orientation (zigzag and armchair) of wafer-size graphene on various substrates before manufacturing. Recently, Sasaki et al. reported friction anisotropy on graphene studied by molecular mechanics simulation. It revealed the possibility of identifying the lattice orientation on graphene theoretically . Although AFM can image surface of material in atomic resolution [30, 31], the imaging conditions are very strict, especially in air under ambient conditions. The imaging process is easily affected by factors such as environment (including humidity, temperature, etc.) and probes. Additionally, the repeatability is very low. Even if the researchers who have rich atomic observation experiences, it also needs to take hours to obtain a stable and clear atomic resolution image.
In this paper, a wavelet transform-based frequency ratio identifying method is developed to determine the lattice orientation of graphene theoretically and experimentally. The uniqueness of the proposed method lies in using one or two friction scanning lines that can quickly distinguish graphene lattice orientation. Both theoretical and experimental results at the atomic scale have shown that the frequency ratio vary with the lattice orientation on graphene, based on which the lattice orientation on graphene can easily be distinguished. The findings in this paper will play a critical role in wafer-size graphene engineering and in the development of graphene-based nanodevices.
The friction measurements on graphene were performed with a Multimode AFM (now Bruker) in air under ambient conditions (43 to 47 % relative humidity, 23 to 26 °C). AFM probes with rectangular cantilevers were used with scan size 5 nm across. Its radius, length, width, height, and thickness are 10, 450, 50, 10, and 2 μm, respectively. The normal spring constant is 0.2 N/m. The scan rate has to be more 10 Hz. The total number of lines per image, which defines the pixel resolution of the image, was kept constant at 256. All images, except otherwise indicated, were flattened with a first-order line-wise correction fit. Graphene (Additional file 1: Figure S1) was prepared by using micromechanical cleavage of bulk graphite . Monolayer CVD graphene (Fig. 6) was transfer to an electrode chip by using bubbling transfer . The electrodes were fabricated by standard photolithography and lift-off techniques. The morphology and structure of monolayer CVD graphene were characterized using an optical microscope (KH-7700, Hirox Inc.), AFM (Veeco Dimension 3100, tapping mode). The wavelet transforms [33, 34] were used here to obtain frequency information and signal filtering. It was performed with Daubechies wavelet (db9). Four-step wavelet decomposition (see Additional file 1: Figure S3) is chosen.
Results and Discussion
Fiction measurements for different directions were performed by changing the scan angles (0°, 5°, 14°, 25°, 30°, 35°, 44°, 49°, and 55°, respectively. See Additional file 1: Figure S1). For the comparison of simulation and experiment results, the lattice angle 0° in experiments is defined as a zigzag orientation, which is in the anticlockwise direction nearest to the scanning direction. Thus 30° indicates an armchair orientation. The parameters used in the simulation were depicted in Additional file 1.
Rotation (30°-θ) by anticlockwise, and scan a single line to calculate δ.
If this δ shows the armchair direction, then the calculated θ (0° < θ < 30°) should be the real lattice angle.
Otherwise, the real lattice angle should be (60°-θ).
The properties of graphene strongly rely on its edge structures. However, there is no rapid way to determine the lattice orientation on graphene. A simple and controllable method is developed to distinguish the lattice orientation of graphene appropriately. The method proposed in the manuscript only needs one or two scan lines to obtain the frequency ratio based on wavelet transform. Both theoretical and experimental results at the atomic scale have shown that the frequency ratios vary with the lattice orientations on graphene. In addition, an adjusted angle to the desired lattice orientation can be easily calculated based on the frequency ratio and the distribution obtained in this work, ultimately providing the platform for graphene engineering and graphene based nanodevices. In the future, the effects of the structural complexities of graphene on frequencies will be investigated, such as local strain, defects, and puckering. Recently, some studies [37–41] have shown that the structural complexities of 2D materials strongly influence friction forces. Therefore, they will influence the frequency also. These effects will be systematically studied in the next step.
This work supported by the National Natural Science Foundation of China (Grant No. 61375107, 61175103), Bureau of International Cooperation, Chinese Academy of Sciences (Grant No. 17321KYSB20130006), Project supported by Science and Technology Development Project of Jilin Province, China (Grant No. 20160520098JH), Key Project of High Education and Scientific Research of Jinlin Province of China (Grant No. JGJX2015C55), the12th Five-Year Plan Project of Education and Sciences of Jinlin Province of China (Grant No. GH150554), and the CAS FEA International Partnership Program for Creative Research Teams.
YZ designed and performed the measurements, carried on the data analysis, and drafted the manuscript. FY, GL, and UCW participated in the revision of the manuscript and discussed the results. LL and NX participated in the monitoring the experimental work, data analysis, discussion, and revision of the manuscript. GL, ZZ, and YW helped to coordinate the experiments and revise the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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- Novoselov KS, Geim AK, Morozov SV, Jiang D, Zhang Y, Dubonos SV, et al (2004) Electric field effect in atomically thin carbon films. Science 306:666–669View ArticleGoogle Scholar
- Novoselov KS, Falko VI, Colombo L, Gellert PR, Schwab MG, Kim K (2012) A roadmap for graphene. Nature 490:192–200View ArticleGoogle Scholar
- Han SJ, Reddy D, Carpenter GD, Franklin AD, Jenkins KA (2012) Current saturation in submicrometer graphene transistors with thin gate dielectric: experiment, simulation, and theory. ACS Nano 6:5220–5226View ArticleGoogle Scholar
- Guom Z, Dong R, Chakraborty PS, Lourenco N, Palmer J, et al (2013) Record maximum oscillation frequency in C-face epitaxial graphene transistors. Nano Lett 13:942–947View ArticleGoogle Scholar
- Han SJ, Garcia AV, Oida S, Jenkins KA, Haensch W (2014) Graphene radio frequency receiver integrated circuit. Nat Comm 5:3086Google Scholar
- Ghadiry M, Ismail R, Saeidmanesh M, Khaledian M, Manaf A (2014) Graphene nanoribbon field-effect transistor at high. Nanoscale Res Lett 9:604View ArticleGoogle Scholar
- Wu YQ, Lin YM, Bol AA, Jenkins KA, Xia F, Farmer DB, et al (2011) High-frequency, scaled graphene transistors on diamond-like carbon. Nature 472:74–78View ArticleGoogle Scholar
- Polat EO, Kocabas C (2013) Broadband optical modulators based on graphene supercapacitors. Nano Lett 13:5851–5857View ArticleGoogle Scholar
- Karimi H, Yusof R, Rahmani R, Hosseinpour H, Ahmadi MT (2014) Development of solution-gated graphene transistor model for biosensors. Nanoscale Res Lett 9:71View ArticleGoogle Scholar
- Wang Y, Yang T, Lao J, Zhang R, Zhang Y, Zhu M (2015) Ultra-sensitive graphene strain sensor for sound signal acquisition and recognition. Nano Res 8:1627–1636View ArticleGoogle Scholar
- Wang Y, Yang R, Shi ZW, Zhang LC, Shi DX, Wang E, Zhang GY (2011) Super-elastic graphene ripples for flexible strain sensors. acs nano 5:3645–3650View ArticleGoogle Scholar
- Zhu WJ, Neumayer D, Perebeinos V, Avouris P (2010) Silicon nitride gate dielectrics and band gap engineering in graphene layers. Nano Lett 10:3572–3576View ArticleGoogle Scholar
- Kim M, Safron NS, Han E, Arnold MS, Gopalan P (2010) Fabrication and characterization of large-area, semiconducting nanoperforated graphene materials. Nano Lett 10:1125–1131View ArticleGoogle Scholar
- Ouyang FP, Peng S, Liu Z, Liu Z (2011) Bandgap opening in graphene antidot lattices: the missing half. ACS Nano 5:4023–4030View ArticleGoogle Scholar
- Feng J, Li WB, Qian XF, Qi JS, Qi L, Li J (2012) Patterning of graphene. Nanoscale 4:4883–4899View ArticleGoogle Scholar
- Datta SS, Strachan DR, Khamis SM, Johnson ATC (2008) Crystallographic etching of few-layer graphene. Nano Lett 8:1912–1915View ArticleGoogle Scholar
- Ci L, Xu Z, Wang L, Gao W, Ding F, Kevin FK, Boris IY, Pulickel MA (2008) Controlled nanocutting of graphene. Nano Res 1:116–122View ArticleGoogle Scholar
- Campos LC, Manfrinato VR, Sanchez-Yamagishi JD, Kong J, Jarillo-Herrero P (2009) Anisotropic etching and nanoribbon formation in single-layer graphene. Nano Lett 9:2600–2604View ArticleGoogle Scholar
- Gao L, Ren W, Liu B, Wu Z, Jiang C, Cheng HM (2009) Crystallographic tailoring of graphene by nonmetal SiOx nanoparticles. J Am Chem Soc 131:13934–13936View ArticleGoogle Scholar
- Giesbers AJM, Zeitler U, Neubeck S (2008) Nanolithography and manipulation of graphene using an atomic force microscope. Sol St Comm 147:366–369View ArticleGoogle Scholar
- Tapaszto L, Dobrik G, Lambin P, Biro LP (2008) Tailoring the atomic structure of graphene nanoribbons by scanning tunnelling microscope lithography. Nat Nano 3:397–401View ArticleGoogle Scholar
- Weng L, Zhang LY, Chen YP, Rokhinson LP (2008) Atomic force microscope local oxidation nanolithography of graphene. Appl Phys Lett 93:093107View ArticleGoogle Scholar
- Zhang Y, Liu L, Xi N, Wang Y, Dong Z, Wejinya UC (2011) Dielectrophoretic assembly and atomic force microscopy modification of reduced graphene oxide. J Appl Phys 110:114515View ArticleGoogle Scholar
- Zhang Y, Liu L, Xi N, Wang Y, Dong Z, Wejinya UC (2012) Cutting forces related with lattice orientations of graphene using an atomic force microscopy based nanorobot. Appl Phys Lett 101:213101View ArticleGoogle Scholar
- Zhang L, Diao S, Nie Y, Yan K, Liu N, Dai B, et al (2011) Photocatalytic patterning and modification of graphene. J Am Chem Soc 133:2706–2713View ArticleGoogle Scholar
- Fischbein MD, Drndic M (2008) Electron beam nanosculpting of suspended graphene sheets. Appl Phys Lett 93:113107, Appl. Phys. Lett.2008; 93:113107View ArticleGoogle Scholar
- Bell DC, Lemme MC, Stern LA, Marcus CM (2009) Precision cutting and patterning of graphene with helium ions. Nanotechnology 20:455301View ArticleGoogle Scholar
- Lemme MC, Bell DC, Williams JR (2009) Etching of graphene devices with a helium ion beam. ACS Nano 3:2674–2676View ArticleGoogle Scholar
- Naruo S, Hideaki O, Noriaki I, Kouji M (2010) Atomic-scale friction of monolayer graphenes with armchair- and zigzag-type edges during peeling process. e-J Surf Sci Nanotech 8:105–111View ArticleGoogle Scholar
- Hembacher S, Giessibl FJ, Mannhart J, Quate CF (2003) Revealing the hidden atom in graphite by low-temperature atomic force microscopy. PNAS 100:12539–12542View ArticleGoogle Scholar
- Lee CG, Li Q, Kalb W, Liu XZ, Berger H (2010) Frictional characteristics of atomically thin sheets. Science 328:76–80View ArticleGoogle Scholar
- Gao L, Ren W, Xu H, Jin L, Wang Z, Ma T, et al (2012) Repeated growth and bubbling transfer of graphene with millimetre-size single-crystal grains using platinum. Nat Comm 3:699View ArticleGoogle Scholar
- Burrus CS, Gopinath RA, Guo H (1997) Introduction to wavelets and wavelet transformsGoogle Scholar
- Ding W, Qin S, Miao L, Xi N, Li H (2012) The application of wavelet transform and wavelet lifting in signal processing of EGG. J Biomed Eng 29:745–749Google Scholar
- Holscher H, Schwarz UD, Zwomer O, Wiesendanger R (1998) Consequences of the stick-slip movement for the scanning force microscopy imaging of graphite. Phys Rev B 57:2477–2481View ArticleGoogle Scholar
- Yu QK, Jauregui LA, Wu W, Colby R, Tian J, Su Z, Cao H (2011) Control and characterization of individual grains and grain boundaries in graphene grown by chemical vapour deposition. Nat Mater 10:443–449View ArticleGoogle Scholar
- Rastei MV, Heinrich B, Gallani JL (2013) Puckering stick-slip friction induced by a sliding nanoscale contact. Phys Rev Lett 111:084301View ArticleGoogle Scholar
- Rastei MV, Heinrich B, Gallani JL (2014) Sliding speed-induced nanoscale friction mosaicity at the graphite surface. Phys Rev B 90:041409View ArticleGoogle Scholar
- Choi JS, Kim JS, Byun IS, Lee DH, Lee MJ, Park BH, et al (2011) Friction anisotropy-driven domain imaging on exfoliated monolayer graphene. Science 333:607–610View ArticleGoogle Scholar
- Boland MJ, Nasseri M, Hunley DP, Ansary A, Strachan DR (2015) Striped nanoscale friction and edge rigidity of MoS2 layers. RSC Adv 5:92165–92173View ArticleGoogle Scholar
- Gallagher P, Lee M, Amet F, Maksymovych P, Wang J, Wang S, et al. One-dimensional ripple superlattices in graphene and hexagonal boron nitride. 2015; arXiv preprint arXiv: 1504.05253.Google Scholar