ARTICLE pubs.acs.org/JPCC
Adsorption of Villin Headpiece onto Graphene, Carbon Nanotube, and C60: Effect of Contacting Surface Curvatures on Binding Affinity Guanghong Zuo,†,‡,§ Xin Zhou,§ Qing Huang,† Haiping Fang,*,†,‡ and Ruhong Zhou*,^,|| †
Shanghai Institute of Applied Physics, Chinese Academy of Sciences, P.O. Box 800-204, Shanghai 201800, China T-Life Research Center, Department of Physics, Fudan University, Shanghai 200433, China § Asia Pacific Center for Theoretical Physics, Pohang, Gyeongbuk, 790-784, South Korea ^ IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States Department of Chemistry, Columbia University, New York, New York 10027, United States
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bS Supporting Information ABSTRACT: The adsorption of protein villin headpiece (HP35) onto a graphene has been investigated using large scale molecular dynamics simulations, and the results are compared with similar adsorptions onto a single-wall carbon nanotube and a fullerene, C60. It is found that HP35 loses most of its native secondary and tertiary structures after the adsorption onto graphene. The π π stacking interactions between the graphene and HP35’s aromatic residues play a key role in this adsorption. The graphene’s softness also helps the binding by adapting its own shape to fit better with aromatic residues in forming stronger π π stacking interactions. Interestingly, the mechanism of HP35 adsorption onto the other two graphitic nanomaterials is found to be somewhat different, in which the π π stacking interactions play a lesser role than the dispersion interactions between the nanomaterial and HP35’s aliphatic side chains. These findings indicate that in addition to the chemical composition, the shape of the nanoparticle is also an important factor in determining its interaction with proteins: the contacting surface curvature can lead to different adsorption mechanisms.
’ INTRODUCTION The interaction between nanomaterial and biomolecules is essential to nanoparticle-based biotechnology and biomedical applications, such as gene delivery,1 cellular imaging,2 tumor therapy,3 and biological experimental technology.4 It is also critical for the understanding of the growing concerns about the biosafety of these nanomaterials.5 11 There have been extensive studies recently on the adsorption of proteins onto nanomaterial, particularly graphitic nanomaterials such as carbon nanotubes and fullerenes, both experimentally and theoretically, and it is shown that these adsorptions can affect both protein structures and functions. For example, Wu and co-workers have found that there are local structural distortions after protein streptavidin is bound onto a single-wall carbon nanotube (SWCNT).12 Karajanagi et al. have also observed changes in both conformation and activity of two enzymes, α-chymotrypsin and soybean peroxidase, upon adsorption onto SWCNTs.13 We have recently found that SWCNTs can plug into the hydrophobic cores of signaling and pathway regulatory proteins, WW domains, to form stable complexes14 and can also win the competitive binding over the native binding ligand (proline-rich motifs) on the SH3 domain,15 which are two possible routes of nanoparticles affecting protein functions. Protein graphene interactions, on the other hand, are relatively less studied. Graphene is a flat monolayer of carbon atoms r 2011 American Chemical Society
tightly packed into a two-dimensional honeycomb lattice.16 18 It is a basic building block for fullerenes, carbon nanotubes (CNTs), and graphite. Due to its unique structural, mechanical, and electronic properties, graphene has attracted much research interest in the field of nanoscience and nanotechnology. Some graphene-based biodevices have also been proposed recently.19 21 It has been shown experimentally that the antibody-functionalized graphene sheet is an excellent candidate for mammalian and microbial detection and diagnosis devices.21 More interestingly, Huang and co-workers have shown that sheets of graphene oxide are highly effective at killing bacteria, which means graphene could be useful in hygiene products or in food packaging to keep food fresh longer.22 These experiments improve our understanding of the interaction between graphene and biomolecules. Some recent MD simulations of short peptides onto graphenes have also shed light on this challenging problem.23 25 For example, it was found that tryptophan residues have strong binding with graphene.25 Moreover, very recent studies have showed that both CNTs and graphene have the capability to disrupt α-helical structures of short peptides,26,27 and graphene may possess a higher capability to break α-helices due to its more Received: September 16, 2011 Revised: October 19, 2011 Published: October 19, 2011 23323
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The Journal of Physical Chemistry C favorable surface curvature.28 However, it is unclear if these findings can be extended to larger proteins. More importantly, how graphenes affect protein structures and what kind of role the shapes of graphitic nanomaterial play are still far from being understood. Thus, it is of interest to simulate the interaction between graphene and larger proteins and compare its effect on protein structures with other graphitic materials, such as fullerenes and CNTs. In this paper, we will report our results on the adsorption of HP35 protein onto graphene using large scale molecular dynamics (MD) simulations, and the results will then be compared with similar adsorptions onto single-wall carbon nanotubes and fullerene (C60). The HP35 protein is an independently folding three-helix bundle that is widely used as a model protein in the study of protein folding.29 31 Our simulations show that HP35 adsorbs onto the graphene surface very quickly (within a few to a few tens of nanoseconds) and then extends to be fully unfolded on the graphene surface. Extensive conformational changes are observed in both the secondary and tertiary structures. The π π stacking interactions are found to dominate the protein nanoparticle interaction and control both the adsorption kinetics and thermodynamics. However, this seems to be somewhat different from the interactions of the same HP35 protein with the SWCNT and C60, in which the hydrophobic interactions between SWCNT/C60 and aliphatic side chains are found to be the driving force for the adsorption, indicating the nanomaterial surface curvature can play a significant role in the protein adsorption. We believe this surface curvature effect might have general applications in the adsorption of other globular proteins.
’ COMPUTATIONAL METHODS Villin headpiece (HP) is an F actin-binding domain that resides in the far C-terminal of the super villin, which is a tissue-specific actin-binding protein associated with the actin core bundle of the brush border.32 The subdomain HP35 has only 35 residues and is a fast and independently folding three-helix bundle. Because of its small size and fast folding kinetics, HP35 is a commonly studied protein in MD simulations.29 31 In our simulation, the native structure of the HP35 was prepared from the Protein Data Bank (PDB code: 1YRF33) and modeled by the Amber03 force field.34 Three classes of graphitic nanomaterials were used for the interaction with the protein: graphene (with dimensions of 47.3 Å 40.6 Å), (5, 5)-armchair SWCNT (19.54 Å in length and 6.73 Å in diameter), and C60 (3.34 Å in radius). The carbon atoms of graphitic nanomaterials were modeled as uncharged Lennard-Jones particles with a cross section of σcc = 3.40 Å and a depth of the potential well of εcc = 0.36 kJ/mol.35,36 Initially, the graphitic nanomaterials and protein were well separated, with their distances (defined as the distance between the geometric center of protein domain and the graphitic nanomaterial’s surface) of 30.0, 25.0, and 20.0 Å for the cases of graphene, SWCNT, and C60, respectively. The combined systems were then solvated in a rhombic dodecahedral periodic box with the distance between the solutes and box boundary at least 10 Å. The TIP3P water model was used for solvation, and two Cl atoms were added into solution to neutralize the system. The total numbers of atoms are 38446, 16051, and 8447, for the cases of graphene, SWCNT, and C60, respectively. The solvated system was then simulated with MD, which is widely used in the study of biomolecules37 54 and nanomaterials.55 60 Here, the MD simulations were performed by using the Gromacs package 4.0.61 In the simulations, the covalent bonds involving
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Figure 1. (a) A typical structure of HP35 adsorbed on the graphene surface. Here, HP35 is shown as a cartoon with red helix and green loop, the graphene is shown as the cyan lines. (b) The superposition of the adsorbed HP35 structure on graphene (red) with its native structure (green). The images were created with PyMOL.
H atoms were constrained by the LINCS algorithm, which allowed a time step of 2 fs. The long-range electrostatic interactions were treated with the particle-mesh Ewald method62 with a grid spacing of 1.2 Å. The cutoff for the van der Waals interaction was set to 10 Å. After energy minimization, all systems were equilibrated by MD simulations for 200 ps at a constant pressure of 1 bar and a temperature of 298 K using Berendsen coupling.63 Then, the production simulation was performed in an NVT ensemble at 298 K. Five trajectories, 500 ns each, were obtained for each graphitic nanomaterial protein system, with an aggregate MD simulation time of 7.5 μs.
’ RESULT AND DISCUSSION In all five trajectories for the graphene-HP35 system, the protein HP35 was adsorbed onto the surface of the graphene and lost its secondary and tertiary structures in various degrees. Figure 1a shows a typical extended structure of the HP35 on graphene, in which three of its five aromatic residues (F10, W23, and F35, shown in blue stacks) bind to the graphene in a flat mode. To investigate the conformational changes of HP35, we displayed the superposition of the HP35 structure on the graphene with its native structure in Figure 1b. It was found that the main change in the protein conformation after adsorption onto the graphene was in the third α-helix. It lost almost all the α-helical content. A secondary structure analysis also showed that part of the third α-helix was converted into a 310-helix (see Figure S1 of the Supporting Information). All contacts between the third α-helix and the other two α-helixes were broken. Many residues of the protein, particularly those aromatic ones, were lying flat on the graphene surface due to the strong interactions with the graphene. Meanwhile, the inherent globular structural characters of HP35 also induced the graphene sheet to adapt itself into a slightly rugged shape (due to its softness and flexibility) to fit better with aromatic residues of HP35. Overall, these structural changes have made HP35 firmly adsorbed on the graphene surface. Figure 2a displays some representative snapshots of HP35 and graphene at different simulation times to show the adsorption kinetic process. The interface area between HP35 and the graphene (denoted by S, shown in Figure 2b) is used to illustrate this process. Thus, at t = 0, S = 0, since HP35 and the graphene were well separated initially. HP35 approached the graphene very quickly. As a result, S rose to ∼250 Å2 within only 3 ns, and maintained around this value for about 3 ns. The snapshot at t = 3 ns in Figure 2a shows that the residue F35 forms a flat binding mode with graphene (F35 is at the C-terminal and has high mobility). 23324
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Figure 2. A representative trajectory of HP35 adsorbing onto the graphene. (a) Representative snapshots at various time points. The proteins are shown in cartoons with red helix and green loop, and the graphene is shown in wheat. The aromatic residues that form the π π stacking interactions are shown in blue, others are shown in green. (b) The contacting surface area of HP35 with the graphene. (c) The rmsd of HP35 from its native structure and the number of residues in the α-helix structure. Here, the secondary structures are determined by the DSSP program.65 (d) The distance between the graphene and the aromatic residues, including F35, W23, F10, F17, and F06. To show the adsorbing process clearer, the x-axis had been truncated and rescaled. The figures were plotted by program R.66
That is, the residue F35 binds with the graphene by the π π stacking interaction, which is a very strong interaction between graphitic material and the aromatic residues.64 It seems that the residue F35 behaves like an “anchor”, which is “thrown” by HP35 to the graphene to lock itself onto the surface of graphene. At ∼7 ns, there was a jump of S from 250 to 300 Å2. As shown in the snapshot at t = 7 ns in Figure 2a, the third α-helix was adsorbed onto the graphene surface, which led to some spatial rearrangements of the three α-helixes and, thus, the increase in contacting surface area. Interestingly, the residue W23, which is an aromatic residue on the other end of the third α-helix, formed the same flat binding mode with the graphene after this jump. Thus, by utilizing these two “anchors”, F35 and W23, the third α-helix was now firmly “fixed” on the graphene surface. In the next 140 ns, the contact surface area, S, maintained at ∼300 Å2, with some fluctuations along the way. The last jump in S happened at ∼145 ns, in which S increased dramatically from ∼300 to ∼750 Å2. As shown in the snapshots at t = 143 and t = 147 ns, the main change was that the second α-helix was adsorbed onto the surface of the graphene, and the residue F10, which is located at the loop between the first and the second α-helixes, formed the same flat binding mode with the graphene. At t = 147 500 ns, there were only minor fluctuations of S at ∼750 Å2. On the other hand, the overall conformational change of HP35 was not very notable until t = 147 ns. Most of the α-helical secondary structures were kept intact, despite some spatial rearrangements of the three helices at about 7 ns. The main conformational changes of HP35 happened only after it had been adsorbed onto the surface of graphene. To investigate the detailed conformational changes of HP35, we calculated the root-mean-square deviation (rmsd) from its native structure and the number of α-helical residues (α-helix is the only secondary structure that can be disrupted in the HP35) during the adsorption progress (shown in Figure 2c). As a comparison, we also performed control runs for HP35 in solvent only (without any graphenes). It was found that without the disruption of graphene or any other nanomaterial, the rmsd of HP35 remained at ∼1.8 Å
(see Figure S2 of the Supporting Information), indicating a very stable HP35 structure in water. In addition, ∼23 24 residues out of a total of 35 are in the α-helical form (see Figure S3 of the Supporting Information). As illustrated in Figure 2c, the conformational changes of HP35 can be described roughly as three stages. In the first stage, from 0 to 7 ns, the HP35 kept its native structure with the rmsd of ∼1.5 Å and the number of residues in α-helices ∼24, even though HP35 had already “anchored” itself on the graphene surface through residues F35 for about 3 4 ns. At about 7 ns, the third α-helix of HP35 was adsorbed onto the graphene surface, which led to a spatial rearrangement of the three α-helices of HP35. Thus, the rmsd increased from 1.5 to ∼3.0 Å. It also resulted in a small change in the secondary structures. The number of α-helical residues decreased from ∼24 to ∼20. From 7 to 145 ns (the second stage), despite some small conformational fluctuations, no significant conformational change was observed for HP35. After 145 ns (the third stage), HP35 began to extend on the graphene surface after the second α-helix was adsorbed onto the surface. Significant changes in both the secondary structure and tertiary structure were observed. As shown in Figure 2c, the rmsd of HP35 increased from ∼3.0 to ∼7.5 Å during this stage, with most of its α-helices (the only secondary structures) lost, with the α-helical residue number dropping from ∼20 to ∼10. Among the three α-helices of HP35, the third α-helix is mostly broken, with some portion converted to 310-helix and bends. The first α-helix is also partially converted to turns, whereas the second α-helix is only slightly affected by the adsorption (see Figure S1 of the Supporting Information). The different behaviors of three helices may be due to their different helical propensity of the constituent amino acids. It should be emphasized that the major conformational changes of HP35 happened after the protein was adsorbed onto the graphene surface, which indicates that it is the strong interaction with graphene (π π stacking) that leads to the collapse of the protein secondary and tertiary structures. It had been reported that the π π stacking interactions between the aromatic residues and the carbon-based nanomaterial 23325
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Figure 3. Boxplot for the interaction energy among each residue of HP35 and graphene, (5, 5)-SWCNT, and C60. For every point of the boxplot, the bold line in the box indicates the median of the data, the upper/lower edge of the box indicates the upper/lower quartile (the 75th/25th percentiles) of the data, and the end of whiskers indicates the maximum and the minimum of the data. The color of the points indicates the probability of the residue in contact with the graphene (see text for more details): 0 20% (red), ∼20 40% (yellow), ∼40 60% (green), ∼60 80% (cyan), and ∼80 100% (blue).
played an important role in the interaction between proteins and these nanomaterials in both simulations14,25,67,68 and experiments.69,70 There are five aromatic residues in HP35, F06, F10, F17, W23, and F35. From the snapshots of the protein and the graphene shown in Figure 2a, we can also see the contributions of the aromatic residues in the adsorption of HP35 onto the graphene. For example, the protein anchored itself on the graphene surface by residue F35. To further understand the role of the π π stacking interaction in the adsorption, we calculated the distances between the side chains of aromatic residues and the graphene with the simulation time (see Figure 2d). Here, the distance of a residue is defined as the average distance of its side chain nonhydrogen atoms from the graphene. Generally, when a benzene or indole ring is adsorbed onto the graphene in the flat mode (i.e., the π π stacking mode), the distance between them is ∼4.0 Å. As shown in Figure 2b d, there is one new aromatic residue forming π π stacking with the graphene at every key transition in HP35 structural change: for example, residue F35 for “anchoring” HP35 on the surface of graphene at about 3 ns, residue W23 (along with F35) for “fixing” the third α-helix on the graphene at about 7 ns, and residue F10 for adsorbing the second α-helix onto the graphene at ∼145 ns. These findings indicate that the aromatic residues of HP35 control the adsorption kinetic process on the graphene surface. Interestingly, π π stacking does not seem to be the dominant driving force in the interaction of HP35 with (5, 5)-SWCNT and C60. Figure 3 shows the “contact probability” and the distribution of the interaction energy of each residue in HP35 with the graphene, SWCNT, and C60, respectively. Here the “contact probability” is indicated by the color of the points in the boxplot (e.g., 0 20% in red and 80 100% in blue), and a residue is considered to be in contact with the nanoparticle if the distance between any of its non-hydrogen atoms and any of the nanomaterial’s carbon atoms is less than 5 Å. As shown in Figure 3, for all classes of graphitic nanomaterials, residues at the C-terminal of HP35 (e.g., the third α-helix) have larger probabilities to be in contact with the carbon-based nanomaterial.
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Notably, residue F35 has a higher than 80% probability to contact with both the graphene and (5, 5)-SWCNT in all trajectories. Similarly, it was also found that the F35 residue of HP35 acted as an “anchor” in the binding with SWCNT. Clearly, F35 played a unique role in HP35’s interactions with both graphene and SWCNT, largely through the following two important factors. First, the interaction between F35 and graphitic nanomaterial is the so-called π π stacking, which has a very high binding affinity. Second, F35 locates at the C-terminal of this subdomain, which is more mobile than other residues. Interestingly, this unique role of F35 was not observed in HP35’s interaction with C60, on the other hand. This is probably due to the small size of C60, which makes C60 more mobile on its own, and furthermore, C60 contacts with only a few residues of HP35 when it is adsorbed onto the surface of HP35, which by itself is unable to distort the three α-helix bundle structures to expose aromatic residues. The distributions of the interaction energies between the nanomaterial and residues of HP35 were also calculated and are shown in Figure 3 by the boxplot. For each residue, only when the residue is in “contact” with the nanoparticle were the interaction energies counted. Generally speaking, residues that possess stronger interaction energies are more important for the binding between the protein and the graphene/SWCNT/C60. As shown in Figure 3, the interaction energies of residues with the graphene are globally lower than those with the SWCNT or the C60. This is because the available contacting surface of the graphene is larger and more flexible than that of SWCNT and the C60. The relative values in the interaction energies, particularly for those residues with high “contact probabilities” (>50%), are even more notable, which reveals the important role of these key residues during HP35’s interaction with the nanomaterial. In the graphene case, the interaction energies of the three aromatic residues F35, W23, and F10 are significantly lower than other residues (in both the median and minimum), especially for W23. In addition, the deviations for the interaction energies of these three aromatic residues are small relative to the median. These findings indicate that these aromatic residues bind with the graphene through relatively uniform π π stacking interactions. For the interaction of HP35 with the (5, 5)-SWCNT, on the other hand, the situations are not as straight. Even though the minima of residues F35 and W23 are lower than other residues, the medians are similar or even higher than that of some other residues, such as residues Q26 and K30. That is, the contribution of the aromatic residues interacting with the SWCNT is not as notable as that in the graphene case, suggesting the probability of their forming the flat π π stacking with the SWCNT is lower. For the case of C60 HP35 binding, the contribution of these aromatic residues is even less significant than that in the SWCNT, with similar contributions to other hydrophobic residues. Therefore, even though the graphene, SWCNT, and C60 have the same chemical components, the surface curvature and the hardness of these graphitic nanomaterials will affect their interaction with proteins, particularly the interaction with aromatic residues. Obviously, the different surface curvatures in these graphitic nanomaterials played a significant role in their binding to HP35. The π π stacking interactions between the protein and graphene, SWNT, and C60 strongly depend on the probability of aromatic residues' forming a stable and flat conformation with the nanomaterial surface. For curved nanoparticles such as SWCNT and C60 in our simulation, the lower probability of forming flat π π stackings with aromatic residues reduced their 23326
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The Journal of Physical Chemistry C overall binding affinity with HP35. Meanwhile, the dispersion interaction itself between an aromatic residue and graphene/ SWCNT/C60 also depends on the surface curvature, since the total number of carbon atoms (aside from the immediate benzene ring) from the nanomaterial to be in contact with the residue decreases from graphene, to SWCNT, to C60. On the other hand, the hydrophobic aliphatic side chains are very flexible, which can adapt to the curved carbon surfaces and form favorable interactions with SWCNT and C60, leading to a more significant role of these residues than aromatic ones. Of course, in addition to the nanomaterial surface curvature (or nanoparticle size), the protein sequence and structure can also affect the interaction between proteins and graphitic nanomaterials. For example, as shown in our previous studies, aromatic residues can play a key role in the interaction between protein WW-domain and SWCNTs (with sizes from (4, 4), (5, 5), to (6, 6)).14 For some short peptides, aromatic residues might also contribute strongly in the binding with curved graphitic surfaces.25 It should also be pointed out that a graphene sheet is very soft and flexible, which might also play an important role, in addition to the surface curvature. As shown in Figure 1a, the graphene sheet bent itself to fit better with the aromatic residues in HP35 to form stronger π π stacking interactions. The importance of the graphene flexibility was also reported previously in its binding with an Au(111) surface.71 We are currently further investigating this effect, and the results will be reported elsewhere.
’ CONCLUSION To summarize, we have investigated the adsorption of villin headpiece (HP35) onto graphene by large scale MD simulations and also compared the results with those similar adsorptions onto a SWCNT and a C60. It is found that during the adsorption, HP35 anchors itself onto the graphene surface through residue F35 first, and then the third α-helix in HP35 attaches to the surface, which is followed by further extensions of other helices on the surface. The conformational changes of HP35, in both the secondary and tertiary structures, happen mainly after the adsorption onto graphene. Most α-helices have been changed to 310-helix, bends, and turns. The aromatic residues F35, W23, and F10 are found to play a central role by forming π π stacking interactions with the graphene in every important step of the adsorption. The analyses on the contact probability and interaction energy of each residue with the graphene clearly indicate that these three aromatic residues are main contributors to the interaction with graphene (almost always in the flat binding mode, i.e., π π stacking). Therefore, we believe the π π stacking interaction is the dominant driving force for the binding between HP35 and the graphene, which controls both the kinetics and thermodynamics. This seems to be somewhat different from the interactions of HP35 with the SWCNT and C60, in which the hydrophobic interactions between aliphatic residues of HP35 and SWCNT/C60 have more contributions, particularly in the C60 case. The different contacting surface curvatures, such as the flat surface of graphene that matches the flat benzene and indole rings perfectly, are found to be responsible for the different mechanisms in the interaction of HP35 with either the flat graphene or the curved SWCNT and C60. Therefore, our studies illustrate that despite the same chemical components, the surface curvature of different nanomaterials can also be an important factor in determining their interactions with proteins.
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’ ASSOCIATED CONTENT
bS
Supporting Information. Variation of the secondary structure of the HP35; histogram of the rmsd and the number of residues in α-helical structure. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*E-mails: (H.F.)
[email protected], (R.Z.) ruhongz@ us.ibm.com.
’ ACKNOWLEDGMENT We thank Peng Xiu for helpful discussions and critical reading. This research is supported in part by grants from NNSFC (10825520), NBRPC (973 Program) (2007CB936000 and 2007CB814800), the Shanghai Leading Academic Discipline Project (B111), and the Shanghai Supercomputer Center of China. R.Z. acknowledges support from the IBM BlueGene Science Program. ’ REFERENCES (1) Rosi, N. L.; Giljohann, D. A.; Thaxton, C. S.; Lytton-Jean, A. K. R.; Han, M. S.; Mirkin, C. A. Science 2006, 312, 1027. (2) Michalet, X.; Pinaud, F. F.; Bentolila, L. A.; Tsay, J. M.; Doose, S.; Li, J. J.; Sundaresan, G.; Wu, A. M.; Gambhir, S. S.; Weiss, S. Science 2005, 307, 538. (3) Wang, X.; Yang, L. L.; Chen, Z.; Shin, D. M. CA Cancer J. Clin. 2008, 58, 97. (4) Li, H. K.; Huang, J. H.; Lv, J. H.; An, H. J.; Zhang, X. D.; Zhang, Z. Z.; Fan, C. H.; Hu, J. Angew. Chem., Int. Ed. 2005, 44, 5100. (5) Service, R. F. Science 2000, 290, 1526. (6) Donaldson, K.; Aitken, R.; Tran, L.; Stone, V.; Duffin, R.; Forrest, G.; Alexander, A. Toxicol. Sci. 2006, 92, 5. (7) Gilbert, N. Nature 2009, 460, 937. (8) Nel, A.; Xia, T.; Madler, L.; Li, N. Science 2006, 311, 622. (9) Zhao, Y.; Xing, G.; Chai, Z. Nat. Nanotechnol. 2008, 3, 191. (10) Chen, Z.; Meng, H.; Xing, G. M.; Chen, C. Y.; Zhao, Y. L.; Jia, G.; Wang, T. C.; Yuan, H.; Ye, C.; Zhao, F.; et al. Toxicol. Lett. 2006, 163, 109. (11) Zhang, Y. B.; Ali, S. F.; Dervishi, E.; Xu, Y.; Li, Z. R.; Casciano, D.; Biris, A. S. ACS Nano 2010, 4, 3181. (12) Zhong, J.; Song, L.; Meng, J.; Gao, B.; Chu, W. S.; Xu, H. Y.; Luo, Y.; Guo, J. H.; Marcelli, A.; Xie, S. S.; et al. Carbon 2009, 47, 967. (13) Karajanagi, S. S.; Vertegel, A. A.; Kane, R. S.; Dordick, J. S. Langmuir 2004, 20, 11594. (14) Zuo, G. H.; Huang, Q.; Wei, G. H.; Zhou, R. H.; Fang, H. P. ACS Nano 2010, 4, 7508. (15) Zuo, G. H.; Gu, W.; Fang, H. P.; Zhou, R. H. J. Phys. Chem. C 2011, 115, 12322. (16) Novoselov, K. S.; Geim, A. K.; Morozov, S. V.; Jiang, D.; Katsnelson, M. I.; Grigorieva, I. V.; Dubonos, S. V.; Firsov, A. A. Nature 2005, 438, 197. (17) Zhang, Y. B.; Tan, Y. W.; Stormer, H. L.; Kim, P. Nature 2005, 438, 201. (18) Novoselov, K. S.; Geim, A. K.; Morozov, S. V.; Jiang, D.; Zhang, Y.; Dubonos, S. V.; Grigorieva, I. V.; Firsov, A. A. Science 2004, 306, 666. (19) Xu, M. S.; Fujita, D.; Hanagata, N. Small 2009, 5, 2638. (20) Lu, C. H.; Yang, H. H.; Zhu, C. L.; Chen, X.; Chen, G. N. Angew. Chem., Int. Ed. 2009, 48, 4785. (21) Mohanty, N.; Berry, V. Nano Lett. 2008, 8, 4469. (22) Hu, W.; Peng, C.; Luo, W.; Lv, M.; Li, X.; Li, D.; Huang, Q.; Fan, C. ACS Nano 2010, 4, 4317. 23327
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