Cytotoxicity of graphene: recent advances and future perspective.

Advanced Review

Cytotoxicity of graphene: recent advances and future perspective Ruhong Zhou1,2∗ and Huajian Gao3∗ Graphene, a unique two-dimensional single-atom-thin nanomaterial with exceptional structural, mechanical, and electronic properties, has spurred an enormous interest in many fields, including biomedical applications, which at the same time ignites a growing concern on its biosafety and potential cytotoxicity to human and animal cells. In this review, we present a summary of some very recent studies on this important subject with both experimental and theoretical approaches. The molecular interactions of graphene with proteins, DNAs, and cell membranes (both bacteria and mammalian cells) are discussed in detail. Severe distortions in structures and functions of these biomacromolecules by graphene are identified and characterized. For example, the graphene is shown to disrupt bacteria cell membranes by insertion/cutting as well as destructive extraction of lipid molecules directly. More interestingly, this cytotoxicity has been shown to have implications in de novo design of nanomedicine, such as graphene-based band-aid, a potential ‘green’ antibiotics due to its strong physical-based (instead of chemical-based) antibacterial capability. These studies have provided a better understanding of graphene nanotoxicity at both cellular and molecular levels, and also suggested therapeutic potential by using graphene’s cytotoxicity against bacteria cells. © 2014 Wiley Periodicals, Inc.

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WIREs Nanomed Nanobiotechnol 2014. doi: 10.1002/wnan.1277

INTRODUCTION

T

he widespread application of nanomaterials in biomedicine (such as gene delivery,1 cellular imaging,2 and tumor therapy3 ) is also accompanied by rapidly increasing interest in understanding their interactions with tissues, cells, and biomolecules,4 and how they might affect the integrity of cell membranes,5,6 as well as the structure and function of proteins and nucleic acids.7,8 A detailed molecular level understanding of the interaction between nanomaterial ∗ Correspondence brown.edu

to:

[email protected];

Huajian_Gao@

1 Computational

Biology Center, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA 2 Department of Chemistry, Columbia University, New York, NY, USA 3 School

of Engineering and Institute for Molecular and Nanoscale Innovation, Brown University, Providence, RI, USA Conflict of interest: The authors have declared no conflicts of interest for this article.

and biomolecules is essential to the safe usage of nanoparticle-based biomedical technologies.5,9–14 There have been extensive studies recently on the interactions between proteins, nucleic acids (such as DNA), and cell membranes with nanomaterial, particularly graphitic nanomaterials [such as zero-dimensional (0D) fullerenes and one-dimensional (1D) carbon nanotubes (CNTs)], both experimentally and theoretically, and it has been shown that these interactions can affect both the structures and functions of biological systems, resulting in serious cytotoxicity and biosafety concerns. For example, Gao et al. showed that a DNA molecule could be spontaneously inserted into a single-wall carbon nanotube (SWCNT) in aqueous solution,15,16 and a SWCNT can inhibit the function of HIV-1 protease by binding to the active site of this biomolecule and preventing its active flaps from opening up.17 Wu and coworkers showed that binding with a SWCNT causes local structural distortions in the protein streptavidin.18 Karajanagi et al.

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discovered changes in both conformation and activity of two enzymes, 𝛼-chymotrypsin and soybean peroxidase, upon adsorption onto SWCNT.19 Zhou and coworkers observed that SWCNTs can plug into the hydrophobic cores of the signaling and pathway regulatory proteins, WW domains, to form stable complexes,20 and can also win the competitive binding over the native binding ligand (proline rich motifs) on the SH3 domain,21 demonstrating two possible routes of nanoparticles affecting protein functions. Kang et al. also showed that metallofullerenol Gd@C82 (OH)22 can inhibit tumor growth and metastasis by inhibiting protein MMP-9 through a combined in vivo, in vitro, and in silico approach.8 Recent studies on the interaction of fullerenes22,23 and CNTs24,25 with cell membranes showed that these nanomaterials can enter cells either through direct penetration26,27 (usually for small-sized nanoparticles) or by endocytosis.24,28 The reader is further referred to recent reviews by Albanese et al.29 and Gao30 for more details of these recent experimental and modeling studies. The graphene–biomolecule interactions and resulting cytotoxicity, on the other hand, are relatively less studied. Graphene is a flat monolayer of carbon atoms densely packed into a 2D honeycomb lattice,31–33 which can be regarded as the basic building block for fullerenes, CNTs, and graphite. Due to its unique structural, mechanical, and electronic properties, graphene has attracted worldwide research interests in the field of nanoscience and nanotechnology. Its high specific surface area is a major advantage in high-density bio-functionalization, which is essential for nanotechnology-based drug delivery.34–38 Titov et al.39 performed coarse-grained molecular dynamics (MD) simulations to study the interaction of few-layer graphene (FLG) nanosheets with a lipid bilayer and reported stable graphene–lipid hybrid structures. Guo et al.40 and Wang et al.41 worked on the translocation of small graphene nanosheets across lipid bilayers. The smooth, continuous topography, and biopersistence of graphene play a unique role in its foreign-body-induced carcinogenesis and tumor progression studies.42,43 Moreover, graphene presents ultrahigh in vivo tumor uptake in mice, suggesting potential use for effective photothermal ablation of tumors.44 Meanwhile, recent studies have also demonstrated strong antibacterial activity45–48 of graphene and graphene-oxide (GO), with severe cytotoxicity to bacteria such as Escherichia coli.45–47 This graphene-induced cytotoxicity is hypothesized to arise from direct interactions between graphene and bacteria cell membranes. Those interactions can result in serious physical damages to cell membranes.45–47

However, the graphene-induced cytotoxicity is largely reduced when graphene nanosheets are surrounded by proteins,49 such as serum proteins. It has also been tested experimentally that the antibody-functionalized graphene sheet is an excellent candidate for mammalian and microbial detection and diagnosis devices.50 These experiments have improved our understanding of the interactions between graphene and biomolecules. Additionally, the recent MD simulations of proteins, DNAs, and cell membranes interacting with graphene nanosheets have also shed light on this challenging problem.51–53 For example, it was found that the 𝜋–𝜋 stacking interactions play a significant role in the binding between peptides and graphene.53 Moreover, graphene is found to display stronger capability in disrupting protein structures than CNTs and fullerenes54–56 due to its more favorable surface curvature.56,57 This review article is aimed to go over some of the recent advances, including some from our own studies, in this exciting new research area. It is by no means complete and has at best touched on a few interesting topics based on our own views that have captivated an array of researchers from different perspectives. Interested readers are also referred to some other recent reviews on this important topic.58–60 For example, Bianco described the recent controversies in the toxicity of graphene and its derivatives, with some in vitro and in vivo studies clearly showing no particular risks, while others indicating that they might become health hazards. Yang et al.59 reviewed the toxicity of graphene by describing its behavior in different microorganisms, cells, and animals, and pointed out that the physicochemical properties such as surface functional groups, charges, coatings, sizes, and structural defects of graphene may affect its behavior as well as its toxicity in biological systems. Seabra et al.60 discussed recent results from both cytotoxicity and genotoxicity studies and also critically examined the methodologies employed in evaluating these toxicities. The environmental impact from the manipulation and application of graphene materials was also reviewed, with additional insights on the mechanistic aspects of graphene toxicity. Meanwhile, the toxicity of graphene nanoflakes was also evaluated in detail by cell-based electrochemical impedance biosensing.61 The following sections in this review describe the molecular mechanisms underlying the disruptions of graphene on proteins; DNAs; cell membranes; potential usage of graphene as antibacterial ‘green antibiotics’ (band-aid) based on the cytotoxicity studies; and provide a summary and future perspective for the subject.


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Cytotoxicity of graphene

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FIGURE 1 | 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 which form the 𝜋–𝜋 stacking interactions are shown in blue stick, while the rest 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. (d) The distance between the graphene and the aromatic residues, including F35, W23, F10, F17, and F06. In order to make the adsorbing process clearer, the x -axis had been truncated and rescaled. The figures were plotted by program R. (Reprinted with permission from Ref 56. Copyright 2011 Journal of Physical Chemistry)

GRAPHENE DISRUPTION TO PROTEIN STRUCTURE AND FUNCTION Graphene has been shown to disrupt the structures and functions of proteins through exceptionally strong molecular interactions. For example, recent studies have shown that both CNTs and graphene are able to disrupt the 𝛼-helical structures of peptides,54,55 but with graphene showing much higher capability in breaking the 𝛼-helices since it has more favorable surface curvature57 for binding with protein residues such as tryptophan.53 In this section, we review graphene’s interaction with protein using villin headpiece (HP35) as an example.

Graphene Interaction with Protein HP35 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.62 The subdomain HP35 contains only 35 residues and is a fast and independently-folding three-helix bundle. Due to its small size and fast folding kinetics, HP35 is commonly studied in MD simulations.63–65 In a recent study, Zhou and coworkers56 modeled the interaction of HP35 with graphene, starting with the native structure of HP35 (PDB code: 1YRF66 ) using the AMBER03 force field.67 For comparison, three different classes of graphitic nanomaterials were used

in the study: graphene, (5, 5)-armchair SWCNT, and C60. The carbon atoms of graphitic nanomaterials were modeled as uncharged Lennard–Jones particles with a cross-section of 𝜎 cc = 3.40 Å and a depth of the potential well of 𝜖 cc = 0.36 kJ/mol.68,69 The entire solvated system was simulated with MD, which is widely used in the studies of biomolecules70–77 and nanomaterials,78–84 with an aggregate simulation time of 7.5 microseconds. Figure 1(a) shows one representative trajectory of HP35 absorption on a graphene nanosheet. The interface area between the HP35 and graphene (denoted by S, shown in Figure 1(b)) is used to help illustrate this process. When t = 0, S = 0, since HP35 and the graphene were well separated initially. The HP35 approached the graphene very quickly. As a result, S rose to about 250 Å2 within only 3 nanoseconds, and maintained around this value for about 3 nanoseconds. The corresponding snapshot shows that the residue F35 forms a flat binding conformation with the graphene (F35 is at the C-terminal and has high mobility), i.e., the residue F35 binds with graphene by the 𝜋–𝜋 stacking interaction.85 It seems that the residue F35 behaves like an ‘anchor’ that is ‘thrown’ by HP35 to lock itself on the surface of the graphene. There was an accompanied quick jump of S from 250 to 300 Å2 . During this period, the third 𝛼-helix was adsorbed onto the graphene surface, which led to some spatial rearrangements of the three 𝛼-helices and thus the increase of contacting



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surface area. Interestingly, the residue W23, which is another aromatic residue on the other end of the third 𝛼-helix, formed a similar flat binding configuration with graphene after this jump. By utilizing these two ‘anchors’, F35 and W23, the third 𝛼-helix was now firmly ‘attached’ to the graphene surface. The last jump in S occurred at about 145 nanoseconds, in which S increased dramatically from ∼300 to ∼750 Å2 . As shown in the snapshots, 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 second 𝛼-helices, formed a similar flat binding configuration with graphene. From t = ∼150 to 500 nanoseconds, there were only minor fluctuations of S at ∼750 Å2 . The root mean square deviation (RMSD) from its native structure and the number of 𝛼-helical residues (𝛼-helix is the only secondary structure in HP35) further confirmed this stepwise binding process (Figure 1(c)). Without the disruption of graphene, the RMSD of HP35 remained at ∼1.8 Å, indicating a very stable HP35 structure in water. Also, ∼23–24 residues out of total 35 are in the 𝛼-helical form. However, interaction with graphene resulted in significant changes as illustrated in Figure 1(c). The RMSD of HP35 increased to ∼7.5 Å during this stage, and most of its 𝛼-helices were lost, with the number of 𝛼-helical residues dropped from ∼23–24 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 transformed to turns; while the second 𝛼-helix is only slightly affected by the adsorption. The different behaviors of three helices may be due to their different helical propensity of the constituent amino acids. It has been reported in both simulations20,53,87,88 and experiments89,90 that the 𝜋–𝜋 stacking interactions between aromatic residues and graphene lattice play an important role in the interaction between proteins and carbon-based nanomaterials. There are five aromatic residues in HP35, namely F06, F10, F17, W23, and F35. To further understand the role of the 𝜋–𝜋 stacking interaction in the adsorption, Zhou and coworkers56 computed the distances between the side chains of aromatic residues and graphene (defined as the average distance of its side chain heavy atoms from the graphene) with the simulation time (see Figure 1(d)). Generally, when a benzene or indole ring is adsorbed onto the graphene in the flat configuration of 𝜋–𝜋 stacking, the distance between them is about 4.0 Å. As shown in Figure 1(b)–(d), during the structural change in HP35, there is one new aromatic residue forming 𝜋–𝜋 stacking with graphene at every key transition: first, residue F35 for ‘anchoring’

HP35 on the surface of graphene, then residue W23 (along with F35) for ‘fixing’ the third 𝛼-helix on the graphene, and finally residue F10 for adsorbing the second 𝛼-helix onto the graphene. These findings indicate that the aromatic residues of HP35 control the kinetic process of adsorption on the graphene surface. Other MD trajectories show similar results, with HP35’s secondary and tertiary structures largely lost on graphene, indicating a serious disruption to the protein structure and function. Typical HP35 structures (largely extended) on graphene reveal that many of its aromatic residues bind to the graphene in a flat mode. The main distortion in the HP35 conformation after adsorption was in its third 𝛼-helix, having lost almost all the 𝛼-helical content and contacts with the other two 𝛼-helixes. Many residues of the protein, particularly the aromatic ones, F10, W23, and F35, were lying flat on the graphene surface due to strong 𝜋–𝜋 stacking interactions. Meanwhile, the inherent nature of the globular structure of HP35 also induced the flexible graphene sheet to adapt itself into a slightly rugged shape in order to fit better with the aromatic residues of HP35.

Comparison among C60, CNT, and Graphene For comparison, Zhou and coworkers56 also studied the interaction of HP35 with a carbon nanotube and fullerene. Interestingly, the 𝜋–𝜋 stacking does not seem to be the dominant driving force in the interaction of HP35 with (5, 5)-SWCNT and C60. Figure 2 shows the distribution of the interaction energy and the ‘contact probability’ of each residue in HP35 with graphene, SWCNT, and C60, respectively. Here the ‘contact probability’ is shown 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 heavy atoms and any of the nanomaterial’s carbon atoms is less than 5 Å. As shown in Figure 2, for all classes of graphitic nanomaterials, residues at the C-terminal of HP35 (e.g., the third 𝛼-helix) have larger probabilities to contact the carbon-based nanomaterial. Notably, residue F35 has a higher than 80% probability to contact both graphene and (5, 5)-SWCNT in all trajectories. Similar to the case with graphene, the F35 residue of HP35 also serves as an ‘anchor’ in the binding with SWCNT. Clearly, F35 plays a unique role in HP35’s interactions with both graphene and SWCNT, largely through the following two important factors: firstly, the 𝜋–𝜋 stacking that has a very high binding affinity, and secondly, the fact that F35 located at the C-terminal of this subdomain




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FIGURE 2 | Boxplot for the interaction energy between each residue of HP35 and graphene, (5, 5)-SWCNT, and C60. For every point of the boxplot, the middle 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 percentile) 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% (orange), 40–60% (green), 60–80% (cyan), and 80–100% (blue). (Reprinted with permission from Ref 56. Copyright 2011 Journal of Physical Chemistry)

is more mobile than other residues. Interestingly, this unique role of F35 was not observed in HP35’s interaction with C60. This is probably due to the smaller size of C60, which makes C60 more mobile on its own. Furthermore, C60 only contacts with 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 structure to expose the aromatic residues. The distributions of the interaction energies between the nanomaterial and residues of HP35 were also computed and shown in Figure 2 by the boxplot. For each residue, only when the residue came in ‘contact’ with the nanoparticle, the interaction energies were counted. Generally speaking, residues which have stronger interaction energies are more important for the binding between the protein and the graphene/SWCNT/C60. As shown in Figure 2, the interaction energies of the residues with graphene are globally lower than those with SWCNT or C60. This is due to the fact that the available contacting surface of graphene is much larger and more flexible than those of SWCNT and 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. For the interaction of HP35 with the (5, 5)-SWCNT, on the other hand, the situations are not as straightforward. Compared to other types of residues such as residues Q26 and K30, even though the minima of residues F35 and W23 are lower, their medians are similar or even higher. 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 them forming the flat 𝜋–𝜋 stacking with SWCNT is lower. For the case of C60-HP35 binding, the contribution of the aromatic residues is even less significant than that in the case of SWCNT, with similar contributions to other hydrophobic residues. Therefore, in spite of identical chemical components, graphene, SWCNT, and C60 possess different geometrical and elastic properties such as surface curvature and bending stiffness, and such differences can play essential roles in their interaction with proteins, particularly the interaction with the aromatic residues. Obviously, the different surface curvatures in these graphitic nanomaterials play a significant role in their binding with 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, the lower probability of forming flat 𝜋–𝜋 stackings with aromatic residues reduces their 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 (asides from the immediate benzene ring) from the nanomaterial to be in direct contact with the residue is expected to decrease from graphene, to SWCNT, to C60. 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. In addition to the surface curvature, the fact that graphene sheet is soft and flexible might also play an important role in its interaction with biomolecules. As shown in Figure 1(a), the graphene sheet bent itself in order to fit better with the aromatic residues in HP35 and to form stronger 𝜋–𝜋 stacking interactions. The importance of the graphene flexibility was also reported previously in its binding with Au(111) surface91 which will be interesting for further investigation.



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GRAPHENE DISRUPTION TO DNA Zhao92 recently studied the interaction of double-stranded DNA segments with the surfaces of graphite in aqueous solution with MD simulations. Several different kinds of self-assembly phenomena were observed. First, it was found that a DNA segment can ‘stand up’ on the graphene surfaces with its helical axis perpendicular to the surfaces of graphene or nanotube arrays to form a forest-like structure. Secondly, a DNA segment can also lie on the graphene surface with its axis parallel to the surface if both of its ends can form stable structure with the carbon surfaces. In the latter case, the ending base pairs of the DNA are broken due to severe deformation, causing potential cytotoxicity to DNA. In their studies, four d-poly(AGTC)3 segments were placed on the surface at the beginning of the simulation, with their axes parallel to the graphite surface. The four DNA segments were aligned, as shown in Figure 3, and labeled as 1–4, respectively. Interestingly, it was observed that the DNA segment can adjust their orientations rapidly and form two types of distinct self-assembled structures on the graphite surface. First, DNA segments 1, 3, and 4 were able to rotate and stabilize in a ‘stand-up’ state (see snapshots in Figure 3). The axes of the double helices turned gradually from a parallel to a perpendicular geometry within 30 nanoseconds. This results in a forest-like structure on the surface by the rotated DNA segments. Given the relatively large size of the DNA molecules and the existence of solvent, such a self-assembly process (within tens of nanoseconds) is surprisingly fast. In the second type of self-assembled structure observed, the DNA segment (i.e., segment 2) was found to lie on the surface keeping its original orientation. Such structure is quite stable since no perturbation was observed up to 50 nanoseconds of simulation time. In contrast to DNA segments 4, 1, and 3, which rotated from parallel to perpendicular geometry, the axis of DNA segment 2 never rotated and retained an angle of about zero degree to the graphite surface. Taking DNA segment 3 as an example, the authors92 found that one of its ending base pairs (A1-T24) interacts with the graphene strongly during the self-assembly process to form very stable 𝜋 stacking structure. Two parameters are monitored to characterize the 𝜋 stacking interaction. The first one is the relative angles (𝛾) between the contacting ending base pair and the graphite surface, and the second one is the distance (d) between the base pair plane and the surface. It was found that very stable 𝜋 interaction was formed as early as at t = 30 nanoseconds. At the beginning, 𝛾 was fluctuating rather randomly. Correspondingly, the value of d was fluctuating between

0.4 and 3.0 nm. Then, the values of 𝛾 and d suddenly dropped to about 0∘ and 3.4 Å, respectively, which represent standard 𝜋 stacking features between two ring structures. The two parameters stay at these two values in the remaining time of the simulation, with negligible fluctuations, implying that the 𝜋 interaction between the base pair and surface is highly stable. The authors then calculated the binding energy of the DNA and surface. Here the binding energy is defined as the potential energy between the bound DNA and surface. The interaction between the DNA and surface starts from about −10 kcal/mol. Along with the orientational change, there is a dramatic drop in the binding energy, which stabilizes at about −53 kcal/mol eventually. It was found that the formation of a stable 𝜋 stacking structure for the DNA and graphite surface happened in a very short period of time (∼3–10 nanoseconds) once in contact.92 The terminal base pairs of DNA segments modeled were either A–T or G–C. Interestingly, it was found that for the first type of self-assembly, the AT end of the DNA is more likely to initiate a 𝜋 interaction with graphite surface than the GC end. For example, the assembled DNA segments 1, 3, and 4 shown in Figure 3(d) all have their AT ends in contact with the graphite surface while their GC ends extend into the solution. This can be explained by the difference in the pairing strength of A–T and G–C. The most significant deformation observed is the breaking of the ending base pairs. The AT base pair only contains two hydrogen bonds, whereas the GC base pair has three. Therefore, it is relatively easier for the ending AT base pair on the DNA to deform. The phenomena observed in this study are consistent with previous studies93 that the interaction between DNA and graphene surfaces are dominated by the 𝜋 stacking force between the carbon rings in graphene and the baseplanes of the ending nucleotides (AT or GC), which are the only exposed hydrophobic surfaces on the DNA. The molecular feature of the second type of self-assembly of DNA segment 2 on graphene surface was also analyzed in detail, which reveals a 𝜋 stacking driving force as well. It is found that the nucleobases on each end of the DNA segment were able to form stable 𝜋 stacking with the graphene. Both the ending base pairs of DNA segment 2 were broken during binding in order for both DNA ends to attach to the surface. In this example, bases C12 and A1 at the two ends are in contact with the graphene surface, while their pairing bases G13 and T24 are dangling in the solution. The hybrid structure formed therein corresponds to a binding energy of about −66 kcal/mol. Such a binding energy is much stronger than the typical hydrogen bonds between the two ending




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FIGURE 3 | Self-assembly of DNA segments on graphene layers. Snapshots were taken at (a) t = 0 nanoseconds, (b) t = 8 nanoseconds, (c) t = 16 nanoseconds, (d) t = 42 nanoseconds. Both top view and side view are shown for each snapshot. Color scheme: gray (C), red (O), blue (N), yellow (P), and white (H). (Reprinted with permission from Ref 92. Copyright 2011 Journal of Physical Chemistry)

base pairs, A1-T24 or C12-G13, resulting in the opening of the base pairs and potential deformation of the DNA structure. In short, the authors92 found from molecular simulations that short DNA segments can self-assemble on graphene surfaces to form stable

hybrid structures. The self-assembly occurs in a very short period of time, usually less than 50 nanoseconds. Two types of assembly patterns were observed for DNA on graphene surface, either ‘standing-up’ on the surface to form a forest-like structure, or ‘lying-flat’ on the surface. The driving force behind both patterns



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is the 𝜋 stacking interaction between the hydrophobic DNA base pairs and graphene carbon rings. In particular, the ending base pairs of the DNA were broken apart when it binds to the surface in the second type of self-assembly. Interestingly, the AT base pairs are more likely to interact with carbon surfaces than the GC basepairs.

GRAPHENE DISRUPTION TO CELL MEMBRANES With applications of graphene and other nanoparticles becoming more significant and important in biomedical fields, there is also rapidly growing interest in understanding their interactions with cells, especially how they might affect the integrity of cell membranes.5,6 Recent studies have shown that graphitic nanomaterials such as fullerenes22,23 and CNTs24,25 can enter cells either through direct penetration26,27 or by endocytosis.24,28 In comparison, the graphene–cell interactions are much less studied. Recently, graphene and GO nanosheets have been demonstrated to display strong antibacterial activity to both Gram-negative and Gram-positive bacteria.45–48 This cytotoxicity of graphene nanosheets is hypothesized to originate from direct interactions between graphene and bacteria cell membranes that cause serious physical damages to the membranes.45–47 The cytotoxicity is reduced significantly when these nanosheets are surrounded by proteins49 such as serum proteins. Therefore, although they are lethal to bacteria, they are less toxic to human or other mammalians. Therefore, they can be potentially used as effective novel antibiotics.

Escherichia coli Membranes In a very recent study, Zhou and coworkers94 revealed an important underlying molecular mechanism of graphene-membrane interaction, applying a combined experimental and theoretical approach. They first used graphene oxide (for water solubility) in the transmission electron microscopy (TEM) experiment, followed by large scale MD simulations using both graphene and graphene oxide for comparison with the experimental results. Figure 4 shows the TEM images of cell morphology of E. coli bacteria that were incubated with 100 μg/mL GO nanosheets at 37∘ C. Roughly three stages (Stages I, II, III) of E. coli cell morphology were observed during the 2.5 h incubation process.94 In Stage I, the bacteria E. coli cells could initially tolerate GO nanosheets for a short period of time, particularly under low concentrations. Figure 4(a) represents the initial morphology

of E. coli (control run or Stage I). In Stage II, E. coli cell membranes were partially damaged, with some exhibiting lower surface phospholipids density, i.e., sparser lipids but no obvious cuts yet (see those cells marked with ‘Type B’ in Figure 4(b) and (c)). In the final stage (Stage III), E. coli cells were found to lose their cellular integrity, with their membranes severely damaged, and some even lost all their cytoplasm, i.e., ‘empty nests’ (see those cells marked with ‘Type A’ in Figure 4(d)–(f).94 Although it is difficult to determine the exact timing of each stage due to the resolution limit and diversity in each individual cell, these rough but representative stages provide insights into the dynamical process at the cellular level for the GO-induced degradation of E. coli cell membranes. Similar results were found in additional experiments with increasing GO lateral sizes and concentrations. Using repeated oxidation processes with Hummers’ method, various GO nanosheets with different lateral sizes, such as ∼500 nm (GO1), ∼200 nm (GO2), and ∼50 nm (GO3) were produced. After the same 2.5 h incubation, larger GO nanosheets displayed a much stronger antibacterial activity than the smaller ones, with efficiency of 90.9, 51.8, and 40.1% for GO1, GO2, and GO3, respectively, under the same 100 μg/mL concentration.94 The increase of GO1 concentration also resulted in persistent increase in the antibacterial activity, with efficiency of 54.3, 71.4, 90.9% for 25, 50, and 100 μg/mL, respectively.94 Similar findings were reported by Liu et al.95 in their recent experiment.

Two Types of Molecular Mechanisms MD simulations were then carried out to investigate the detailed interactions of both graphene and GO nanosheets with E. coli membranes. Both the outer and inner membranes of E. coli were simulated with all-atom lipid models in explicit solvent. Zhou and coworkers first started with graphene nanosheets to model an earlier experiment by Akhavan and Ghaderi,46 where graphene nanosheets were deposited on stainless steel substrates by electrophoretic deposition. They showed that the sharpened edges of graphene nanosheets may act like ‘blades’, which can insert and cut through bacteria cell membranes,46 similar to the phenomenon observed in the Stage III TEM images as shown in Figure 4. The unbiased simulations then showed that the graphene nanosheet ‘suspended’ above the membranes (mimicking the experiment) can enter into both the outer and inner E. coli membranes very quickly. During this spontaneous entrance of the graphene nanosheet into the two membranes (Figure 5), three distinguishable modes were observed: First, the Swing Mode, where the



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E . coli undergoing changes in morphology after incubation with 100 μg/mL graphene-oxide nanosheets at 37∘ C for 2.5 h. Three stages of destruction can be seen: (a) Initial morphology of E. coli (control or Stage 1; two individual TEM images (inset and main image) are shown, the scale bar applies to both)). (b and c) Partial damage of cell membranes with some bacteria showing lower density of surface phospholipids (Stage II). Arrows indicate Type B mechanism, where graphene nanosheets extract phospholipids from the cell membrane. (d–f) Three representative images showing the complete loss of membrane integrity, with some showing ‘empty nests’ and missing cytoplasm (Stage III). (d, f) Representative images showing Type A mechanism, where graphene nanosheets cut off large areas of membrane surfaces. (Reprinted with permission from Ref 94. Copyright 2013 Nature Nanotechnology)

graphene nanosheet with its initially unbiased orientation underwent a swing motion, rocking back and forth, around the restrained atom, for a short period; Second, the Insertion Mode, in which the tail end of the graphene nanosheet was eventually trapped and pulled by the membranes, due to strong van der Waals (vdW) attractions from the membrane lipids and hydrophobic interactions. Once the tail end started to enter, it took only a few nanoseconds for the graphene nanosheet to cut into the lipid membranes. This direct insertion/cutting is referred to as the ‘Type A’ mechanism by Zhou and coworkers (see cells marked with ‘Type A’ in Figure 4). Interestingly, this insertion (Type A mechanism) was also observed by Gao and coworkers96 in another very recent study with three different mammalian cell types (see below); Third, the Extraction Mode, where the graphene nanosheet started to vigorously extract phospholipid molecules from the lipid bilayers onto its own surfaces. The disruptive extraction of phospholipid molecules, caused by the strong pulling

forces from the graphene nanosheet, eventually led to the loss of cell membrane integrity. This surprising lipid extraction is named the ‘Type B’ mechanism; it was first revealed in the MD simulations, and subsequently validated by careful examination of the staged TEM images (see cells marked with ‘Type B’ in Figure 4). This strong extraction-induced deformation might also help to explain the membrane wrapping in endocytosis97 of various nanoparticles.28,98 Further analyses of the interaction energy profiles between the graphene nanosheet and the two E. coli membranes clearly demonstrate the three distinguishable observed modes. The ‘Swing Mode’ of the graphene nanosheet in bulk water is associated with an initial high-energy plateau. Subsequently, the ‘Insertion Mode’ is depicted by a sharp energy collapse, which corresponds to further enhancement in the interaction from graphene’s continuous pulling on the membrane and direct extraction of lipid molecules. This exceptionally strong dispersion interaction mainly results from the



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FIGURE 5 | Graphene nanosheet insertion and lipid extraction. (a) Graphene nanosheet insertion and lipid extraction in the outer membrane (Pure POPE); and (b) in the inner membrane (3:1 Mixed POPE-POPG). Water is shown in ice-blue, and the phospholipids in tan lines with hydrophilic charged atoms in color spheres (hydrogen in white, oxygen in red, nitrogen in blue, and phosphorus in orange). The graphene sheet is shown as a yellow-bonded sheet with a large sphere marked at one corner as the restrained atom in simulations. Those extracted phospholipids are shown in larger spheres with hydrogen in white, oxygen in red, nitrogen in blue, carbon in cyan, and phosphorus in orange. (Reprinted with permission from Ref 94. Copyright 2013 Nature Nanotechnology)

graphene’s unique 2D-structure with atomically dense sp2 -carbons, which is so strong that it can overcome the self-attraction among the lipid molecules within the membrane. As for the ‘Insertion Mode’, it can be shown by the dramatic changes of phospholipid membranes in both the thickness (increase) and area per lipid (decrease), with both E. coli outer and inner membranes displaying similar deformations overall. However, there is no significant change in the phospholipid tail order parameters, indicating the acyl chain orientations are not much affected.

Robust Lipid Extraction by Graphene and GO Nanosheets As described above, during the course of simulations, a novel ‘Type B’ mechanism which leads to the thinning of the lipid densities and eventually the loss of cell membrane integrity was first

discovered. This could be the key mechanism to show the applicability of graphene and GO nanosheets as antibacterial ‘green’ band-aid. To further prove that this lipid extraction is indeed for real and not just due to some kinetic effects, an additional graphene ‘docking’ simulation was performed using the outer membrane (pure POPE) as an example.94 The configuration for the simulation was intentionally set up so that it is very hard for the lipid extraction to occur. Namely, the entire graphene nanosheet was restrained in space, with its plane oriented perpendicular to the membrane surface and its tail barely touching the membrane surface to ensure there is no kinetic effects. At the beginning, the lipid membrane displayed some adaptive motions to adjust to the penetration of the graphene. Shortly after that, larger fluctuations started to occur in nearby phospholipid molecules, perturbing the seemingly smooth membrane surface.




Then, some phospholipids began to climb up along both surfaces of the graphene nanosheet as a consequence of the strong attraction from the graphene. Soon after that, many other phospholipids also joined in this ‘climbing’ activity. Interestingly, this phospholipid climbing seems to be highly cooperative due to the collective movements of their hydrophobic tails at the water/graphene interface. Also, multilayer climbing of phospholipid molecules was observed and the lipid extraction took place simultaneously on both sides of the graphene nanosheet. A few hundred nanoseconds later, significant membrane deformations became noticeable. During the pulling process, the hydrophobic tails of these extracted phospholipids tend to spread out evenly onto the entire graphene nanosheet to maximize their contacts with the hydrophobic graphene surface, while their hydrophilic head groups prefer to be solvated in bulk water. To further demonstrate the robustness of this lipid extraction mechanism,94 the GO nanosheets were also simulated to compare directly with the TEM experiments (which was done with GO for water-dispersibility). The GO nanosheets were based on the Lerf–Klinowski GO structural model with a molecular formula of C10 O1 (OH)1 (COOH)0.5 , which represents typical outcomes from the standard oxidation process.99–101 As expected, both POPE and POPG phospholipids were found to be extracted from the membranes by the GO nanosheets because of the strong vdW attractions between the GO nanosheet and membrane lipids. Similar to the case of graphene nanosheet, once extracted, the hydrophobic interactions also play a dominant role through nanoscale dewetting. Again, the lipid hydrophobic tails tend to spread into the unoxidized hydrophobic regions, while the hydrophilic head groups prefer to contact the polar oxide groups via favorable electrostatic interactions. It is known that large unoxidized residual graphene-like regions (sp2 -domain) can exist on GO nanosheets99,102,103 and such regions have been utilized experimentally to achieve the oxidative cutting and unraveling of CNTs.104 Therefore, the above discussions on graphene nanosheets can also be applied to GO nanosheets largely. Other evidences include the finding by Gomez-Navarro et al. that upon oxidation, isolated highly oxidized areas (few nm in size) are formed, while at least ∼60% of the surface remains undisturbed.102 In addition, further UV–vis spectra data also show that the maximum absorption peak of graphene, resulting from sp2 -domain of carbon atoms, displays an obvious blue-shift upon repeated heavy oxidation due to the presence of oxygen and increased number of sp3 bonds.94

Another insight gained from these findings is that water plays an important role in the lipid structure and orientation at the graphene/water interface. At the beginning, all these complicated and collective movements of phospholipids on the graphene nanosheet began with short-ranged vdW attractions between the graphene (sp2 -domains in GO) and lipid molecules. However, once extracted, the strong hydrophobic interactions between the graphene and lipid tails played another significant role through nanoscale ‘dewetting’ (i.e., expelling water from the graphene surface). This strong hydrophobic packing is similar to that found in many biomolecular self-assemblies, such as cell membrane formation and protein folding, where many recent studies75,76,105 have demonstrated that nanoscale dewetting can provide significant driving forces for the collapse speed and system stability. These strong interactions provide the underlying driving force that causes the newly discovered ‘Type B’ mechanism for graphene’s antibacterial capability. In short, experimental and theoretical approaches have been combined to investigate the molecular mechanisms for the graphene-induced degradation of E. coli cell membranes. This study reveals two types of mechanism: one by severe insertion and cutting, and the other by destructive extraction of lipid molecules. This surprising extraction of phospholipids directly out of lipid membranes was first observed in computer simulations, and then validated by TEM imaging. The graphene’s unique 2D-structure with all sp2 -carbons is the source that facilitates exceptionally strong dispersion interactions between the graphene and lipid molecules which cause the surprisingly robust destructive lipid extraction. Even though these findings are from studies about E. coli, similar mechanisms should also apply to other types of bacteria.46,48 These findings have implications in the design of novel antibiotics and other future clinical applications. In particular, graphene might become a new type of ‘green’ antibacterial materials for everyday use with little bacterial resistance due to its ‘physical damage’-based bacterial killing mechanism, as indicated in a recent attempt to use graphene-coated cotton fabric for band-aid.106

Mammalian Cellular Membranes The lipid extraction behaviors of graphene nanosheets discussed above can effectively kill the bacteria membrane, but such cytotoxicity to mammalian cells seems to be reduced when the nanosheets are surrounded by proteins.49 On the other hand, the internalization of 2D materials with micron-scale lateral dimensions, here referred to as microsheets, can substantially disrupt the cytoskeletal organization of cells and induce



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FIGURE 6 | Cellular uptake and internalization of few-layer graphene microsheets. (a) and (b) Confocal images of human lung epithelial cells (a and b) and mouse macrophages (c) exposed to graphene microsheets (0.5–25 μm lateral dimension) after 24 and 5 h, respectively. The nuclei in (a) and (b), are visualized (blue fluorescence) with 4′ ,6-diamidino-2-phenylindole (DAPI). The microtubules of the lung epithelial cells (a and b) are visualized using antitubulin 𝛽 antibodies conjugated with FITC (green fluorescence), whereas the actin cytoskeleton of macrophages shown in (c) is visualized using rhodamine-phalloidin (red fluorescence). In unexposed lung epithelial cells ( a and b-insert), cytoplasmic microtubules (MT) form a linear network spanning across the cytoplasm. Internalized graphene flakes (yellow arrows; a and b) physically displace the linear microtubular network. In unexposed macrophages (c-insert), filamentous actin (F) is organized into aggregates beneath the plasma membrane. Internalized graphene flakes with large lateral dimension (yellow arrow, c) induce dense aggregates of actin filaments while submicron graphene sheets (yellow arrow head, c) do not disrupt the actin cytoskeleton. Transmission electron micrographs of macrophages (d) and lung epithelial cells (e) exposed to 10 ppm FLG sheets (∼800 nm in lateral dimension) for 5 and 24 h show localization in the cytoplasm within membrane-bound vacuoles (blue inserts). Graphene microsheets inside vacuoles appear as electron-dense linear sections (d inset) or irregular flakes (e inset). (Reprinted with permission from Ref 96. Copyright 2013 PNAS)

size- and geometry-dependent toxicity to mammalian cells. Thus, one important question is whether and how graphene microsheets with atomic-scale thickness but micron-scale lateral dimension can enter cells. This question has been recently addressed by Gao and co-workers96 through a detailed investigation of the interactions between graphene microsheets with three types of mammalian cells and with model lipid bilayers by combining coarse-grained MD, all-atom MD, analytical modeling, confocal fluorescence imaging, and electron microscopic imaging. Confocal fluorescent and ex situ electron micrographs in Figures 6 and 7

show that the FLG microsheets can definitely enter cells and they do so through certain edge or corner penetration modes. Here, three types of mammalian cells are used: mouse macrophages, human lung epithelial cells, and keratinocytes; the latter two types form flat, single cell monolayers in vitro, and are representative of the epithelial lining of the human respiratory tract and the skin, respectively.107 Figure 6(a)–(c) displays indirect immunofluorescence confocal microscopy of microtubular cytoskeletal network of polarized epithelial cells and subcortical distribution of actin filaments of macrophages. The cytoplasm




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FIGURE 7 | Graphene microsheets cutting into three types of cells (Scale bars, 2 μm). (a) Corner penetration observed for a graphene sheet of micron-scale lateral dimension on the surface of a human lung epithelial cell at low and high magnification. (b) Edge penetration of multiple microsheets (G) into a macrophage (M), (c) Edge penetration for a 5 μm graphene sheet interacting with primary human keratinocytes, in which the edge entry appears to have been nucleated at an asperity or protrusion (thick yellow arrow). (d) Corner penetration mode at the surface of a primary human keratinocyte. The graphene microsheets have layer numbers that range from 4 to 25. (Reprinted with permission from Ref 96. Copyright 2013 PNAS).

imaging clearly demonstrated that plate-like graphene microsheets are internalized by human lung epithelial cells (panels a and b) and macrophages (panel c). The graphene microsheets seem to orient their basal planes preferentially in parallel with the basolateral cell surface attached to the substrate, and physically disrupt the cytoskeletal organization of both lung epithelial cells (Figure 6(a) and (b) and macrophages (Figure 6(c)) after passing through cytoskeleton. Some graphene microsheets are found within cytoplasmic vacuoles inside macrophages (Figure 6(d)) and lung epithelial cells (Figure 6(e)), as shown in thin sections using TEM. The TEM imaging (Figure 6(d) and (e)) also revealed that the overall structure and integrity of subcellular organelles are preserved.96 The confocal and TEM imaging protocols in Figure 6 demonstrate graphene internalization and orientation, but they do not show how the graphene microsheets are able to enter the cells. To reveal the entry mode, ex situ field-emission scanning electron micrographs (SEMs) of target cells with the outer membrane enhanced by osmium tetroxide post-fixation is used to capture the initial uptake

process (Figure 7), both with and without critical point drying to check for drying artifacts. The SEM micrographs in Figure 7 presents high-resolution images of cell surfaces exposed to graphene after 5 or 24 h, showing that the graphene microsheets can slice into the cells with their atomically sharp edges and corners. Figure 7(c) and (d) shows particularly clear cases of membrane penetration that appear to have initiated at an asperity or protrusion on the graphene edge (Figure 7(c)) or initiated at a graphene corner (Figure 7(d)). The atomic-scale mechanisms of cell interaction with FLG microsheets that underlie the observed edge- and corner-entry of graphene microsheets into cells have been investigated by coarse-grained and all-atom MD simulations.96 The coarse-grained simulations serve to illustrate the dynamic process of graphene–bilayer interaction, while the all-atom simulations allow one to determine the energy barriers associated with graphene penetration. Preliminary simulations showed that idealized graphene microsheets with smooth edges will not penetrate lipid bilayers at room temperature due to high-energy



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barriers, even in cases where the encounter is strictly edge-on (this is different from the above all-atom simulations on graphene nanosheets, where the smaller lateral size of a monolayer graphene is modeled, which can penetrate into cell membranes without much barrier). This puzzle was eventually resolved by noting that the edges of the graphene sheets are highly irregular (Figure 7(a), (c), and (d)), and that the sharp edge/corner protrusions could reduce the energy barrier for cell entry to near the thermal energy kB T. Figure 8 shows the primary results from the coarse-grained MD simulation of graphene and FLG interacting with lipid bilayers. In the first batch of simulations, a small, rhombic, monolayer graphene nanosheet with edge length of 6.4 nm is placed initially at a distance about 4 nm above, parallel to a square patch of lipid bilayer with 992 lipid molecules and 67,817 water molecules in a cubic box with edge dimension of 24 nm, and subject to periodic boundary conditions in all three dimensions (Figure 8(a)). The nanosheet undergoes Brownian motion, including rapid vibration, rotation, and migration in the vicinity of the bilayer under thermal fluctuations. Spontaneous piercing into the bilayer, facilitated by the

attractive interactions between graphene and the tail groups of lipids, is observed to begin as soon as the nanosheet finds a configuration with one of its sharpest corners oriented nearly orthogonal to the membrane (Figure 8(b) and (c)). The piercing occurs only after the tip of the penetrating corner touches the hydrophobic core of the bilayer, and the nanosheet in the simulation eventually ends up embedded in the bilayer due to its small dimensions. Simulation of a rhombic graphene nanosheet with two different internal angles (30∘ and 60∘ ) demonstrates that orthogonal piercing of the sharpest corner has the lowest energy barrier and is the most preferred entry pathway. Note that the graphene microsheets used in experiments may have a complex chemistry, typically decorated with hydrophilic oxygen functional groups, while in cell culture medium graphene may exhibit adsorbed proteins that can reduce apparent hydrophobicity. To investigate these effects, further simulations of bilayer interaction with graphene nanosheets of different shapes and surface chemistry were carried out to confirm that the nanosheets tend to penetrate into the cell via spontaneous piercing at their sharpest hydrophobic corner. A series of calculations with




different interaction parameters between graphene and lipid molecules demonstrate that the corner-entry mode is robust within a broad range of parameters.96 In the next set of simulations, coarse-grained simulations were carried out to study large FLG sheets interacting with lipid bilayers (Figure 8(f)–(h)). Penetration was not found in the case of ideal, atomically smooth, infinite graphene edge interacting with lipid bilayers. However, it was quickly realized that the edges of experimentally fabricated graphene exhibit atomic-scale roughness (Figure 8(e)), as revealed by atomically resolved scanning tunneling microscopy.108–110 Additionally, most FLG sheets also show very rough edges (Figure 7), as well as terraced or beveled edge structures that become successively thinner toward one of the two faces.111,112 Figure 8(f) shows a model terraced edge structure created on a 5-layer FLG flake interacting with a bilayer. The simulation system contains of a patch of bilayer with 2016 lipid molecules and 133,052 water molecules in a cubic box with dimensions of 24 nm × 48 nm × 24 nm. The plate-like FLG, which has a ragged edge topography mimicking those observed in experiments, is composed of five atomic layers with an equilibrium interlayer distance of 0.34 nm and placed initially at a distance about 3 nm above and orthogonal to the bilayer. Each graphene layer is assigned a different color. The first two and last two layers are set to be symmetrical with respect to the mid-layer. To mimic part of a much larger structure for which Brownian motion is limited, the top edge of the FLG is clamped and periodic boundary conditions are imposed in all three dimensions of the simulation box. The lipid bilayer undergoes Brownian motion in the vicinity of the large graphene edge for 2.19 microseconds under the confinement of a harmonic potential. The latter is then removed and the bilayer membrane is set free to interact with the ragged graphene edge. It is noticed that the FLG penetrates the bilayer despite its size, starting with localized piercing at sharp protrusions along the edge. The penetrated portion of the membrane then propagates along the whole edge, resulting in full penetration. During this process, the energy barrier to penetration is overcome by local piercing at sharp corners along the nominally flat edge, and the full penetration is driven by the attractive interaction between the graphene and the tail groups of lipids once initial piercing is successful. The robustness of this entry mode is tested by carrying out further coarse-grained simulations on monolayer or FLG sheets with an isolated protrusion or a terrace, or by initiating contact near a corner or a locally folded edge.96 The simulation results all show similar pathways that are initiated by localized piercing at an atomically thin graphene

feature (requiring only thermal energy to overcome the small barrier), followed by spreading and complete penetration driven by hydrophobic forces between the graphene and the bilayer core. This entry mechanism is believed to be generic for cell uptake of all 2D hydrophobic nanomaterials with atomic-scale thickness. The coarse-grained simulations demonstrate that graphene penetration of lipid bilayers tends to initiate with local orthogonal piercing at a sharp hydrophobic corner. To understand the reason for orthogonal corner piercing, the free energy of the system is calculated using the so-called thermodynamic integration technique113,114 as a function of two orientation angles (𝜽, 𝝓) of a rhombic graphene nanosheet as one of the sharp corners of the sheet is fixed at a distance of 0.4 nm above the bilayer (81); 𝜽 is the angle between the long diagonal axis of the flake and the bilayer within the graphene plane and 𝝓 is the angle between the vectors normal to the graphene plane and the membrane plane (Figure 8(i)). The free energy associated with orthogonal piercing (90∘ , 90∘ ) is set to 0 as a reference value. As expected, the orientation (30∘ , 0∘ ), which corresponds to the nanosheet lying parallel to the bilayer plane, shows the highest free energy since it induces most severe confinement of thermal motion in this configuration. The free energy is normalized by its peak value at the parallel configuration (30∘ , 0∘ ) and then plotted in Figure 8(i), with results indicating that the orthogonal orientation (90∘ , 90∘ ) exhibits the lowest free energy due to its weakest confinement on the thermal motions, thereby maximizing the entropy, of both membrane and graphenes. The coarse-grained simulations indicated that localized corner piercing at edge asperities plays a critical role during the initial stage of cell uptake of graphene microsheets. To determine the energy evolution associated with such initial piercing events, further simulations are conducted on an all-atom model of corner piercing of a monolayer graphene corner across a bilayer patch of POPC lipid in a box of water molecules. Two types of all-atom simulations were conducted. In type I simulations, a sharp corner of graphene was initially placed in a corner-piercing configuration across the bilayer and then observed to spontaneously move downward, penetrating further into the bilayer, as shown in Figure 9(a). In type II simulations, the energy barrier associated with corner piercing is calculated by steered MD simulations, in which the graphene corner is pulled across the bilayer by a virtual spring. Figure 9(b) shows the graphene–bilayer interaction energy calculated from the all-atom simulations as a function of the



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FIGURE 9 | All-atom molecular dynamics simulations of corner piercing of a monolayer graphene across a lipid bilayer. (a) Simulations directly showing that the corner piercing proceeds spontaneously. (b) Graphene-bilayer interaction energy as a function of the penetration distance, showing the existence of an energy barrier of about 5 kB T associated with corner piercing. The mean value of interaction energy is obtained from 11 independent simulation runs and the error bars show standard deviation. The relatively large fluctuations of interaction energy at large penetration distance are mainly due to random translational and rotational movements of graphene relative to the bilayer membrane and random configurational changes of individual lipids adjacent to the graphene. (c) Analytical model of corner piercing. (Reprinted with permission from Ref 96. Copyright 2013 PNAS)

penetration distance. The calculations confirm that the energy barrier is only ∼5 kB T for the graphene corner to pierce through the top hydrophilic head region of the bilayer. Shortly after this point, the total interaction energy starts to decrease due to favorable interactions between the lipid tails in the core of the bilayer and an ever increasing area of immersed graphene. Thus, both coarse-grained and all-atom simulations reveal that corner piercing involves a small energy barrier comparable to thermal energy and is essentially a spontaneous process. Further study shows that the free energy change associated with the corner piercing of graphene into bilayer can be described by a simple mathematical model in terms of four parameters96 : hH and hT (thicknesses of the head- and tail-groups in the lipid monolayer, respectively, as shown in Figure 9(c)), and 𝛾 H and 𝛾 T (interaction energy densities between one side surface of graphene and head-

and tail-groups of lipids relative to that between solvent and graphene, respectively). The model predicts that, as the graphene penetrates into the bilayer by a distance h, the energy increases in the regime of 0 < h ≤ hH , and decreases in the regimes of hH + 2hT < h ≤ 2hH + 2hT and 2hH + 2hT < h. The peak energy occurs when the graphene tip lies in the hydrophobic core hH < h ≤ hH + 2hT at a critical penetration depth of hcr = (1 − 𝛾 H(/𝛾 T )hH , suggesting an ) energy barrier of Ebarrier = 2 1 − 𝛾H ∕𝛾T h2H 𝛾H tan𝛼. For a graphene corner with internal angle of 45∘ , the energy barrier for piercing is predicted to be about 7 kB T, with peak energy occurring at the critical penetration depth of ∼1.0 nm. These values are in excellent agreement with the results obtained from all-atom simulations as shown in Figure 8(b). In summary, live-cell and ex situ TEM/SEM bioimaging, coarse-grained, and all-atom MD and analytical modeling have been performed to address




the question whether graphene microsheets can enter mammalian cells.96 This question is important for toxicity studies because, due to their large lateral dimension, the microsheets can disrupt the cytoskeletal organization of cells and induce sizeand geometry-dependent toxicity to cells. It is found that direct bilayer penetration of graphene begins with localized piercing at sharp hydrophobic corners or at protrusions along graphene edges followed by propagation along the edge to achieve full penetration. Brownian motion and entropic driving forces in the near-membrane region tend to position these sharp corners orthogonal to the bilayer plane, which then leads to spontaneous corner piercing. All-atom steered MD simulations reveal that there exists only a small energy barrier, comparable to kB T associated with graphene corner piercing. In the absence of sharp corners or edge protrusions, the cell membrane has a high intrinsic energy barrier against penetration by long graphene edge segments even though they are atomically thin. Such uniform, atomically smooth, horizontally aligned, long-length graphene edges are rare, however, in practice cell penetration is spontaneous due to the presence of atomic- or nano-scale edge roughness that essentially eliminates the energy barrier. Experimental imaging studies confirm graphene penetration of cell membranes in a dominant edge- or corner-first mode for each of three cell types studied: human lung epithelial cells, human keratinocytes, and mouse macrophages. The experiments showed penetration and successful uptake of FLGs as large as 5–10 μm in lateral dimension, which supports the prediction that penetration activation barriers are not intrinsically length dependent, because of initiation at local sharp features. Once the initial energy barrier for spontaneous membrane penetration has been overcome, interaction between the hydrophobic basal surfaces of graphene microsheets with the inner hydrophobic region of the plasma membrane promotes cellular uptake.

APPLICATIONS OF GRAPHENE NANOTOXICITY TO GREEN ANTIBIOTICS Antibacterial materials are now widely used to protect the public health. A wide range of materials, including antibiotics, metal ions, and quaternary ammonium compounds, have been known to prevent attachment and proliferation of microbes on material surfaces.106 However, these materials also raise concerns about antibiotic resistance, environmental pollution, relatively complex processing, and high cost.115 More recently, antibacterial nanomaterials have been

explored to meet these challenges, such as silver nanoparticles,115 titanium oxide nanoparticles,116 and CNTs.117,118 CNTs have been found to be cytotoxic to both bacteria and human cells. The use of nanomaterials for antibacterial is a trend based on its unique advantages, but few practical or commercial applications of antibacterial materials made from nanomaterials have been reported. In a very recent study, Fan and coworkers106 provided a new approach to fabricate antibacterial cotton fabrics by attaching single-layer GO. Interestingly, GO can be attached with cloth very easily and still maintains its good antibacterial activity. GO is a derivative of graphene with suspended hydroxyl, epoxyl, and carboxyl functional groups on its plane and edges, which makes it readily dispersed in water. Fan and coworkers106 prepared the GO nanosheets following a modified Hummer’s method.119 The thickness of GO sheet is ∼1.1 nm as measured by atomic force microscopy (AFM), suggesting of a single-layer 2D structure. Three individual approaches are used to prepare GO modified cotton fabrics as presented in Scheme 1. In the first routine, GO is adsorbed onto cotton fabric by filtering directly without using any other chemical reagents, resulting in the so-called Cotton-GO fabric. In the second approach, cotton fabric is pre-soaked in the trially isocyanurate (TAIC) solution to introduce the crosslinking agent into the cotton matrix, and then GO is adsorbed onto cotton fabric by filtering. Then, the fabric is irradiated by 𝛾-ray to activate the vinyl bonds in TAIC to form a crosslinking embedding GO nanoparticles. The resulting fabric is called Cotton-rx-GO herein, where rx means radiation-induced crosslinking. In the third approach, cotton fabric is soaked in TAIC solution prior to be filtrated by ethanol solution containing GO and thermal initiator-ammonium persulfate. And then the fabric is heated to activate the vinyl bonds in TAIC to form a crosslinking network involving GO nanoparticles. The fabric produced this way is called Cotton-cx-GO herein, where cx means chemical crosslinking. The antibacterial activity of pristine and GO modified cotton fabrics were qualitatively evaluated using Gram-negative E. coli and Gram-positive Bacillus subtilis bacteria cells according to the revised Japanese Industrial Standard (JIS L1902-2002). After 4 h’ incubation at 37∘ C, fewer E. coli cells and colonies were found on all GO modified cotton fabrics (Figure 10(a)), implying the superior antibacterial effect of such GO modified cotton fabrics.106 In contrast, control studies on pristine cotton fabric show a great number of colony-forming units (CFU). SEM studies further confirmed that E. coli cells on the



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SCHEME 1 | Preparation of graphene-oxide (GO) modified cotton fabrics by means of adsorption (Cotton-GO), radiation-induced crosslinking (Cotton-rx-GO), and chemical crosslinking (Cotton-cx-GO). (Reprinted with permission from Ref 106. Copyright 2013 Advanced Healthcare Material)

GO modified cotton fabrics lost the integrity of membranes, which was responsible for the bacteria-killing effect of the GO on the surface of the GO modified cotton fabrics. Further results showed that Cotton-GO, Cotton-cx-GO, and Cotton-rx-GO have bactericidal efficiency up to 99.2, 99.8, and 98.6%, respectively106 (Figure 10(b)). As reference control, the inactivation efficiency of Cotton-rx was only 0.2%, indicating insignificant antibacterial activity. Cotton-GO was a perfect candidate to be used as an antibacterial material due to its simplicity in preparation. Inactivation efficiency of the three materials for Gram-positive bacteria (B. Subtilis) was also very good (Figure 10), even though the Gram-positive bacteria possess more complicated cell wall structures. These results show that GO in suspension solution can firmly combine with cotton fabrics by adsorption or by radiation-induced crosslinking using TAIC as a crosslinker. The GO modified cotton fabrics can inactivate >98% of bacteria in only less than 4 h, especially the directly filtrating GO modified cotton fabric. Cotton-GO was also laundering durable, with inactivation efficiency of bacteria >90% even after being washed 100 times. In contrast, the skin irritation tests indicate that Cotton-GO was safe.106 In short, Cotton-GO is a low-cost, high efficiency, and novel nanomaterial. The flexible, foldable, and recyclable GO based cotton fabric can be

potentially used in medical and food (for food preservation) industries to effectively kill bacteria.

SUMMARY AND FUTURE PERSPECTIVE In this review article, we have discussed some of the recent advances in cytotoxicity of graphene, including its interaction and disruption on the structure and function of proteins, DNAs, and cell membranes, with an emphasis on the molecular level understanding of its interactions with biological systems. Both experimental and theoretical approaches have shown that graphene can have significant disruptions to protein and DNA structures due to the strong 𝜋–𝜋 stacking interactions, and also damage the integrity of cell membranes (both bacteria and human cells). More interestingly, two types of molecular mechanisms for the graphene-induced degradation of cell membranes have been identified, one by severe insertion and cutting, and the other by destructive extraction of lipid molecules. This strong attraction between graphene and membrane lipids is largely derived from graphene’s unique 2D-structure with all sp2 -carbons, which facilitates exceptionally strong interactions between graphene and lipid molecules. Cooperative movements of extracted lipid molecules were also observed on the graphene 2D-surface due to the redistribution of the hydrophobic tails in order to




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(a)

100 80 60 40 20 0

Loss of B. subtilis viability (%)

(c)

Cotton-GO

Cotton-rx-GO Cotton-cx-GO

Cotton-GO

Cotton-rx-GO Cotton-cx-GO

100 80 60 40 20 0

FIGURE 10 | Antibacterial activity of Cotton-graphene-oxide (GO) and inactivation efficiency for GO modified cotton fabrics. About 100 𝜇L elute was vaccinated in agar plate and then incubated overnight (a). Inactivation efficiency for GO modified cotton fabrics tested by Escherichia coli (b) and Bacillus subtilis (c). (Reprinted with permission from Ref 106. Copyright 2013 Advanced Healthcare Material)

maximize hydrophobic interactions with the graphene surface. We have also reviewed some of the latest progress on the potential applications of graphene nanosheets in ‘green antibiotics’ based on its cytotoxicity to bacteria. Both the severe graphene insertion and destructive lipid extraction suggest that graphene nanosheets can induce serious membrane stress, and thus significantly reduce cell viability. This capability has implications in the design of novel antibiotics with little bacterial resistance due to its ‘physical damage’-based bacterial killing mechanism.106

Further studies on the toxicity of graphene and graphene derivatives to other cell lines, tissues, and animal models will be highly desired for a deeper understanding of the underlying molecular mechanisms, using combined in vivo, in vitro, and in silico approaches. We also envision more development efforts on applying graphene as a new type of antibacterial materials and other clinical applications for everyday use. These studies on graphene cytotoxicity may also stimulate and facilitate the cytotoxicity studies of other nanomaterials in this emerging field of nanotoxicology.

REFERENCES 1. Rosi NL, Giljohann DA, Thaxton CS, Lytton-Jean AKR, Han MS, Mirkin CA. Oligonucleotide-modified gold nanoparticles for intracellular gene regulation. Science 2006, 312:1027–1030. 2. Michalet X, Pinaud FF, Bentolila LA, Tsay JM, Doose S, Li JJ, Sundaresan G, Wu AM, Gambhir SS, Weiss S. Quantum dots for live cells, in vivo imaging, and diagnostics. Science 2005, 307:538–544. 3. Grossman JH, McNeil SE. Nanotechnology in cancer medicine. Phys Today 2012, 65:38–42. 4. Nel AE, Madler L, Velegol D, Xia T, Hoek EMV, Somasundaran P, Klaessig F, Castranova V,

Thompson M. Understanding biophysicochemical interactions at the nano-bio interface. Nat Mater 2009, 8:543–557. 5. Zhao Y, Xing G, Chai Z. Nanotoxicology: Are carbon nanotubes safe? Nat Nanotechnol 2008, 3:191–192. 6. Nel A, Xia T, Mädler L, Li N. Toxic potential of materials at the nanolevel. Science 2006, 311:622–627. 7. Ge C, Du J, Zhao L, Wang L, Liu Y, Li D, Yang Y, Zhou R, Zhao Y, Chai Z, et al. Binding of blood proteins to carbon nanotubes reduces cytotoxicity. Proc Natl Acad Sci USA 2011, 108:16968–16973.



Advanced Review

8. Kang SG, Zhou G, Yang P, Liu Y, Sun B, Huynh T, Meng H, Zhao L, Xing G, Chen C, et al. Molecular mechanism of pancreatic tumor metastasis inhibition by Gd@C82(OH)22 and its implication for de novo design of nanomedicine. Proc Natl Acad Sci USA 2012, 109:15431–15436. 9. Service RF. Is nanotechnology dangerous? Science 2000, 290:1526–1527. 10. Donaldson K, Aitken R, Tran L, Stone V, Duffin R, Forrest G, Alexander A. Carbon nanotubes: a review of their properties in relation to pulmonary toxicology and workplace safety. Toxicol Sci 2006, 92:5–22. 11. Gilbert N. Nanoparticle safety in doubt. Nature 2009, 460:937. 12. Nel A, Xia T, Madler L, Li N. Toxic potential of materials at the nanolevel. Science 2006, 311:622–627. 13. Chen Z, Meng H, Xing GM, Chen CY, Zhao YL, Jia G, Wang TC, Yuan H, Ye C, Zhao F, et al. Acute toxicological effects of copper nanoparticles in vivo. Toxicol Lett 2006, 163:109–120. 14. Zhang YB, Ali SF, Dervishi E, Xu Y, Li ZR, Casciano D, Biris AS. Cytotoxicity effects of graphene and single-wall carbon nanotubes in neural phaeochromocytoma-derived PC12 cells. ACS Nano 2010, 4:3181–3186. 15. Gao HJ, Kong Y, Cui DX, OZkan CS. Spontaneous insertion of DNA oligonucleotides into carbon nanotubes. Nano Lett 2003, 3:471–473. 16. Gao HJ, Kong Y. Simulation of DNA-nanotube interactions. Annu Rev Mater Res 2004, 34:123–150. 17. Cheng Y, Li D, Ji B, Shi X, Gao H. Structure-based design of carbon nanotubes as HIV-1 protease inhibitors: atomistic and coarse-grained simulations. J Mol Graph Model 2010, 29:171–177. 18. Zhong J, Song L, Meng J, Gao B, Chu WS, Xu HY, Luo Y, Guo JH, Marcelli A, Xie SS, et al. Bio-nano interaction of proteins adsorbed on single-walled carbon nanotubes. Carbon 2009, 47:967–973. 19. Karajanagi SS, Vertegel AA, Kane RS, Dordick JS. Structure and function of enzymes adsorbed onto single-walled carbon nanotubes. Langmuir 2004, 20:11594–11599. 20. Zuo GH, Huang Q, Wei GH, Zhou RH, Fang HP. Plugging into proteins: poisoning protein function by a hydrophobic nanoparticle. ACS Nano 2010, 4:7508–7514. 21. Zuo GH, Gu W, Fang HP, Zhou RH. Carbon nanotube wins the competitive binding over proline-rich motif ligand on SH3 domain. J Phys Chem C 2011, 115:12322–12328. 22. Wong-Ekkabut J, Baoukina S, Triampo W, Tang IM, Tieleman DP, Monticelli L. Computer simulation study of fullerene translocation through lipid membranes. Nat Nanotechnol 2008, 3:363–368.

23. Qiao R, Roberts AP, Mount AS, Klaine SJ, Ke PC. Translocation of C60 and its derivatives across a lipid bilayer. Nano Lett 2007, 7:614–619. 24. Shi X, von dem Bussche A, Hurt RH, Kane AB, Gao H. Cell entry of one-dimensional nanomaterials occurs by tip recognition and rotation. Nat Nanotechnol 2011, 6:714–719. 25. Wallace EJ, Sansom MSP. Blocking of carbon nanotube based nanoinjectors by lipids: a simulation study. Nano Lett 2008, 8:2751–2756. 26. Zhang Y, Ali SF, Dervishi E, Xu Y, Li Z, Casciano D, Biris AS. Cytotoxicity effects of graphene and single-wall carbon nanotubes in neural phaeochromocytoma-derived PC12 cells. ACS Nano 2010, 4:3181–3186. 27. Yang K, Ma YQ. Computer simulation of the translocation of nanoparticles with different shapes across a lipid bilayer. Nat Nanotechnol 2010, 5:579–583. 28. Vácha R, Martinez-Veracoechea FJ, Frenkel D. Receptor-mediated endocytosis of nanoparticles of various shapes. Nano Lett 2011, 11:5391–5395. 29. Albanese A, Tang PS, Chan WC. The effect of nanoparticle size, shape, and surface chemistry on biological systems. Annu Rev Biomed Eng 2012, 14:1–16. 30. Gao HJ. Probing mechanical principles of cell-nanomaterial interactions. J Mech Phys Solids 2014, 62:312–339. 31. Novoselov KS, Geim AK, Morozov SV, Jiang D, Katsnelson MI, Grigorieva IV, Dubonos SV, Firsov AA. Two-dimensional gas of massless Dirac fermions in graphene. Nature 2005, 438:197–200. 32. Zhang YB, Tan YW, Stormer HL, Kim P. Experimental observation of the quantum Hall effect and Berry’s phase in graphene. Nature 2005, 438:201–204. 33. Novoselov KS, Geim AK, Morozov SV, Jiang D, Zhang Y, Dubonos SV, Grigorieva IV, Firsov AA. Electric field effect in atomically thin carbon films. Science 2004, 306:666–669. 34. Liu Z, Robinson JT, Sun X, Dai H. PEGylated nanographene oxide for delivery of water-insoluble cancer drugs. J Am Chem Soc 2008, 130:10876–10877. 35. Feng L, Liu Z. Graphene in biomedicine: opportunities and challenges. Nanomedicine 2011, 6:317–324. 36. Sanchez VC, Jachak A, Hurt RH, Kane AB. Biological Interactions of graphene-family nanomaterials: an interdisciplinary review. Chem Res Toxicol 2011, 25:15–34. 37. Sun X, Liu Z, Welsher K, Robinson JT, Goodwin A, Zaric S, Dai H. Nano-graphene oxide for cellular imaging and drug delivery. Nano Res 2008, 1:203–212. 38. Yang X, Wang Y, Huang X, Ma Y, Huang Y, Yang R, Duan H, Chen Y. Multi-functionalized graphene oxide based anticancer drug carrier with dual-targeting




function and pH-sensitivity. J Mater Chem 2011, 21:3448–3454. 39. Titov AV, Kral P, Pearson R. Sandwiched graphene-membrane superstructures. ACS Nano 2010, 4:229–234. 40. Guo R, Mao J, Yan LT. Computer simulation of cell entry of graphene nanosheet. Biomaterials 2013, 34:4296–4301. 41. Wang JL, Wei YJ, Shi XH, Gao HJ. Cellular entry of graphene nanosheets: the role of thickness, oxidization and surface adsorption. RSC Adv 2013, 3:15776–15782. 42. Okada F. Beyond foreign-body-induced carcinogenesis: impact of reactive oxygen species derived from inflammatory cells in tumorigenic conversion and tumor progression. Int J Cancer 2007, 121:2364–2372. 43. Soldano C, Mahmood A, Dujardin E. Production, properties and potential of graphene. Carbon 2010, 48:2127–2150. 44. Yang K, Zhang S, Zhang G, Sun X, Lee S-T, Liu Z. Graphene in mice: ultrahigh in vivo tumor uptake and efficient photothermal therapy. Nano Lett 2010, 10:3318–3323.

53. Tomasio SM, Walsh TR. Modeling the binding affinity of peptides for graphitic surfaces. Influences of aromatic content and interfacial shape. J Phys Chem C 2009, 113:8778–8785. 54. Balamurugan K, Gopalakrishnan R, Raman SS, Subramanian V. Exploring the changes in the structure of 𝛼-helical peptides adsorbed onto a single walled carbon nanotube using classical molecular dynamics simulation. J Phys Chem B 2010, 114:14048–14058. 55. Ou LC, Luo Y, Wei GH. Atomic-level study of adsorption, conformational change, and dimerization of an 𝛼-helical peptide at graphene surface. J Phys Chem B 2011, 115:9813–9822. 56. Zuo G, Zhou X, Huang Q, Fang H, Zhou R. Adsorption of villin headpiece onto graphene, carbon nanotube, and C60: effect of contacting surface curvatures on binding affinity. J Phys Chem C 2011, 115:23323–23328. 57. Balamurugan K, Singam ERA, Subramanian V. Effect of curvature on the 𝛼-helix breaking tendency of carbon based nanomaterials. J Phys Chem C 2011, 115:8886–8892. 58. Bianco A. Graphene: safe or toxic? The two faces of the medal. Angew Chem Int Ed Engl 2013, 52:4986–4997.

45. Hu W, Peng C, Luo W, Lv M, Li X, Li D, Huang Q, Fan C. Graphene-based antibacterial paper. ACS Nano 2010, 4:4317–4323.

59. Yang K, Li Y, Tan X, Peng R, Liu Z. Behavior and toxicity of graphene and its functionalized derivatives in biological systems. Small 2013, 9:1492–1503.

46. Akhavan O, Ghaderi E. Toxicity of graphene and graphene oxide nanowalls against bacteria. ACS Nano 2010, 4:5731–5736.

60. Seabra AB, Paula AJ, de Lima R, Alves OL, Duran N. Nanotoxicity of graphene and graphene oxide. Chem Res Toxicol 2014, 27:159–168.

47. Liu S, Zeng TH, Hofmann M, Burcombe E, Wei J, Jiang R, Kong J, Chen Y. Antibacterial activity of graphite, graphite oxide, graphene oxide, and reduced graphene oxide: membrane and oxidative stress. ACS Nano 2011, 5:6971–6980.

61. Yoon OJ, Kim I, Sohn IY, Kieu TT, Lee N-E. Toxicity of graphene nanoflakes evaluated by cell-based electrochemical impedance biosensing. J Biomed Mater Res A 2014:102.

48. Krishnamoorthy K, Veerapandian M, Zhang L-H, Yun K, Kim SJ. Antibacterial efficiency of graphene nanosheets against pathogenic bacteria via lipid peroxidation. J Phys Chem C 2012, 116:17280–17287.

62. Friederich E, Vancompernolle K, Louvard D, Vandekerckhove J. Villin function in the organization of the actin cytoskeleton – correlation of in vivo effects to its biochemical activities in vitro. J Biol Chem 1999, 274:26751–26760.

49. Hu W, Peng C, Lv M, Li X, Zhang Y, Chen N, Fan C, Huang Q. Protein corona-mediated mitigation of cytotoxicity of graphene oxide. ACS Nano 2011, 5:3693–3700.

63. Duan Y, Kollman PA. Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. Science 1998, 282:740–744.

50. Mohanty N, Berry V. Graphene-based singlebacterium resolution biodevice and DNA transistor: interfacing graphene derivatives with nanoscale and microscale biocomponents. Nano Lett 2008, 8:4469–4476. 51. Gianese G, Rosato V, Cleri F, Celino M, Morales P. Atomic-scale modeling of the interaction between short polypeptides and carbon surfaces. J Phys Chem B 2009, 113:12105–12112. 52. Raffaini G, Ganazzoli F. Surface ordering of proteins adsorbed on graphite. J Phys Chem B 2004, 108:13850–13854.

64. Shaw DE, Maragakis P, Lindorff-Larsen K, Piana S, Dror RO, Eastwood MP, Bank JA, Jumper JM, Salmon JK, Shan YB, et al. Atomic-level characterization of the structural dynamics of proteins. Science 2010, 330:341–346. 65. Snow CD, Nguyen N, Pande VS, Gruebele M. Absolute comparison of simulated and experimental protein-folding dynamics. Nature 2002, 420:102–106. 66. Chiu TK, Kubelka J, Herbst-Irmer R, Eaton WA, Hofrichter J, Davies DR. High-resolution x-ray crystal structures of the villin headpiece subdomain, an ultrafast folding protein. Proc Natl Acad Sci USA 2005, 102:7517–7522.



Advanced Review

67. Duan Y, Wu C, Chowdhury S, Lee MC, Xiong GM, Zhang W, Yang R, Cieplak P, Luo R, Lee TS, et al. A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J Comput Chem 2003, 24:1999–2012. 68. Gong XJ, Li JY, Lu HJ, Wan RZ, Li JC, Hu J, Fang HP. A charge-driven molecular water pump. Nat Nanotechnol 2007, 2:709–712. 69. Hummer G, Rasaiah JC, Noworyta JP. Water conduction through the hydrophobic channel of a carbon nanotube. Nature 2001, 414:188–190. 70. Garcia AE, Paschek D. Simulation of the pressure and temperature folding/unfolding equilibrium of a small RNA hairpin. J Am Chem Soc 2008, 130:815–816. 71. Zhou RH, Eleftheriou M, Royyuru AK, Berne BJ. Destruction of long-range interactions by a single mutation in lysozyme. Proc Natl Acad Sci USA 2007, 104:5824–5829. 72. Karplus M, Gao YQ, Ma J, van der Vaart A, Yang W. Protein structural transitions and their functional role. Philos Transact A Math Phys Eng Sci 2005, 363:331–355; discussion 355-356. 73. Hua L, Huang X, Liu P, Zhou R, Berne BJ. Nanoscale dewetting transition in protein complex folding. J Phys Chem B 2007, 111:9069–9077. 74. Huang X, Hagen M, Kim B, Friesner RA, Zhou R, Berne BJ. Replica exchange with solute tempering: efficiency in large scale systems. J Phys Chem B 2007, 111:5405–5410. 75. Liu P, Huang X, Zhou R, Berne BJ. Observation of a dewetting transition in the collapse of the melittin tetramer. Nature 2005, 437:159–162. 76. Zhou R, Huang X, Margulis CJ, Berne BJ. Hydrophobic collapse in multidomain protein folding. Science 2004, 305:1605–1609. 77. Fitch BG, Rayshubskiy A, Eleftheriou M, Ward TJC, Giampaga M, Zhestkov Y, Pitman MC, Suits F, Grossfield A, Pitera J, et al. Blue Matter: Strong Scaling of Molecular Dynamics on Blue Gene/L. Berlin, Heidelberg: Springer; 2006. 78. Tu YS, Xiu P, Wan RZ, Hu J, Zhou RH, Fang HP. Water-mediated signal multiplication with Y-shaped carbon nanotubes. Proc Natl Acad Sci USA 2009, 106:18120–18124. 79. Li JY, Gong XJ, Lu HJ, Li D, Fang HP, Zhou RH. Electrostatic gating of a nanometer water channel. Proc Natl Acad Sci USA 2007, 104:3687–3692. 80. Wang CL, Lu HJ, Wang ZG, Xiu P, Zhou B, Zuo GH, Wan RZ, Hu JZ, Fang HP. Stable liquid water droplet on a water monolayer formed at room temperature on ionic model substrates. Phys Rev Lett 2009, 103:137801. 81. Wan RZ, Li JY, Lu HJ, Fang HP. Controllable water channel gating of nanometer dimensions. J Am Chem Soc 2005, 127:7166–7170.

82. Kang SG, Huynh T, Zhou R. Metallofullerenol Gd@ C(8)(2)(OH)(2)(2) distracts the proline-rich-motif from putative binding on the SH3 domain. Nanoscale 2013, 5:2703–2712. 83. Kang SG, Huynh T, Zhou R. Non-destructive inhibition of metallofullerenol Gd@C(82)(OH)(22) on WW domain: implication on signal transduction pathway. Sci Rep 2013, 2:957. 84. Das P, Zhou R. Urea-induced drying of carbon nanotubes suggests existence of a dry globule-like transient state during chemical denaturation of proteins. J Phys Chem B 2010, 114:5427–5430. 85. Yang ZX, Wang ZG, Tian XL, Xiu P, Zhou RH. Amino acid analogues bind to carbon nanotube via 𝜋-𝜋 interactions: comparison of molecular mechanical and quantum mechanical calculations. J Chem Phys 2012, 136:025103. 86. Team RDC. R: A Language and Environment for Statistical Computing. Vienna, Austria: 2008. 87. Fan WJ, Zeng J, Zhang RQ. Quantum mechanical quantification of weakly interacting complexes of peptides with single-walled carbon nanotubes. J Chem Theory Comput 2009, 5:2879–2885. 88. Ge C, Du JF, Zhao L, Wang L, Liu Y, Li DH, Yang Y, Zhou RH, Zhao YL, Chai ZF, et al. Competitive binding of human serum proteins on single-wall carbon nanotubes reduces cytotoxicity. Proc Natl Acad Sci USA 2011, 108:16968–16973. 89. Wang S, Humphreys ES, Chung SY, Delduco DF, Lustig SR, Wang H, Parker KN, Rizzo NW, Subramoney S, Chiang YM, et al. Peptides with selective affinity for carbon nanotubes. Nat Mater 2003, 2:196–200. 90. Zorbas V, Smith AL, Xie H, Ortiz-Acevedo A, Dalton AB, Dieckmann GR, Draper RK, Baughman RH, Musselman IH. Importance of aromatic content for peptide/single-walled carbon nanotube interactions. J Am Chem Soc 2005, 127:12323–12328. 91. Song B, Li D, Qi W, Elstner M, Fan C, Fang H. Graphene on Au(111): a highly conductive material with excellent adsorption properties for high-resolution bio/nanodetection and identification. ChemPhysChem 2010, 11:585–589. 92. Zhao XC. Self-assembly of DNA segments on graphene and carbon nanotube arrays in aqueous solution: a molecular simulation study. J Phys Chem C 2011, 115:6181–6189. 93. Zhao X, Johnson JK. Simulation of adsorption of DNA on carbon nanotubes. J Am Chem Soc 2007, 129:10438–10445. 94. Tu Y, Lv M, Xiu P, Huynh T, Zhang M, Castelli M, Liu Z, Huang Q, Fan C, Fang H, et al. Destructive extraction of phospholipids from Escherichia coli membranes by graphene nanosheets. Nat Nanotechnol 2013, 8:594–601.




95. Liu S, Hu M, Zeng TH, Wu R, Jiang R, Wei J, Wang L, Kong J, Chen Y. Lateral dimension-dependent antibacterial activity of graphene oxide sheets. Langmuir 2012, 28:12364–12372.

108. Girit CO, Meyer JC, Erni R, Rossell MD, Kisielowski C, Yang L, Park CH, Crommie MF, Cohen ML, Louie SG, et al. Graphene at the edge: stability and dynamics. Science 2009, 323:1705–1708.

96. Li Y, Yuan H, von dem Bussche A, Creighton M, Hurt RH, Kane AB, Gao H. Graphene microsheets enter cells through spontaneous membrane penetration at edge asperities and corner sites. Proc Natl Acad Sci USA 2013, 110:12295–12300.

109. Huang JY, Ding F, Yakobson BI, Lu P, Qi L, Li J. In situ observation of graphene sublimation and multi-layer edge reconstructions. Proc Natl Acad Sci USA 2009, 106:10103–10108.

97. Doherty GJ, McMahon HT. Mechanisms of endocytosis. Annu Rev Biochem 2009, 78:857–902. 98. Jiang W, Kim BYS, Rutka JT, Chan WCW. Nanoparticle-mediated cellular response is sizedependent. Nat Nanotechnol 2008, 3:145–150. 99. Lerf A, He H, Forster M, Klinowski J. Structure of graphite oxide revisited. J Phys Chem B 1998, 102:4477–4482. 100. Shih CJ, Lin S, Sharma R, Strano MS, Blankschtein D. Understanding the pH-dependent behavior of graphene oxide aqueous solutions: a comparative experimental and molecular dynamics simulation study. Langmuir 2012, 28:235–241. 101. Medhekar NV, Ramasubramaniam A, Ruoff RS, Shenoy VB. Hydrogen bond networks in graphene oxide composite paper: structure and mechanical properties. ACS Nano 2010, 4:2300–2306. 102. Gómez-Navarro C, Meyer JC, Sundaram RS, Chuvilin A, Kurasch S, Burghard M, Kern K, Kaiser U. Atomic structure of reduced graphene oxide. Nano Lett 2010, 10:1144–1148. 103. Ganguly A, Sharma S, Papakonstantinou P, Hamilton J. Probing the thermal deoxygenation of graphene oxide using high-resolution in situ X-ray-based spectroscopies. J Phys Chem C 2011, 115:17009–17019. 104. Kosynkin DV, Higginbotham AL, Sinitskii A, Lomeda JR, Dimiev A, Price BK, Tour JM. Longitudinal unzipping of carbon nanotubes to form graphene nanoribbons. Nature 2009, 458:872–876. 105. Berne BJ, Weeks JD, Zhou R. Dewetting and hydrophobic interaction in physical and biological systems. Annu Rev Phys Chem 2009, 60:85–103. 106. Zhao J, Deng B, Lv M, Li J, Zhang Y, Jiang H, Peng C, Li J, Shi J, Huang Q, et al. Graphene oxide-based antibacterial cotton fabrics. Adv Healthcare Mater 2013, 2:1259–1266. 107. Gibson MC, Perrimon N. Apicobasal polarization: epithelial form and function. Curr Opin Cell Biol 2003, 15:747–752.

110. Xie L, Wang H, Jin C, Wang X, Jiao L, Suenaga K, Dai H. Graphene nanoribbons from unzipped carbon nanotubes: atomic structures, Raman spectroscopy, and electrical properties. J Am Chem Soc 2011, 133:10394–10397. 111. Warner JH, Schäffel F, Rümmeli MH, Büchner B. Examining the edges of multi-layer graphene sheets. Chem Mater 2009, 21:2418–2421. 112. Song B, Schneider GF, Xu Q, Pandraud G, Dekker C, Zandbergen H. Atomic-scale electron-beam sculpting of near-defect-free graphene nanostructures. Nano Lett 2011, 11:2247–2250. 113. Leroy F, Dos Santos DJ, Muller-Plathe F. Interfacial excess free energies of solid-liquid interfaces by molecular dynamics simulation and thermodynamic integration. Macromol Rapid Commun 2009, 30: 864–870. 114. Gu Y, Sun W, Wang G, Fang N. Single particle orientation and rotation tracking discloses distinctive rotational dynamics of drug delivery vectors on live cell membranes. J Am Chem Soc 2011, 133:5720–5723. 115. Stewart PS, Costerton JW. Antibiotic resistance of bacteria in biofilms. Lancet 2001, 358: 135–138. 116. Wei C, Lin WY, Zainal Z, Williams NE, Zhu K, Kruzic AP, Smith RL, Rajeshwar K. Bactericidal activity of TiO2 photocatalyst in aqueous media: toward a solar-assisted water disinfection system. Environ Sci Technol 1994, 28:934–938. 117. Magrez A, Kasas S, Salicio V, Pasquier N, Seo JW, Celio M, Catsicas S, Schwaller B, Forro L. Cellular toxicity of carbon-based nanomaterials. Nano Lett 2006, 6:1121–1125. 118. Schipper ML, Nakayama-Ratchford N, Davis CR, Kam NW, Chu P, Liu Z, Sun X, Dai H, Gambhir SS. A pilot toxicology study of single-walled carbon nanotubes in a small sample of mice. Nat Nanotechnol 2008, 3:216–221. 119. Hummers WS, Offeman RE. Preparation of graphitic oxide. J Am Chem Soc 1958, 80:1339–1339.


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