Understanding the Roles of Solution Chemistries and

Article pubs.acs.org/JPCC

Understanding the Roles of Solution Chemistries and Functionalization on the Aggregation of Graphene-Based Nanomaterials Using Molecular Dynamic Simulations Huan Tang, Ying Zhao, Xiaonan Yang,* Dongmei Liu, Sujie Shan, and Fuyi Cui* State Key Laboratory of Urban Water Resource and Environment and School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China S Supporting Information *

ABSTRACT: Microscopic aggregation processes of graphene-related nanomaterials (GNs) (including graphene, graphene oxide, carbon nanotubes, carboxylic carbon nanotubes, fullerene, and fullerol) were explored by molecular dynamic (MD) simulations. The aggregation exhibited a strong dependence on solution chemistries and the presence of oxygen-containing functional groups, and the mechanisms were uncovered. The aggregate configurations observed in MD simulations were consistent with the results obtained using density functional theory calculations. Upon the aggregation, the configurations of the GNs were changed, and the electrical properties were affected. The statistics of the Brownian trajectories of the GNs were investigated and were found to vary in the presence of oxygen-containing functional groups and the pH conditions. In addition, the aggregation behavior of GNs was found to be size- and density-dependent, with the density affecting the aggregation efficiency and the size of the nanostructure. Overall, our studies provide a platform for investigating the aggregation of GNs in water, which can also be employed to investigate the behavior of other nanomaterials.

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INTRODUCTION In 2010 the Nobel Prize in Physics was given to Andre Geim and Konstantin Novoselov for their groundbreaking work with two-dimensional graphene (G).1 Since then, G has gained tremendous attention. Ideal G is a two-dimensional single layer of sp2-hybridized carbon.2 Its extended honeycomb network is a basic building block for other important graphene-related nanomaterials (GNs). It can be rolled to form 1D carbon nanotubes (CNTs) and wrapped to form 0D fullerenes (C60).3 Other types of GNs are their derivatives, such as carboxylic CNTs (CNT-COOH), fullerenols (C60-OH), and graphene oxide (GO). Due to their exceptional mechanical, electronic, optical, and catalytic properties, GNs could enable a wide range of potential applications, including optics, cosmetics, nanocomposites, and pharmaceuticals.4−6 Currently, the production and industrial application of GNs are booming. The commercial market for graphene-related products is projected to be as large as $986.7 million by 2022.7 Due to their various applications and large-scale production, release of GNs into the environment is inevitable. Recent studies have shown that GNs can be toxic toward organisms, including bacteria and humans, and the toxicities are related to their dispersion state.8 Since the fate and transport of GNs are governed primarily by their stability in natural and engineered aquatic systems,9 it is necessary to understand the aggregation of GNs in water. Previous investigations on the aggregation of GNs mainly focused on measuring the kinetics, and the mechanisms underlying the distinct stabilities under different solution chemistries are still not sufficiently understood from a microscopic perspective. For example, Chowdhury et al. © XXXX American Chemical Society

showed that higher pH suppressed the aggregation of GO and attributed this to electrostatic repulsion induced by carboxyl groups, but the effect of water molecules was neglected.8 Water is abundant and supposed to play key roles in the aqueous-phase interaction;10 however, the microscopic effect of water is inaccessible experimentally. Atomic-scale investigations with molecular dynamics (MD) simulations could contribute significantly to understanding the microscopic process and furnish many details that are not accessible experimentally;11,12 moreover, the stability of colloids has garnered a great deal of interest. For example, MD simulations were performed to understand the origin of the interactions between the heteropolyanions, and aggregation behavior was suggested to be due to short-range attractions and long-range repulsions.13 Aggregation kinetics and stability mechanisms of pristine and oxidized nanocarbons in polar solvents were explored, and the role of surface modification in their dispersibility enhancement was explicated.14 George et al. studied the size- and shape-dependent behavior of nested C60 in water and claimed water molecules played key roles in mediating the cluster structure.15 Shih and co-workers contributed a lot to understanding the colloidal dispersion stability of GNs using MD;16−18 however, the roles of metal cations and natural organic matter (NOM) were not incorporated. Received: April 4, 2017 Revised: June 9, 2017 Published: June 13, 2017 A

DOI: 10.1021/acs.jpcc.7b03193 J. Phys. Chem. C XXXX, XXX, XXX−XXX

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The Journal of Physical Chemistry C

plane, while the edges are functionalized with carboxyl groups.19 The distribution of oxidized groups in GO was set to be random. 16 The model of GO was set to be C10O1(OH)1(COOH)0.5 (i.e., 1 epoxy, 1 hydroxyl, and 0.5 carboxyl per 10 carbon atoms are attached to the graphene basal plane); a similar model has been employed by other researchers and was proposed to perform well for exploring the properties of GO.16,19,20 Chiral CNTs (n = 7, m = 6) were employed. The extent of oxidation of CNT-COOH reported in the literature varied from 1.6% to 11.3%;21 in our model of CNT-COOH, the oxygen content was 5.2% (C210H29(COOH)7), and carboxyl groups were attached to both the side walls and the open ends.22 The chemical formula of C60OH was set to be C60(OH)10. Surface functional groups, such as carboxyl and phenol groups, are deprotonated depending on the pKa value and solution pH. The pKa for the carboxyl functional group on aromatic rings is usually lower than 4.2; for phenol groups, the pKa values are generally around 10.23 The range of pH usually observed in the aquatic environment is from 5 to 9,8,24 indicating only carboxyl groups in GO and CNT-COOH will be deprotonated. The fullerol was suggested to have a negative charge surface over the wide range of pH > 3, implying a certain proportion of deprotonated surface sites.25 Therefore, C10O1(OH)1(COOH)0.5, C210H29(COOH)7, and C60(OH)10 were employed to simulate the behavior of GO, CNT-COOH, and C60-OH at low pH values, and C10O1(OH)1(COO−)0.5, C210H29(COO−)7, and C60(OH)5(O−)5 were employed to simulate the behavior of GO, CNT-COOH, and C60-OH at high pH values. Model of NOM. NOM is a natural substance with complicated structures varying with the origin, and the principal functional groups of NOM are well characterized.26 A number of structure models have been proposed and validated,27−29 and the models have many common features; for example, all those models incorporate carboxylic groups, carbonyl groups, phenol groups, amine groups, aromatic functional groups, and other R−OH alcohol groups.30,31 Herein, we selected tannic acid (TA) as a surrogate of dissolved organic matter (DOM), which provides a good structural and compositional analogue of NOM and is commonly used in experimental studies.32 The model of TA used in our simulations is shown in Figure S1 (Supporting Information). MD Simulation Details. To obtain the table structure of GN in water, we built a system that contained single GN and water. Then we performed minimization and equilibration processes to obtain its minimized configuration in water, and this structure was used as the starting point in the following simulations. To simulate the aggregation of GNs in water, we built systems that contained two GNs and water. Initially, two GNs were well separated (Figure 1), and the initial distance between GNs was about 2 nm. The GNs were then solvated in a periodic box, with the distance between the solutes and box boundary at least 1.5 nm. For the GN−GN system, a ∼6 × 6 × 6 nm3 simulation box which contains ∼6655 water molecules was used. For the GN−TA system, a ∼14 × 7 × 9 nm3 simulation box which contains ∼30000 water molecules was used. To explore the effect of ionogenic functional groups and the pH on the aggregation of GNs, deprotonated GNs were employed; 10 mM Na+ and Cl− ions were added to the system to compensate for the net charges in the simulation box, and 200 mM Na+ and Cl− ions were added to study the effect of metal ions during the aggregation process. To study the size-

This motivated the current work, in which we considered the atom level aggregation of GNs with different chemical compositions under different solution chemistries. This work provides a molecular level of understanding on the aggregation process of different GNs in different solution chemistries and shows that MD, if used properly, can be applied as a useful tool to obtain insights into the behavior of nanomaterilas in the environment.

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METHODS

Models of GNs. G, CNTs, C60, GO, CNT-COOH, and C60-OH were employed to explore the aggregation of GNs. The models of these GNs are shown in Figure 1. GO was built in accordance with the Lerf−Klinowski model, which suggested hydroxyl and epoxide groups are mainly attached to the basal

Figure 1. Models of GNs and setup of the simulation systems (H in white, O in red, C in black, and Na2+ in blue; the tiny red lines represent water molecules): (a) models of G, CNTs, C60, GO, CNTCOOH, and C60-OH, (b) model systems used to study the aggregation of G, CNTs, C60, GO, CNT-COOH, and C60-OH in water, (c) model systems used to study the aggregation of GO, CNTCOOH, and C60-OH in water in the presence of 200 mM NaCl, (d) model systems used to study the aggregation of GO, CNT-COOH, and C60-OH in water in the presence of tannic acid and NaCl. The initial distance between each adjacent molecule was 2 nm. B


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Figure 2. Evolutions of distance, L-J potential energy, and electrostatic potential energy during aggregation: (a, left) aggregation of G, (b, middle) aggregation of CNTs, (c, right) aggregation of C60. The initial distance between GNs was about 2.0 nm; “distance” here means the distance between the geometric centers of the GNs. The L-J potential indicates the vdW interaction energy. A negative potential value indicates attraction between GNs, and a positive value indicates repulsion.

Figure 3. Representative snapshots of the aggregation process of (a) C60, (b) CNT-COOH, and (c) GO in the presence of TA and 200 mM NaCl.

employed to represent the dynamics of GNs in our simulations. Force field parameters are given in the Supporting Information. All the sp2 carbon atoms that are not connected with oxygenated groups are treated as uncharged spheres; the C atoms near the oxygenated groups are charged, and the specific charges are provided in the force field parameters in the Supporting Information. The Lennard-Jones (L-J) interactions were treated with a cutoff of 1 nm. Short-range electrostatic interactions, up to a distance of 1 nm between the interacting atoms, were directly calculated using the Coulomb law. Long-

and density-dependent aggregation of GNs, different GN and system sizes were employed (Table S1, Supporting Information). The optimized potentials for liquid simulations all-atom (OPLS-AA) force field33 implemented in the GROMACS 5.1 software package34 was used for all simulations. The OPLS allatom force field has been tested and was suggested to be broadly applicable.33 Recently, the OPLS force field has been used widely and was proposed to perform well for exploring the properties of GNs.16,35−41 Therefore, the OPLS potential was C


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Figure 4. Configurations of the aggregates of GNs: MD and DFT results.

range electrostatic interactions beyond 1 nm were accounted for employing the particle-mesh Ewald (PME) summation method.42 A simple point charge (SPC) model43 was used for water molecules, which has been extensively tested in the literature for interfacial studies.44 Bond lengths were constrained with LINCS,45 and water geometries were constrained with SETTLE.46 Periodic boundary conditions were applied in all three directions. For each system, static structure optimization was first performed to ensure that the maximum force is less than 1000.0 kJ/(mol·nm). Then the system was equilibrated for 100 ps at a constant temperature of 300 K and a pressure of 1 bar using a modified Berendsen thermostat.47 During the minimization and equilibration processes, the GNs were constrained. Then the GNs were released, and MD simulations were performed. This first-constrain-then-release method has been used widely by other researchers.18,20,48−50 The equations of motion were integrated with a time step of 2 fs using the leapfrog algorithm,19 and data were collected every 10 ps. The NPT ensemble (constant number of atoms, constant pressure P = 1 bar, and constant temperature T = 298.15 K) was employed in all simulations. In the course of production runs, the pressure was coupled to an isotropic Parrinello−Rahman barostat,51 and the temperature was regulated using the Nose−Hoover thermostat.52 For equilibration purposes, a Berendsen barostat53 was implemented to keep the system at constant pressure. The equilibrium MD trajectories resulting from all production runs were processed to extract the structural, dynamical, and energetic properties of the simulated systems. The criteria for the formation of a hydrogen bond (H-bond) were a donor−acceptor distance smaller than 0.35 nm and a hydrogen−donor−acceptor angle less than 30°.34,54 These criteria for H-bond formation have been used in previous studies.55,56 Density Functional Theory Calculations. Density functional theory (DFT) calculations were performed using Gaussian 0957 to test and correlate with our MD results. Distances in van der Waals (vdW) interactions and H-bonds were calculated using the B3LYP hybrid functional at the 6311++G (d,p) level. The highest occupied molecular orbital (HOMO)−lowest unoccupied (LUMO) molecular orbital energy gap was calculated to inspect the change of the electronic properties of the GNs upon aggregation using timedependent DFT. The polarization continuum model (PCM) was employed to consider the effect of the solvent.

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RESULTS AND DISCUSSION

Microscopic Aggregation Processes of G, CNTs, and C60. Representative snapshots of the aggregation, final stable configurations, interaction energy profiles, and movements of the geometric center of GNs are shown in Figures 2 and 3 and Table S2 (Supporting Information). To illustrate the microscopic process, two specific time points should be noted (indicated by red arrows in the energy−time curve): (1) The LJ potential values became constantly negative after the first time point. (2) The L-J potential and distance reached their maximum/minimum values after the second time point. On the basis of these two points, the aggregation was divided into three stages: Brownian motion, aggregation, and adjustment. In the Brownian motion stage, GNs moved randomly without touching each other and adjust their motion directions. As the distance−time curve shows, GNs first separated and then approached one another. Five forces may drive this approach: electrostatic interaction, van der Waals (vdW) attraction, H-bond interaction, π−π interaction, and hydrophobic interaction.58,59 Since the models of GNs in our simulations were neutrally or negatively charged, there were no long-range electrostatic attractions between GNs. The distance between GNs in the Brownian motion stage (around 2 nm) was beyond the scope of short-range forces (cutoff for vdW attraction, 1 nm; cutoff for H-bond interaction, 0.35 nm;60 cutoff for π−π interaction, 0.35 nm61), indicating the driving force was hydrophobic interaction.62 Therefore, the hydrophobic interaction is the dominant driving force in this stage, which controls both the kinetics and thermodynamics. After the motion direction adjustment, GNs moved along their direction of approach. Once in close vicinity, GNs continuously diffused and rotated relative to each other, making transient contacts at many points until vdW attraction formed between the GNs (the L-J potential became negative) and the aggregation stage was initiated. By utilizing vdW attraction, GNs approached one another quickly and more atom pairs were connected, leading to a rapid decrease in the distance. However, vdW interactions have both long-range attractive terms and short-range highly repulsive components; once GNs were close enough, vdW repulsions occurred between those close atom pairs, leading to an interlayer spacing between two GNs. As shown in Figure 2, the contributions from vdW forces were much higher than those from electrostatic interactions, indicating vdW attractions were the major driving force in this stage. D


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energy indicated the reactivity of GNs would increase upon aggregation. As discussed above, we can conclude that the dominant mechanism of aggregation is different in the three stages. In the Brownian motion stage, the driving force is hydrophobic interaction. Once the vdW attraction appears between GNs, the aggregation stage begins and vdW attractions play a major role. In the adjustment stage, π−π interaction plays a dominant role. The behavior of GNs in the Brownian motion stage is essential in the aggregation process. If GNs are not getting closer in this stage, they will not aggregate. The effect of the chemical compositions and solution chemistries can also be elucidated by the behavior of GNs in the Brownian motion stage. Effect of Ionogenic Functional Groups and the pH. Ionogenic functional groups, such as carboxyl and hydroxy, are precursors to various compounds via ammonification, acylation, and esterification reactions.69 GNs can be converted to functionalized GNs to improve their performance in synthesizing materials, and functionalized GNs have also attracted significant attention.70 Therefore, the effect of these functional groups on the aggregation of GNs was studied. MD results showed the effect of functional groups was related to the pH (Figure 6 and Figure S3, Supporting Information). At high pH, upon the deprotonations of the carboxyl and hydroxy groups, the GNs exhibited a much weaker tendency to aggregate in comparison with the pristine GNs. GO and C60-OH remained stable in water with no aggregation involved. Although CNT-COOH moieties aggregated, a longer time (∼14000 ps) was needed than that for CNTs (∼4000 ps). As two CNTs got closer, the carboxyl groups on one CNTCOOH started to repel the basal carbon atoms on the opposing CNT-COOH, which compensated for the π−π interaction between the basal carbon atoms on the two CNT-COOH moieties. The positive electrostatic energy values proved there are electrostatic repulsions between GNs upon the deprotonation of ionogenic functional groups, and the increasing stability of GNs at high pH was expected to originate from electrostatic repulsions.8 However, the magnitude of the L-J energy was much higher than that of the electrostatic energy, implying the electrostatic repulsions could be compensated by vdW attractions. Furthermore, even if the carboxyl and hydroxy groups were all protonated at low pH, a longer time was required for GO and CNT-COOH to aggregate than G and CNTs, with C60-OH even remaining stable, indicating electrostatic repulsion was not the only reason for the increase in the stability of functionalized GNs. As we discussed in the previous section, the behavior of GNs in the Brownian motion stage is essential throughout the whole aggregation process. The statistics of the trajectory of the GNs were investigated and were found to vary in the presence of oxygen-containing functional groups and with the pH conditions (Figure 7 and Figure S4, Supporting Information). The Brownian-like trajectories suggested the random movement of GNs during the aggregation process. Only the overlapped red and blue dots indicated the aggregation state of the GNs. For G, CNTs, and C60, almost 50% of the trajectories were overlapped. In the presence of oxygencontaining functional groups, most trajectories showed a random walk behavior and a lower percentage resulted in an aggregation trajectory; this behavior was particularly evident under high-pH conditions, indicating functional groups and the pH played key roles in determining the swimming direction of GNs in water. Since hydrophobic interaction is the dominant

In the adjustment stage, GNs adjusted their orientation and formed final stable configurations. Nishio et al. suggested that attractive interaction between aromatic moieties was a factor in determining the preferential conformation;63 therefore, the aggregate configurations should maximize the π−π interaction between GNs. Three possible stable structures for π−π interaction were suggested (Figure S2, Supporting Information): offset face-to-face (sometimes referred to as parallel displaced), T-shaped edge-to-face, and tilted-T structures.64 As shown in Figure 4, GN aggregates possessed an offset face-toface mode, with a displacement of benzene rings. π−π electron interaction is an important repulsive force, which is roughly proportional to the area of benzene ring overlap.65 The displacement helps to realize a minimization of repulsive π interaction and a maximization of attractive interaction.66 The interlayer distance of G, CNTs, and C60 is 0.35 nm, according well with the interaction distance (0.35 nm) of the offset faceto-face π−π interaction,67 indicating that π−π interaction played a dominant role in the adjustment stage. After the configuration adjustment, stable aggregates formed. It should be noted that, in the adjustment stage, the distance curve of C60 fluctuated at a higher frequency and larger interval compared with that of CNTs, which in turn fluctuated at a higher frequency and larger interval than that of G. Xie showed that C60 crystalline structures are formed through weak association between C60 molecules; this association can be readily broken in an aqueous environment.68 As we discussed above, this weak association was a π−π interaction. The plane structure of G enables a stronger π−π interaction than that of CNTs and C60, thus leading to a more stable configuration. The aggregate configuration was also calculated using DFT and was observed to be consistent with our MD results (Figure 4); for example, MD results showed the interlayer distance between G is 0.35 Å with a displacement of benzene rings, according well with the aggregate structure observed in DFT calculations. Upon the aggregation and adjustment stages, the electrical properties of GNs were affected. The effect of aggregation on the electronic properties of GNs was inspected by calculating the alteration in the values of the HOMO−LUMO energy gap. Figure 5 presents the HOMO and LUMO energy diagrams.

Figure 5. HOMO and LUMO energy diagrams of nonaggregated and aggregated graphene. The green color indicates the positive phase.

For G, both HOMO and LUMO levels increase upon aggregation and the shift is larger for the HOMO. By comparing the nonaggregated HOMO−LUMO gap energy of G to that of aggregated G, it can be established that the gap energy was decreased from 0.00512 to 0.00443 eV, indicating that the electronic properties of GNs are sensitive toward aggregation. The other GN energy levels are all affected by aggregation, and the results are shown in Table S3 (Supporting Information). It should be noted that the decrease of the gap E


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Figure 6. Evolutions of distance, L-J potential energy, and electrostatic potential energy during aggregation: (a) aggregation of GO at high pH, (b) aggregation of GO at low pH. The initial distance between GOs was about 2.0 nm; “distance” here means the distance between the geometric centers of GOs. The L-J potential indicates the vdW interaction energy. A negative potential value indicates attraction between GOs, and a positive value indicates repulsion. (c) Amount of H-bonds formed between GO and water when GO is protonated or deprotonated.

Figure 7. Trajectories of GNs during the simulation of 10 ns: (a) trajectories of G, (b) trajectories of GO under low-pH conditions, (c) trajectories of GO under high-pH conditions. The red and blue dots represent two different GNs.

H-bonds formed during aggregation were also investigated. There were four kinds of H-bonds: (1) H-bonds between functional groups attached to the same GN, (2) H-bonds between functional groups attached to the same GN bridged by water molecules, (3) H-bonds between GNs and water molecules, (4) H-bonds between different GNs bridged by water molecules. The details of the above H-bonds were explored by DFT calculations, and the results are shown in Table S4 (Supporting Information). With the introduction of oxygen-containing functional groups, the aggregate configurations of GO and CNT-COOH were different from those of G and CNTs. The final interlayer spacing between GO platelets increased to 0.7 nm due to the electrostatic repulsion and steric effect of the functional groups. Marcano et al. suggested that the interlayer spacing of pristine graphene without oxygen functional groups is around 0.37 nm and the spacing of the materials is proportional to the degree of oxidation.72 It should also be noted that there are a bunch of water molecules confined within the interlayer cavities maintained by H-bonds, forming a GO−water−GO sandwich structure. Shenoy et al. also claimed individual GO platelets can be interlinked via a nonuniform network of H-bonds mediated by oxygen-containing functional groups and water molecules.73 vdW attraction and π−π interactions between GOs will be screened by the water molecules confined between two GO sheets, leading the GO to be more stable in an aqueous environment than G. This sandwich-like structure was also obtained by DFT calculations with an interlayer spacing of ∼6.5 Å (Figure 4), according well with our MD results; the properties of the H-bonds formed between GO and the water molecules confined within the interlayer were calculated and are shown in Table S4. The local aggregation of CNT-COOH occurred in areas which facilitated π−π interaction and Hbonds; one CNT-COOH was vertical to another, and the

driving force in the Brownian motion stage, the effects of functionalization and the pH conditions are speculated to be related to the change of the hydrophilicity of the GNs. Functionalized GNs have both hydrophilic (e.g., carboxyl and hydroxy groups) and hydrophobic (e.g., the graphitic domains) segments. If hydrophobicity dominates over hydrophilicity, GNs will approach to each other driven by hydrophobic interaction, and conversely, GNs will be trapped in the Brownian motion stage longer before the aggregation is initiated or dissolved into the bulk water.8 The hydrophilicity of GNs can be measured by the ability to form H-bonds with water. The greater the number of H-bonds formed between GNs and water, the stronger the hydrophilicity the GNs exhibited. As shown in Figure S5 (Supporting Information), upon functionalization, H-bonds formed between GNs and water. At high pH, more H-bonds appeared; therefore, GNs were more hydrophilic and it was difficult for the aggregation to proceed. The critical role of water-mediated H-bonds in stabilizing colloids has also been reported by Wipff and Chaumont.71 To understand the contribution of H-bonds during aggregation, the number of H-bonds formed between GNs and the surrounding water molecules was calculated. As shown in Figure S6 (Supporting Information), more H-bonds formed between a GN and water than between two GNs. On the basis of the relative quantity of different kinds of H-bonds, we can conclude that H-bonds play a double role in the aggregation process. On one hand, the water molecules involved in a network of H-bonds can act as a long-range driving force to bring and hold the GNs together. On the other hand, water is abundant, so GN···water H-bonds exist in strong competition with GN···GN H-bonds. Only GN···GN H-bonds that are much stronger than GN···water H-bonds can contribute substantially to the overall aggregation. The configurations of F


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Figure 8. (a) Evolutions of distance, L-J potential energy, and electrostatic potential energy during the aggregation of GO in the presence of 200 mM NaCl. (b) RDF of the initial system of GO−Na+. (c) RDFs collected after NPT equilibrium and 5000 ps of the aggregation process of GO−Na+.

interaction, and GNs did not aggregate until the end of the simulation time. At higher ionic strength (300 mM NaCl), the negative charges of the GN−TA system were mostly neutralized. TA adsorbed on one GN and spread to another GO, bridging two GNs together and forming GN−TA−GN aggregates (Figure 9). With a conformational change and by π−π interaction, TA anchored onto the GNs.

distance between the two CNT-COOH moieties was 3.5 Å, consistent with the configuration observed from DFT calculations. The vertical configuration decreased the scale of π−π interaction occurring at the side wall, which was supposed to maintain the connection between CNT-COOH moieties, and this small scale of π−π interaction will be readily broken in an aqueous environment. In summary, our MD results showed the introduction of oxygen-containing functional groups will lead GNs to be more hydrophilic and form less stable aggregate configurations, thus increasing the stability of the GNs. By utilizing the potential of mean force and translational kinetic energy, previous studies also demonstrated that functional groups enhance the confinement of the water molecules between nanocarbons, thus increasing solvent-induced repulsion and leading the nanocarbons to be less motivated to form aggregates.14 Effect of Metal Cations. Previous studies suggested that metal ions can accelerate the aggregation of GNs.8 To understand the microscopic behavior of ions, MD simulations were carried out. As shown in Figure 8a and Figure S7 (Supporting Information), in the presence of 200 mM Na+, negatively charged GNs aggregated. To trace the behavior of ions during the aggregation process, radial distribution functions (RDFs) which describe how the density of Na+ varies as a function of the distance from the GNs were calculated. Initially, Na+ ions were distributed randomly around the GNs (Figure 8b and Figure S8, Supporting Information). For GO, after NPT equilibrium (Figure 8c), a peak (∼1.25 nm) appeared in the RDF curve, indicating most Na+ ions appeared 1.25 nm away from the GO. After 5000 ps of simulation, the location of the peaks shifted to about 0.5 nm and both the peak height and peak area increased. The shift of the peak together with the increase of the peak height and peak area demonstrated that Na+ approached the GO as the MD simulation proceeded. Xing et al. claimed that, for the GO− metal system, the aggregation process is accompanied by surface adsorption.74 Metal cations can bond to the hydrophobic aromatic surface through cation−π interactions in addition to binding to oxygen-containing groups and electrostatic attractions. During aggregation, metal ions tend to move toward GNs, align themselves around the GNs, and facilitate the aggregation process. Effect of Natural Organic Matter. The effect of TA on the behaviors of GNs was related to the ionic strength (Table S5, Supporting Information). At lower ionic strength (50 mM NaCl), TA adsorbed on GN by π−π interaction or H-bonds and competed with GNs for aggregation sites. Furthermore, TA and GNs were negatively charged. The TA molecules between GNs would inhibit aggregation by repulsive electrostatic

Figure 9. Configurations of the GN−TA−GN aggregate.

As discussed above, the effect of functional groups was related to the pH, and the effect of TA on the behaviors of GNs was related to the ionic strength, indicating the behavior of the GNs in water was determined by the cooperation of many factors. An aqueous environment is a complex system, and the increasing amount of waste from industry or civil life may introduce unexpected mechanisms or mutual influence; therefore, investigations of the stabilities of nanomaterials should incorporate more than one environmental factor. Effect of the GN Size and Density. The aggregation behavior of GNs was found to be size-dependent. In the Brownian motion stage, GNs with larger size approached one another immediately after the simulation began (Videos S1−S4, Supporting Information); for smaller GNs, a longer time was needed for the motion direction to be adjusted. The sizedependent behavior in the Brownian motion stage was assumed to be related to the hydrophobicity of the GNs. The domains of CNTs and G are hydrophobic; larger GNs contain more sp2 carbons and, therefore, are more hydrophobic. Smaller GNs with oxygen-containing functional groups have a higher edgeto-area ratio and should be more hydrophilic. Despite the stronger hydrophilicity, small GNs aggregated more quickly than large GNs (Figure S9, Supporting Information), which could be attributed to the size-dependent random reorientation of GNs.75 According to the Stokes−Einstein formula (D = kBT/ 6πδru, where D is the self-diffusivity of GNs, kB is the Boltzmann constant, T is the temperature, δ is the viscosity of the solution, and ru is the GN radius),76 small GNs diffused more quickly and, therefore, resulted in more aggregation trajectories and aggregated quickly. The size of the nanoparticles also had an effect on the rate of oriented aggregation; as the size of the primary nanoparticles increased, the rate of production of goethite nanorods decreased dramatically.77 G


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The Journal of Physical Chemistry C Notes

Density affected the aggregation efficiency and the size of nanostructures (Figure S10, Supporting Information). An increase of the density of GNs enhanced their aggregation, presumably due to the higher probability of particle collisions. At higher density regimes, GNs tended to approach one another during the Brownian motion stage, resulting in the formation of relatively larger aggregates. Previous dynamic light scattering studies also showed the sizes of the aggregates were observed to increase with increasing density.78 The rate (J) of colloid aggregation is given by J = 8πDV02R0/W, where D is the diffusion coefficient, V0 is the number of primary particles, R0 is the minimum separation of the particle center, and W is the stability ratio.79 High density will increase the values of V0 and R0, thus accelerating the aggregation process.

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research is supported by the National Natural Science Foundation of China (Grant Nos. 51278147, 50808052, and 51408162), HIT Environment and Ecology Innovation Special Funds (Grant No. HSCJ201606), China Postdoctoral Science Special Foundation (Grant No. 2016T90303), and State Key Laboratory of Urban Water Resource and Environment (Grant No. 2016DX02).

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(1) Virtual Issue: Graphene and Functionalized Graphene; Prezhdo, O. V., Kamat, P. V., Schatz, G. C., Eds. J. Phys. Chem. C 2011, 115, 3195− 3512. (2) Chee, W. K.; Lim, H. N.; Zainal, Z.; Huang, N. M.; Harrison, I.; Andou, Y. Flexible Graphene-Based Supercapacitors: A Review. J. Phys. Chem. C 2016, 120, 4153−4172. (3) Allen, M. J.; Tung, V. C.; Kaner, R. B. Honeycomb Carbon: A Review of Graphene. Chem. Rev. 2010, 110, 132−145. (4) Talyzin, A. V.; Luzan, S.; Anoshkin, I. V.; Nasibulin, A. G.; Kauppinnen, E. I.; Dzwilewski, A.; Kreta, A.; Jamnik, J.; Hassanien, A.; Lundstedt, A.; et al. Hydrogen-Driven Cage Unzipping of C60 into Nano-Graphenes. J. Phys. Chem. C 2014, 118, 6504−6513. (5) Wang, Q.; Zhang, S.; Xu, B.; Li, S.; Yang, B.; Yuan, W.; Hou, J. Efficient Fullerene-Free Polymer Solar Cells Based on Alkylthio Substituted Conjugated Polymers. J. Phys. Chem. C 2017, 121, 4825− 4833. (6) Kumar, V. B.; Borenstein, A.; Markovsky, B.; Aurbach, D.; Gedanken, A.; Talianker, M.; Porat, Z. Activated Carbon Modified with Carbon Nanodots as Novel Electrode Material for Supercapacitors. J. Phys. Chem. C 2016, 120, 13406−13413. (7) Ren, X.; Li, J.; Tan, X.; Shi, W.; Chen, C.; Shao, D.; Wen, T.; Wang, L.; Zhao, G.; Sheng, G.; et al. Impact of Al2O3 on the Aggregation and Deposition of Graphene Oxide. Environ. Sci. Technol. 2014, 48, 5493−5500. (8) Chowdhury, I.; Duch, M. C.; Mansukhani, N. D.; Hersam, M. C.; Bouchard, D. Colloidal Properties and Stability of Graphene Oxide Nanomaterials in the Aquatic Environment. Environ. Sci. Technol. 2013, 47, 6288−6296. (9) Zhao, J.; Wang, Z.; White, J. C.; Xing, B. Graphene in the Aquatic Environment: Adsorption, Dispersion, Toxicity and Transformation. Environ. Sci. Technol. 2014, 48, 9995−10009. (10) Picaud, S.; Collignon, B.; Hoang, P. N.; Rayez, J.-C. Molecular Dynamics Simulation Study of Water Adsorption on Hydroxylated Graphite Surfaces. J. Phys. Chem. B 2006, 110, 8398−8408. (11) Zhou, X.; El Gabaly, F.; Stavila, V.; Allendorf, M. D. Molecular Dynamics Simulations of Hydrogen Diffusion in Aluminum. J. Phys. Chem. C 2016, 120, 7500−7509. (12) Velasco, M. I. I.; Franzoni, M. B.; Franceschini, E. A.; Gonzalez Solveyra, E.; Scherlis, D. A.; Acosta, R. H.; Soler-Illia, G. J. Water Confined in Mesoporous TiO2 Aerosols: Insights from Nmr Experiments and Molecular Dynamics Simulations. J. Phys. Chem. C 2017, 121, 7533−7541. (13) Bera, M. K.; Qiao, B.; Seifert, S.; Burton-Pye, B. P.; Olvera de la Cruz, M.; Antonio, M. R. Aggregation of Heteropolyanions in Aqueous Solutions Exhibiting Short-Range Attractions and LongRange Repulsions. J. Phys. Chem. C 2016, 120, 1317−1327. (14) Azar, N. S.; Pourfath, M. Aggregation Kinetics and Stability Mechanisms of Pristine and Oxidized Nanocarbons in Polar Solvents. J. Phys. Chem. C 2016, 120, 16804−16814. (15) Makarucha, A. J.; Baldauf, J. S.; Downton, M. T.; Yiapanis, G. Size-Dependent Fullerene-Fullerene Interactions in Water: A Molecular Dynamics Study. J. Phys. Chem. B 2016, 120, 11018−11025. (16) Shih, C.-J.; Lin, S.; Sharma, R.; Strano, M. S.; Blankschtein, D. Understanding the pH-Dependent Behavior of Graphene Oxide

CONCLUSION In this work, MD simulations were employed to explore the aggregation processes of GNs. There are three microscopic stages of the process: Brownian motion, aggregation, and adjustment. The dominant driving forces of each respective stage are hydrophobic interaction, vdW interaction, and π−π interaction. The behavior of GNs in the Brownian motion stage is essential throughout the whole aggregation process. Ionogenic functional groups, the pH conditions, metal ions, and the presence of natural organic matter all have effects on the aggregations of GNs. The effects of the pH and ionogenic groups originate from electrostatic repulsions and the increased hydrophilicity of GNs. In the presence of metal ions, the aggregation process is accompanied by surface adsorption: metal ions bond to GNs, screen the negative charges, and then accelerate the aggregation. The effect of TA on the behaviors of GNs is related to the ionic strength. TA tends to bridge GNs and form GN−TA−GN aggregates at higher ionic strength. In addition, the aggregation behavior of GNs is size- and densitydependent. Overall, our studies provide a platform for investigating the aggregation of GNs in water, which can also employed to investigate the behavior of other nanomaterials.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcc.7b03193. Additional information regarding details of the models used in our simulations, snapshots of the aggregation processes of GNs in water, energy diagrams of the nonaggregated and aggregated GNs, details of the hydrogen bonds formed during aggregation, MD results of the aggregation of CNTs, CNT-COOH, C60, and C60OH, and effects of the GN size and density on aggregation (PDF) Video S1-The aggregation of larger GO (AVI) Video S2-The aggregation of smaller GO (AVI) Video S3-The aggregation of larger CNT (AVI) Video S4-The aggregation of smaller CNT (AVI)

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REFERENCES

AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. Phone: 86-15245085119. *E-mail: [email protected]. Phone: 86-13904503191. ORCID

Fuyi Cui: 0000-0002-4107-9398 H


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The Journal of Physical Chemistry C Aqueous Solutions: A Comparative Experimental and Molecular Dynamics Simulation Study. Langmuir 2012, 28, 235−241. (17) Shih, C.-J.; Lin, S.; Strano, M. S.; Blankschtein, D. Understanding the Stabilization of Single-Walled Carbon Nanotubes and Graphene in Ionic Surfactant Aqueous Solutions: Large-Scale CoarseGrained Molecular Dynamics Simulation-Assisted Dlvo Theory. J. Phys. Chem. C 2015, 119, 1047−1060. (18) Lin, S.; Shih, C.-J.; Sresht, V.; Rajan, A. G.; Strano, M. S.; Blankschtein, D. Understanding the Colloidal Dispersion Stability of 1D and 2D Materials: Perspectives from Molecular Simulations and Theoretical Modeling. Adv. Colloid Interface Sci. 2016, DOI: 10.1016/ j.cis.2016.07.007. (19) Lerf, A.; He, H.; Forster, M.; Klinowski, J. Structure of Graphite Oxide Revisited. J. Phys. Chem. B 1998, 102, 4477−4482. (20) 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. (21) Khan, I. A.; Afrooz, A. N.; Flora, J. R.; Schierz, P. A.; Ferguson, P. L.; Sabo-Attwood, T.; Saleh, N. B. Chirality Affects Aggregation Kinetics of Single-Walled Carbon Nanotubes. Environ. Sci. Technol. 2013, 47, 1844−1852. (22) Smith, B.; Yang, J.; Bitter, J. L.; Ball, W. P.; Fairbrother, D. H. Influence of Surface Oxygen on the Interactions of Carbon Nanotubes with Natural Organic Matter. Environ. Sci. Technol. 2012, 46, 12839− 12847. (23) Jiang, Y.; Raliya, R.; Fortner, J. D.; Biswas, P. Graphene Oxides in Water: Correlating Morphology and Surface Chemistry with Aggregation Behavior. Environ. Sci. Technol. 2016, 50, 6964−6973. (24) Crittenden, J. C.; Trussell, R. R.; Hand, D. W.; Howe, K. J.; Tchobanoglous, G. MWH’s Water Treatment: Principles and Design; John Wiley & Sons: Hoboken, NJ, 2012. (25) Brant, J. A.; Labille, J.; Robichaud, C. O.; Wiesner, M. Fullerol Cluster Formation in Aqueous Solutions: Implications for Environmental Release. J. Colloid Interface Sci. 2007, 314, 281−288. (26) Sein, L. T.; Varnum, J. M.; Jansen, S. A. Conformational Modeling of a New Building Block of Humic Acid: Approaches to the Lowest Energy Conformer. Environ. Sci. Technol. 1999, 33, 546−552. (27) Schulten, H.-R.; Schnitzer, M. Chemical Model Structures for Soil Organic Matter and Soils. Soil Sci. 1997, 162, 115−130. (28) Schulten, H.-R.; Schnitzer, M. A State of the Art Structural Concept for Humic Substances. Naturwissenschaften 1993, 80, 29−30. (29) Shevchenko, S. M.; Bailey, G. W.; Akim, L. G. The Conformational Dynamics of Humic Polyanions in Model Organic and Organo-Mineral Aggregates. J. Mol. Struct.: THEOCHEM 1999, 460, 179−190. (30) Iskrenova-Tchoukova, E.; Kalinichev, A. G.; Kirkpatrick, R. J. Metal Cation Complexation with Natural Organic Matter in Aqueous Solutions: Molecular Dynamics Simulations and Potentials of Mean Force. Langmuir 2010, 26, 15909−15919. (31) Zhu, X.; Chen, D.; Wu, G. Molecular Dynamic Simulation of Asphaltene Co-Aggregation with Humic Acid During Oil Spill. Chemosphere 2015, 138, 412−421. (32) Lin, D.; Xing, B. Tannic Acid Adsorption and Its Role for Stabilizing Carbon Nanotube Suspensions. Environ. Sci. Technol. 2008, 42, 5917−5923. (33) Jorgensen, W. L.; Maxwell, D. S.; Tirado-Rives, J. Development and Testing of the Opls All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids. J. Am. Chem. Soc. 1996, 118, 11225−11236. (34) Van Der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A. E.; Berendsen, H. J. Gromacs: Fast, Flexible, and Free. J. Comput. Chem. 2005, 26, 1701−1718. (35) Lazar, P.; Karlický, F. e.; Jurečka, P.; Kocman, M. s.; Otyepková, E.; Šafárǒ vá, K. r.; Otyepka, M. Adsorption of Small Organic Molecules on Graphene. J. Am. Chem. Soc. 2013, 135, 6372−6377. (36) Schlierf, A.; Yang, H.; Gebremedhn, E.; Treossi, E.; Ortolani, L.; Chen, L.; Minoia, A.; Morandi, V.; Samorì, P.; Casiraghi, C.; et al. Nanoscale Insight into the Exfoliation Mechanism of Graphene with

Organic Dyes: Effect of Charge, Dipole and Molecular Structure. Nanoscale 2013, 5, 4205−4216. (37) Karlický, F. e.; Otyepková, E.; Banás,̌ P.; Lazar, P.; Kocman, M. s.; Otyepka, M. Interplay between Ethanol Adsorption to High-Energy Sites and Clustering on Graphene and Graphite Alters the Measured Isosteric Adsorption Enthalpies. J. Phys. Chem. C 2015, 119, 20535− 20543. (38) Worley, B. C.; Haws, R. T.; Rossky, P. J.; Dodabalapur, A. Chemical Understanding of the Mechanisms Involved in Mitigation of Charged Impurity Effects by Polar Molecules on Graphene. J. Phys. Chem. C 2016, 120, 12909−12916. (39) Gulde, M.; Rissanou, A. N.; Harmandaris, V.; Müller, M.; Schäfer, S.; Ropers, C. Dynamics and Structure of Monolayer Polymer Crystallites on Graphene. Nano Lett. 2016, 16, 6994−7000. (40) DeFever, R. S.; Geitner, N. K.; Bhattacharya, P.; Ding, F.; Ke, P. C.; Sarupria, S. Pamam Dendrimers and Graphene: Materials for Removing Aromatic Contaminants from Water. Environ. Sci. Technol. 2015, 49, 4490−4497. (41) Osella, S.; Minoia, A.; Beljonne, D. Combined Molecular Dynamics and Density Functional Theory Study of Azobenzene− Graphene Interfaces. J. Phys. Chem. C 2016, 120, 6651−6658. (42) Darden, T.; York, D.; Pedersen, L. Particle Mesh Ewald: An N· Log (N) Method for Ewald Sums in Large Systems. J. Chem. Phys. 1993, 98, 10089−10092. (43) Berendsen, H.; Grigera, J.; Straatsma, T. The Missing Term in Effective Pair Potentials. J. Phys. Chem. 1987, 91, 6269−6271. (44) Jian, C.; Zeng, H.; Liu, Q.; Tang, T. Probing the Adsorption of Polycyclic Aromatic Compounds onto Water Droplets Using Molecular Dynamics Simulations. J. Phys. Chem. C 2016, 120, 14170−14179. (45) Hess, B.; Bekker, H.; Berendsen, H. J.; Fraaije, J. G. LINCS: A Linear Constraint Solver for Molecular Simulations. J. Comput. Chem. 1997, 18, 1463−1472. (46) Miyamoto, S.; Kollman, P. A. Settle: An Analytical Version of the Shake and Rattle Algorithm for Rigid Water Molecules. J. Comput. Chem. 1992, 13, 952−962. (47) Lemak, A.; Balabaev, N. On the Berendsen Thermostat. Mol. Simul. 1994, 13, 177−187. (48) Blanchard, A. E.; Arcario, M. J.; Schulten, K.; Tajkhorshid, E. A Highly Tilted Membrane Configuration for the Prefusion State of Synaptobrevin. Biophys. J. 2014, 107, 2112−2121. (49) Bai, Q.; Shi, D.; Zhang, Y.; Liu, H.; Yao, X. Exploration of the Antagonist Cp-376395 Escape Pathway for the CorticotropinReleasing Factor Receptor 1 by Random Acceleration Molecular Dynamics Simulations. Mol. BioSyst. 2014, 10, 1958−1967. (50) Miño-Galaz, G. A. Allosteric Communication Pathways and Thermal Rectification in PDZ-2 Protein: A Computational Study. J. Phys. Chem. B 2015, 119, 6179−6189. (51) Parrinello, M.; Rahman, A. Polymorphic Transitions in Single Crystals: A New Molecular Dynamics Method. J. Appl. Phys. 1981, 52, 7182−7190. (52) Lysenko, W. Equilibrium Phase-Space Distributions and Space Charge Limits in Linacs; Los Alamos Scientific Laboratory Report LA7010-MS; Los Alamos Scientific Laboratory: Los Alamos, NM, October 1977. (53) Berendsen, H. J.; Postma, J. v.; van Gunsteren, W. F.; DiNola, A.; Haak, J. Molecular Dynamics with Coupling to an External Bath. J. Chem. Phys. 1984, 81, 3684−3690. (54) Luzar, A.; Chandler, D. Hydrogen-Bond Kinetics in Liquid Water. Nature 1996, 379, 55−57. (55) Chen, M.; Lu, X.; Liu, X.; Hou, Q.; Zhu, Y.; Zhou, H. Retardation of Water Reorientation at the Oil/Water Interface. J. Phys. Chem. C 2015, 119, 16639−16648. (56) Tang, H.; Liu, D.; Zhao, Y.; Yang, X.; Lu, J.; Cui, F. Molecular Dynamics Study of the Aggregation Process of Graphene Oxide in Water. J. Phys. Chem. C 2015, 119, 26712−26718. (57) Frisch, M.; Trucks, G.; Schlegel, H.; Scuseria, G.; Robb, M.; Cheeseman, J.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G.; I


Article

The Journal of Physical Chemistry C et al. Gaussian 09, revision D. 01; Gaussian, Inc.: Wallingford, CT, 2009. (58) Wei, N.; Lv, C.; Xu, Z. Wetting of Graphene Oxide: A Molecular Dynamics Study. Langmuir 2014, 30, 3572−3578. (59) Li, X.; Gámiz, B.; Wang, Y.; Pignatello, J. J.; Xing, B. Competitive Sorption Used to Probe Strong Hydrogen Bonding Sites for Weak Organic Acids on Carbon Nanotubes. Environ. Sci. Technol. 2015, 49, 1409−1417. (60) Feenstra, K. A.; Hess, B.; Berendsen, H. J. Improving Efficiency of Large Time-Scale Molecular Dynamics Simulations of HydrogenRich Systems. J. Comput. Chem. 1999, 20, 786−798. (61) Główka, M.; Martynowski, D.; Kozłowska, K. Stacking of SixMembered Aromatic Rings in Crystals. J. Mol. Struct. 1999, 474, 81− 89. (62) Galamba, N. Water Tetrahedrons, Hydrogen-Bond Dynamics, and the Orientational Mobility of Water around Hydrophobic Solutes. J. Phys. Chem. B 2014, 118, 4169−4176. (63) Kunieda, N.; Endo, H.; Hirota, M.; Kodama, Y.; Nishio, M. The Conformational Analysis of 1-P-Tolyl-2-Phenyl-1-Propanols, 1-PTolyl-2-Phenylethanol, and 1-P-Tolyl-2-Phenyl-1-Propanone by Means of Nmr Spectroscopy. Bull. Chem. Soc. Jpn. 1983, 56, 3110− 3117. (64) Jennings, W. B.; Farrell, B. M.; Malone, J. F. Attractive Intramolecular Edge-to-Face Aromatic Interactions in Flexible Organic Molecules. Acc. Chem. Res. 2001, 34, 885−894. (65) Li, Y.; Li, H.; Zhang, K.; Liew, K. M. The Theoretical Possibility of a Graphene Sheet Spontaneously Scrolling Round an Iron Nanowire. Carbon 2012, 50, 566−576. (66) Devic, T.; Yuan, M.; Adams, J.; Fredrickson, D. C.; Lee, S.; Venkataraman, D. The Maximin Principle of Π-Radical Packings. J. Am. Chem. Soc. 2005, 127, 14616−14627. (67) Jiang, Y.; Li, H.; Li, Y.; Yu, H.; Liew, K. M.; He, Y.; Liu, X. Helical Encapsulation of Graphene Nanoribbon into Carbon Nanotube. ACS Nano 2011, 5, 2126−2133. (68) Xie, B.; Xu, Z.; Guo, W.; Li, Q. Impact of Natural Organic Matter on the Physicochemical Properties of Aqueous C60 Nanoparticles. Environ. Sci. Technol. 2008, 42, 2853−2859. (69) Cano, M.; Khan, U.; Sainsbury, T.; O’Neill, A.; Wang, Z.; McGovern, I. T.; Maser, W. K.; Benito, A. M.; Coleman, J. N. Improving the Mechanical Properties of Graphene Oxide Based Materials by Covalent Attachment of Polymer Chains. Carbon 2013, 52, 363−371. (70) Jaggernauth, A.; Silva, R. M.; Neto, M. A.; Hortigüela, M. J.; Gonçalves, G.; Singh, M. K.; Oliveira, F. J.; Silva, R. F.; Vila, M. Nanographene Oxide Functionalization with Organic and Hybrid Organic−Inorganic Polymers by Molecular Layer Deposition. J. Phys. Chem. C 2016, 120, 24176−24186. (71) Chaumont, A.; Wipff, G. Interactions between Keggin Anions in Water: The Higher Their Charge, the Higher Their Condensation? A Simulation Study. Eur. J. Inorg. Chem. 2013, 2013, 1835−1853. (72) Marcano, D. C.; Kosynkin, D. V.; Berlin, J. M.; Sinitskii, A.; Sun, Z.; Slesarev, A.; Alemany, L. B.; Lu, W.; Tour, J. M. Improved Synthesis of Graphene Oxide. ACS Nano 2010, 4, 4806−4814. (73) Medhekar, N. V.; Ramasubramaniam, A.; Ruoff, R. S.; Shenoy, V. B. Hydrogen Bond Networks in Graphene Oxide Composite Paper: Structure and Mechanical Properties. ACS Nano 2010, 4, 2300−2306. (74) Yang, K.; Chen, B.; Zhu, X.; Xing, B. Aggregation, Adsorption and Morphological Transformation of Graphene Oxide in Aqueous Solutions Containing Different Metal Cations. Environ. Sci. Technol. 2016, 50, 11066−11075. (75) Buttinoni, I.; Volpe, G.; Kümmel, F.; Volpe, G.; Bechinger, C. Active Brownian Motion Tunable by Light. J. Phys.: Condens. Matter 2012, 24, 284129. (76) Sharma, M.; Yashonath, S. Size Dependence of Solute Diffusivity and Stokes-Einstein Relationship: Effect of van der Waals Interaction. Diffus. Fundam. 2007, 7, 11.1−11.15. (77) Penn, R. L.; Tanaka, K.; Erbs, J. Size Dependent Kinetics of Oriented Aggregation. J. Cryst. Growth 2007, 309, 97−102.

(78) Pettibone, J. M.; Cwiertny, D. M.; Scherer, M.; Grassian, V. H. Adsorption of Organic Acids on Tio2 Nanoparticles: Effects of Ph, Nanoparticle Size, and Nanoparticle Aggregation. Langmuir 2008, 24, 6659−6667. (79) Wiese, G.; Healy, T. Effect of Particle Size on Colloid Stability. Trans. Faraday Soc. 1970, 66, 490−499.

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