Computer Simulation of the Effects of Nanoparticles' Adsorption on the

Aug 1, 2012 - ABSTRACT: Supported lipid bilayer (SLB) represents a kind of well-established model cell membrane and is also used for many biosensors o...
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Computer Simulation of the Effects of Nanoparticles' Adsorption on the Properties of Supported Lipid Bilayer Xubo Lin, Changling Wang, Meng Wang, Kun Fang, and Ning Gu* State Key Laboratory of Bioelectronics and Jiangsu key Laboratory for Biomaterials and Devices, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, People's Republic of China S Supporting Information *

ABSTRACT: Supported lipid bilayer (SLB) represents a kind of well-established model cell membrane and is also used for many biosensors or biodevices. Here, for the first time, we use molecular dynamics simulation to study the effects of nanoparticle (NP) adsorption on SLB. In our simulations, the surface charge properties and the heating effects of NPs are investigated. Results show that NPs' adsorption behavior, SLB's diffusion ability, and local order parameter distribution are largely dominated by the property of the NPs' surface charge. Meanwhile the NPs' heating can increase the nearby lipids' and water's thermal motions, thus disrupting the surface charge's domination on the aforementioned properties. Besides, we find that the solid support may induce more intense thermal motions but poorer diffusion ability for the lipid leaflet closer to the support. This study provides useful insights on the NPs' disruption to the functioning of the biological membrane and the performance of SLB-based biosensors or biodevices.

1. INTRODUCTION One of the most significant advantages of supported lipid bilayers (SLBs) is their stability and possibility to be studied by surface-sensitive techniques,1−4 such as atomic force microscopy, ellipsometry, waveguide spectroscopy, X-ray and neutron reflectivity, and quartz crystal microbalance. Besides, they also can be easily assembled on hydrophilic surfaces (mica, glass, and silicon oxide) by one of two main strategies: the Langmuir−Blodgett approach and the vesicle fusion technique.5 Hence, SLBs have been an excellent model for studying cell membranes,5−7 the T cell immunological synapse,8,9 neuronal interactions,10 and the triggering of EphA2 receptor tyrosine kinase in breast epithelial cancer cells11 and also are used for many biosensors or biodevices.7,12−14 With the development of nanotechnology, nanoparticles' (NPs') applications and potential toxicity have attracted much attention. In view of SLB's advantages, many research studies focus on studying the interactions between NPs and SLB to find an elegant balance between NPs' biomedical applications and potential toxicity.7,12−19 On the one hand, SLB provides a good alternative to cell membrane in experiments.5−7 Unlike real cell membranes, SLB is very simple and can provide much molecular-level information for understanding the interactions between NPs and cell membranes. On the other hand, the interactions between NPs and SLB are also very essential for the performance of SLB-based biosensors or biodevices.7,12−14 © 2012 American Chemical Society

These research studies can promote the design, the optimization, and the applications of SLB-based biosensors or biodevices. To date, the studies of the interactions between NP and SLB are mainly in vitro experiments. However, the results in different laboratories sometimes cannot achieve absolute consensus due to the difficulty in controlling experimental parameters that affect SLBs' formation and their consequent behavior.20,21 Hence, there is a need to use theory or simulations to compensate for the disadvantages of experiments. Molecular dynamics (MD) simulation is a very important computational tool for understanding the physical basis of a certain biological process. MD has been successfully used to simulate the dynamic properties of SLB,19,22,23 the interactions between NPs and lipid bilayers,24−28 etc. Therefore, it is possible and also necessary to study the interactions between NPs and SLB using MD. We use coarse-grained MD (CGMD) simulations to probe the effects of NPs' adsorption on SLB using the MARTINI force field.29,30 The NP's diameter is 10 nm. We choose 10 nm NPs because these particles easily can be synthesized in experiments and can be simulated with proper computational resources. We studied the surface charge Received: June 13, 2012 Revised: August 1, 2012 Published: August 1, 2012 17960

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Figure 1. Coarse-grained representation of the NP−SLB system and different surface charge density of NP. For clarity, water molecules are not shown. The snapshot is rendered by VMD.31

it means that the NP was heated. As for systems of chargeneutral NP, we named them as Neutral and Neutral-T. Besides, we performed a simulation with no NP (ref-non-NP) as a contrast. For the sake of concision, we defined the lipid leaflet closer to the NP as the upper lipid leaflet and the lipid leaflet closer to the support as the lower lipid leaflet (Figure 1). For all simulations, systems were set in the NAPzT ensemble in which we kept the area of the system constant throughout the simulations while allowing the normal pressure to fluctuate around the reference value (1 bar) as described in ref 22. A cutoff of 1.2 nm was used for van der Waals (vdW) interactions, and the Lennard−-Jones potential was smoothly shifted to zero between 0.9 and 1.2 nm to reduce cutoff noise. For electrostatic interactions, the Coulombic potential, with a cutoff of 1.2 nm, was smoothly shifted to zero from 0 to 1.2 nm. The relative dielectric constant was 15, which was the default value of the force field.30 DPPC, water, NP, and the support were coupled separately to Berendsen heat baths32 with a coupling constant τ = 1 ps. It is worth mentioning that in all of our simulations, the support CG beads were fixed at their initial positions completely to mimic the properties of the bulk solid. The system was simulated using semi-isotropic pressure coupling (Berendsen coupling scheme,32 coupling constant of 1 ps, compressibility in the lateral direction of 0 and in the normal direction of 5 × 10−6 bar−1). All of the simulations were performed with the GROMACS simulation package.33 A time step of 40 fs and periodic boundary condition were used in all of the simulations. The neighbor list for nonbonded interactions was updated every 10 steps.

properties and the heating effects of the NP on the properties of SLB. The results may provide insights for SLB-relative researches and promote applications of SLB.

2. SIMULATION DETAILS Recently, the Martini force field has been successfully extended to study SLB.22,23 To simulate the SLB systems, the water− water nonbonded interaction is weakened to 76% of that for the free lipid bilayer systems. By doing this, the water behavior with proper fluidity and density was reproduced. On the basis of this modification, we performed a CGMD simulation to probe the effects of adsorbed NPs on SLB. We studied the surface charge properties and the heating effects of the NP on the properties of SLB. We first constructed the SLB system consisting of a hydrophilic support, DPPC bilayer (2 × 1296 DPPC molecules), and 103680 CG water molecules. The CG presentation of DPPC and water was the same as in the references.22,23 The hydrophilic support consisted of 9216 completely fixed Nda type Martini beads29,30 on a grid with a spacing of 0.3 nm. In the x and y dimensions, the box size was carefully chosen to make sure the minimum distance between support beads was 0.3 nm considering the lattice spacing under periodic boundary conditions (PBCs). The thickness of the water layer between the lipid bilayer and the support was set to be about 1 nm as reported.6,23 The initial box size was 28.80 × 28.80 × 20.41 nm3. Then, a water hole was generated, and the FCC-stacked spherical NP with a diameter of 10 nm was inserted correspondingly. The building block of the NP was also a Nda type bead.29,30 The NP of this type can easily adsorb onto the lipid bilayer,27 which can ensure that we study the effects of the NPs' adsorption on SLB. To reduce the simulation time, the NP was first pulled to the surface of the SLB, and then, the system with the center-of-mass of NP and lipid bilayer constrained was relaxed until the total energy reached its equilibrium value. By changing the properties of the NP, we obtained different simulation systems. (As for the system with charged NP, a certain amount of water molecules were replaced by the relative counterions Na+/Cl−.) Then, the constraints were removed in all systems and relaxed to get their new equilibrium states, and another 100 ns simulations were performed at these states. The last 50 ns was used for analyzing the relative properties. For systems of charged NPs, we named them in the format of q-X-Y-T. As shown in Figure 1, “X” represents NPs' surface charged bead number, which can be 1278, 942, and 438, respectively. “Y” represents charge properties, which can be “plus” or “minus”. When “T” appears,

3. RESULTS AND DISCUSSION Surface charge properties are very important in the interactions between the NP and the lipid bilayer. In our simulations, we consider both positively and negatively charged NPs with different charge densities. Magnetic NPs' heating effects under time-varied magnetic field or gold NPs' light-induced heating effects may play an important role in the interactions between these NPs and SLB. Thus, we coupled NPs to Berendsen heat baths at 338 K and others to Berendsen heat baths at 298 K in our simulations to model NPs' heating effects (TNP > Tothers, 298 K is near the room temperature). 3.1. NP's Adsorption Behavior. For the free-standing lipid bilayer, surface charge properties play an important role on the NPs' adsorption on the lipid bilayer.27 A Nda type NP can easily adsorb onto the lipid bilayer, and electrostatic interactions can modulate this nonspecific adsorption. As for 17961

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Figure 2. Distance (z) between the NP and the support averaged over the last 50 ns.

Figure 3. Local membrane thickness distribution for all simulation systems averaged over the last 50 ns. The pink line corresponds to the NP's trajectory projected in the x−y plane.

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equipartition theorem (3/2kBT = ⟨1/2mυx2 + 1/2mυy2 + 1/ 2mυz2⟩). Then we calculate the lipids' local temperature distribution averaged over the last 50 ns simulation time using the equipartition theorem. We find that the heated NP causes more intense thermal motions for the nearby lipids, especially those in the upper leaflet (Figure 5), while nonheated NP (TNP = Tothers) seldom affects lipids' thermal motions as compared to the system ref-non-NP (Figure S3a in the Supporting Information). Besides, the lower leaflet seems to have a higher temperature than the upper leaflet for all simulation systems. This seems special. We further confirm by statistics that the temperature of the lower leaflet is about 1 K higher than that of the upper leaflet (Figure S3b in the Supporting Information). The question that immediately arises is which driving force should be responsible for this. It is widely reported that water molecules (interfacial water) near the hydrophilic support behave quite abnormally as compared with the bulk water. The interfacial water molecules often exist as a monolayer.22,23,34,35 Besides, Wang et al.34,35 found that water droplets of 30 Å can be formed at room temperature on a continuous water monolayer of ∼4 Å and elucidated the phenomenon (“water that does not wet a water monolayer”) in theory, which were verified by James et al. using a combination of XRR and AFM.36 Because the hydrophilic support can bring about abnormal water dynamic properties, the driving force for this thermal motion difference most possibly derives from the hydrophilic support, which exists in all of our simulation systems. By calculating the temperature distribution along the z-axis, we can clearly find that the interfacial water has a higher temperature than the bulk water (Figures 6 and S4 in the Supporting Information). Here, the interfacial water refers to the water between the hydrophilic support and the lipid bilayer with a thickness of about 1 nm, and the bulk water refers to the water layer far from the hydrophilic support. The upper lipid leaflet is in direct contact with the bulk water and the lower lipid leaflet with the interfacial water. The different thermal motions of the interfacial water and bulk water in our system may be responsible for the special phenomenon in all of our simulations. Water dynamics influence the nearby DPPC molecules' dynamics, which explains why the lower lipid leaflet has a higher temperature than the upper leaflet for all of our simulation systems. Besides, we use another classical heat bath method (nose-hoover) to simulate all systems and get the similar temperature distribution information (Figure S4c in the Supporting Information), which proves the reliability of the Berendsen heat bath used in all of our simulation systems. Hence, this special phenomenon should be an interesting but long-neglected phenomenon for SLB, which may be useful for the applications of SLB. 3.3. Lipid Lateral Diffusion. Lipid lateral diffusion is an important dynamic process for the lipid bilayer. It can be measured by experimental methods such as quasi-elastic neutron scattering.37 Thus, the lipid lateral diffusion coefficient is often calculated and discussed in simulations to combine with the experiments directly. One common method is to calculate the mean squared displacement (MSD) at first and then use an Einstein relation to get the lipid lateral diffusion coefficient.22,38,39 Besides, Ilpo Vattulainen et al. recently developed a useful method to calculate the lipid lateral diffusion coefficient to study the relative diffusion mechanism.40−42 However, these computational methods can seldom provide the local information of the lipid's diffusion in the bilayer. In view of

SLB, the existence of the support prevents the large deformation of the lipid bilayer. Thus, we can turn to the distance (z) between the NP and the support (Figure 2) and membrane thickness (Figure 3) to evaluate NPs' different adsorption behaviors. The shorter distance (z) corresponds to the tighter adsorption. As shown in Figure 2, NPs' adsorption behavior is largely dominated by the surface charge properties with little influence from NPs' heating effects. The relations between the adsorption behavior and the charge density are quite different for positively and negatively charged NPs. As for positively charged NPs, the adsorption becomes dramatically weaker with the decrease of the charge density. However, the adsorption of the negatively charged NP is weakened more gently than that of the positively charged NP. In general, negatively charged NPs tend to adsorb onto the lipid bilayer more easily. This difference for positively and negatively charged NPs is mainly induced by the dipole layer formed by charged head groups of DPPC molecules with a positively charged group outside.29,30 NPs' adsorption is strengthened with the increase of the charge density, but the trends are quite different for positively and negatively charged NPs, which is also proved by the local distribution of membrane thickness for all simulation systems (Figure 3). It is well-known that the thickness of the bilayer is not uniform.27,29 We reproduced this typical undulation of membrane thickness in our simulations. Membrane thinning (blue regions in Figure 3) corresponds to NPs' tight adsorption. NP only causes membrane thinning of the contact region, and the degree of thinning is determined by the surface charge properties as described above. 3.2. Temperature Distribution. The radial temperature distribution with the center of the NP as the reference point shows that the core of the NP has a higher temperature, while the temperature of NP surface and its environment vary gradually (Figure 4). This is natural for the situation that heated particles are immerged in the cool environment, which verifies the feasibility of our temperature treatment. What we are interested in is the SLB's local temperature distribution after interaction with the heated rigid NP. By analyzing the trajectory of our simulations, we can obtain velocities of all lipids at every recorded time. As we know, molecules' thermal motions are tightly related to the corresponding macroscopic temperature according to the

Figure 4. Radial temperature distribution corresponding to the center of mass of the NP (the diameter of the NP is 10 nm). 17963

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Figure 5. Temperature distribution (TNP > Tothers): The left two columns are for the upper lipid leaflet; the right two columns are for lower lipid leaflet. The pink line corresponds to the NP's trajectory projected in the x−y plane.

the “update rate” (Δt). Using the algorithm described in the Supporting Information, we analyzed the trajectory of all simulations and got the local distribution of the update rate for both the upper and the lower lipid leaflet averaged over the last 50 ns (Figures S5 and S6 in the Supporting Information). For the lower lipid leaflet, the update rate distribution shows little differences. Hence, we performed further analysis only for the upper lipid leaflet. We treat NPs' average position as the center and calculate the radial distribution of the update rate (Figure 7b). Results show that NPs' adsorption induces the decrease of the directly contacted or nearby lipid molecules' update rate. The lipid molecules far from NP (>6 nm) show little changes as compared to the system ref-non-NP with no NPs. By further analyzing the lipid molecules with a decreased update rate (r < 6 nm), we can figure out the relations between this decrease and the surface charge properties and heating effects, as shown in Figure 7c. For both positively and negatively charged NPs, a larger charge density induces a weaker lipid diffusion ability of the local region (r < 6 nm). When NP is heated, the nearby lipids' thermal motions increase, and thus, the diffusion ability is promoted to a certain extent, and this extent varies with the surface charge density quite differently for positively and negatively charged NPs. The dynamics of water molecules influences the dynamics of DPPC molecules. Hence, the difference of dynamics between the interfacial water and the bulk water is likely responsible for the differences between the effects of NP on the upper and the

Figure 6. Temperature distribution along the z-axis for all simulation systems.

this shortcoming, we turn to the microscopic process of lipid's diffusion. As we know, during a certain period, a lipid molecule will diffuse from one place to another. In other words, molecules will flow in and out of a selected region during this period (Figure 7a). Suppose that there are N molecules in the selected region at the initial time, and then, after a period (Δt), n new molecules enter into the region. Obviously, a larger value of n/N corresponds to the better diffusion ability. Thus, we can use n/N to reflect the lipid's diffusion ability, and we call n/N 17964

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Figure 8. Lateral diffusion of the interfacial water (a, e), the bulk water (b, f), the lower lipid leaflet (c, d), and the upper lipid leaflet (d, h) of the system ref-non-NP. Panels a−d represent the snapshots at t = 50 ns; panels e−h represent the snapshots at t = 100 ns. Beads in red (a− h) represent marked water or DPPC molecules. All snapshots are rendered using VMD.31

the diffusion ability of the DPPC molecules is in tight correlation with that of the surrounding water molecules, and the difference of diffusion ability between the two lipid leaflets is driven by the difference between the interfacial water and the bulk water. 3.4. Order Parameter. NPs' nonspecific adsorptions induce corresponding local membrane thinning and thus may affect the membrane's physical properties such as the order parameter. By using the algorithm described in the Supporting Information, we obtained the local distribution of the order parameter for the lipid heads and tails of both of the two leaflets of the SLB. Figures 9 and 10 show the local order parameter distribution of the lipid head and tail of all systems' upper lipid leaflet. The upper lipid leaflet is more sensitive to NPs' adsorption, while the lower lipid leaflet shows little influences on the order parameter by NPs' nonspecific adsorption. As compared to the SLB system without NPs, NPs with positive charges induce the decrease of the local head order parameter, while NPs with negative charges bring about the increase of the local head order parameter of the upper lipid leaflet, and the degree of the increase or decrease is related to the charge density. The larger the charge density, the larger the degree of the increase or decrease. As a higher value of the order parameter corresponds to more ordered orientation, the above results are consistent with the experimental results,43,44 which can be explained mainly by electrostatic interactions between the NPs and the lipid head groups. However, all charged NPs make the local tail order parameter of the upper lipid leaflet decrease. The larger the charge density is, the larger the degree of the decrease. Besides, the NP of negative charge has more significant effects. Charge-neutral NPs have little influence on the upper leaflet's order parameter distribution. As for the local tail order parameter, there are some deviations for the effects of NPs on SLB in our simulation and liposome in the experiment.43 We ascribe this to the

Figure 7. Update rate of the upper lipid leaflet for all simulation systems: (a) the schematic diagram for the definition of update rate, (b) the radial distribution of the update rate with the center of mass of NP as the reference point averaged over the last 50 ns, and (c) results averaged over the region (r < 6 nm).

lower lipid leaflet. To confirm this assumption, we further analyzed the dynamics of the whole SLB system. The lateral motions of all components of SLB system over the last 50 ns are monitored directly. The lateral diffusion ability of the bulk water and the upper leaflet is significantly higher than the interfacial water and the lower leaflet correspondingly (Figure 8), which verifies our assumption. Thus, we can conclude that 17965

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Figure 9. Order parameter (PO4, TNP > Tothers): The left two columns are for the upper leaflet; the right two columns are for lower leaflet. The pink line corresponds to the NP's trajectory projected in the x−y plane.

ature) but poorer diffusion ability. This special phenomenon is driven by the difference of the interfacial water and the bulk water, which exist in SLB system. The interfacial water is about 1 nm thick, confined between the hydrophilic support and the lipid bilayer. The property that the interfacial water has more intense thermal motion but poorer diffusion ability than the bulk water may be crucially important in a vast number of technical applications and scientific fields as described by a recent review.46 Besides, this property found in our simulations can also be tested by a method developed by Joly et al.47 By introducing the definition of “update rate”, we studied the local distribution of lipids' lateral diffusion. For both positively and negatively charged NPs, a larger charge density induces a weaker lipid diffusion ability of the local region (r < 6 nm) of the upper lipid leaflet. The trend is similar to the situation of NPs' adsorption behavior. Besides, the diffusion ability of the upper lipid leaflet is quite higher than that of the lower lipid leaflet. The order parameter of the upper lipid leaflet is also locally disturbed mainly by the NPs' surface charge density. The difference is that the NPs with positive charges induce lipids' head orientation more disordered locally, and NPs with negative charges are to the contrary. However, they both cause more disordered lipids' tail orientation locally, and NPs with negative charges are more significant. Besides, a larger charge density corresponds to larger local changes of the order parameter. The lower lipid leaflet shows little changes with the interactions of NP. Our simulation results bring new information about SLB, helps elucidate the complex inter-

difference between model systems SLB and liposome. In model system SLB, the support can stabilize the system and regulate the effects of other molecules on the properties of the lipid bilayer.44 As for model system liposome, the role of its variable shape and curvature effects45 on modulating the phase behavior of the lipid bilayer after disruptions may distinguish itself from other systems such as SLB. Systems with the interactions of nonheated NPs have similar results. Thus, we can conclude that the surface charge properties rather than the heating factor show the most influence on the local disruption of the lipid bilayer's order parameter.

4. CONCLUSIONS In summary, we have performed CGMD simulations to investigate the interactions between hydrophilic NPs (10 nm) and SLB. The surface charge properties and the heating effect of NP were considered. NPs' adsorption behavior is largely dominated by the surface charge properties and is disturbed a little when the NP is heated. The higher surface charge density induces better adsorption and thus relative local membrane thinning. However, the trend that NPs' adsorption behavior varies with the charge density is quite different for positively and negatively charged NP. The NPs' heating effect shows little influence on the NPs' adsorption on the SLB. While it increases local lipids' thermal motions. Interestingly, in all of the simulations, the lower lipid leaflet shows more intense thermal motions (higher temper17966

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Figure 10. Order parameter (C2A, TNP > Tothers): The left two columns are for the upper leaflet; the right two columns are for lower leaflet. The pink line corresponds to the NP's trajectory projected in the x−y plane.

and BK2011036), and the Outstanding Ph.D. Student Program of China's Ministry of Education.

actions between NP and lipid bilayer, and thus may provide insights into the applications of SLB.

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ASSOCIATED CONTENT

S Supporting Information *

Algorithm for calculating the local distribution of the bilayer's certain properties, snapshots for all simulation systems, lipid leaflet's local temperature distribution of the system with noheated NP, interfacial and bulk water's temperature distribution, and lipid leaflet's local update rate distribution. This material is available free of charge via the Internet at http:// pubs.acs.org.

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REFERENCES

(1) Picas, L.; Montero, M. T.; Morros, A.; Oncins, G.; HernandezBorrell, J. J. Phys. Chem. B 2008, 112, 10181−10187. (2) Koenig, B. W.; Krueger, S.; Orts, W. J.; Majkrzak, C. F.; Berk, N. F.; Silverton, J. V.; Gawrisch, K. Langmuir 1996, 12, 1343−1350. (3) Richter, R. P.; Brisson, A. R. Biophys. J. 2005, 88, 3422−3433. (4) Vockenroth, I. K.; Rossi, C.; Shah, M. R.; Köper, I. Biointerphases 2009, 4, 19−26. (5) Czolkos, I.; Jesorka, A.; Orwar, O.. Soft Matter 2011, 7, 4562− 4576. (6) Tanaka, M.; Sackmann, E. Nature 2005, 437, 656−663. (7) Castellana, E. T.; Cremer, P. S. Surf. Sci. Rep. 2006, 61, 429−444. (8) Manz, B. N.; Jackson, B. L.; Petit, R. S.; Dustin, M. L.; Groves, J. T. Proc. Natl. Acad. Sci. U.S.A. 2011, 108, 9089−9094. (9) Hartman, N. C.; Nye, J. A.; Groves, J. T. Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 12729−12734. (10) Pautot, S.; Lee, H.; Isacoff, E. Y.; Groves, J. T. Nat. Chem. Biol. 2005, 1, 283−289. (11) Salaita, K.; Nair, P. M.; Petit, R. S.; Neve, R. M.; Das, D.; Gray, J. W.; Groves, J. T. Science 2010, 327, 1380−1385. (12) Lohmuller, T.; Triffo, S.; O’Donoghue, G.; Xu, Q.; Coyle, M. P.; Groves, J. T. Nano Lett. 2011, 11, 4912−4918. (13) Rumpel, A.; Novak, M.; Walter, J.; Braunschweig, B.; Halik, M.; Peukert, W. Langmuir 2011, 27, 15016−15023. (14) Lee, Y. K.; Nam, J. K. Small 2012, 8, 832−837. (15) Jing, B. X.; Zhu, Y. X. J. Am. Chem. Soc. 2011, 133, 10983− 10989.

AUTHOR INFORMATION

Corresponding Author

*Tel: +86(0)25-83272476. Fax: +86(0)25-83272460. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We appreciate Prof. Yi Y. Zuo for his careful reading of our manuscript and Prof. Yeng-Long Chen for his insightful suggestions on our research during attendance in the KITPC biophysics program. We acknowledge the support of this research from the National Important Basic Research Program of China (No. 2011CB933503), the National Natural Science Foundation of China (Nos. 60725101 and 61127002), the Basic Research Program of Jiangsu Province (Nos. BK2009013 17967

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The Journal of Physical Chemistry C

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(16) Leroueil, P. R.; Berry, S. A.; Duthie, K.; Han, G.; Rotello, V. M.; McNerny, D. Q.; Baker, J. R.; Orr, J. B. G.; Holl, M. M. B. Nano Lett. 2008, 8, 420−424. (17) Bendix, P. M.; Reihani, S. N. S.; Oddershede, L. B. ACS Nano 2010, 4, 2256−2262. (18) Marques, J. T.; de Almeida, R. F. M.; Viana, A. S. Soft Matter 2012, 8, 2007−2016. (19) Liang, Q.; Chen, Q. H.; Ma, Y. Q. J. Phys. Chem. B 2010, 114, 5359−5364. (20) Seeger, H. M.; Marino, G.; Alessandrini, A.; Facci, P. Biophys. J. 2009, 97, 1067−1076. (21) Seu, K. J.; Pandey, A. P.; Haque, F.; Proctor, E. A.; Ribbe, A. E.; Hovis, J. S. Biophys. J. 2007, 92, 2445−2450. (22) Xing, C. Y.; Faller, R. J. Phys. Chem. B 2008, 112, 7086−7094. (23) Xing, C. Y.; Ollila, O. H. S.; Vattulainen, I.; Faller, R. Soft Matter 2009, 5, 3258−3261. (24) Shi, X. H.; Bussche, A.; Hurt, R. H.; Kane, A. B.; Gao, H. J. Nature Nanotechnol. 2011, 6, 714−719. (25) Loverde, S. M.; Klein, M. L.; Discher, D. E. Adv. Mater. 2011, 24, 3823−3830. (26) Yang, K.; Ma, Y. Q. Nature Nanotechnol. 2010, 5, 579−583. (27) Li, Y.; Gu, N. J. Phys. Chem. B 2010, 114, 2749−2754. (28) Lin, X. B.; Li, Y.; Gu, N. J. Comput. Theor. Nanosci. 2010, 7, 269−276. (29) Marrink, S. J.; de Vries, A. H.; Mark, A. E. J. Phys. Chem. B 2004, 108, 750−760. (30) Marrink, S. J.; Risselada, H. J.; Yefimov, S.; Tieleman, D. P.; de Vries, A. H. J. Phys. Chem. B 2007, 111, 7812−7824. (31) Humphrey, W.; Dalke, A.; Schulten, K. J. Mol. Graphics 1996, 14, 33−38. (32) Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F.; DiNola, A.; Haak, J. R. J. Chem. Phys. 1984, 81, 3684−3690. (33) Lindahl, E.; Hess, B.; der Spoel, D. V. J. Mol. Model. 2001, 7, 306−317. (34) Wang, C. L.; Lu, H. J.; Wang, Z. G.; Xiu, P.; Zhou, B.; Zuo, G. H.; Wan, R. Z.; Hu, J.; Fang, H. P. Phys. Rev. Lett. 2009, 103, 137801/ 1−137801/4. (35) Wang, C. L.; Zhou, B.; Xiu, P.; Fang, H. P. J. Phys. Chem. C 2011, 115, 3018−3024. (36) James, M.; Ciampi, S.; Darwish, T. A.; Hanley, T. L.; Sylvester, S. O.; Gooding, J. J. Langmuir 2011, 27, 10753−10762. (37) Armstrong, C. L.; Kaye, M. D.; Zamponi, M.; Mamontov, E.; Tyagi, M.; Jenkins, T.; Rheinstadter, M. C. Soft Matter 2010, 6, 5864− ̅ 5867. (38) Böckmann, R. A.; Hac, A.; Heimburg, T.; Grubmüller, H. Biophys. J. 2003, 85, 1647−1655. (39) Liu, B.; Li, X. Y.; Li, B. L.; Xu, B. Q.; Zhao, Y. L. Nano Lett. 2009, 9, 1386−1394. (40) Falck, E.; Róg, T.; Karttunen, M.; Vattulainen, I. J. Am. Chem. Soc. 2008, 130, 44−45. (41) Javanainen, M.; Monticelli, L.; de la Serna, J. B.; Vattulainen, I. Langmuir 2010, 26, 15436−15444. (42) Apajalahti, T.; Niemela,̅ P.; Govindan, P. N.; Miettinen, M. S.; Salonen, E.; Marrink, S. J.; Vattulainen, I. Faraday Discuss. 2010, 144, 411−430. (43) Wang, B.; Zhang, L. F.; Bae, S. C.; Granick, S. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 18171−18175. (44) Oreopoulos, J.; Christopher, M. Y. J. Struct. Biol. 2009, 168, 21− 36. (45) Risselada, H. J.; Marrink, S. J. Phys. Chem. Chem. Phys. 2009, 11, 2056−2067. (46) Giovambattista, N.; Rossky, P. J.; Debenedetti, P. G.. Annu. Rev. Phys. Chem. 2012, 63, 179−200. (47) Joly, L.; Ybert, C.; Bocquet, L. Phys. Rev. Lett. 2006, 96, 046101/ 1−046101/4.

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dx.doi.org/10.1021/jp305784z | J. Phys. Chem. C 2012, 116, 17960−17968