Direct Observation of Nanoparticle Self-Assembly Dynamics at the

Direct Observation of Nanoparticle Self-Assembly Dynamics at the Water−Air ... self-assembly of gold nanoparticles (GNPs) at the water−air (w/a) i...
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J. Phys. Chem. C 2009, 113, 1209–1216

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Direct Observation of Nanoparticle Self-Assembly Dynamics at the Water-Air Interface Using Differential Interference Contrast Microscopy Lehui Xiao, Rui Zhou, Yan He,* Yongjun Li, and Edward S. Yeung* Biomedical Engineering Center, College of Chemistry and Chemical Engineering, State Key Laboratory of Chemo/Biosensing and Chemometrics, Hunan UniVersity, Changsha 410082, People’s Republic of China ReceiVed: September 2, 2008; ReVised Manuscript ReceiVed: NoVember 20, 2008

Understanding the dynamics of the nanoparticle (NP) self-assembly process is very important for both fundamental and applied research of functional nanomaterials. In this report, high-contrast differential interference contrast microscopy was used for real time observation of evaporation-mediated self-assembly of gold nanoparticles (GNPs) at the water-air (w/a) interface. GNPs were found to be trapped at the w/a interface and gradually assembled into clusters. The dynamic cluster formation process was driven by evaporation-induced electrostatic repulsion decrease and can be divided into two stages. During the whole period, individual particles moved in random patterns that were indistinguishable from those produced by computer simulation of two-dimensional random walk, but interactions with surrounding particles and clusters caused a more directional flow motion. The observed dynamics of GNP cluster formation is more like the reaction-limited cluster formation model where the sticking probability is less than unity and anisotropic repulsion is responsible for the low dimensionality of GNP clusters formed. The sticking probability is correlated with the average reversible sticking time between individual particles. Introduction Self-assembly of nanoparticles (NPs) is becoming a leading methodology in fabrication of functional materials with unique optical, electronic, chemical, and biological properties.1-5 To achieve controlled nanostructure and properties, it is important to develop imaging techniques that are able to track the motion of a single NP in real time and study particle-particle interactions, spatial and temporal intermediate states, and other dynamic behaviors of the NP self-assembly process at the nanometer scale.6-8 However, traditional nanoimaging techniques such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), and atomic force microscopy can just provide static information and are therefore mostly used for characterization of the final products.9 Conventional brightfield optical microscopy is usually limited by strong background signals and out-of-focus light. Confocal fluorescence microscopy can only be used to study NPs which are self-fluorescent or labeled with fluorophores.10-12 One solution for nanoscale live imaging is differential interference contrast (DIC) microscopy.13 In DIC, the object is probed by a pair of lineally polarized light rays separated by a small shear distance. The response is detectable only when there is an optical path difference (OPD) between the two rays, usually resulting from sample thickness or refractive index difference. Therefore DIC is able to detect nanometer-sized objects if the optical properties are distinct from the surroundings, and the background signal can be minimized because the homogeneous media cannot produce measurable optical-path difference. For example, Allen14 and Inoue15 used video-enhanced DIC (VE-DIC) to study microtubes whose diameter was only 25 nm. Sheetz16 used VE-DIC to track kinesin-driven bead movements in vitro with a precision of 1-2 nm. Kang17 and co-workers applied transmission DIC to directly observe single lambda DNA molecules in flow streams. * Corresponding authors, [email protected] and yeung@ameslab. gov.

Many types of self-assembly processes occur at interfaces such as solid-liquid, liquid-liquid, and vapor-liquid boundaries, where the abrupt change of local environment results in lower interfacial potentials than the bulk phase and leads to spontaneous aggregation and spatial rearrangement of NPs. One area of continuous interest over the past few decades is selfassembly of colloidal particles with nanometer to micrometer sizes at the liquid-air interface,7,18-20 which both served as model systems to illustrate important physical and biological phenomena and provided simple robust routes for the fabrication of two-dimensional (2D) and three-dmensional (3D) functional nanostructures. Although extensive work has been done to reveal the dynamic aspects of colloidal aggregation and organization at the water-air interface, they are either computer simulations20-22 or optical imaging of large particles or clusters over at least a few hundred nanometers in size.23,24 Those results may not be applicable to the real interfacial behaviors of much smaller NPs. In this article, we applied high-contrast DIC microscopy to directly monitor evaporation-mediated self-assembly of 18 nm gold nanoparticles (GNPs) at the water-air interface for the first time. The dynamics of the GNP cluster formation process and the diffusional behaviors of GNPs were followed in real time. The mechanisms of the self-assembly process and factors that affect this process are thus revealed. These findings should provide insights into controlled formation of nanoparticle assemblies at liquid-vapor interfaces, and our method could be potentially applied to study other nanoparticle self-assembly processes occurred at interfaces. Experimental Methods GNP Preparation and Characterization. AR grade HAuCl4 · 3H2O and sodium citrate were obtained from Shanghai Sinopharm. The procedure for monodisperse GNP preparation was described elsewhere.25 SEM and TEM measurements were performed using a JEM-3010 and a JSM-6700 (JEOL, Japan)

10.1021/jp807776w CCC: $40.75  2009 American Chemical Society Published on Web 01/05/2009

1210 J. Phys. Chem. C, Vol. 113, No. 4, 2009 microscopes, respectively. SEM sample preparation was performed by adding 7 µL of freshly synthesized GNP sample solution on the surface of a silicon wafer that was freshly cleaned using piranha solution and distilled water (MilliPore). Dynamic light scattering measurement was performed in a Zetasizer 3000HS (U.K.). Sample Cell Preparation. A 22 × 22 mm2 microscope cover glass was cleaned with piranha solution and then sonicated with methanol and distilled water (MilliPore). A small glass ring was adhered onto the cover glass with Norland Optical Adhesive immediately. At the start of the measurement, a 7 µL drop of GNP sample solution was added into the small sample cell and covered with another cover glass in order to minimize disturbance from the surrounding environment and to reduce the evaporation rate of water. DIC Microscopy. A Nikon TE2000U inverted microscope operated under high-contrast DIC mode was used for all the DIC measurements, if not mentioned otherwise. Images were collected using either a Plan Apo 40× objective or a Plan Apo 20× objective and sent into an air-cooled CCD (Cool Snap HQ2, 14 bits, cooled to -30 °C, Photometrics). The CCD exposure time was 50 ms. MetaVue (Universal Imaging Corp) was used to control the CCD camera. All images obtained were further processed using Image J. Fractal Dimension Calculation. The cluster′s fractal dimension was calculated using Image J. The TEM image was first converted into an 8-bit binary format (black and white). This algorithm was based on box counting. A set of different box sizes was predetermined. The fractal dimension was determined by the slope of the linear least-squares regression line for the log-log plot of box size and count. Here, box size is the size of individual boxes used to cover an object and the count refers to the number of effective pixels contained in a box.26 Computer Simulation. Computer simulation of two-dimensional Brownian motion was based on the following three assumptions.27 First, the particle steps toward a random direction once during each time interval and moves at a fixed step size. Second, the probability of going toward any direction is equal on the plane and is independent of the previous steps. Third, the particles move independently from other particles. The simulated individual particle tracks for display and MSD plot calculation are composed of a series of jumps. Each jump contains 100 small steps. The time interval of a jump is set to be equal to the frame rate of CCD camera. The length of each small step is determined by (4Dt)1/2 where D is the initial diffusion coefficient. Results and Discussion Single GNP Identification. Figure 1 shows the DIC and TEM images of the GNPs used in this study. The 2D (Figure 1A insert) image is a snapshot of GNPs dispersed in solution. Each spot consists of a bright and dark portion, which is a unique characteristic of DIC imaging. Figure 1A is the 3D plot of the snapshot. The uniform peak shape and similar peak height indicate that each spot is likely a single particle. The TEM result (Figure 1B) confirms that the GNPs are monodispersed with a diameter of 18.18 ( 0.17 nm and dynamic light scattering measurement of the GNP solution also gives a narrow particlesize distribution of 21.7 ( 5.3 nm. The Nikon microscope has both standard and high-contrast DIC (HC-DIC) modules installed. We found that clear DIC images of individual particles in motion can be obtained only in the high contrast DIC mode using 20× or 40× objectives. Under standard DIC mode, GNPs immobilized on a glass surface are still barely visible, but those

Xiao et al.

Figure 1. (A) 2D (insert) and 3D plots of the DIC image of GNPs in bulk solution, scale bar 3 µm. (B) TEM image of single GNPs.

in free solution are hardly distinguishable from the background. The reason lies in the fact that high-contrast DIC has a shear distance that is twice that of standard DIC. This enhances the optical path difference and contrast (though at a cost of lower resolution), making it easier to detect isolated thin specimens such as GNPs. It should be noted that although individual small particles are “seen” and the capability of single particle tracking is demonstrated, the optical resolution of HC-DIC is still restricted by the diffraction limit. Therefore, the observed individual single-particle-like entities are not necessarily single GNPs. They could also be small clusters. In the case of a static or slowly moving particle, however, its 2D coordinate can be obtained with nanometer resolution by using certain imageprocessing methods.16 For simplicity, the term “individual particle” in later sections refers to single-particle-like entity observed, which could be either a single GNP or a small cluster of GNPs that cannot be resolved by the microscope. Evaporation-Mediated GNP Self-Assembly. The selfassembly of 18 nm GNP at a water-air (w/a) interface is started by adding a 7 µL drop of GNP solution onto the surface of a glass coverslip. The cover glass was made hydrophilic by cleaning with piranha solution, and the GNP drop is protected by a roofed glass ring to eliminate disturbance from ambient air. The depth of field of DIC is very shallow, so one can easily

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Figure 2. Snapshots of the GNP self-assembly process at the water-air interface, scale bar 20 µm.

distinguish particles that are at the interface from those that are in the solution. Such information is not available in bright-field microscopy (which projects all objects onto a 2D plane) or darkfield microscopy (where scattering causes objects to be hidden from each other at high number densities). By changing the focal plane, we could observe GNPs spontaneously moving to the upper surface of the drop from the bulk solution. At room temperature, the 7 µL drop shrinks gradually with water evaporation and dries in about 1 h. During that period, 100frame sequences of DIC images of the same area of the w/a interface with a frame-to-frame time interval of 56 ms is taken every 2 min. The focus of microscope objective is adjusted constantly to maintain best focusing as the height of the w/a interface from the slide decreases. Figure 2 shows six snapshots at different times. It can be seen that initially the viewing area is filled with isolated individual particles plus a few small clusters. Some region is out of focus because the liquid surface is curved. As time passes, the number of individual particles and the total number of particles slowly decrease and individual particles gradually assembled into small clusters and then into large branchlike clusters with various shapes and sizes. The initial accumulation of GNPs at the w/a interface is because particles trapped at the w/a interface would minimize the Helmholtz free energy of this system. The reduced free energy ∆E, resulting from single GNP being captured from the bulk solution onto the w/a surface potential energy well, is given by12

∆E ) -(πr2 ⁄ γw/a) × [γw/a - (γp/w - γp/a)]2

(1)

where r is radius of the particle, the γp/w is the particle-water interfacial energy, γp/a is the particle-air interfacial energy, and

γw/a is the negative energy of the water-air interface. By substituting Young′s equation

γp/a ) γp/w + γw/a cos θ

(2)

into the above equation, we get

∆E ) -πr2γw/a(cos θ + 1)2

(3)

where θ is the contact angle of GNP. In our case, the contact angle θ is taken to be 60°,28 T is 294 K, and the w/a interfacial energy γw/a is 72 erg/cm2, so ∆E is about -4.12 × 10-10 erg for 18 nm GNP. Obviously, the value of ∆E is always negative regardless the contact angle of GNP, so GNPs have a tendency to be captured by the surface potential energy well spontaneously. On the other hand, to escape from the surface potential energy well, GNPs must absorb -∆E to overcome the energy barrier. The probability is very low but still exists because ∆E decreases with the second power of the particle diameter. During experiments where the microscope objective is focused on the w/a interface, we occasionally observed single GNPs disappear into the bulk solution, probably due to vigorous Brownian motion. GNP aggregation can occur without the addition of salts. Negatively charged GNPs trapped at the w/a interface should not aggregate because of the strong electrostatic repulsion between them. This is the same reason why gold colloidal solution is stable. With evaporation of the drop, the electrolyte concentration and the ionic strength of the solution both increase. This leads to partial shielding of the surface charge and reduction of the repulsive electrostatic force. So, particles are able to approach each other close enough so that van der Waals attraction force can cause adhesion. However, if the initial

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Figure 3. The variation of (A) number of clusters, (B) the average cluster size, and (C) the standard deviation of cluster size vs time.

electrolyte content of the solution is reduced substantially by dialysis, aggregation of GNPs cannot be observed until the drop is almost dried. Figure 3 illustrates the time course of the evaporation mediated GNP self-assembly process. It can be seen that the number of clusters increases initially and then decreases (Figure 3A) but both the average size (Figure 3B) and the width of the size distribution of the clusters (Figure 3C) increase monotonically with time. We also note that there exist two different rates of increase in each of the latter two quantities. The break points of all three curves occur at about the same time of ∼25 min. Therefore, the GNP self-assembly process at the air-water interface can be divided into two stages. This two-stage kinetics is apparently related to the gradually decreasing repulsive electrostatic force. During the first stage, the ionic strength of the solution is low and the electrostatic repulsion among particles is strong, so the rate of GNP aggregation is slow. Single GNPs in the bulk solution keep moving up to the air-water interface, and small clusters appear gradually at different regions of the interface. Thus the total number of clusters increases. Because clusters form in similar environments and do not have much interaction with each other, they have similar sizes and both their average size and the width of their size distribution increases slowly with time. During the second stage, ionic strength is high and electrostatic repulsion is weak, so small clusters can readily aggregate into large clusters. Both the average size and size distribution width of clusters rise rapidly. Because large clusters result from small ones and the supply of single GNPs from the bulk solution eventually becomes exhausted, the total number of clusters decreases with time.

Xiao et al. Diffusional Behaviors of Individual Particles. The ability to visualize single particles allowed us to analyze the diffusional behaviors of the observed individual particles during the selfassembly process. Particles moving at the w/a interface can be described by a 2D random walks model according to the equation MSD ) 4DT, and the diffusion coefficient D can be determined by plotting the mean square displacement MSD versus lag time T. For each 100-frame sequence, we randomly picked four to seven individual particles and tracked their motion within the sequence. A total of 164 individual particle tracks were obtained, and none of the particles stuck irreversibly with other particles or clusters throughout the sequence. We plotted the average MSD of the 164 tracks versus lag time T, and a good linear relationship (R2 ) 0.99) is obtained. Therefore, the ensemble behaviors of individual particles at the w/a interface can still be described by free diffusion, even though they encounter frequent collisions with nearby particles or clusters and the experimental conditions span a range of ionic strengths. From the slope, the average diffusion coefficient is calculated to be 1.03 µm2 s-1. Interestingly, this value is much smaller than the theoretical diffusion coefficient of 23.80 µm2 s-1 in water for a single 18 nm GNP calculated from the StokesEinstein equation D ) kBT/6πηr (where kB is Boltzmann′s constant, T is the temperature, η is the viscosity of water, and r is the hydrodynamic radius of the particle).27 There are two possible reasons: one is that these individual particles are single particles but they must overcome surface tension at the w/a interface and therefore experience a larger dragging force than those in the bulk solution; the other is that they are small clusters resulting from fast aggregation of GNPs at the interface. The origin of this phenomenon is under further study and will be reported later. At the single-particle level, the trajectory of each individual particle is determined by both its own Brownian motion and its interaction with nearby particles or clusters. The resulting individual tracks exhibit various shapes and their MSD plots assume different profiles. The majority of the 164 MSD plots can be classified into three different patterns:29 normal diffusion (linear), directional flow with diffusion, and anomalous diffusion (subdiffusion), which correspond to pure random walk diffusion, diffusion in the presence of flow, and restricted motion of the particles, respectively (Figure 4A). The rest of them are combinations of multiple patterns and are denoted as irregular. For comparison, a simulation based on 2D random motion that does not consider any particle-particle interactions was performed 164 times. D ) 1.0 µm2 s-1 was used as the initial input. The results show that the patterns of the tracks and the MSD plots from the simulation are indistinguishable from those obtained from the measurements. This indicates that during the whole period of the aggregation process, the random nature of individual particle motion is unaltered. Figure 4B shows the distribution of the three patterns in the 164 MSD plots from our measurements and from simulation. It can be seen that the number of plots with the anomalous diffusion pattern from our measurements is the same as that from simulation. However, the number of the directional flow pattern from our measurements, at the expense of the number of the linear pattern, is much larger than simulation results. This indicates that rather than being more restricted in motion, interaction with other particles or clusters leads to more directed flow of individual particles, which probably “helps” them to find their final binding targets. Similarly, the number of linear patterns from measurements is much smaller because the simulation program does not consider any particle-particle interactions. Since the

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Figure 4. (A) Three typical trajectories and their MSD plots from direct measurement. S and E denote the start and end point of the track, respectively. The left one is directional flow with diffusion. Its MSD vs Tlag was fitted using the function 〈r2〉 ) 4Dt + (Vt)2. The middle one is the normal brownian diffusion. Its MSD vs Tlag was fitted by 〈r2〉 ) 4Dt. The right one is anomalous diffusion. Its MSD vs Tlag was fitted by 〈r2〉 ) 4Dta. (B) Distribution of the three diffusion patterns from our measurements and from the simulation of 164 single-particle tracks.

ensemble behaviors of these particles are random, the observed discrepancy demonstrates that our techniques can reveal individual particle behaviors buried under ensemble measurements. To calculate the diffusion coefficient of individual particles, only the first 10 data points of each MSD plot are used. The variation of the measured D values versus time is shown in Figure 5 and appears to fit the two stage kinetics. Before t ) ∼25 min, the D values scattered randomly around 1.0 µm2 s-1 and the width of its distribution stays roughly constant. After t ) ∼25 min, there are more anomalously large D values, causing both the average value and the width of the distribution to increase slowly with time. Close examination of individual movies (Supporting Information) indicates that those particles with anomalously large D values all have one or more large clusters in the nearby region and that their tracks can be ascribed to the directional flow pattern. It is known that large clusters can cause irregular deformation of the surrounding w/a interface to change the local surface tension,30 leading to directed

Figure 5. Time variation of the distribution of individual diffusion coefficients from our measurements.

movement of nearby small particles. So, directional flow induced by the large clusters formed in the second stage of the GNP aggregation process is the most likely reason why those GNPs apparently diffuse faster. Another reason of more observed directional flow patterns in Figure 4B is probably due to

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Figure 6. (A) SEM image of dendritic GNPs cluster. (B) DIC image of the final GNP clusters formed, scale bar 20 µm. (C) Proposed anisotropic particle-cluster interaction model.

heterogeneous charge distribution on the surface of large clusters (see discussions below), which leads to anisotropic electrostatic repulsion force experienced by individual particles and prevents them from random diffusion at certain directions. GNP Cluster Formation Mechanisms. Generally, the dynamics of cluster formation of colloidal particles at the w/a interface can be described by two limiting regimes of irreversible colloid aggregation.31-33 One is the diffusion-limited cluster aggregation (DLCA) regime, where clusters stick together upon first contact due to negligible interparticle repulsive forces. The other is the reaction-limited cluster aggregation (RLCA) regime, where the cluster sticking probability is low because of a substantial repulsive barrier between the particles. In both regimes, the motion of colloidal particles is purely controlled by diffusion. Differentiations between these two regimes usually

rely on indirect proof from the calculation of fractal dimension Df of the clusters formed. Df describes the amount of branching of a cluster.34 Because clusters could take more time to rotate locally35 and sample all possible configurations before they aggregate, the fractal dimension, Df, resulting from RLCA is always larger than that from DLCA. In the case of 2D clusters, a Df of 1.55 indicates RLCA and a Df of 1.45 indicates DLCA.31 However, this criterion is not always reliable.36 For evaporationmediated aggregation of 18 nm GNPs, the movies obtained using HC-DIC give direct evidence on the mechanism. We have observed numerous collision events between particles, between clusters, and between particle and clusters. In all cases, there was no permanent or strong adhesion on first contact. Individual particles and small clusters usually recoil immediately and large clusters keep reorganizing themselves. Movie S1 (Supporting

Nanoparticle Self-Assembly Dynamics

Figure 7. (A) Reversible collision demonstrated by a 36-frame sequence of continuous interaction of three individual particles. The frame rate is 100 ms. Each arrow points to an individual particle. Two particles are believed to stick together if their images overlap with each other. Each star denotes an occurrence of at least two particles sticking together. A reversible contact event is defined as a set of frames with consecutive stars. (B) Time variation of average reversible sticking time.

Information) shows one example of cluster-cluster interaction. It can be seen that the upper smaller cluster moved close to the top end of the lower larger cluster, initiated a no-touch encounter with one end, turned counterclockwise slightly, made the first contact with its second end, stuck together for a moment, detached, turned clockwise for about 120°, and finally stuck together with the lower cluster. Our results indicate that there is a significant repulsive barrier between particles. However, clusters do keep forming and growing, indicating that the repulsive barrier is surmountable as the total electrolyte concentration gradually increases with evaporation. Therefore, the cluster growth mechanism is more like RLCA rather than DLCA based on their definitions as to whether the observed sticking probability is much less than unity. This actually demonstrates the power of HC-DIC in studying 2D colloid aggregation; that is, we can derive the mechanism directly from the dynamics of individual particles during their assembly process instead of ensemble or postanalysis of structural properties of aggregated particles. Such real time information of individual particles cannot be obtained using existing techniques such as dynamic light scattering, small-angle X-ray scattering, and neutron scattering. On the other hand, from both the SEM (Figure 6A) and the DIC (Figure 6B) images, the structure of the clusters is open

J. Phys. Chem. C, Vol. 113, No. 4, 2009 1215 and loose with few branching points. The fractal dimension calculated from the SEM result (Figure 6A, based on the cluster within the box), however, gives Df ) 1.36, a value less than that predicted from the RLCA model. We ascribe this discrepancy to anisotropic cluster growth; i.e., irreversible adhesion is more likely to occur at the cluster tips. Movie S2 (Supporting Information) shows the interaction path between an individual particle and a cluster. It can be seen that an individual particle approached a cluster from its side for the first time. However, it did not stick together there; instead, it bounced along the side of the cluster several times and finally settled at one of its terminals. This indicates that charge or repulsive barrier is not uniformly distributed on the surface of the cluster. The repulsive barrier is much smaller when a particle or a cluster approaches another cluster at its terminal rather than on its side (Figure 6C), since nearest neighbors can contribute cooperatively to the repulsive force. As a result, clusters prefer to grow on the ends, leading to less branching and a smaller Df. Anisotropic cluster formation of silica microspheres in a DLCA situation has been reported by Hurd and Schaefer,36 but what we observed here is a RLCA-like behavior. The contradiction between the RLCAlike cluster formation and the small Df cannot be explained by invoking only diffusion and short-range particle-particle interactions. One possible explanation is that in the classical DLCA or RLCA regime, the colloidal particles reside in a homogeneous environment with constant ionic strength. However the GNPs here experience a heterogeneous environment with increasing ionic strength due to drying of the colloidal droplet. With a relatively large surface-to-volume ratio and comparably fast evaporation rate of the solution, the local ionic strength, surface tension, and microflow across the surface could be quite diverse and all contribute to the observed individual particle dynamics and anisotropic cluster growth. Moreover, here the droplet dries in about an hour. But it is known that in the traditional RLCA regime, the cluster formation rate is many times slower than the corresponding DLCA regime. As a result, the small Df clusters obtained may belong to an intermediate of a RLCA regime that has not reached its most stable state before complete drying of the solution. Therefore, dynamics at the single/individual particle level may not necessarily comply with the traditional RLCA or DLCA regime derived from bulk or static measurements. More studies are underway to elucidate this phenomenon. Besides directly showing that the sticking probability is less than unity, our individual particle tracking technique also allows us to get a deeper understanding of this important parameter by analyzing individual collision events. The sticking probability, in the simplest form, should be determined by both Brownian motion and the competition between electrostatic repulsion and van der Waals attraction between the two particles or clusters. Because it is difficult to count the exact number of sticking and nonsticking collision events, a direct quantitative evaluation of its value is not readily achieved if not impossible. Since multiple reversible contacts are needed before an irreversible adhesion can happen, we suspect that the sticking probability is related to the reversible sticking time, ∆trev, for which two particles or clusters stick together during a reversible contact before detaching. Figure 7A shows a 36-frame sequence of interaction of three individual particles, taken at ∼20 min after the start of evaporation. Four reversible contacts are identified. It can be seen that ∆trev of the four events are not equal even for the same particles over just 3.6 s. Two of them last no more than 100 ms, but the other two last more than 400 ms. Because the electrostatic repulsion force decreases with evaporation of

1216 J. Phys. Chem. C, Vol. 113, No. 4, 2009 the solution, the average sticking probability should increase accordingly. Figure 7B shows the time variation of the average ∆trev, in agreement with the expected trend of sticking probability. Two distinct groups before and after about 25 min are clearly visible, indicating that the transition of sticking probability from small to large values is not smooth. This two-stage kinetics is in accord with the observed cluster growth behaviors observed in Figure 3. Conclusions Direct observation of evaporation mediated self-assembly of 18 nm GNP at the water-air interface was achieved by HCDIC microscopy for the first time. The high time resolution and optical section capability of HC-DIC allowed us to track the motion of individual particles and monitor the dynamic clustering process of GNPs consecutively. The results presented in this study provide a clear picture about the GNP self-assembly process and factors that affect this process. The surface potential energy well captured GNPs from the bulk solution onto the w/a interface, Brownian motion allows particles and clusters to make contact with each other, and competition between gradually decreasing electrostatic repulsion and the van der Waals attraction force determines whether two particles or clusters will stick together. Further studies are under way to control the selfassembly process by adjusting these three factors separately. Acknowledgment. This work was supported by NSFC (20605008), Program for New Century Excellent Talents in University, and Hunan University 985 fund. E.S.Y. thanks the Ames Laboratory for partial support of this work. Supporting Information Available: Two movies show the GNP cluster-cluster interaction (S1) and particle-cluster interaction (S2). This material is available free of charge via the Internet at http://pubs.acs.org. References and Notes (1) Freeman, R. G.; Grabar, K. C.; Allison, K. J.; Bright, R. M.; Davis, J. A.; Guthrie, A. P.; Hommer, M. B.; Jackson, M. A.; Smith, P. C.; Walter, D. G.; Natan, M. J. Science 1995, 267, 1629.

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