J. Phys. Chem. C 2009, 113, 10517–10520
10517
Melting Phase Transitions and Catalytic Activity of Bilayer Gold Nanoclusters Yanting Wang† and Sergey N. Rashkeev* Center for AdVanced Modeling & Simulation, Idaho National Laboratory, Idaho Falls, Idaho 83415-3553 ReceiVed: April 1, 2009; ReVised Manuscript ReceiVed: April 28, 2009
Recent experiments in oxidation catalysis indicate that bilayer gold nanostructures exhibit exceptional catalytic activity at ambient temperatures. Here we use molecular dynamics simulations to show that an unsupported bilayer gold nanocluster has a melting transition smeared out over a broad temperature range. The transition is characterized by an interplay between the intralayer and interlayer diffusion processes, and the transition temperature region ranges from about 300 to 1200 K. We suggest that surface thermal instabilities of partially melted bilayer gold nanoclusters result in their exceptional catalytic activity at ambient temperatures. For gold nanoclusters with more than two layers, the melting transition temperature range narrows, and the activity of the cluster decreases due to the suppression of surface fluctuations. These results systematically explain experimental observations showing that catalytic ability of gold nanoclusters decreases with size. 1. Introduction Nanogolds have many perspective applications in various fields, such as bioelectrocatalysis,1 sensors,2 and nanotechnology.3 Also, in contrast to the inertness of larger gold clusters, supported small gold nanocrystals (e5 nm) have exceptional properties in oxidation catalysis including the oxidation of carbon monoxide at room temperature,4 but the origin of their catalytic activity is still not fully understood. The enhanced activity of small Au particles was attributed to different nanoparticle features (perimeter sites,5 low-coordination atoms,6 charge transfer,7 dynamic structural fluxionality,8 nonmetallic properties,9 oxidation state10), or to the combination of several of them that collectively result in the observed behavior.11 Recent aberration-corrected scanning transmission electron microscopy (STEM) analysis for Au nanoclusters on iron oxide supports the correlation between the catalytic activity with the presence of bilayer clusters that are ∼0.5 nm in diameter and contain only ∼10 gold atoms.12 This is consistent with an earlier observation that a bilayer gold structure on titania is significantly more active than a monolayer.13 Another recent experiment showed that very small gold entities (∼1.4 nm) derived from 55-atom gold clusters and supported at inert materials (BN, SiO2, and carbon) are efficient catalysts for selective oxidation of styrene by dioxygen at 100 °C.14 It is also well-known that the melting temperature of small gold clusters decreases with their size.15-17 Computer simulations17,18 have shown that gold nanoclusters with a moderate size of 600-5000 atoms (∼2-5 nm in diameter) in vacuum form icosahedral solid structures at low temperatures, which agrees well with recent STEM data.19 With increasing temperature, the surface of an icosahedral nanogold softens due to the increasing mobility of the vertex and edge atoms, but does not melt until reaching the “bulk” melting temperature, when both the interior and the outside layers simultaneously become liquid. It is questionable whether such a mechanism still holds for even smaller gold nanoclusters. Very small Au clusters (∼10 atoms) have the capability to exhibit several structural forms * To whom correspondence should be addressed. E-mail: sergey.
[email protected]. † Also at the Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China.
Figure 1. The quenched configuration of the 54-atom gold nanocluster at T ) 100 K: (a) interior and (b) surface layer.
(isomers) of comparable energies, and to interconvert between such isomers at finite temperatures (dynamic structural fluctionality).20 This structural variability may influence the chemical reactivity of Au-based nanocatalytic systems by forming isomers with high chemical reactivity and by adapting the structure and lowering the reaction free-energy path. Clusters with 50-600 atoms are not expected to exhibit such a high fluxionality in a solid state, but they are expected to exhibit different structural rearrangements (especially at their surface) while the temperature increases. How will these rearrangements affect their catalytic activity? To address this question, we have conducted molecular dynamics (MD) simulations for clusters with 54, 146, 308, and 560 gold atoms in vacuum. These numbers correspond to the entities that consist of 2, 3, 4, and 5 complete atomic layers, respectively. To our knowledge, no systematic study has been done for the thermal stability and melting behavior of such small nanoclusters. The cluster with 54 atoms has a special interest for us because (i) it is a bilayer gold structure, (ii) the experiment14 shows extreme oxidation activity of similar size clusters at inert substrates (which are not expected to play any role in the catalysis) at a temperature of 100 °C, and (iii) most of its atoms (46) are under-coordinated and positioned at its surface (the remaining 8 atoms form a cubic interior, see Figure 1). The surface of this cluster has a nonideal cubic shape, with
10.1021/jp902995x CCC: $40.75 2009 American Chemical Society Published on Web 05/26/2009
10518
J. Phys. Chem. C, Vol. 113, No. 24, 2009
Wang and Rashkeev
Figure 2. (a) Caloric curve of the 54-atom gold nanocluster. The two dashed lines are extrapolated from the linear parts in the two ends of the curve. (b) Constant-volume heat capacity Cv of the 54-atom gold nanocluster vs. temperature T.
Figure 3. Snapshots of the last configurations at several temperatures for the 54-atom gold nanocluster. Gray spheres represent gold atoms positioned at the cluster surface at the beginning of the MD simulation at T ) 100 K, and green spheres represent atoms that were initially positioned in the cluster interior.
slightly distorted hexagonal structures at the facets, which is consistent with the cubic interior.
ends of the curve, the transition starts at the onset temperature of Ts ≈ 300 K and ends at Te ≈ 1200 K, and the curve changes its slope slowly and continuously in the transition region. For a system with a finite number of particles, the constant-volume heat capacity (that is nearly equal to the constant-pressure heat capacity because the external pressure on the isolated gold nanocluster is close to zero) can be calculated from the fluctuations of Ep:24
2. Simulation Methods The ground state of a solid gold nanocluster at low temperatures was obtained by the simulated annealing procedure. A random configuration with 54 atoms went through a constant temperature MD simulation at T ) 1500 K for 286 ns (108 steps), with a time step of 2.86 fs. The empirical glue potential21 was employed to model the many-body interactions between the gold atoms. The temperature of the system was kept constant by rescaling the velocities of the atoms at every MD step. The final structure was then cooled step by step from 1500 to 100 K, with a temperature interval of 100 K. The thermal fluctuations were quenched by the conjugate gradient minimization method,22 and the surface atoms were identified by the cone algorithm.17 The final structure of the cluster at T ) 100 K is shown in Figure 1. Using the parallel tempering simulation23 approach, we confirmed that the relaxed structure is indeed the global energy minimum structure at T ) 100 K. Due to the large surface-to-volume ratio of this cluster, it does not form either a face-centered-cubic structure as bulk gold metal or an icosahedral structure as midsized gold nanoclusters. The obtained stable solid structure was then heated step by step in the temperature range between 100 to 1500 K, with a temperature interval of 100 K (a finer interval of 20 K was used for the temperatures between 300 and 900 K). At each temperature, potential energy was sampled evenly 105 times, and instantaneous configurations were sampled evenly 104 times. 3. Results and Discussion The caloric curve (average potential energy per atom Ep vs. temperature T) obtained for the 54-atom gold cluster is plotted in Figure 2a. In contrast to the first-order melting transition for bulk gold that takes place at a single, well-defined temperature, the melting transition for this nanocluster spans a wide temperature range. According to the extrapolations of the two linear
Cv )
(〈Ep2〉 - 〈Ep〉2) kBT 2
3 + NkB 2
(1)
where kB is the Boltzmann’s constant, T is the system temperature, N is the number of atoms, and the acute brackets mean the ensemble average. For the 54-atom gold nanocluster, the heat capacity vs. temperature plot exhibits a broad peak that also starts at Ts ≈ 300 K and ends at Te ≈ 1200 K (Figure 2b). A visual examination of individual atomic trajectories suggests that the intralayer and interlayer diffusion processes at the cluster take place at different temperatures (Figure 3). At T ) 100 K, the gold nanocluster retains its original solid structure after 108 MD steps. At T ) 560 K, the surface layer gets an irregular structure after 108 steps, but interior atoms do not appear at the surface yet. A more detailed analysis confirmed that at this temperature both the surface and interior atoms diffuse a lot within their original layer as in a liquid state, but without any interlayer exchange. At T ) 620 K, several interior atoms come up to the surface, and the interlayer diffusion becomes more pronounced at T ) 760 K. To quantify the interlayer diffusion, the fractions of moVed atoms were calculated for the surface layer and the interior, respectively. At each temperature, the cone algorithm17 was used to identify the surface and interior atoms for all the saved configurations. An atom is marked as “moved” at a given temperature if it moves from the layer where it was positioned at the beginning of the simulation to the other layer at least
Catalytic Activity of Bilayer Gold Nanoclusters
J. Phys. Chem. C, Vol. 113, No. 24, 2009 10519 TABLE 1: Comparison of the Melting Transition Onset Temperatures Ts, Transition End Temperatures Te, and Interlayer Diffusion Temperatures Ti for Gold Nanoclusters with Different Number of Layers
Figure 4. (a) Fractions of moved atoms for the surface layer and the interior, showing the degrees of the interlayer diffusion at various temperatures for the 54-atom gold nanocluster. (b) Diffusion coefficient D vs. temperature T from 100 to 600 K for the 54-atom gold nanocluster.
once during the full time interval of the MD simulation. For each layer, the fraction of the moved atoms is calculated as a ratio of the number of moved atoms in this layer and the total number of atoms positioned in this layer at the initial moment of the simulation. As shown in Figure 4a, the interlayer diffusion does not happen at temperatures below Ti ) 560 K, all of the interior atoms “move” at temperatures higher than 660 K, and all the surface atoms “move” at temperatures higher than 760 K. The atomic diffusion process (that includes the contributions from both the interlayer and intralayer diffusion) was also quantified by using the mean square displacement (MSD), defined as M
∆r2(t) )
layers
Ts
Te
Ti
54 146 308 560
2 3 4 5
300 350 400 550
1200 1000 900 850
560 300 400 500
(Figure 4a). Therefore, below and at Ti ) 560 K, the growth of the diffusion coefficient is related to the intralayer atomic diffusion only, while at higher temperatures both interlayer and intralayer diffusion processes occur. Consistent with the caloric curve, the diffusion coefficient plot grows continuously without disruptive jumps. The nanocluster retains a solid state below Ts ≈ 300 K, and becomes a complete liquid drop above Te ≈ 1200 K. In the temperature range between 300 and 1200 K, the dynamics of its partially melted states strongly depends on the temperature. Similar MD simulations have also been performed for three larger gold nanoclusters with 146 atoms (3 complete atomic layers), 308 atoms (4 layers), and 560 atoms (5 layers). The simulated annealing simulations indicate that at low temperatures the cluster surfaces consist mainly of {111} facets. When the number of layers increases, the structure of the cluster becomes closer and closer to an icosahedron.17-19 The onset temperature Ts, the end temperature Te of the melting transition, and the interlayer diffusion temperature Ti for all of these clusters are compared in Table 1. It can be seen that the transition temperature range narrows as the cluster size increases. Except for the 54-atom cluster, the interlayer diffusion starts at a temperature Ti that is lower than Ts. The above-described behavior of small gold nanoclusters is consistent with the one suggested for the midsize icosahedral gold nanoclusters (600-5000).17,18 In midsize icosahedral gold nanoclusters, the vertex and edge atoms on its surface have a smaller binding energy than the atoms positioned in the center
N
∑ ∑ [ri(tj + t) - ri(tj)]2
1 MN j)1
atoms
(2)
i)1
where ri, i ) 1, ..., N, are the positions of the N atoms, t is the time interval over which the motion takes place, and tj ≡ tj-1 + t. The ensemble average 〈∆r2(t)〉 is taken for all the available data. The diffusion coefficient D as a function of the temperature T was determined by fitting 〈∆r2(t)〉 to the early time linear part of MSD by using the relation 〈∆r2(t)〉 ) 6Dt (Figure 4b). At T ≈ Ts ) 300 K, D becomes larger than 0.01 Å2/ns (corresponding to a displacement of each atom on an average Au-Au interatomic distance within a time interval of 100 ns), which roughly indicates that the motion in the gold nanocluster starts to exhibit a diffusive component in addition to the vibrations in the solid-state phase. Therefore, atoms start to diffuse at temperatures close to Ts, which is consistent with the behavior of the caloric and heat capacity curves at this temperature (Figure 2). At T > Ts, the diffusion coefficient grows rapidly with the temperature. At Ti ) 560 K, the diffusive motion is already quite intensive, and the structure of the cluster is very different from its low-temperature, solid-state structure. However, the interlayer diffusion does not happen until Ti ) 560 K
Figure 5. The distributions of instantaneous coordination numbers for surface Au atoms for (a) a bilayer cluster (54 atoms) and (b) a fivelayer cluster (560 atoms). The distributions are shown for T ) 300 (blue bars) and 1000 K (red bars). The cutoff radius for counting coordination numbers is 11% larger than an average distance between gold atoms.
10520
J. Phys. Chem. C, Vol. 113, No. 24, 2009
of the stable {111} facets. Therefore, the edge and vertex atoms diffuse at temperatures lower than the system melting temperature, resulting in the softening of the {111} facets. For gold nanoclusters with thousands of atoms, the portion of the vertex and edge surface atoms is so small that their “premelting” contributes too little to the caloric and heat capacity curves. Most of the gold atoms melt concurrently at the bulk melting temperature Tm. Therefore, the melting transition of these clusters exhibits a first-order transition behavior.17,18 When the size of the cluster decreases, the fraction of atoms located in the cores of the {111} facets and below the surface also declines. For the 54-atom gold nanocluster, the facets are so small that all of the surface atoms are effectively vertex and edge atoms. Thus, the surface premelts at a very low temperature, and the melting transition in the whole cluster is spanned over a wider temperature range. The complete melting transition occurs at higher temperatures when the interlayer diffusion processes also become significant, and the whole cluster reaches the real liquid state. Such an unusual multistep melting transition in small nanoclusters should significantly affect their catalytic activity. Structural fluctuations and surface diffusion that start near the onset melting transition temperature (Ts) will form surface sites with the coordination that is lower than the coordination of the same sites in equilibrium at low temperatures. Figure 5a clearly shows that for the bilayer cluster the distribution of the instantaneous coordination numbers of surface Au atoms has a maximum at the coordination number n ) 6 and does not dramatically change when the temperature increases from 300 (which is the onset temperature Ts for this cluster) to 1000 K (when the cluster is nearly liquid). At T ) 300 K, the fraction of surface sites with n e 5 is about 0.36. For the five-layer cluster (Figure 5b), the fraction of surface sites with n e 5 changes from 0.04 to 0.22 while the temperature changes from 300 to 1000 K. If we follow ref 11 and assume that only the sites with n e 5 are active for the CO oxidation by dioxygen (for the sites with n e 5, the oxidation barrier is lower than the binding energy of the O2 molecule), we estimate that the bilayer cluster contains about 9 times more active sites per surface Au atom than the five-layer one. Such a dynamic mechanism of the formation of catalytically active surface sites by the structural fluctuations is somehow similar to the dynamic fluxionality for smaller clusters (∼10 atoms) described in ref 20 and performs the same functions: (i) it forms catalytically active surface sites and (ii) the cluster structure adapts to adsorbed reacting molecules and lowers the reacting barrier. 4. Conclusions In conclusion, we have found that the 54-atom (bilayer) gold nanocluster forms a hexagonal-covered cubic-shaped solid structure. It exhibits a broad and mild melting transition, with the intralayer diffusion starting at a lower temperature than the interlayer diffusion. With increasing number of atoms (layers), the transition temperature range narrows. The distribution of the instantaneous coordination numbers for surface atoms
Wang and Rashkeev indicates that the fraction of low-coordinated sites at the bilayer cluster is about 9 times higher in the bilayer than in the fivelayer cluster at 300 K, which explains the high catalytic activity of 55-atom clusters at inert substrates described in ref 14. Also, the unusual multistep melting transition in small nanoclusters may explain their other physical and chemical properties and facilitate their use in different applications. Acknowledgment. We would like to acknowledge the INL Laboratory Directed Research and Development program and the U.S. Department of Energy, Office of Nuclear Energy under DOE Idaho Operations Office Contract DE-AC07-051D14517 for financial support. This research used resources of the National Energy Research Scientific Computing Center (NERSC), which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. It was also supported in part by a grant of computer time from High Performance Computer Center at Idaho National Laboratory. References and Notes (1) Xiao, Y.; Patolsky, F.; Katz, E.; Hainfeld, J. F.; Willner, I. Science 2003, 299, 1877. (2) Obare, S. O.; Hollowell, R. E.; Murphy, C. J. Langmuir 2002, 18, 10407. (3) Daniel, M.-C.; Astruc, D. Chem. ReV. 2004, 104, 293. (4) Haruta, M.; Tsubota, S.; Kobayashi, T.; Kageyama, H.; Genet, M. J.; Delmon, B. J. Catal. 1993, 144, 175. (5) Haruta, M. J. New Mater. Electrochem. Syst. 2004, 7, 163. (6) Remediakis, I. N.; Lopez, N.; Nørskov, J. K. Angew. Chem., Int. Ed. 2005, 44, 1824. (7) Liu, Z.-P.; Gong, X.-Q.; Kohanoff, J.; Sanchez, C.; Hu, P. Phys. ReV. Lett. 2003, 91, 266102. (8) Yoon, B.; Ha¨kkinen, H.; Landman, U.; Wo¨rz, A. S.; Antonietti, J.-M.; Abbet, S.; Judal, K.; Heiz, U. Science 2005, 307, 403. (9) Valden, M.; Lai, X.; Goodman, D. W. Science 1998, 281, 1647. (10) Guzman, J.; Gates, B. C. J. Am. Chem. Soc. 2004, 126, 2672. (11) Rashkeev, S. N.; Lupini, A. R.; Overbury, S. H.; Pennycook, S. J.; Pantelides, S. T. Phys. ReV. B 2007, 76, 035438. (12) Herzing, A. A.; Kiely, C. J.; Carley, A. F.; Landon, P.; Hutchings, G. J. Science 2008, 321, 1331. (13) Chen, M. S.; Goodman, D. W. Science 2004, 306, 252. (14) Turner, M.; Golovko, V. B.; Vaughan, O. P. H.; Abdulkin, P.; Berenguer-Murcia, A.; Tikhov, M. S.; Johnson, B. F. G.; Lambert, R. M. Nature 2008, 454, 981. (15) Buffat, P.; Borel, J.-P. Phys. ReV. A 1976, 13, 2287. (16) Ercolessi, F.; Andreoni, W.; Tosatti, E. Phys. ReV. Lett. 1991, 66, 911. (17) Wang, Y.; Teitel, S.; Dellago, C. J. Chem. Phys. 2005, 122, 214722. (18) Wang, Y.; Teitel, S.; Dellago, C. Chem. Phys. Lett. 2004, 394, 257. (19) Li, Z. Y.; Young, N. P.; Di Vece, M.; Palomba, S.; Palmer, R. E.; Bleloch, A. L.; Curley, B. C.; Johnston, R. L.; Jiang, J.; Yuan, J. Nature 2008, 451, 46. (20) Ha¨kkinen, H.; Abbet, S.; Sanchez, A.; Heiz, U.; Landman, U. Angew. Chem., Int. Ed. 2003, 42, 1297. (21) Ercolessi, F.; Parrinello, M.; Tosatti, E. Philos. Mag. A 1988, 58, 213. (22) Press, W. H.; Teukolsky, S. A.; Vetterling, W. T.; Flannery, B. P. Numerical Recipes in C: The Art of Scientific Computing; Cambridge University Press: Cambridge, UK, 1992. (23) Frenkel, D.; Smit, B. Understanding Molecular Simulation: From Algorithms to Applications; Academic Press: San Diego, CA, 2002. (24) Allen, M. P.; Tildesley, D. J. Computer Simulation of Liquids; Clarendon Press: Oxford, UK, 1987.
JP902995X
