Structural Motifs of Bimetallic Pt - American Chemical Society

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Structural Motifs of Bimetallic Pt101−xAux Nanoclusters

Maribel Dessens-Félix,† Rafael Pacheco-Contreras,‡,§ Giovanni Barcaro,⊥ Luca Sementa,⊥ Alessandro Fortunelli,*,⊥ and Alvaro Posada-Amarillas*,‡ †

Programa de Doctorado en Ciencias de Materiales, Departamento de Investigación en Polímeros y Materiales and ‡Departamento de Investigación en Física, Universidad de Sonora, Boulevard Luis Encinas and Calle Rosales, Colonia Centro, 83000 Hermosillo, Sonora, México § División de Ingeniería y Tecnología, Universidad de la Sierra, 84560 Moctezuma, Sonora, México ⊥ CNR, Consiglio Nazionale delle Ricerche, Pisa 56124, Italy S Supporting Information *

ABSTRACT: The evolution of the structure of bimetallic Pt101−xAux (x = 0− 101) clusters is theoretically studied as a function of composition. The basin hopping method using the Gupta empirical potential (EP) is used to perform an exhaustive sampling of the potential energy surface (PES). Several highly symmetric morphologies such as Marks decahedra, incomplete icosahedra, two types of anti-Mackay-covered 5-fold structures, Leary tetrahedra and closepacked structures are identified and reoptimized at the first-principles density functional theory (DFT) level to take into account electronic effects. Alloyed configurations at very low Pt content and ubiquitous Pt(core)Au(shell) segregated motifs with different morphology and core shape are found as the lowest energy structural motifs at the empirical potential level, with an appreciable influence of Pt concentration on the nanocluster structure and a strong competition between different structural motifs, especially in the region of the lowest values of mixing energy. At variance with these predictions, at the DFT level a core−shell crystalline motif (which is only marginally present as a global minimum at the Gupta level) becomes dominant over a broad range of compositions including pure particles. This shows the importance of adopting a combined DFT/empirical−potential investigation for thirdrow transition metal clusters, also in connection with the prediction of the catalytic properties of these systems.

1. INTRODUCTION The study of nanoscale systems has become a subject of great interest for science and technology in the past few years,1−3 mainly because the displayed properties may differ significantly from those shown at the macroscopic level. These differences are associated with the increase in the surface/volume ratio and finite-size electronic effects causing morphology, chemical ordering, and composition changes.4−6 New technological applications in fields such as medicine,7 catalysis,8−10 and pollution control,11 to mention a few, can then take advantage of this peculiarity of nanoscale materials to improve device performance and component materials quality. Beyond doubt, before any technological development, quantification of the physical and chemical properties of these systems is mandatory as well as the development of synthesis methods which allow the production of tailor-made nanoparticles (NPs). There is also the intellectual need of profound atomic level knowledge of nanoscale materials, which might contribute to further future progress. Binary nanoparticles have recently raised considerable interest in this field12−15 mainly because they are excellent candidates for designing advanced functional nanomaterials. Experimental approaches have been devised to control the chemical ordering in bimetallic nanoclusters with the aim of © 2013 American Chemical Society

tailoring properties, establishing reproducible methodologies that make mass production feasible through chemical or physical methods.16,17 Among bimetallic clusters, Pt-based NPs are of special importance because of a wide range of applications, which include chemical,18 food,19 and renewable energy20 industries. Motivated by their potential use in fuel cell technology, core−shell Pt−Au NPs were recently synthesized, focusing on Pt−Au NPs supported on carbon substrates, quantifying the support’s influence on catalytic activity and selectivity.21 Also experimentally demonstrated has been the possibility of controlling nanoscale alloying and phase segregation of small-sized Pt−Au nanoparticles,22 analyzing also the effect of temperature on the chemical ordering and, consequently, the catalytic activity. Herbani et al.23 fabricated alloyed binary (Pt−Au and Pt−Ag) and ternary (Au−Pt−Ag) nanoparticles by irradiating mixed solutions of noble metals Au, Pt, and Ag ions with femtosecond laser pulses. They claimed to find good agreement with Vegard’s law when measuring the interplanar spacing of Pt-containing NPs, finding intermediate values between those of the pure elements. This was invoked as Received: July 9, 2013 Revised: September 7, 2013 Published: September 12, 2013 20967

dx.doi.org/10.1021/jp406780e | J. Phys. Chem. C 2013, 117, 20967−20974

The Journal of Physical Chemistry C

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evidence of alloy formation in binary nanoparticles. However, it has been recently demonstrated that Vegard’s law is ambiguous at the nanoscale;24 i.e., theory and experiment may disagree on the description of the structure in this size regime. While theory predicts that the thermodynamically favored arrangement of a system is core−shell, X-ray diffraction detects randomly mixed alloys, probably due to hindered chemical ordering kinetics.25 The importance of having alternative schemes for characterizing unambiguously the structural differences in binary NPs is highlighted by this discrepancy in theoretical and experimental results. Careful studies have revealed the complexity of binary nanoparticles (nanoalloys), validated by experimental and theoretical results26−29 which exhibit a broad diversity of shapes even for a small number of atoms and a fixed composition. Transmission electron microscopy (TEM) observations and X-ray diffraction measurements corroborate this, while a theoretical explanation is given in terms of the large number of minima associated with the potential energy surface (PES), which is sampled through mathematical algorithms designed to search and find the lowest energy structures, i.e., local and global minima on the PES. This type of theoretical approach provides a physical representation and previous knowledge of their structural properties, which is desirable prior to experimental synthesis and characterization works in order to optimize resources and, in due case, to reduce the pollution impact on the environment. Such an increase in structural complexity in binary NPs is due to the existence of homotops,30 and the problem of minima becomes a combinatorial one. PES exploration on nanoalloys through specialized algorithms has been the subject of a number of reports, where different global search methods have been utilized. Several recent reviews describe, for example, the basin hopping (BH) method31 and improvements to this, applied to both mono- and bimetallic clusters.32−36 Important contributions include also the use of genetic algorithms (GA) in global geometry optimization of clusters,37 the structural and energetical analysis of various homogeneous and heterogeneous nanoclusters,38−42 and also strategies using semiempirical potentials through approaches to explore the energy landscape, producing as a result a representation in terms of a tree graphic (disconnectivity graphs) which contains topological information on the potential energy hypersurface.43−47 The most recent methodologies, however, try to combine different theoretical levels and to validate/refine the prediction of empirical potentials via first-principles calculations. This is both feasible and necessary when the studied system is composed of several tens of atoms,48−52 and a deep understanding of electronic properties is required in order to predict its behavior for specific applications, such as catalysis.53−55 In particular, a very effective approach proposed by some of us49,51,52 is to conduct a systematic exploration at the empirical potential level, single out the structural motifs predicted by this lower level approach, and then conduct firstprinciples local optimizations starting from the previously identified database of competing motifs. The idea behind this method is that the empirical potential is accurate enough to predict correctly the energetic order among configurations belonging to the same structural family, but possibly not the relative energetics of different families, so that structural diversity is explored by including in the first-principles optimizations configurations representative of the various families as singled out by the empirical potential search.56

In this paper we adopt this combined methodology and report a thorough study on the structural properties of 101atoms Pt−Au nanoparticles, looking for morphological trends associated with chemical composition in these clusters. At the empirical potential level, putative global minima in the full range of compositions are found using the BH method. Our results shed light on the type of structural motifs that can be found for these bimetallic nanoclusters as the number of platinum atoms changes. Marks decahedron appears to be the lowest energy geometry in ca. 50% of the possible compositions, all of which exhibit, for mixed clusters, Pt atoms segregated into the core. A structural transformation pattern emerges, driven by the change in Pt composition, also related to the effect of the core’s shape on the adopted lowest energy structures. Several recent investigations have presented detailed analyses of stable, structure, and size selected Pt−Au nanoparticles,57−60 and composition-induced structural transitions have also recently been reported for binary Lennard-Jones clusters.61 In this work we give insight into the composition-induced effects on the lowest energy structural motifs of 101-atom Pt−Au clusters through the use of an empirical potential and show that density functional theory (DFT) analysis provides a quite different, and much simpler, energy landscape. A clear prevalence of crystalline motifs is apparent, and a residual competition only survives between fcc and Marks decahedra, especially at Pt concentration lower than 30%. The discrepancy between empirical potential (EP) and DFT predictions is then attributed to directionality effects which play a role for all metals and become crucial in third-row transition and noble metal atoms.48,62 The article is structured as follows. Section 2 describes the adopted methodology. Section 3 reports and discusses results, while section 4 summarizes the main findings of this work.

2. METHODOLOGY 2.1. Gupta Potential. We modeled the interatomic interactions by the Gupta potential, which is a many-body potential based on the Friedel’s tight-binding model and consists of a two-body repulsive component and a many-body attractive term. In this model, the configurational energy of a cluster is written as the sum over all of the atoms of attractive and repulsive energy components63 N

Vclus =

∑ {V r(i) − V m(i)} (1)

i r

where the Born−Mayer pair repulsive term V (i) is expressed as V r(i) =

N

⎡

j≠i

⎣

⎞⎤ − 1⎟⎥ ⎝ r0(α , β) ⎠⎥⎦ ⎛

∑ A(α , β) ln⎢⎢ − p(α , β)⎜

rij

(2)

and the many-body attractive term Vm(i) is expressed as V m(i) =

N

⎡

j≠i

⎣

⎞⎤ − 1⎟⎥ ⎝ r0(α , β) ⎠⎥⎦ ⎛

∑ ξ 2(α , β) ln⎢⎢ − 2q(α , β)⎜

rij

(3)

α and β represent the atomic species of atoms i and j, respectively. A, ξ, p, and q are the potential parameters that are usually fitted to experimental properties of bulk metals and alloys, such as the cohesive energy, lattice parameters, and independent elastic constants for the reference crystal structure 20968



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at 0 K; r0 denotes the nearest neighbor distance of the pure bulk elements, often taken as the average of the pure distances, but it can also be taken as the experimental nearest neighbor distance in some specific ordered bulk alloy; and rij is the distance between atoms i and j. Values of the Gupta potential parameters describing Pt−Pt and Au−Au interactions are taken from the work of Cleri and Rosato.63 The heteronuclear (Au− Pt) parameters used in this work are the average parameters introduced by Logsdail et al.,64 which have been successfully used in previous theoretical studies of bimetallic clusters.65 Table 1 shows the parameter values used in this work.

where E(PtNAuM) is the energy of a given structure composed by N atoms of Pt and M atoms of Au and E(Pt101) and E(Au101) are the energies of the lowest energy structures of the pure platinum and gold clusters, respectively. Thus, a correlation between the morphological changes and the clusters’ relative stability is devised, finding in this way atomic composition regions depicted by different structural motifs. 2.3. DFT Calculations. DFT calculations were performed using the Quantum ESPRESSO package,70 employing a basis set of plane waves, ultrasoft pseudopotentials,71 and the Perdew−Burke−Ernzerhof (PBE) exchange-correlation functional (xc-functional).72 The chosen pseudopotentials include relativistic effects for the Pt atom at the scalar relativistic level. Values of 40 and 240 Ry (1 Ry = 13.606 eV) were used as the energy cutoff for the selection of the plane-wave basis set for the description of the wave function and the electron density, respectively. Eigenvalues and eigenstates of the Kohn−Sham Hamiltonian have been calculated at the Γ point only of a cubic cell of side of approximately 20 Å, applying a Gaussian smearing technique with a broadening of the one-particle levels of 0.03 eV. The DFT local relaxations were performed by fully relaxing the coordinates of the metal atoms until the forces were smaller than 0.05 eV Å−1 and have been checked with relaxations using a threshold of 0.01 eV Å−1, finding total energy values differing by a few hundredths of an electronvolt.

Table 1. Gupta Potential Parameters parameter

Au−Au

Pt−Pt

Pt−Au

A ξ p q r0

0.2061 1.790 10.229 4.036 2.884

0.2975 2.695 10.612 4.004 2.7747

0.250 2.20 10.42 4.02 2.830

As it will be shown in the following, we found significant differences between the empirical potential and the DFT modeling of these nanoalloys. This led us to be particularly careful and to conduct as thorough an investigation at the empirical level as possible, exploring as much diversity52 as achievable within the given approach to minimize the possibility that other structural motifs, not predicted by the empirical potential, might result in being competitive at the DFT level. 2.2. Basin Hopping Method. The basin hopping algorithm has been thoroughly utilized in pioneering work by Doye and Wales as a stochastic search strategy to explore hypersurfaces of cluster systems and applied to atomic clusters with simple interaction models.31 Despite its simplicity, the BH method has proven to be an effective strategy to study a number of systems described by empirical potentials or force fields.66−69 This method is based on a mathematical transformation of the function that models the potential energy to be explored, which is transformed into a collection of interpenetrating staircases.31 For each cluster’s chemical composition our optimizations started from random configurations, increasing the likelihood of unveiling their structural complexity by generating 400 initial configurations that underwent the optimization procedure. In our searches we assign 5000 Monte Carlo variable size steps, with an initial step size of 1.2 Å and a thermal energy of 0.086 eV (kBT). A detailed description of our BH code will be published elsewhere. An energetic analysis was performed on the resulting lowest energy structural motifs, as well as a structural characterization, aimed at understanding the effect of the amount of Pt atoms in the mixed clusters on the morphological changes identified through the analysis of the structural diversity of these nanoclusters. To this end, we calculated the excess or mixing energy which is an unbiased quantity defined as zero for the global minima of the pure clusters; in binary clusters this quantity characterizes their relative stability48 with the most stable clusters exhibiting the most negative values. The excess energy has been calculated according to the following expression48

3. RESULTS AND DISCUSSION Figure 1 shows a plot of the excess energy at the Gupta level as a function of Pt content for the putative global minima (GM)

Figure 1. Excess energy for the lowest energy Pt101−xAux nanoclusters calculated at the Gupta level. Structural motifs are indicated by colored circles: Dh-Mk-101, blue; Ih-Mc-inc, yellow; Dh-AMc-inc, orange; cLeary, red; cp(T), gray. White-colored circles correspond to other geometry structures.

of the Pt101−xAux (x = 0−101) nanoclusters. The excess energy is calculated according to eq 4. By mining the large database collected in the investigation performed through the extensive BH technique, nine structural motifs have been singled out and deeply investigated over a broad range of compositions (from 30 to 70% Pt content) at both the Gupta and the DFT levels. In more detail, for any given structural motif, the chemical order has been determined at the Gupta level by applying an exchange-only BH algorithm; the same chemical order has been used as input for the DFT calculations. The selected motifs are the following:

Eexc = E(PtN AuM ) − NE(Pt101)/101 − ME(Au101)/101 (4) 20969



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already theoretically observed in the case of 34-atoms PdPt clusters;49 the core is formed by two tetrahedra in hcp stacking, whereas the surface atoms form a local decahedral neighborhood in order to reduce the surface energy of the cluster. Amorphous structural motifs have not been found as lowenergy motifs by the BH search using our empirical parameters; consequently, this family of structural motifs has not been investigated, although it cannot be excluded that a competition exists with the morphologies here considered (as shown in ref 73). The percentage of occurrence for each of the structural motifs found in our searches in the whole composition range is given in the Supporting Information. According to the Gupta EP results, a common feature of the GM found over a large part of the composition spectrum is the phase separation between Pt and Au, with Au atoms segregating at the cluster surface. Some few exceptions are given by the structures at very low Pt content, as discussed below. These results are in agreement with the segregation tendency observed in the bulk alloy over a broad composition range.74 The tendency of Au atoms to segregate at the surface of the cluster can be ascribed to the larger values of cohesion energy and surface energies and to the smaller lattice parameter of Pt with respect to Au. It can be added that at the electronic structure level, charge transfer could also play a role in stabilizing Au surface segregation, although reports of electronic effects on AuPt bimetallic nanoparticles have been contradictory up to now.75−77 Through an analysis of the results, we can identify a structural transformation from that of the pure decahedral Au101 cluster to the pure decahedral Pt101 nanoparticle. This occurs through the series of morphologies shown in Figure 3,

(a) Dh-Mk-101 is found from Pt0Au101 to Pt11Au90 and from Pt62Au39 to Pt101Au0 (see Figure 2a); this motif is the perfect Marks decahedron with indices (2,3,2).2

Figure 2. (a−i) Nine structural motifs considered in the present investigation. In the left visualization of each structure, the core atoms are displayed as big blue balls, whereas the surface atoms are as small yellow balls; on the right visualization, only the core atoms are displayed as small blue balls.

(b) Ih-Mc-inc is found from Pt12Au89 to Pt35Au66 (see Figure 2b); this motif is a fragment of the Mackay icosahedron which is characterized by a structural shell closure at size 147. (c) Dh-AMc-inc is found from Pt36Au65 to Pt46Au55 (see Figure 2c); the core is a fragment of the 39-atoms Ino decahedron, whereas the atoms on the surface grow according to an anti-Mackay arrangement. (d) c-Leary is found from Pt47Au54 to Pt59Au42 (see Figure 2d); this motif is formed by a capped 98-atoms Leary tetrahedron; 98 is the size of the structural shell closure for this motif. (e) cp(T) is found from Pt60Au41 to Pt61Au40 (see Figure 2e); this motif belongs to the same family of Leary structures; its core is formed by a closed-packed tetrahedral structure, covered by closed-pack atoms in hcp stacking; this motif has a structural shell closure at size 100. (f) Dh-inc is found sometimes in the case of Pt-poor compositions (see Figure 2f), this motif is a fragment of a larger Marks decahedron. (g) The fcc (see Figure 2g) motif is a perfect crystalline structure, only missing one surface atom, exposing (111) facets and very small (100) facets. The previous motifs have been selected, as they resulted in being global minima at certain compositions; two further motifs have been added to this database, as they were found to be lowlying homotops at some compositions. They are the following: (h) The Ih-AMc-inc (see Figure 2h) motif is a fragment of the 147-atoms anti-Mackay icosahedron. (i) The Dh-cp(DT) (see Figure 2i) motif belongs to the same structural family of the Leary tetrahedron, and it has been

Figure 3. Structural transformation pathway as the Pt concentration in the cluster increases. Representative structures compositions are as follows: Au101 and Pt101 for Dh-Mk-101, and Pt3Au98, Pt35Au66, Pt40Au61, Pt46Au55, Pt53Au48, and Pt61Au40 for the structures fcc, Ih-Mcinc, Dh-AMc-inc, Ih-AMc-inc, c-Leary, and cp(T), respectively.

reminiscent of a structural phase transition produced by the increase in Pt concentration. The structural rearrangement of the Pt atoms, segregated into the core, induces a shape change in the given range of compositions, as detailed in the following. Regarding the morphology of the particles, the Dh-Mk-101 motif is the most frequent structure, found in almost 50% of the possible compositions as the lowest energy structure. In particular, this motif results as the global minimum for both 20970



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Figure 4. Excess energy (eV) at the (a) Gupta (EP) and (b) DFT levels for the nine motifs considered in the paper, as a function of the number of Pt atoms.

decahedral particles is indeed predicted to occur around 100atoms size.78 This is thus consistent with the prevalence of DhMk-101 for both Au-rich and Pt-rich clusters at the empirical potential level. Between 10 and 62% Pt content, we find a strong competition between structural motifs, as shown in the plot of Figure 4a, where the excess energy has been calculated separately for all of the nine motifs considered in this work. For compositions lower than 10% in Pt content, the structure predicted by the Gupta potential is the Dh-Mk-101 where the few Pt atoms prefer to occupy subsurface positions by maximizing the number of mixed bonds. This effect can be ascribed to the appearance of unusual chemical ordering patterns driven by a subtle bond energetics as a function of coordination, as in the case of crystalline PdPt alloyed nanoparticles.79 When increasing the Pt content (from 10 to 30%, more or less), the dominant motif becomes Ih-Mc-inc (with a sporadic

Pt-poor clusters (below 10% of Pt content) and Pt-rich clusters (above 62% of Pt content). In general terms, this result is not unexpected: focusing on icosahedral, decahedral, and crystalline motifs, in previous work it has been shown that a crossing should occur as a function of size from icosahedra (favored for small particles) to decahedra (favored for intermediate size) and finally crystalline configurations (i.e., fcc in the present case of Au and Pt, favored for large crystals and the bulk).78 The size at which these crossings occur essentially depends on a parameter which is the “stickiness” of the metal−metal bonding, i.e., the relative easiness with which a metal−metal bond is strained: the greater the stickiness, the smaller are the sizes at which icosahedra transitions to decahedra, and then decahedra to fcc structures. Stickiness in general increases going down a column of the periodic table, and it is maximum for third-row transition and noble metals and in particular for Au and Pt, for which the crossing between icosahedra and 20971



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Table 2. Calculated Binding (Eb) and Surface Energies (σ100, σ111), and Enthalpy of Formation (ΔHF) of Investigated Pure Materialsa r0 (Å)

a

σ100/σ111

Eb (eV/atom)

ΔHF (eV)

sample

DFT

EP

DFT

EP

DFT

EP

DFT

EP

Au Pt

2.948 (2.889) 2.844 (2.775)

2.884 2.775

3.041 (3.819) 5.262 (5.863)

3.779 5.853

1.358 1.394

1.150 1.114

−3.04 −5.26

−3.80 −5.85

EP and experimental values were taken from ref 63. Experimental values are indicated in parentheses.

5-fold motifs,48,62 in agreement with previous studies pointing out that the discrepancies between EP and DFT for the Au−Pt pair increase with the Pt content of the system.59 When considering other motifs, we see that the two antiMackay-covered structures (Dh-AMc-inc and Ih-AMc-inc) are quite high in energy, whereas the curves of the three mixed Dhcp motifs (Dh-cp(DT), c-Leary, and cp(T)) run almost parallel, as it could be expected as they belong to the same structural family. Finally, the pure Ih-Mackay structure (Ih-Mc-inc) is energetically very competitive for Pt-poor compositions, whereas it moves at higher energies for Pt-rich clusters again due to directionality effects. Even though the present work concerns nanoscale systems, it should be mentioned that thermochemical properties calculations on pure Au and Pt bulk systems show substantial differences between DFT and EP (and experimental) data as reported in Table 2, where we present the results of binding and surface energies and the enthalpy of formation for bulk gold and platinum. These differences might be one of the sources of the discrepancies revealed here between EP and DFT in the case of alloyed nanoclusters, although we think that the directionality effects discussed above are more important.

appearance of the Dh-inc motif at few compositions), where the effects of the internal distribution of the Pt atoms begin to be noticeable: the Pt atoms start segregating by forming a core in an off-site position with respect to the center of the cluster. These kinds of off-center asymmetric cores (also called Janus patterns) have been recently theoretically predicted to be favored with respect to more symmetric structures under appropriate conditions for binary nanoalloys characterized by poor miscibility and large lattice mismatch, even though the case of AuPt was not previously considered.69 When the composition of the cluster exceeds 30% in Pt content, the number of Pt atoms is such that a core−shell pattern characterized by a central Pt region covered by Au atoms is realized; this core−shell pattern is preserved across the changes in the structural motifs observed at increasing Pt content: at about 35% in Pt content from Ih-Mc-inc to DhAMc-inc, where a change takes place in both the structure of the core (from Ih to Dh) and of the external shell (from Mackay to anti-Mackay mode); at about 50% in Pt content, where a transition toward the c-Leary structure is observed; and, finally, at about 60% in Pt content, where the Dh-Mk-101 structure is restored. In previous studies of Pt−Pd nanoalloys, the 98-atoms Leary tetrahedron (LT) was found to be the putative global minimum in a range of compositions,41,60 thus meaning that LT can be a lowest energy structure on the potential energy landscape of binary transition metal nanoparticles at the EP level, although the situation can change when energetics is predicted via DFT methods. When the structures proposed by the empirical potential analysis are locally optimized at the DFT level, the energy landscape completely changes and the fcc motif becomes energetically favored over a broad range of compositions between 30 and 70% in Pt content. We focused our DFT calculations on this composition range, as this is the range characterized by the lowest values of the excess energy. In Figure 4b, the excess energy of the nine structural motifs is shown at the DFT level: the curve corresponding to the fcc motif is quite well separated from the other curves, and the only motif in competition is Dh-inc especially at 30% in Pt content, where the fcc and the Dh-inc structures are practically isoenergetic. This competition is effective also in pure Au clusters, where the fcc is lower in energy by only about 0.7 eV with respect to the Dh-inc motif and by about 1 eV with respect the Dh-Mk-101 motif. When considering Pt-rich clusters, instead, it seems that the fcc is much more stable than the other motifs, lying about 1 eV below the Dh-Mk-101 motif and about 2 eV below the Dh-inc motif. The fact that fcc structures become more and more favored as the Pt content increases can be rationalized as due to the smaller lattice constant of Pt with respect to Au and to increased directionality effects brought about by the increased overlap among d-orbitals in Pt bonding with respect to Au bonding. These increased directionality effects thus play a more important role in Pt-rich clusters and, hence, favor crystalline, less tensioned structures with respect to

4. SUMMARY A systematic and exhaustive exploration of the potential energy hypersurface of 101-atoms Au−Pt nanoclusters is performed by means of a global-search basin hopping algorithm and DFT local optimizations of selected structural motifs. Nine different morphologies (structural families) are identified by mining the large database accumulated by the BH search. At the Gupta level, a stabilization of Dh-Marks is observed when the Pt content is below 10% or higher than 62%; between 10 and 62%, a strong competition between all of the several structural motifs is observed. When reoptimized at the DFT level, a stabilization of the crystalline motif results over a broad range of compositions between 30 and 70% in Pt content. Below 30% Pt content, it is suggested that decahedral motifs can be competitive, whereas, above 30%, it seems that the crystalline motif is appreciably more stable than any other morphology. These results underline the importance of a proper account of quantum mechanical (thus, e.g., directionality) effects in determining the structural properties of third-row transition and noble metal clusters in the nanometer-size range and shed light on the thermodynamic preference for crystalline motifs in these systems which should have a bearing on their promising catalytic activity.21−23,75−77

■

ASSOCIATED CONTENT

S Supporting Information *

Figures showing the atom-by-atom structural evolution of Pt101−xAux (x = 0−101) and the percentage of occurrence for each of the structural motifs found in our PES explorations in 20972



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the entire composition range. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS M.D.-F. is grateful to CONACYT for the award of a Ph.D. scholarship. A.P.-A. acknowledges financial support from CONACYT through Grant No. 180424. A.F. is grateful for financial support from the COST Action MP0903 for a STSM to Hermosillo and the SEPON project within the ERC-AG.

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REFERENCES

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Structural Motifs of Bimetallic Pt - American Chemical Society

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