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Understanding Segregation Behaviour In AuPt, NiPt And AgAu Bimetallic Nanoparticles Using Distribution Coefficients Srikanth Divi, and Abhijit Chatterjee J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.6b08325 • Publication Date (Web): 09 Nov 2016 Downloaded from http://pubs.acs.org on November 21, 2016

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The Journal of Physical Chemistry

Understanding Segregation Behaviour In AuPt, NiPt And AgAu Bimetallic Nanoparticles Using Distribution Coefficients Srikanth Divi and Abhijit Chatterjee* Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India *E-mail: [email protected] . Tel: +91-222-576-7242

Abstract Bimetallic nanoparticles (BNPs) often possess peculiar segregation behaviour as the particle size, composition, shape and temperature are varied. However, a thermodynamic model for this phenomena has been lacking thus far. We show for the first time that the distribution of metal species within a nanoparticle can be adequately captured in terms of distribution coefficients calculated for the facets, facet edges and bulk regions. Thermodynamic relations for the distribution coefficients are derived. Only m distribution coefficients from the m(m-1) distribution coefficients are independent, where m denotes the number of regions. The theory is applied to AuPt, NiPt and AuAg BNPs. Distribution coefficients are calculated at 400 and 600 K using Monte Carlo (MC) simulations of varying BNP sizes and compositions. A wide range of mixing behaviour from alloying to partial- or full-segregation, and core-shell to onion-like structures can be observed. A key finding is that the distribution coefficients are independent of the BNP size. The observed size-dependent segregation can be attributed to the relative availability of surface and bulk sites, i.e., the area-to-volume (A/V) ratio. This implies that two bimetallic nanostructures of different sizes and shapes but same A/V ratio may exhibit nearly identical segregation behaviour. Thus nanothermodynamic segregation in bimetallic alloys may be described concisely using a handful of distribution coefficients. 1

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1.

Introduction

Recent years have witnessed a surge in interest over the potential applications of bimetallic alloy nanoparticles (BNPs) in catalysis1–3 and fuel cells4–7. The lower cost and enhanced catalytic activity of BNPs compared to pure metal counterparts makes these materials exciting8. Metal species distribution at the surface is believed to be one of the crucial parameters that influences the activity, selectivity and stability of the catalyst9. For instance, a monolayer shell of Pt atoms in Au@Pt bimetallic core-shell structure promotes oxygen reduction reaction (ORR) and Au atoms in subsurface hinders the poisoning of catalyst by preventing CO molecules to adsorb at the Pt surface10,11. Other types of Au-Pt phase behaviour have also been reported but they exhibit varied levels of catalytic activity. Au-Pt distribution depends on a number of factors such as kinetics12, thermodynamics, particle size13,14 , shape6 , composition9, temperature, and the synthesis route15. Unfortunately, a systematic theoretical model to predict the Au-Pt distribution within the BNP of a given size, shape, composition and temperature has been lacking. This is also true for other bimetals. Addressing this issue is the objective of this work. While our focus lies mainly on AuPt BNPs, we also study the segregation behaviour of NiPt and AuAg systems. Bulk AuPt exhibits a miscibility gap over a wide range of compositions16 and has a lattice mismatch of 4% that induces a large strain within the material structure. Au has a lower surface energy than Pt, therefore Au should segregate towards the BNP surface to lower the overall energy of the thermodynamic system. Experimentally different types of phase behaviour have been reported, e.g., core-shell Pt@Au17–19, Au@Pt20–23, mixed alloy19,24–29 and segregated phases19,28,30. Recently, pair distribution functions (PDF) from XAFS combined with Reverse Monte Carlo methods, XRD, TEM techniques have also provided insights into the surface and bulk distributions31–33. Several investigations have 2


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concluded that at high temperatures core-shell structures are observed regardless of the initial phase of BNP. It appears that the synthesis route taken is responsible for the variety of phases observed in the experiments. This aspect has motivated studies on understanding the unique phase behaviour at equilibrium. Equilibrium molecular simulations have offered key insights into mechanisms, equilibrium structures, segregation behaviour, spatial arrangement of Au and Pt, and phase diagram of BNPs34–40. Monte Carlo (MC) simulations combined with density functional theory and cluster expansion method can enable one to find the size and shape distribution for Pt ensembles in AuPt surface alloys/Pt(111)41 when the total number of Au and Pt atoms in the surface are pre-specified. In many cases, the number of Au and Pt atoms in the surface might not be known to us in the first place. Size and overall composition effects on the surface composition have been observed in AuPt BNP using MC simulations14. Segregated and core-shell structures were observed with increasing Pt composition. Contrary to the phase behaviour in bulk, alloy formation was reported in AuPt43 BNPs smaller than 6 nm. This behaviour was attributed to the negative heat of formation associated with the alloyed structure. Yun et al. studied equilibrium structures using MC simulations and concluded that heat of formation and surface energy play a key role in determining BNP surface composition44. More recent MC studies of AuPt BNPs show that onion-like and core-shell structures form35,42 even at small BNP size. Interestingly, the compositions where the transitions between these structures are observed is a function of the BNP size. Attempts to link the heat of formation, surface energy and cohesive energy to the phase behaviour have also been made with other BNPs, e.g. AgPd45 , AgPt46, AuAg47 and PtPd48,49. Rather than qualitatively understand the Au-Pt arrangement for different BNP shapes, sizes, compositions, and temperatures, our objective is to model equilibrium Au and Pt 3



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fractions at the facets, facet edges and vertices, and bulk regions using distribution coefficients. Each region has a unique atomic environment. Thermodynamic expressions for distribution coefficients for pairs of regions are derived in Sec. 2. Material balance equations, which add a constraint on how the Au and Pt atoms are distributed between the regions, are also discussed. As we show later such a thermodynamic model can explain the sizedependent segregation behaviour observed in this and previous studies. Details of our MC implementation are provided. Other new aspects of this work, namely, the plane analysis method used to determine the region an atom belongs, and an improved embedded atom method (EAM) potential are presented. The distribution coefficients are calculated using MC simulations performed for truncated octahedron BNPs in Sec. 3. The behaviour of the distribution coefficients for AuPt, NiPt and AuAg is analysed. The distribution coefficients are found to be independent of size. A weak temperature-dependence is also observed. Reasons for these observations are provided. The main implication of this work is that nanothermodynamic segregation in bimetallic alloys can be described concisely using handful of distribution coefficients. Conclusions are presented in Sec. 4.

Fig. 1. Effect of size, composition and temperature on the Au and Pt distribution at four different regions of BNP are investigated in this study. The four regions of a 4.3 nm truncated octahedron BNP, namely, {111}, {100}, bulk and edge-vertex are shown in different colours. 4


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2.

Methods Among the other possible shapes, the truncated-octahedron BNP is preferred because

of its low surface energy per unit volume. The truncated-octahedron comprises of 8 {111} facets, 6 {100} facets, 12 {111}-{111} edges, 24 {111}-{100} edges and 24 vertices. Each region is unique in the manner in which Au and Pt can arrange. We consider four separate regions in our analysis, namely, bulk, {111} facets, {100} facets and edges/vertices denoted as Bu, 111, 100 and EV, respectively (see Fig. 1). Subsurface atoms are also regarded as bulk atoms. Thermodynamic relations for the distribution coefficients are derived for these regions. The derivation can be readily extended to other nanostructures as well as other bimetallic alloys. 2.1 Distribution coefficient Consider a AuxPt1-x BNP of size d in the canonical ensemble. Within the lattice approximation, the Au and Pt atoms can reside at specific lattice sites. The number of sites in region α, α=Bu, 111, 100 or EV, is denoted Nα. The sum of sites over all regions will equal the number of atoms Natoms in the BNP. Unlike typical thermodynamic phases where the number of atoms in each phase may vary, as we shall see later the number of atoms Nα in the region α being fixed greatly simplifies the calculation of the Au-Pt distribution. Au and Pt atoms are distributed between these regions such that the free energy is minimized. Let x αAu α and x Pt denote the Au and Pt mole fractions in α, where xαAu + xPtα = 1 . The average number of α α Au and Pt atoms is given by N Au , respectively. Our objective is to = N α xαAu and N Ptα = N α xPt α predict x Pt .

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Ignoring for now details of the atomic arrangements favoured inside each region, we turn our attention to exchange of Au-Pt species between two regions α and β. Each Pt atom that is transferred from α to β is in turn replaced by a Au atom that is transferred from β to α. At equilibrium, the forward and backward transition rates for the swap P t [α ] + A u [ β ] ↔ P t [ β ] + A u [ α ]

(1)

are equal. Here Pt[α] implies a Pt atom at an α-site. Thus, the equilibrium distribution are determined by the forward and backward rates in Eq. (1). The transition probability for the forward rate depends on the product of two terms. The first term gives the probability of selecting a pair of Pt[α]-Au[β] atoms for swapping, i.e., β 2 N Ptα N Au / N atoms ( N atoms − 1) . This expression is valid even when phase separation is witnessed

within a region as in the case of AuPt. The second term involves the probability of accepting the swap move the Metropolis criterion50 min(1,exp(-∆Uα-β/kBT)) that ensures that detailed balance is satisfied. Here ∆Uα-β is the energy change associated with the forward move in Eq. (1) for a given atomic configuration of the BNP, kB is the Boltzmann constant and T is the absolute temperature. When ∆Uα-β 111>100>EV sites. Consistent with the bulk phase diagram Au- and Pt-rich phases are formed and random alloy of Au and Pt in the bulk were not witnessed. At low Pt compositions xPt = 0.1-0.3 (where xPt=1-x), a cluster of Pt atoms are found in the subsurface layers just below the Au surface. Subsurface sites below edges, vertices and {100} facets are most preferred by the Pt atoms. Similar phase behaviour was reported in previous studies of AuPt BNPs despite the differences in the interatomic potential employed35. Our calculation for heat of segregation as a function of the distance from the surface layer (see Supporting Information) indicates that Pt atoms avoid the surface layer due to the positive segregation energy. The combination of a positive heat of mixing for Au-Pt (see Fig. 2) and the negative heat of segregation for the sub-surface layer promotes Pt enrichment at the subsurface. As the composition increases, the Pt clusters grow in size while still maintaining a thickness of 2 atoms. At xPt=0.3 a cage-like structure connecting the Pt clusters is visible in 3D views of the BNP. We observe onion-like structures consisting of Au core, Pt shell and Au outer layer at xPt= 0.4-0.5 once all subsurface layer sites are occupied by the Pt atoms. Thereafter, the size of the Au core shrinks while the Pt shell expands in thickness. The effect of temperature visible in the cross-sectional views is that Pt shells tend to remain more compact at 400 K than at 600 K (Fig. 4 A1-A5 and B1-B5). Beyond xPt = 0.6, the Pt atoms have filled all the available sites in the bulk region and they begin appearing at the surface, i.e., a Pt core- Au-Pt

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shell structure is observed. Finally, at xPt=0.9 Pt enrichment is witnessed mainly at the {100} and {111} facets while Au atoms decorate the EV sites.

Fig. 4. Effect of composition on the Au and Pt distribution in a 2.7 nm diameter truncated octahedron. Cross-sectional views are shown. Yellow (brown) spheres denote Au (Pt). The temperature is 400 K for panel A1-A9 and 600 K for panels B1-B9. The nine snapshots shown in panels A1-A9 and B1-B9 represent total Pt fraction between 0.1 to 0.9 in steps of 0.1.

The 4.3 nm BNP shows a behaviour which is qualitatively similar to the 2.7 nm one. Cross-sectional views of the 4.3 nm BNP at 400 K are shown in Fig. 5. The behaviour can once again be explained by the preference for Pt atoms for Bu>111>100>EV sites. At Pt fractions less than 0.4, the Pt atoms prefer subsurface sites forming a Pt-rich layer that is two atoms thick. The Pt-rich layer grows in thickness with increasing values of xPt, eventually occupying all Bu sites when the Pt fraction reaches 0.7 (see Table 1), and not 0.6 as in the case of the 2.7 nm BNP. Beyond xPt =0.7, the Pt atoms show more preference for 111 and 100 sites than the EV sites. Similar behaviour is observed for 600 K.

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Fig. 5. Effect of composition on the Au and Pt distribution in a 4.3 nm diameter truncated octahedron. Cross-sectional views are shown. Yellow (brown) spheres denote Au (Pt). Temperature is 400 K. Panels A1-A9 represent total Pt compositions between 0.1 to 0.9 in steps of 0.1.

A comparison between the Pt fractions as a function of composition, size and temperature is made in Fig. 6. The 2.7 and 4.3 nm Pt fractions are shown in filled circles and squares, respectively. Pt fractions at 400 and 600 K are shown in green and magenta colour, respectively. It is not that the surface sites are completely occupied by Au atoms at low Pt fractions. Fig. 6 shows that early on the Pt surface fractions lie in the range of 10-4-10-3. A steady rise in the surface fraction can be observed with increasing values of xPt. Interestingly, similar values are observed for Pt fractions at 111, 100 and EV. At the same time, the bulk Pt fraction increases towards a value of 1 with increasing xPt. Once xPtBu reaches 1, a sharp jump in the surface Pt fraction is witnessed. The effect of temperature on the Pt fractions is barely noticeable except for EV where higher Pt fraction can be observed at 600 K. At 600 K and low xPt, Pt atoms at EV sites is slightly more favoured than 111 and 100 sites. This scenario reverses at intermediate xPt, resulting in a hump-like feature in Fig. 6c. Arguments based on ensemble-averaged energy difference can explain this trend. Since Pt atoms first accumulate 17



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at the subsurface sites below edges and vertices (see Fig. 4 and 5), a Pt atom introduced at the EV site can form a dimer with a subsurface Pt atom. Dimer formation is less likely when the Pt atom is introduced at 111 and 100 sites at low xPt. Later dimers can form since subsurface layer below 111 and 100 are occupied by Pt at the intermediate xPt. The BNP size appears to have a significant effect on the Pt fractions in the entire range of xPt. One explanation is that the large A/V ratio of the 2.7 nm BNP results in a higher probability for Pt atoms to occupy the surface sites. While A/V effect can be clearly seen in the material balance Eq. (7), the distribution coefficients might also be a function of size. Other important questions that arise include why the surface Pt fractions lie in the range of 10-4-10-3 when xPtBu < 0.5 and the temperature has an insignificant effect on the Pt distribution.

α Fig. 6. Pt fraction xPt for different regions α,namely, a) 111, b) 100, c) EV and d) Bu are shown. Circles and squares denote results obtained with 2.7 and 4.3 nm, respectively. Green and magenta colours denote results for 400 and 600 K, respectively.

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Fig. 7 shows the fractions in each region plotted against the 111 Pt fraction. Since the variation in xPtBu for different values of xPt is nearly identical for the onion-shell structures of 2.7 and 4.3 nm BNPs, the differences observed in x PtBu − x111 Pt plot in Fig. 7 in this composition range can be attributed to how x111 Pt varies as a function of size. A preference for 111 sites over other sites by the Pt atoms is observed at larger values of xPt once core-shell structures 100 EV are formed. Fig. 7b and c shows that x111 Pt can be 10 and 1000 times larger than x Pt and x Pt ,

respectively.

α Fig. 7. Pt fraction xPt for different regions α=Bu, 100 and EV are shown as a function of

x111 Pt . Circles and squares denote results obtained with 2.7 and 4.3 nm, respectively. Green and magenta colours denote results for 400 and 600 K, respectively.

The distribution coefficients are calculated using the probability of selecting Au-Pt atoms in pairs of regions α-β from the MC calculations. Fig. 8a shows ∆111-Bu as function of the Pt fraction xPt. An important point is that distribution coefficient appears to be 19



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independent of size when xPt0.7 for 4.3 nm), a clear trend showing an increase in ∆111-Bu with respect to x111 Pt is observed in Fig. 9a. This behaviour can be explained in terms of the relative ease of finding pairs of Pt[111]Au[Bu] atoms. Similarly, from Fig. 9c we find that the likelihood of finding Pt[111]-Au[EV] atoms can be 1000 times that of Pt[EV]-Au[111] and as more Pt is accommodated in the 111 21



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region. Figs. 8 and 9 show that ∆ Bu − EV > ∆ Bu −100 > ∆ Bu −111 > 1 , ∆ 111− EV > ∆ 111−100 > 1 and ∆ 100 − EV > 1 . An order of magnitude difference in the distribution coefficient for 2.7 and 4.3 nm BNPs is observed in Fig. 9. Although all 111 and 100 sites have equal number of NN surface sites, i.e., 6 and 4, respectively, the number of second NN for sites at the center of a region will be different from the ones at the boundary. Thus, the missing second NN sites will result in a different value of energy change for inserting/removing an atom from boundary sites when compared to the sites at the center. This has a direct effect on the Pt atom arrangement for 2.7 and 4.3 nm BNPs. The insets in Fig. 9b shows the Pt arrangements at 111 can be qualitatively different because of size, which may explain the behaviour observed with the distribution coefficients. When the BNP size increases to large values, most sites will remain far from the boundary and the size-effect on the distribution coefficient will be less evident. As we shall show later, the distribution coefficient can be assumed to be independent for BNPs greater than 4.3 nm.

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Fig. 9. Values of distribution coefficients as a function of x111 Pt . Circles and squares denote results obtained with 2.7 and 4.3 nm, respectively. Green and magenta colours denote results for 400 and 600 K, respectively. Left-inset in panel b: Top-view of 2.7 nm BNP with xPt=0.7 at 400 K. Right-inset in panel b: Top-view of 4.3 nm BNP with xPt=0.8 at 400 K.

Why the distribution coefficients possess certain range of values for the Au-Pt BNPs can be understood in terms of the Boltzmann terms in Eq. (2). Fig. 10 shows the ensembleaveraged Boltzmann terms for [111-Bu] and [100-Bu] regions. Consider the move Pt[111]+ Au[Bu] Pt[Bu]+Au[111] for 2.7 nm BNP at 400 K. The average value of the Boltzmann term remains nearly constant when xPt0.6, the entire bulk is comprised of Pt and Pt atoms start occupying the preferred sites at the {111} surface first. Therefore, the Boltzmann term decreases to a smaller value at large xPt when the less preferred surface sites are filled by the Pt atoms. Increasing the temperature to 600 K increases the value of the Boltzmann term. Fig. 10a shows that the Boltzmann term can increases by a factor of 5 times. The value of the Boltzmann term for 4.3 nm BNP is similar to that of 2.7 nm BNP when xPt100>EV. The distribution coefficients are found to depend on composition, greater dependence on temperature is visible than AuPt and NiPt, and no size-dependence is observed. In summary, we have studied three bimetallic systems that exhibit diverse mixing behaviour: from alloyed to partial to nearly perfect segregation. In each example, the distribution coefficient was found to be practically independent of the size. This implies that the distribution coefficients calculated for one size could have been directly used with other sizes, and possibly shapes.

4.

Conclusions The bimetallic nanoparticle (BNP) surface composition is known to play an important

role in the catalytic activity. In this work, we have developed a thermodynamic model for predicting the distribution of metal species in a AuPt BNP for a given size, shape, composition and temperature. In the absence of such a model, previous studies have largely provided a qualitative understanding of the richness in the phase behaviour of the BNPs without possessing the capability to systematically predict the segregation behaviour for conditions that were not included in the study.

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We show that when the BNP is divided into regions on the basis of the local atomic environment, one can define distribution coefficients for pairs of these regions that capture the thermodynamic preference of the metal species for one region over the other. Here we have focused on the bulk, {111}, {100} facets and edge and vertex sites of Au-Pt, Ni-Pt and Au-Ag BNPs. The composition in each region can be determined using material balance once the distribution coefficients are known to us. The A/V ratio effect of the segregation arises due to two reasons. First, one metal (e.g., Pt in Au-Pt) prefers bulk sites more than other (e.g., Au in Au-Pt) does, therefore, the relative availability of the sites in each region, which is a function of size and shape, dictates the Au-Pt distribution. Second, the distribution coefficients themselves can depend on the size, shape, temperature and composition. AuPt, NiPt and AuAg BNPs display an interesting characteristic wherein one can largely ignore the dependence on size and temperature and still obtain reasonable estimates for the species composition in the different regions. The weak temperature-dependence arises from the cancellation of temperature effects in Eq. (2). The weak size-dependence arises primarily because the Boltzmann terms in Eq. (2) depend on the local environment, which remains practically unchanged as the system size increases. For the same reason, we expect the distribution coefficient to be independent of the shape as well, as shown for AuPt. In some cases such as AuPt, the distribution coefficient can be assumed to be even independent of the composition, as shown in Sec. 3. We observed a diverse range of phase behaviour within the BNP which gives rise to alloyed, onion-like and segregated core-shell structures in these systems. As we have shown, such rich behaviour results from an interplay of the heat of mixing of the alloy material, segregation energy as a function of the distance from the surface layer, interactions within the local atomic environment, and configurational aspects, namely, the relative number of sites in 30


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each region. Knowing the overall composition in a region one can employ statistical mechanics techniques, e.g., the quasichemical approximation59, to find the phase behaviour within a region. This study highlights that future studies on BNPs could benefit by including distribution coefficients to understand the segregation behaviour.

Supporting Information The Supporting Information is available free of charge on the ACS Publications website at http://pubs.acs.org. Tables for species composition at different regions of NiPt and AgAu BNPs at 400 K, and figures showing cross-sectional views of the BNPs. The authors declare no competing financial interest.

ACKNOWLEDGEMENTS AC acknowledges support from Science and Engineering Research Board, Department of Science and Technology Grant No. SB/S3/CE/022/2014 and Indian National Science Academy Grant No. SP/YSP/120/2015/307.

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TOC figure:

40


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{111} atoms


Edge atoms Vertex

{100} atoms


Heat of mixing (eV/atom)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

0.2


θ =1

0.15 0.1

Experiment (Ref. [55])

0.05 0

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θ =0.707 0

0.2 0.4 0.6 ACS Paragon Plus Environment x Pt

0.8

1

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Radial distribution function g(r)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41


5

a) Bulk Au

3 1 5

b) Bulk Au Pt 0.6

0.4

3 1 5

c) Bulk Pt

3 1 0

2

4

6 8 ACS Paragon Plus Environment r (Å) ij

10

12

A1

A2

A3

A4

A5

A7

A8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

B1

B2

B3

A6

B4

B5

B6

A9

B7

B8

B9



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A1

A2

A3

A4

A5

A6

A7

A8

A9

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2.7 nm 10

Pt

xα

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

0

10

0

4.3 nm

10

0

10

-1

10

-1

10

-1

10

-2

10

-2

10

-2

10

-3

10

-3

10

-3

10

-4

0.0

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10

0

EV 100 -4 -1 10 10 10 1.0 0.0 1.0 0.0 1.0 0.0 ACS Paragon Plus Environment Pt fraction, x Pt

111

-4

Bu 1.0

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Pt

a)

x Pt Pt

x

EV

x

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

Bu

10

0

10

-1

10

-1

10

-2

10

-3

10

-1

b)

c)

-2

10 -3 10 -4 10 -4 10

-3

-2

10 10 10 ACS Paragon Plus Environment 111 Pt fraction x Pt

-1

10

0

∆

EV-Bu

∆

111-100

∆

100-EV

∆

111-EV

∆

100-Bu

∆

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59

111-Bu

-1

10 a) -3 10


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-5

10 -1 10 b) -3 10 -5

10 4 10 c) 2 10 0 10 3 10 2 d) 10 1 10 0 10 1 10 e) 0 10

10

-3

10

-5

10

-7

f)

0

0.6 0.4 ACS Paragon Plus Environment Pt fraction x Pt

0.2

0.8

1

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∆

111-EV

∆

111-100

∆

111-Bu

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41


10

-1

10

-3

10

-5

10

1

10

0

10

4

10

2

10

0

0.0

a)

b)

c)

0.4 0.6 0.8 ACS Paragon Plus Environment 111 Pt fraction x

0.2

Pt

1.0


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

2.7 nm @ 400 K 2.7 nm @ 600 K 0 10 a) -1 10 10

-2

10

-3

10

-4

10

-5

10

-6

10

-7

10

-8

Pt[111]+Au[Bu] Pt[Bu]+Au[111]

Pt[Bu]+Au[111] Pt[111]+Au[Bu]

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4.3 nm @ 400 K 4.3 nm @ 600 K b)

Pt[100]+Au[Bu] Pt[Bu]+Au[100]

Pt[Bu]+Au[100] Pt[100]+Au[Bu]

0.1 0.3 0.5 0.7 0.3 0.5 0.7 0.9 ACS Paragon Plus Environment Pt fraction x Pt

Page 51 1 of 53

a)

111-Bu

10 10

-1

10

-3

10

1

10

-1

10

-3

10

2

10

0

10

2

10

0

10

1

10

0

10

-1

10

-3

∆

EV-Bu

∆

111-100

∆

100-EV

∆

111-EV

∆

100-Bu

∆

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

0.0


b)

c)

d)

e)

f)


0.2

0.4 0.6 Pt fraction x Pt

0.8

1.0

111-Bu

0

∆

EV-Bu

∆

111-100

∆

100-EV

∆

111-EV

∆

100-Bu

∆

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

10 -1 10 10

-2

10

-1

10

-2

10

1

10

0

10

1

10

0

10

1

10

0

10

-1

10

-2

10

-3

0.0

a)


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b)

c)

d)

e)

f)


0.2

0.4 0.6 Au fraction x Au

0.8

1.0

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2.7 nm

Pt fraction in region

10

0

10

0

4.3 nm 10

0

10

-1

10

-1

10

-1

10

-2

10

-2

10

-2

10

-3

10

-3

10

-3

10

0

=

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

Pt

Au


Thermodynamic model 10 -4 Pt fraction in region = f(xPt ; distribution coefficients)

EV 100 -4 -1 10 10 10 1.0 0.0 1.0 0.0 0.0 1.0 0.0 ACS Paragon Plus Environment Overall Pt fraction, x Pt 111

-4

Bu 1.0

Understanding Segregation Behavior in AuPt, NiPt ... - ACS Publications

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