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Modeling of equilibrium conformation of Pt2Ru3 nanoparticles using the density functional theory and Monte Carlo simulations

Published online by Cambridge University Press:  23 February 2017

Md. Khorshed Alam ,
Shuhei Saito  and
Hiromitsu Takaba
Md. Khorshed Alam
Affiliation:
Department of Environmental Chemistry and Chemical Engineering, School of Advanced Engineering, Kogakuin University, Hachioji, Tokyo 192-0015, Japan
Shuhei Saito
Affiliation:
Department of Environmental Chemistry and Chemical Engineering, School of Advanced Engineering, Kogakuin University, Hachioji, Tokyo 192-0015, Japan
Hiromitsu Takaba*
Affiliation:
Department of Environmental Chemistry and Chemical Engineering, School of Advanced Engineering, Kogakuin University, Hachioji, Tokyo 192-0015, Japan
*
a)Address all correspondence to this author. e-mail: [email protected]
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Abstract

In this study, the density functional theory (DFT) and Monte Carlo (MC) simulations were conducted to determine the equilibrium conformation of Pt2Ru3 nanoparticles with diameters 1.0–3.5 nm at finite temperature. DFT calculations were carried out to estimate the binding energy using slab configurations and energy could be correlated with some structural descriptors and multilinear regression equations to calculate the binding energy from descriptors related to the number of a specific bond to neighboring atoms. MC simulations were carried out to obtain the equilibrium conformation of atoms in Pt2Ru3 at 150–363 K. MC simulations’ result shows that atoms of the same element tend to segregate each other, and Pt/Ru ratio on the surface increases with increasing particle size; also, most of the Pt are located on the surface whereas most of the Ru are located on the subsurface or at the core sites. It is qualitatively exhibited that the Pt/Ru ratio on the surface decreases with increasing temperature.

Keywords

Pt2Ru3 alloynanoparticlepolymer electrolyte fuel celldensity functional theoryMonte Carlo simulation
Type
Articles
Information
Journal of Materials Research , Volume 32 , Issue 8 , 28 April 2017 , pp. 1573 - 1581
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Copyright
Copyright © Materials Research Society 2017 

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Footnotes

Contributing Editor: Susan B. Sinnott

References

REFERENCES

Gasteiger, H.A., Markovic, N.M., and Ross, P.N.: H2 and CO electrooxidation on well-characterized Pt, Ru, and Pt–Ru. 1. Rotating disk electrode studies of the pure gases including temperature effects. J. Phys. Chem. 99, 8290 (1995).CrossRefGoogle Scholar
Leiva, E.P.M., Santos, E., and Iwasita, T.J.: The effect of adsorbed carbon monoxide on hydrogen adsorption and hydrogen evolution on platinum. J. Electroanal. Chem. Interfacial Electrochem. 215, 357 (1986).CrossRefGoogle Scholar
Petrii, O.A. and Tsirlina, G.A.: Electrocatalytic activity prediction for hydrogen electrode reaction: Intuition, art, science. Electrochim. Acta 39, 1739 (1994).CrossRefGoogle Scholar
Ponce, V. and Bond, G.C.: Catalysis by Metals and Alloys; Studies in Surface Science and Catalysis 95 (Elsevier, Amsterdam, 1995).Google Scholar
Krausa, M. and Vielstich, W.: Study of the electrocatalytic influence of Pt/Ru and Ru on the oxidation of residues of small organic molecules. J. Electroanal. Chem. 379, 307 (1994).CrossRefGoogle Scholar
Frelink, T., Visscher, W., and Vanveen, J.A.R.: On the role of Ru and Sn as promotors of methanol electro-oxidation over Pt. Surf. Sci. 335, 353 (1995).CrossRefGoogle Scholar
Tong, Y.Y., Kim, H.S., Babu, P.K., Waszczuk, P., Wieckowski, A., and Oldfield, E.: An NMR investigation of CO tolerance in a Pt/Ru fuel cell catalyst. J. Am. Chem. Soc. 124, 468 (2002).CrossRefGoogle Scholar
Watanabe, M. and Motoo, S.: Electrocatalysis by ad-atoms: Part II. Enhancement of the oxidation of methanol on platinum by ruthenium ad-atoms. J. Electroanal. Chem. Interfacial Electrochem. 60, 267 (1975).CrossRefGoogle Scholar
Yajima, T., Wakabayashi, N., Uchida, H., and Watanabe, M.: Adsorbed water for the electro-oxidation of methanol at Pt–Ru alloy. Chem. Commun. 7, 828 (2003).CrossRefGoogle Scholar
Watanabe, M. and Motoo, S.: Electrocatalysis by ad-atoms: Part III. Enhancement of the oxidation of carbon monoxide on platinum by ruthenium ad-atoms. J. Electroanal. Chem. 60, 275 (1975).CrossRefGoogle Scholar
Hanand, B.C. and Ceder, G.: Effect of coadsorption and Ru alloying on the adsorption of CO on Pt. Phys. Rev. B: Condens. Matter Mater. Phys. 74, 205418 (2006).Google Scholar
Babu, P.K., Kim, H.S., Oldfield, E., and Wieckowski, A.: Electronic alterations caused by ruthenium in Pt–Ru alloy nanoparticles as revealed by electrochemical NMR. J. Phys. Chem. B 107, 7595 (2003).CrossRefGoogle Scholar
Babu, P.K., Kim, H.S., Kuk, S.T., Chung, J.H., Oldfield, E., Smotkin, E.S., and Wieckowski, A.: Activation of nanoparticle PtRu fuel cell catalysts by heat treatment: A 195 Pt NMR and electrochemical study. J. Phys. Chem. B 109, 17192 (2005).CrossRefGoogle Scholar
Takeguchi, T., Yamanaka, T., Asakura, K., Muhamad, E.N., Uosaki, K., and Ueda, W.: Evidence of non electrochemical shift reaction on a CO-tolerant high-entropy state Pt–Ru anode catalyst for reliable and efficient residential fuel cell systems. J. Am. Chem. Soc. 134, 14508 (2012).CrossRefGoogle Scholar
Bratlie, K.M., Lee, H., Komvopoulos, K., Yang, P.D., and Somorjai, G.A.: Pt nanoparticle shape effects on benzene hydrogenation selectivity. Nano Lett. 7, 3097 (2007).CrossRefGoogle ScholarPubMed
Xiong, Y.J., McLellan, J.M., Chen, J.Y., Yin, Y.D., Li, Z.Y., and Xia, Y.N.: Kinetically controlled synthesis of triangular and hexagonal nanoplates of palladium and their SPR/SERS properties. J. Am. Chem. Soc. 127, 17118 (2005).CrossRefGoogle ScholarPubMed
Park, K.H., Jang, K., Kim, H.J., and Son, S.U.: Near-mono disperse tetrahedral rhodium nanoparticles on charcoal the shape-dependent catalytic hydrogenation of arenes. Angew. Chem., Int. Ed. 46, 1152 (2007).CrossRefGoogle Scholar
Zhou, S.H., Varughese, B., Eichhorn, B., Jackson, G., and McIlwrath, K.: Pt–Cu core–shell and alloy nanoparticles for heterogeneous NO x reduction: Anomalous stability and reactivity of a core–shell nanostructure. Angew. Chem., Int. Ed. 117, 4515 (2005).CrossRefGoogle Scholar
Alayoglu, S., Nilekar, A.U., Mavrikakis, M., and Eichhorn, B.: Ru–Pt core–shell nanoparticles for preferential oxidation of carbon monoxide in hydrogen. Nat. Mater. 7, 333 (2008).CrossRefGoogle ScholarPubMed
Park, J.I., Kim, M.G., Jun, Y.W., Lee, J.S., and Lee, W.R.: Characterization of super paramagnetic “core–shell” nanoparticles and monitoring their anisotropic phase transition to ferromagnetic “solid solution” nanoalloys. J. Am. Chem. Soc. 126, 9072 (2004).CrossRefGoogle Scholar
Hills, C.W., Nashner, M.S., Frenkel, A.I., Shapley, J.R., and Nuzzo, R.G.: Carbon support effects on bimetallic Pt–Ru nanoparticles formed from molecular precursors. Langmuir 15, 690 (1999).CrossRefGoogle Scholar
Nashner, M.S., Frenkel, A.I., Adler, D.L., Shapley, J.R., and Nuzzo, R.G.: Structural characterization of carbon-supported platinum–ruthenium nanoparticles from the molecular cluster precursor PtRu5C(CO)16 . J. Am. Chem. Soc. 119, 7760 (1997).CrossRefGoogle Scholar
Long, J.W., Stroud, R.M., Swider-Lyons, K.E., and Rolison, D.R.: How to make electrocatalysts more active for direct methanol oxidation avoid PtRu bimetallic alloys. J. Phys. Chem. B 104, 9772 (2000).CrossRefGoogle Scholar
Yuge, K.: Segregation of Pt28Rh27 bimetallic nanoparticles: A first-principles study. J. Phys.: Condens. Matter 22, 245401 (2010).Google ScholarPubMed
Alayoglu, S., Zavalij, P., Eichhorn, B., Wang, O., Frenkel, A.I., and Chupas, P.: Structural and architectural evaluation of bimetallic nanoparticles: A case study of Pt–Ru core-shell and alloy nanoparticles. ACS Nano 3, 3127 (2009).CrossRefGoogle ScholarPubMed
Koper, M.T.M., Jansen, A.P.J., van Santen, R.A., Lukkien, J.J., and Hilbers, P.A.J.: Monte Carlo simulations of a simple model for the electrocatalytic CO oxidation on platinum. J. Chem. Phys. 109, 6051 (1998).CrossRefGoogle Scholar
Andreaus, B. and Eikerling, M.: Active site model for CO adlayer electrooxidation on nanoparticle catalysts. J. Electroanal. Chem. 607, 121 (2007).CrossRefGoogle Scholar
Saravanan, C., Markovic, N.M., Head-Gordon, M., and Ross, P.N.: Stripping and bulk CO electro-oxidation at the Pt–electrode interface: Dynamic Monte Carlo simulations. J. Chem. Phys. 114, 6404 (2001).CrossRefGoogle Scholar
Maillard, F., Lu, G.-Q., Wieckowski, A., and Stimming, U.: Ru-decorated Pt surfaces as model fuel cell electrocatalysts for CO electrooxidation. J. Phys. Chem. B 109, 16230 (2005).CrossRefGoogle ScholarPubMed
Andreaus, B., Maillard, F., Kocylo, J., Savinova, E.R., and Eikerling, M.: Kinetic modeling of CO ad monolayer oxidation on carbon-supported platinum nanoparticles. J. Phys. Chem. B 110, 21028 (2006).CrossRefGoogle Scholar
Petukhov, V.: Effect of molecular mobility on kinetics of an electrochemical Langmuir–Hinshelwood reaction. Chem. Phys. Lett. 277, 539 (1997).CrossRefGoogle Scholar
Saravanan, C., Koper, M.T.M., Markovic, N.M., Head-Gordon, M., and Ross, P.N.: Modeling base voltammetry and CO electrooxidation at the Pt(111)-electrolyte interface: Monte Carlo simulations including anion adsorption. Phys. Chem. Chem. Phys. 4, 2660 (2002).CrossRefGoogle Scholar
Dowben, P.A. and Miller, A., eds.: Surface Segregation Phenomena (CRC Press, Boca Raton, Florida, 1990).Google Scholar
Rodriguez, J.A.: Physical and chemical properties of bimetallic surfaces. Surf. Sci. Rep. 24, 223 (1996).CrossRefGoogle Scholar
Polak, M. and Rubinovich, L.: The interplay of surface segregation and atomic order in alloys. Surf. Sci. Rep. 38, 127 (2000).CrossRefGoogle Scholar
Shan, B., Wang, L., Yang, S., Hyun, J., Kapur, N., Zhao, Y., Nicholas, J., and Cho, K.: First-principles-based embedded atom method for PdAu nanoparticles. Phys. Rev. B: Condens. Matter Mater. Phys. 80, 035404 (2009).CrossRefGoogle Scholar
Wang, G., Van Hove, M.A., and Ross, P.N.: Monte Carlo simulations of segregation in Pt–Ni catalyst nanoparticles. J. Chem. Phys. 122, 024706 (2005).CrossRefGoogle ScholarPubMed
Yuge, K., Seko, A., Kuwabara, A., Oba, F., and Tanaka, I.: First-principles study of bulk ordering and surface segregation in Pt–Rh binary alloys. Phys. Rev. B: Condens. Matter Mater. Phys. 74, 174202 (2006).CrossRefGoogle Scholar
Sato, T., Okaya, K., Kunimatsu, K., Yano, H., Watanabe, M., and Uchida, H.: Effect of particle size and composition on CO-tolerance at Pt–Ru/C catalysts analyzed by in situ attenuated total reflection FTIR spectroscopy. ACS Catal. 2, 450 (2012).CrossRefGoogle Scholar
Sato, T., Kunimatsu, K., Okaya, K., Yano, H., Watanabe, M., and Uchida, H.: In situ ATR-FTIR analysis of the CO-tolerance mechanism on Pt2Ru3/C catalysts prepared by the nano capsule method. Energy Environ. Sci. 4, 433 (2011).CrossRefGoogle Scholar
Delley, B.: Fast calculation of electrostatics in crystals and large molecules. J. Chem. Phys. 100, 6107 (1996).CrossRefGoogle Scholar
Delley, B.: From molecules to solids with the DMol3 approach. J. Chem. Phys. 113, 7756 (2000).CrossRefGoogle Scholar
Perdew, J.P., Burke, K., and Ernzerhof, M.: Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865 (1996).CrossRefGoogle ScholarPubMed
Alam, Md.K., Saito, S., and Takaba, H.: Density functional theory study on the adsorption of H, OH, and CO and coadsorption of CO with H/OH on the Pt2Ru3 surfaces. J. Mater. Res. 31, 2617 (2016).CrossRefGoogle Scholar
Koper, M.T.M., Shubina, T.E., and van Santen, R.A.: Periodic density functional study of CO and OH adsorption on Pt–Ru alloy surfaces: Implications for CO tolerant fuel cell catalysts. J. Phys. Chem. B 106, 686 (2002).CrossRefGoogle Scholar
Shubina, T.E. and Koper, M.T.M.: Quantum-chemical calculations of CO and OH interacting with bimetallic surfaces. Electrochim. Acta 47, 3621 (2002).CrossRefGoogle Scholar
Lee, C.T., Yang, W.T., and Parr, R.G.: Development of the Colle Salvetti correlation energy formula into a functional of the electron density. Phys. Rev. B: Condens. Matter Mater. Phys. 37, 785 (1988).CrossRefGoogle ScholarPubMed
Hansen, E.W. and Neurock, M.: First-principles-based Monte Carlo simulation of ethylene hydrogenation kinetics on Pd. J. Catal. 196, 241 (2000).CrossRefGoogle Scholar
Reuter, K., Frenkel, D., and Scheffler, M.: The steady state of heterogeneous catalysis, studied by first-principles statistical mechanics. Phys. Rev. Lett. 93, 116104 (2004).CrossRefGoogle ScholarPubMed
Allen, M.P. and Tildesley, D.J.: Computer Simulation of Liquids (Clarendon Press, Oxford, 1987).Google Scholar
Eriksson, L., Johansson, E., Muller, M., and Wold, S.J.: On the selection of training set in environmental QSAR when compounds are clustered. Chemometrics 14, 599 (2000).3.0.CO;2-8>CrossRefGoogle Scholar
Tropsha, A., Gramatica, P., and Gombar, V.K.: The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci. 22, 69 (2003).CrossRefGoogle Scholar
Todeschini, R., Consonni, V., Mauri, A., and Pavan, M.: Detecting “bad” regression models: Multicriteria fitness functions in regression analysis. Anal. Chim. Acta 515, 199 (2004).CrossRefGoogle Scholar
Topliss, J. and Edwards, P.J.: Chance factors in studies of quantitative structure-activity relationships. J. Med. Chem. 22, 1238 (1979).CrossRefGoogle ScholarPubMed
Consonni, V., Ballabio, D., and Todeschini, R.J.: Comments on the definition of the Q 2 parameter for QSAR validation. J. Chem. Inf. Model. 49, 1669 (2009).CrossRefGoogle Scholar
Antolini, E., Giorgi, L., Cardellini, F., and Passalacqua, E.: Physical and morphological characteristics and electrochemical behavior in PEM fuel cells of PtRu/C catalysts. J. Solid State Electrochem. 5, 131 (2001).CrossRefGoogle Scholar
Yamamoto, T.A., Kageyama, S., Seino, S., Nitani, H., Nakagawa, T., Horioka, R., Honda, Y., Ueno, K., and Daimon, H.: Methanol oxidation catalysis and substructure of PtRu/C bimetallic nanoparticles synthesized by a radiolytic process. Appl. Catal., A 396, 68 (2011).CrossRefGoogle Scholar
Cheng, D., Huang, S., and Wang, W.: The structure of 55-atom Cu-Au bimetallic clusters: Monte Carlo study. Eur. Phys. J. D 39, 41 (2005).CrossRefGoogle Scholar
Cheng, D., Huang, S., and Wang, W.: Structures of small Pd–Pt bimetallic clusters by Monte Carlo simulation. Chem. Phys. 330, 423 (2006).CrossRefGoogle Scholar
Roy, P.P., Paul, S.P., Mitra, I., and Roy, K.: On two novel parameters for validation of predictive QSAR models. Molecules 14, 1660 (2009).Google Scholar
Katritzky, A.R., Kuanar, M., Slavov, S., and Dennis Hall, C.: Quantitative correlation of physical and chemical properties with chemical structure: Utility for prediction. Chem. Rev. 110, 5714 (2010).CrossRefGoogle ScholarPubMed
Nitani, H., Nakagawa, T., Daimon, D., Kurobe, Y., Ono, T., Honda, Y., Koizumi, A., Seino, S., and Yamamoto, T.A.: Methanol oxidation catalysis and substructure of PtRu bimetallic nanoparticles. Appl. Catal., A 326, 194 (2007).CrossRefGoogle Scholar
Wang, G., Hove, M.A.V., Ross, P.N., and Baskes, M.I.: Quantitative prediction of surface segregation in bimetallic Pt–M alloy nanoparticles (M = Ni, Re, Mo). Surf. Sci. 79, 28 (2005).Google Scholar
Wang, G., Hove, M.A.V., Ross, P.N., and Baskes, M.I.: Monte Carlo simulations of segregation in Pt–Ni catalyst nanoparticles. J. Chem. Phys. 122, 024706 (2005).CrossRefGoogle ScholarPubMed