Surface free energies of solid metals: Estimation from liquid surface tension measurements

Tyson, W. R.; Miller, W.

doi:10.1016/0039-6028(77)90442-3

Cited by 1,799 publications

(763 citation statements)

References 10 publications

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“…We also in Table III give the calculated surface energies of the relaxed low index surfaces for pure Pt and pure Ni using our MEAM potentials. The calculated surface energies using the MEAM potentials agree quite well with the ab initio calculations [34][density-functional theory (DFT) with the generalized gradient approximation (GGA)] and experiments [35,36]. It is noticeable that the surface energies in Table III for pure Pt and pure Ni are pretty close.…”

Section: A Development Of the Potentialssupporting

confidence: 71%

“…the cohesive energy E c (eV), the equilibrium nearest-neighbor distance r e (Å), the exponential decay factor for the universal energy function α, the scaling factor for the embedding energy A, the four exponential decay factors for the atomic densities β (i) , the four weighting factors for the atomic densities t (i) , and the density scaling factor ρ 0 . b Reference [35].…”

Section: Discussionmentioning

confidence: 99%

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Monte Carlo simulations of segregation in Pt-Ni catalyst nanoparticles

Wang

Hove

Ross

et al. 2004

The Journal of Chemical Physics

133

144

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We have investigated the segregation of Pt atoms in the surfaces of Pt-Ni nanoparticles, using Modified Embedded Atom Model potentials and the Monte Carlo method. The nanoparticles are assumed to have disordered fcc configurations at two fixed overall concentrations (50 at.% Pt and 75 at.% Pt). We use four kinds of nanoparticle shapes [cube, tetrahedron, octahedron, and cubo-octahedron] terminated by {111} and {100} facets to examine the extent of the Pt segregation to the nanoparticle surfaces and determine the equilibrium structures of Pt-Ni nanoparticles at T=600 K. The model particles contain between 560 and 4631 atoms (particle size ranging from 2.5 to 5 nm).Our results imply that a complete {100}-facet reconstruction could make the cubooctahendral Pt-Ni nanoparticles most energetically favorable, consistent with experimental observations. We predict that at 600 K due to segregation the equilibrium Pt 50 Ni 50 nanoparticles with fewer than 2000 atoms and Pt 75 Ni 25 nanoparticles with fewer than 4000 atoms would achieve a surface-sandwich structure, in which the Pt atoms are enriched in the outermost and third atomic shells while the Ni atoms are enriched in the second atomic shell. We also find that due to an order-disorder transition the Pt 50 Ni 50 cubo-octahedral nanoparticles containing more than 2000 atoms would form a core-shell structure with a Pt-enriched surface and a Pt-deficient homogenous core. *Corresponding

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Section: A Development Of the Potentialssupporting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Monte Carlo simulations of segregation in Pt-Ni catalyst nanoparticles

Wang

Hove

Ross

et al. 2004

The Journal of Chemical Physics

133

144

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“…2) for Pt and Re using our MEAM potentials. For the surfaces of fcc Pt and hcp Re, the calculated surface energies using the MEAM potentials agree quite well with the ab initio calculations [37][density-functional theory (DFT) with the generalized gradient approximation (GGA)] and experiments [38,39]. For both fcc and hcp phases, Pt has significantly lower surface energies than Re.…”

Section: Thermodynamics Considerationssupporting

confidence: 68%

Monte Carlo simulations of segregation in Pt-Re catalyst nanoparticles

Wang

Hove

Ross

et al. 2004

The Journal of Chemical Physics

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. We predict that due to the segregation process the equilibrium Pt-Re nanoparticles would achieve a core-shell structure, with a P t-enriched shell surrounding a Pt-deficient core. For fcc cubo-octahedral Pt 75 Re 25 nanoparticles, the shells consist of almost 100 at.% of Pt atoms. Even in the shells of hcp truncated hexagonal bipyramidal Pt 50 Re 50 nanoparticles, the concentrations of Pt atoms exceed 85 at. % (35 at.% higher than the overall concentration of Pt atoms in these nanoparticles).Most prominently, all Pt atoms will segregate to the surfaces in the hcp truncated hexagonal bipyramidal Pt 25 Re 75 nanoparticles containing less than 1 000 atoms. We also find that the Pt atoms segregate preferentially to the vertex sites, less to edge sites, and least to facet sites on the shell of Pt-Re

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“…Second, compared to SiO iron has a very high surface energy. Tyson & Miller (1977) report a value of 2123 erg cm −2 for iron at the melting point as well as a semi-empirical temperature dependence. As we have seen previously for SiO, higher values for the surface energy of the condensed phase significantly inhibit nucleation.…”

Section: Adding Iron and Magnesium To (Sio) N Nucleimentioning

confidence: 94%

A Model of Silicate Grain Nucleation and Growth in Circumstellar Outflows

Paquette

Ferguson

Nuth

2011

ApJ

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Based on its abundance, high bond energy, and recent measurements of its vapor pressure SiO is a natural candidate for dust nucleation in circumstellar outflows around asymptotic giant branch stars. In this paper, we describe a model of the nucleation and growth of silicate dust in such outflows. The sensitivity of the model to varying choices of poorly constrained chemical parameters is explored, and the merits of using scaled rather than classical nucleation theory are briefly considered. An elaboration of the model that includes magnesium and iron as growth species is then presented and discussed. The composition of the bulk of the grains derived from the model is consistent with olivines and pyroxenes, but somewhat metal-rich grains and very small, nearly pure SiO grains are also produced.

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Surface free energies of solid metals: Estimation from liquid surface tension measurements

Cited by 1,799 publications

References 10 publications

Monte Carlo simulations of segregation in Pt-Ni catalyst nanoparticles

Monte Carlo simulations of segregation in Pt-Ni catalyst nanoparticles

Monte Carlo simulations of segregation in Pt-Re catalyst nanoparticles

A Model of Silicate Grain Nucleation and Growth in Circumstellar Outflows

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