Paper Title:
Superficial Phase Transitions in Nanoalloys
  Abstract

In order to build the phase diagram of Cu-Ag nanoalloys, we study a 405-atom nanoparticle by means of Monte Carlo simulations with relaxations using N-body interatomic potentials. We focus on a range of nominal concentrations for which the cluster core remains Cu-pure and the (001) facets of the outer shell exhibit two original phenomena. Within the (N,mAg-mCu,P,T) ensemble, a structural and chemical bistability is observed, which affects all the (001) facets together. For a nanoparticle assembly, this will result in a bimodal distribution of clusters, some of them having their (001) facets Cu-rich with the usual square shape, the other ones having their (001) facets Ag-rich with a diamond shape. This bistability is replaced in the (NAg,NCu,P,T) ensemble by a continuous evolution of both the structure and the concentration of the (001) facets from Cu-rich square-shaped to Ag-rich diamond-shaped facets as the number of Ag atoms increases. For a nanoparticle assembly, this will result in an unimodal distribution of the cluster population concerning the properties of the (001) facets. This comparison between pseudo grand canonical and isothermal-isobaric results shows that the distribution of a population of bimetallic nanoparticles depends strongly on the conditions under it is elaborated.

  Info
Periodical
Solid State Phenomena (Volumes 172-174)
Edited by
Yves Bréchet, Emmanuel Clouet, Alexis Deschamps, Alphonse Finel and Frédéric Soisson
Pages
658-663
DOI
10.4028/www.scientific.net/SSP.172-174.658
Citation
M. Briki, J. Creuze, F. Berthier, B. Legrand, "Superficial Phase Transitions in Nanoalloys", Solid State Phenomena, Vols. 172-174, pp. 658-663, 2011
Online since
June 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Li Min Liu, Xiao Ping Fan
Chapter 10: Environmentally Sustainable Manufacturing Processes and Systems
Abstract:Traditional clustering ensemble combines all of the available clustering partitions to get the final clustering result. But in supervised...
2760
Authors: Xiao Na Liu, Qing Yin Zhang
Chapter 4: Other Related Topics
Abstract:In this article, we use the molecular dynamic simulation to study the structure and transmission properties of polar fluid which is in the...
657
Authors: Xiao Wei Sun, Hong Bo Zhou
Chapter 2: Information Technologies and Information Processing Algorithms
Abstract:An ensemble consists of a set of independently trained classifiers whose predictions are combined when classifying novel instances. Previous...
506
Authors: Xiao Wei Sun, Hong Bo Zhou
Chapter 5: Information and Applied Technology
Abstract:Boosting algorithms are a means of building a strong ensemble classifier by aggregating a sequence of weak hypotheses. An ensemble consists...
513