Roy L. Johnston

Nanoalloys: From Theory to Applications of Alloy Clusters and Nanoparticles

5.1. Nanoalloys of Group 11 (Cu, Ag, Au) 865 5.1.1. Cu−Ag 866 5.1.2. Cu−Au 867 5.1.3. Ag−Au 870 5.1.4. Cu−Ag−Au 872 5.2. Nanoalloys of Group 10 (Ni, Pd, Pt) 872 5.2.1. Ni−Pd 872 * To whom correspondence should be addressed. Phone: +39010 3536214. Fax:+39010 311066. E-mail: ferrando@fisica.unige.it. † Universita di Genova. ‡ Argonne National Laboratory. § University of Birmingham. | As of October 1, 2007, Chemical Sciences and Engineering Division. Volume 108, Number 3

Atomic and molecular clusters

Clusters: Types, Sizes and Experiments. Rare Gas Clusters. Molecular Clusters. Metal Clusters I: Models. Metal Clusters II: Properties. Semiconductor Clusters. Ionic Clusters. Clusters in Action: Past, Present and Future.

Three-dimensional atomic-scale structure of size-selected gold nanoclusters

An unambiguous determination of the three-dimensional structure of nanoparticles is challenging. Electron tomography requires a series of images taken for many different specimen orientations. This approach is ideal for stable and stationary structures. But ultrasmall nanoparticles are intrinsically structurally unstable and may interact with the incident electron beam, constraining the electron beam density that can be used and the duration of the observation. Here we use aberration-corrected scanning transmission electron microscopy, coupled with simple imaging simulation, to determine with atomic resolution the size, three-dimensional shape, orientation and atomic arrangement of size-selected gold nanoclusters that are preformed in the gas phase and soft-landed on an amorphous carbon substrate. The structures of gold nanoclusters containing 309±6 atoms can be identified with either Ino-decahedral, cuboctahedral or icosahedral geometries. Comparison with theoretical modelling of the system suggests that the structures are consistent with energetic considerations. The discovery that nanoscale gold particles function as active and selective catalysts for a variety of important chemical reactions has provoked much research interest in recent years. We believe that the detailed structure information we provide will help to unravel the role of these nanoclusters in size- and structure-specific catalytic reactions. We note that the technique will be of use in investigations of other supported ultrasmall metal cluster systems.

Theoretical study of Cu–Au nanoalloy clusters using a genetic algorithm

A study has been made of the structures and stabilities of copper and gold clusters and copper–gold nanoalloy clusters, with up to 56 atoms, modeled by the many-body Gupta potential. For pure copper clusters, the lowest energy structures are found to be based on icosahedral packing, while pure gold clusters tend to form less symmetrical (often amorphous) structures. In a number of cases, the replacement of a single gold atom by copper is found to be sufficient to convert the structure to that of the more symmetrical copper cluster. The lowest energy clusters are generally more difficult to find for the bimetallic clusters than for the pure metallic clusters, due to the presence of homotops (related by permuting Cu and Au atoms), as well as geometrical isomers. The structures of the lowest energy bimetallic clusters exhibit primarily icosahedral packing, with (CuAu)M and (CuAu3)M clusters tending to form layered structures and (Cu3Au)M clusters showing greater Cu–Au mixing.

Global optimization of bimetallic cluster structures. I. Size-mismatched Ag–Cu, Ag–Ni, and Au–Cu systems

A genetic algorithm approach is applied to the optimization of the potential energy of a wide range of binary metallic nanoclusters, Ag-Cu, Ag-Ni, Au-Cu, Ag-Pd, Ag-Au, and Pd-Pt, modeled by a semiempirical potential. The aim of this work is to single out the driving forces that make different structural motifs the most favorable at different sizes and chemical compositions. Paper I is devoted to the analysis of size-mismatched systems, namely, Ag-Cu, Ag-Ni, and Au-Cu clusters. In Ag-Cu and Ag-Ni clusters, the large size mismatch and the tendency of Ag to segregate at the surface of Cu and Ni lead to the location of core-shell polyicosahedral minimum structures. Particularly stable polyicosahedral clusters are located at size N = 34 (at the composition with 27 Ag atoms) and N = 38 (at the composition with 32 and 30 Ag atoms). In Ag-Ni clusters, Ag32Ni13 is also shown to be a good energetic configuration. For Au-Cu clusters, these core-shell polyicosahedra are less common, because size mismatch is not reinforced by a strong tendency to segregation of Au at the surface of Cu, and Au atoms are not well accommodated upon the strained polyicosahedral surface.

The Genetic Algorithm: Foundations and Apllications in Structure Solution from Powder Diffraction Data

Recently, new methods based on the use of genetic algorithms have been explored and developed for solving crystal structures directly from powder diffraction data. In implementing genetic algorithms in such applications, several different aspects of the technique and strategy are open to optimization, leading to a versatile and powerful approach. In this paper, the fundamental concepts underlying genetic algorithms are discussed and the implementation of the genetic algorithm for structure solution from powder diffraction data is described. The opportunities, scope and potential for future developments in the foundations and applications of genetic algorithms in this field are highlighted. The genetic algorithm approach adopts the ‘direct-space’ philosophy for structure solution, with trial structures generated independently of the experimental diffraction data and the quality of each structure assessed by comparing the calculated and experimental powder diffraction patterns; in this work, this comparison is made using the profile R factor Rwp. In the genetic algorithm, a population of trial structures is allowed to evolve subject to well defined rules governing mating, mutation and ‘natural selection’. The ‘fitness’ of each structure in the population is a function of its profile R factor. The successful application of the genetic algorithm approach for structure solution of molecular crystals from powder diffraction data is demonstrated with examples of previously known and previously unknown structures.

Global optimization of bimetallic cluster structures. II. Size-matched Ag-Pd, Ag-Au, and Pd-Pt systems

Genetic algorithm global optimization of Ag-Pd, Ag-Au, and Pd-Pt clusters is performed. The 34- and 38-atom clusters are optimized for all compositions. The atom-atom interactions are modeled by a semiempirical potential. All three systems are characterized by a small size mismatch and a weak tendency of the larger atoms to segregate at the surface of the smaller ones. As a result, the global minimum structures exhibit a larger mixing than in Ag-Cu and Ag-Ni clusters. Polyicosahedral structures present generally favorable energetic configurations, even though they are less favorable than in the case of the size-mismatched systems. A comparison between all the systems studied here and in the previous paper (on size-mismatched systems) is presented.

Evolving better nanoparticles: Genetic algorithms for optimising cluster geometries

A review is presented of the design and application of genetic algorithms for the geometry optimisation of clusters and nanoparticles, where the interactions between atoms, ions or molecules are described by a variety of potential energy functions. A general introduction to genetic algorithms is followed by a detailed description of the genetic algorithm program that we have developed to identify the lowest energy isomers for a variety of atomic and molecular clusters. Examples are presented of its application to model Morse clusters, ionic MgO clusters and bimetallic “nanoalloy” clusters. Finally, a number of recent innovations and possible future developments are discussed.

Searching for the optimum structures of alloy nanoclusters.

Recent advances in computational methods for searching for the most stable structures of alloy nanoparticles are reviewed. A methodology based on extensive global optimization searches within an empirical potential model in conjunction with structure recognition algorithms and subsequent density-functional local relaxation of the lowest-energy structures pertaining to each different structural basin is proposed. Applications to different systems, including Cu-Ag, Cu-Au, Ni-Ag, Co-Ag, Co-Au, Ni-Au and Pd-Pt clusters, are presented.

Investigation of the structures of MgO clusters using a genetic algorithm

The application of a genetic algorithm, for optimizing the geometries of stoichiometric and non-stoichiometric MgO clusters, bound by a simple Coulomb-plus-Born–Mayer potential, is investigated. The genetic algorithm is shown to be efficient and reliable for finding, reproducibly the global minima for these clusters. The variation of the structures of MgO clusters are investigated as a function of the formal charges (±q) on the ions—ranging from q = 1 to q = 2. In agreement with previous studies, lower charges are found to favour compact, rocksalt-like cuboidal clusters, while the higher formal charges favour hollow pseudo-spherical structures. Hexagonal stacks are also found to be stable for small (MgO)N clusters with N = 3n. Comparisons are made with experimental mass spectral abundances and the results of previous empirical calculations, as well as with more sophisticated model potential and ab initio calculations. Finally, possible ways in which the genetic algorithm search method could be coupled with more accurate calculation methods are discussed.