Why do gallium clusters have a higher melting point than the bulk?

Density functional molecular dynamical simulations have been performed on Ga$_{17}$ and Ga$_{13}$ clusters to understand the recently observed higher-than-bulk melting temperatures in small gallium clusters [Breaux {\em et al.}, Phys. Rev. Lett. {\bf 91}, 215508 (2003)]. The specific-heat curve, calculated with the multiple-histogram technique, shows the melting temperature to be well above the bulk melting point of 303 K, viz. around 650 K and 1400 K for Ga$_{17}$ and Ga$_{13}$, respectively. The higher-than-bulk melting temperatures are attributed mainly to the covalent bonding in these clusters, in contrast with the covalent-metallic bonding in the bulk.Comment: 4 pages, including 6 figures. accepted for publication in Phys. Rev. Let

Melting Point and Lattice Parameter Shifts in Supported Metal Nanoclusters

The dependencies of the melting point and the lattice parameter of supported metal nanoclusters as functions of clusters height are theoretically investigated in the framework of the uniform approach. The vacancy mechanism describing the melting point and the lattice parameter shifts in nanoclusters with decrease of their size is proposed. It is shown that under the high vacuum conditions (p<10^-7 torr) the essential role in clusters melting point and lattice parameter shifts is played by the van der Waals forces of cluster-substrate interation. The proposed model satisfactorily accounts for the experimental data.Comment: 6 pages, 3 figures, 1 tabl

Size--sensitive melting characteristics of gallium clusters: Comparison of Experiment and Theory for Ga17+_{17}{}^{+} and Ga20+_{20}{}^{+}

Experiments and simulations have been performed to examine the finite-temperature behavior of Ga$_{17}{}^{+}$ and Ga$_{20}{}^{+}$ clusters. Specific heats and average collision cross sections have been measured as a function of temperature, and the results compared to simulations performed using first principles Density--Functional Molecular--Dynamics. The experimental results show that while Ga$_{17}{}^{+}$ apparently undergoes a solid--liquid transition without a significant peak in the specific--heat, Ga$_{20}{}^{+}$ melts with a relatively sharp peak. Our analysis of the computational results indicate a strong correlation between the ground--state geometry and the finite--temperature behavior of the cluster. If the ground--state geometry is symmetric and "ordered" the cluster is found to have a distinct peak in the specific--heat. However, if the ground--state geometry is amorphous or "disordered" the cluster melts without a peak in the specific--heat.Comment: 6 figure

Impurity effects on the melting of Ni clusters

We demonstrate that the addition of a single carbon impurity leads to significant changes in the thermodynamic properties of Ni clusters consisting of more than a hundred atoms. The magnitude of the change induced is dependent upon the parameters of the Ni-C interaction. Hence, thermodynamic properties of Ni clusters can be effectively tuned by the addition of an impurity of a particular type. We also show that the presence of a carbon impurity considerably changes the mobility and diffusion of atoms in the Ni cluster at temperatures close to its melting point. The calculated diffusion coefficients of the carbon impurity in the Ni cluster can be used for a reliable estimate of the growth rate of carbon nanotubes.Comment: 27 pages, 13 figure

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior