Introducing hollow structures into metallic nanoparticles has become a promising route to improve their catalytic performances. A fundamental understanding of thermal stability of these novel nanostructures is of significance for their syntheses and applications. In this article, molecular dynamics simulations have been employed to offer insights into the thermodynamic evolution of hollow bimetallic core-shell nanoparticles. Our investigation reveals that for hollow Pt-core/Au-shell nanoparticle, premelting originates at the exterior surface, and a typical two-stage melting behavior is exhibited, similar to the solid ones. However, since the interior surface provides facilitation for the premelting initiating at the core, the two-stage melting is also observed in hollow Au-core/Pt-shell nanoparticle, remarkably different from the solid one. Furthermore, the collapse of hollow structure is accompanied with the overall melting of the hollow Pt-core/Au-shell nanoparticle while it occurs prior to that of the hollow Au-core/Pt-shell nanoparticle and leads to the formation of a liquid-core/solid-shell structure, although both of them finally transform into a mixing alloy with Au-dominated surface. Additionally, the existence of stacking faults in the hollow Pt-core/Au-shell nanoparticle distinctly lowers its melting point. This study could be of great importance to the design and development of novel nanocatalysts with both high activity and excellent stability.

Image segmentation, as a key step of microarray image processing, is crucial for obtaining the spot expressions simultaneously. However, state-of-art clustering-based segmentation algorithms are sensitive to noises. To solve this problem and improve the segmentation accuracy, in this article, several improvements are introduced into the fast and simple clustering methods (K-means and Fuzzy C means). Firstly, a contrast enhancement algorithm is implemented in image preprocessing to improve the gridding precision. Secondly, the data-driven means are proposed for cluster center initialization, instead of usual random setting. The third improvement is that the multi features, including intensity features, spatial features, and shape features, are implemented in feature selection to replace the sole pixel intensity feature used in the traditional clustering-based methods to avoid taking noises as spot pixels. Moreover, the principal component analysis is adopted for various feature extraction. Finally, an adaptive adjustment algorithm is proposed based on data mining and learning for further dealing with the missing spots or low contrast spots. Experiments on real and simulation data sets indicate that the proposed improvements made our proposed method obtains higher segmented precision than the traditional K-means and Fuzzy C means clustering methods.

High-index-faceted Pt nanoparticles exhibit exceptional electrocatalytic activity owing to the high density of low coordinated sites on their surface, and thus have attracted extensive studies over the past few years. In this study, we have employed atomistic simulations to systematically investigate the structural and thermal stabilities and shape evolution of Pt nanoparticles with different high-index facets, that is, tetrahexahedra enclosed by {hk0} facets, trapezohedra by {hkk} ones, and trisoctahedra by {hhk} ones. The results show that {221} faceted trisoctahedral nanoparticles display the best structural and thermal stabilities while {410} faceted tetrahexahedral ones display the worst. The shape stability of these nanoparticles generally decreases in the order from trapezohedron to tetrahexahedron to trisoctahedron. For the same type of polyhedron, the structural, thermal and shape stabilities of the nanoparticles all decrease according to the order of {2kl}, {3kl} and {4kl} facets. Further analyses have discovered that a large proportion of high-coordinated surface atoms are beneficial for enhancing both the thermal and shape stabilities. This work provides an in-depth understanding of surface structures and thermodynamic evolution of high-index-faceted metallic nanoparticles.