Thermally induced morphological transition of silver fractals

Abstract

We present both experimental and theoretical study of thermally induced morphological transition of silver nanofractals. Experimentally, those nanofractals formed from deposition and diffusion of preformed silver clusters on cleaved graphite surfaces exhibit dendritic morphologies that are highly sensitive to any perturbation, particularly caused by temperature. We analyze and characterize the morphological transition both in time and temperature using the recently developed Monte Carlo simulation approach for the description of nanofractal dynamics and compare the obtained results with the corresponding experimental data. The reported results reveal essential and general features of dynamical systems having dendritic shape.

Efficient 3D kinetic monte carlo method for modeling of molecular structure and dynamics

“…Consider the soft landing process of Ag 800 silver nanoparticles on a graphite substrate at room temperature . The deposited nanoparticles diffuse over the surface and self‐assemble in structures with morphologies that are mainly controlled by the nanoparticle deposition flux rate.…”

“…In the case of Ag 800 , diffusion over a surface this process is the diffusion of a free Ag 800 nanoparticle. Thus, knowing the coefficient of Ag 800 diffusion over the surface, , the time step Δ t can be calculated as where z = 3, denotes the dimensionality of space used in the present simulation (as the nanoparticles could move in the direction perpendicular to the surface). The value z = 2 describes the diffusion of particles constrained in 2D space, that is, describes the situation when particles could not leave the surface.…”

A coarse-grained Monte Carlo approach to diffusion processes in metallic nanoparticles

“…In some cases, models of reduced dimensionality are able to deliver very accurate results, as has been shown in the case of silver cluster fractals on graphite [19,20]. Experimental studies revealed that silver clusters on graphite arrange themselves in dendritic structures [22,23].…”

“…In comparison to the comprehensive MBN package our method can be considered as a "top-down" approach in the sense of accessing nanostructures from the macroscopic side based on microscopy image data, while comparable stochastic Monte Carlo based algorithms, as implemented in the MBN package, are starting at the atomic scale but employ a grouping of atoms into clusters in order to address larger systems. Recently, the latter type of approach has been successfully applied to the description of surface diffusion of Ag 800 nanoparticles on graphite surfaces [19,20], and has also been extended to three dimensions via a stacking of the metallic nanoparticles [21]. As will be shown below, our treatment of surface kinetics via Monte Carlo sampling is very similar to these earlier studies on silver clusters, but with the conceptional difference that in our case fictitious, finite portions of metal, with their volume defined by the resolution of input TEM images, are subject to a random repositioning, and not well-defined concrete metal clusters.…”

Charge localization effects and transport in dendritic nanostructures for photovoltaic applications

“…Metallic nanostructures of this kind are also used as surface enhancement Raman scattering (SERS) active surfaces, for which new techniques based on thermal annealing have been recently developed [8]. Other examples include hierarchical dendritic gold nanostructures [9] prepared by electrochemical techniques, silver nanofractals [10] which exhibit thermally induced morphological transitions or mixed Au/Ag bimetallic dendrites produced by AC electrodeposition method [11]. In addition, during the past few years controllable growth of dendritic nanowire arrays has been achieved [12][13][14].…”