Diffusion of nanoclusters

Jensen, Pablo; Clément, Arnaud; Lewis, Laurent J.

doi:10.1016/j.commatsci.2004.01.031

Cited by 20 publications

(11 citation statements)

References 26 publications

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“…When those bonds break, the nano-clusters can then roll or slide to new positions on the support. This behavior differs from that in previous studies of diffusion, [12][13][14][15] in that the characteristic time-scale is set by the librational motion of the cluster with respect to the support, rather than the frequency of individual atomistic events. According to the crystallite model of sintering, the combined motion of many such clusters typically leads to reduced catalytic activity.…”

Section: Introductioncontrasting

confidence: 96%

“…6 These longer-time whole-cluster dynamics have been previously shown to be important in the characterization of epitaxial nanoclusters. [12][13][14][15] When combined with the stochastic thermal coupling to the support, these lowfrequency modes induce a rattle-like motion in the system, typically at sub-THz frequencies. At longer time scales (tens of ps for supported Pt clusters on γ -alumina at high temperatures), bonding to the support is transient.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic structural disorder in supported nanoscale catalysts

Rehr

Vila

2014

The Journal of Chemical Physics

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We investigate the origin and physical effects of "dynamic structural disorder" (DSD) in supported nano-scale catalysts. DSD refers to the intrinsic fluctuating, inhomogeneous structure of such nano-scale systems. In contrast to bulk materials, nano-scale systems exhibit substantial fluctuations in structure, charge, temperature, and other quantities, as well as large surface effects. The DSD is driven largely by the stochastic librational motion of the center of mass and fluxional bonding at the nanoparticle surface due to thermal coupling with the substrate. Our approach for calculating and understanding DSD is based on a combination of real-time density functional theory/molecular dynamics simulations, transient coupled-oscillator models, and statistical mechanics. This approach treats thermal and dynamic effects over multiple time-scales, and includes bond-stretching and -bending vibrations, and transient tethering to the substrate at longer ps time-scales. Potential effects on the catalytic properties of these clusters are briefly explored. Model calculations of molecule-cluster interactions and molecular dissociation reaction paths are presented in which the reactant molecules are adsorbed on the surface of dynamically sampled clusters. This model suggests that DSD can affect both the prefactors and distribution of energy barriers in reaction rates, and thus can significantly affect catalytic activity at the nano-scale.

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Section: Introductioncontrasting

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Dynamic structural disorder in supported nanoscale catalysts

Rehr

Vila

2014

The Journal of Chemical Physics

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“…Figure a shows an example of a typical cluster center of mass trajectory obtained with MBN Explorer () for the Ag 488 cluster moving during 10 ns at

T = 800

K. In this case the interaction of silver atoms within the cluster was modeled through the Sutton–Chen potential (), while the interaction of the cluster with graphene was described using the Morse potential (), adapted from earlier studies . The atomistic MD simulation provides the description of the dynamics of each atom in the cluster.…”

Section: Theoretical Description Of Morphological Transitionmentioning

confidence: 99%

Thermally induced morphological transition of silver fractals

Solov’yov

Kébaı̈li

et al. 2013

Physica Status Solidi (b)

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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.

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“…The addition of some sort of mobility of the NPs in our simulation is strongly supported by existing literature. According to Jensen et al . and references therein, small gold nanoclusters on carbon substrates move on the substrate in a Brownian‐like fashion with diffusion coefficients from 10 −‐8 cm 2 /s down to 10 −15 cm 2 /s.…”

Section: Discussionmentioning

confidence: 99%

Coalescence of Cluster Beam Generated Sub‐2 nm Bare Au Nanoparticles and Analysis of Au Film Growth Parameters

et al. 2017

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In this work is presented the growth model for Au films grown on a carbon substrate at room temperature by using as building blocks Au nanoparticles (NPs) with 1.4 nm mean size generated via remote cluster beam synthesis and soft landing on the substrate. The key results highlighted in this work are that 1) the deposited nanoparticles coalesce at substrate level in such a way that the film growth is 3D, 2) newly formed nanoparticles at substrate level are predominantly magic number clusters and 3) coalescensce takes place as soon as two neighboring nanopartciles come closer than a critical distance. The film growth was investigated by TEM as a function of Au load, in the range 0-1.2 μg/cm 2 . Two distinct regimes are identified: the "landing regime" and the "coalescence regime". During the latter the film growth is 3D with a dynamic scaling exponent z of 2.13. Particular attention was devoted to the study of the evolution of the NP population from the moment they are generated with the cluster beam generator to the moment they land on the substrate and coalesce with other NPs. Our results show that 1) the NPs generated by the cluster beam are heterogeneous in size and are made by more than 95% by Au Magic numbers, mainly Au 20 and Au 55 and 2) kinetic processes (coalescence) at substrate level is capable of producing NPs populations made of larger Au magic numbers containing up to several thousands of Au atoms. Experimental and simulation results provide insight into the coalescence mechanism and provide strong evidence that the NPs coalesce when the nearest neighbor distance is below a critical mark. The critical distance is at its minimum 0.4-0.5 nm and it is still unclear whether it is constant or not although the best matching simulation results seem to point to a superlinear dependence from the NP size difference between two neighboring candidate coalescing NPs. The coalescence phenomenon investigated in this work pinpoints the unique self-organization properties of these small Au NPs in creating films with a stable edge-to-edge mean nearest neighbor distance of the order of 1.4 nm.

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Diffusion of nanoclusters

Cited by 20 publications

References 26 publications

Dynamic structural disorder in supported nanoscale catalysts

Dynamic structural disorder in supported nanoscale catalysts

Thermally induced morphological transition of silver fractals

Coalescence of Cluster Beam Generated Sub‐2 nm Bare Au Nanoparticles and Analysis of Au Film Growth Parameters

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