91 research outputs found
Dynamics of Chainlike Molecules on Surfaces
We consider the diffusion and spreading of chainlike molecules on solid
surfaces. We first show that the steep spherical cap shape density profiles,
observed in some submonolayer experiments on spreading polymer films, imply
that the collective diffusion coefficient must be an increasing
function of the surface coverage for small and intermediate coverages.
Through simulations of a discrete model of interacting chainlike molecules, we
demonstrate that this is caused by an entropy-induced repulsive interaction.
Excellent agreement is found between experimental and numerically obtained
density profiles in this case, demonstrating that steep submonolayer film edges
naturally arise due to the diffusive properties of chainlike molecules. When
the entropic repulsion dominates over interchain attractions,
first increases as a function of but then eventually approaches zero
for . The maximum value of decreases for increasing
attractive interactions, leading to density profiles that are in between
spherical cap and Gaussian shapes. We also develop an analytic mean field
approach to explain the diffusive behavior of chainlike molecules. The
thermodynamic factor in is evaluated using effective free energy
arguments, and the chain mobility is calculated numerically using the recently
developed dynamic mean field theory. Good agreement is obtained between theory
and simulations.Comment: 16 pages, 13 Postscript figure
Nonexponential decay of velocity correlations in surface diffusion: The role of interactions and ordering
We study the diffusive dynamics of adparticles in two model systems with strong interactions by considering the decay of the single-particle velocity correlation function φ(t). In accordance with previous studies, we find φ(t) to decay nonexponentially and follow a power-law φ(t)∼t−x at intermediate times t, while at long times there is a crossover to an exponential decay. We characterize the behavior of the decay exponent x in detail in various ordered phases and in the vicinity of phase boundaries. We find that within the disordered phase, the behavior of x can be rationalized in terms of interaction effects. Namely, x is typically larger than two in cases where repulsive adparticle–adparticle interactions dominate, while attractive interactions lead to x<2. In ordered phases, our results suggest that the behavior of x is mainly governed by ordering effects that determine the local structure in which adatoms diffuse. Then the decay is characterized by 1<x<2 under conditions where diffusion is truly two-dimensional, while in phases where adatoms diffuse in a one-dimensional fashion along ideal rows of vacancies, we find a regime characterized by x<1. Also, changes in the qualitative behavior of x are closely related to phase boundaries and local ordering effects. Our studies suggest that φ(t) can be used to obtain information about the ordering of the system and about the nature of predominant interactions between adparticles. Our predictions can be tested experimentally by techniques such as scanning tunneling microscopy, in which φ(t) can be measured in terms of discrete adparticle displacements as shown in this work. Finally, our studies suggest that the decay of velocity correlations in collective diffusion follows, qualitatively, the same behavior as the decay of single-particle velocity correlations in tracer diffusion.Peer reviewe
Memory expansion for diffusion coefficients
We present a memory expansion for macroscopic transport coefficients such as the collective and tracer diffusion coefficients DC and DT, respectively. The successive terms in this expansion for DC describe rapidly decaying memory effects of the center-of-mass motion, leading to fast convergence when evaluated numerically. For DT, one obtains an expansion of similar form that contains terms describing memory effects in single-particle motion. As an example we evaluate DC and DT for three strongly interacting surface systems through Monte Carlo simulations, and for a simple model diffusion system via molecular dynamics calculations. We show that the numerical method provides a speedup of about two orders of magnitude in computational time as compared with the standard methods, when collective diffusion is concerned. For tracer diffusion, the speedup is not quite as significant. Our studies using the memory expansion provide information of the nature of memory effects in diffusion and suggest a nontrivial power-law behavior of memory terms at intermediate times. We also discuss the application of the present approach to studies of other transport coefficients.Peer reviewe
Diffusive Spreading of Chainlike Molecules on Surfaces
We study the diffusion and submonolayer spreading of chainlike molecules on
surfaces. Using the fluctuating bond model we extract the collective and tracer
diffusion coefficients D_c and D_t with a variety of methods. We show that
D_c(theta) has unusual behavior as a function of the coverage theta. It first
increases but after a maximum goes to zero as theta go to one. We show that the
increase is due to entropic repulsion that leads to steep density profiles for
spreading droplets seen in experiments. We also develop an analytic model for
D_c(theta) which agrees well with the simulations.Comment: 3 pages, RevTeX, 4 postscript figures, to appear in Phys. Rev.
Letters (1996
A Dynamical Mean Field Theory for the Study of Surface Diffusion Constants
We present a combined analytical and numerical approach based on the Mori
projection operator formalism and Monte Carlo simulations to study surface
diffusion within the lattice-gas model. In the present theory, the average jump
rate and the susceptibility factor appearing are evaluated through Monte Carlo
simulations, while the memory functions are approximated by the known results
for a Langmuir gas model. This leads to a dynamical mean field theory (DMF) for
collective diffusion, while approximate correlation effects beyond DMF are
included for tracer diffusion. We apply our formalism to three very different
strongly interacting systems and compare the results of the new approach with
those of usual Monte Carlo simulations. We find that the combined approach
works very well for collective diffusion, whereas for tracer diffusion the
influence of interactions on the memory effects is more prominent.Comment: 13 pages LaTeX and 6 PostScript figures, style files included. To
appear in Surface Science Letter
Dynamics and Scaling of 2D Polymers in a Dilute Solution
The breakdown of dynamical scaling for a dilute polymer solution in 2D has
been suggested by Shannon and Choy [Phys. Rev. Lett. {\bf 79}, 1455 (1997)].
However, we show here both numerically and analytically that dynamical scaling
holds when the finite-size dependence of the relevant dynamical quantities is
properly taken into account. We carry out large-scale simulations in 2D for a
polymer chain in a good solvent with full hydrodynamic interactions to verify
dynamical scaling. This is achieved by novel mesoscopic simulation techniques
Diffusion of gold nanoclusters on graphite
We present a detailed molecular-dynamics study of the diffusion and
coalescence of large (249-atom) gold clusters on graphite surfaces. The
diffusivity of monoclusters is found to be comparable to that for single
adatoms. Likewise, and even more important, cluster dimers are also found to
diffuse at a rate which is comparable to that for adatoms and monoclusters. As
a consequence, large islands formed by cluster aggregation are also expected to
be mobile. Using kinetic Monte Carlo simulations, and assuming a proper scaling
law for the dependence on size of the diffusivity of large clusters, we find
that islands consisting of as many as 100 monoclusters should exhibit
significant mobility. This result has profound implications for the morphology
of cluster-assembled materials
Visual pattern recognition as a means to optimising building performance?
Visual pattern recognition as a means to optimising building performance
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