2004
DOI: 10.1016/j.susc.2004.07.026
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Surface rearrangement at complex adsorbate–substrate interfaces

Abstract: On the basis of the information theory approach we propose a novel statistical scheme for analyzing the evolution of coupled adsorbate-substrate systems, in which the substrate undergoes the adsorbate-induced transformations. A relation between the substrate morphology and the adsorbate thermodynamic state is established. This allows one to estimate the surface structure in terms of incomplete experimental information and the one concerning the adsorbate thermodynamic response to the structural modifications.

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Cited by 5 publications
(8 citation statements)
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“…For thin films ͑negative K or B͒ we have to spend energy in order to induce both distortions and volume dilatation ͑presumably because of a stronger influence of the film support͒. Interestingly that a quite similar crossover from a quenched to an adsorbate-induced roughness has recently been reported 20 for HCL adsorption on ice films of varying thickness. In the context of the present work the crossover is due to an interplay of the film volume dilatation and the insertion-induced internal distortions.…”
Section: ͑13͒mentioning
confidence: 90%
“…For thin films ͑negative K or B͒ we have to spend energy in order to induce both distortions and volume dilatation ͑presumably because of a stronger influence of the film support͒. Interestingly that a quite similar crossover from a quenched to an adsorbate-induced roughness has recently been reported 20 for HCL adsorption on ice films of varying thickness. In the context of the present work the crossover is due to an interplay of the film volume dilatation and the insertion-induced internal distortions.…”
Section: ͑13͒mentioning
confidence: 90%
“…In that case the distribution (17) does not change and the exponent λ is determined by the distance from the equilibrium state µ ′ (β|ρ i = ρ).…”
Section: Discussionmentioning
confidence: 99%
“…Maximizing H under the constraint (1) and requiring the normalization for f (β) we get the following conditional distribution f (β) = f (β|ρ) [17] f (β|ρ) = e −κθ(ρ|β)…”
Section: Maximum Entropy Approachmentioning
confidence: 99%
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