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UnissResearch
Article . 2009
Data sources: UnissResearch
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
UnissResearch
Article . 2009
Data sources: UnissResearch
The Journal of Chemical Physics
Article . 2009 . Peer-reviewed
Data sources: Crossref
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From thermodynamic cell models to partitioning cellular automata for diffusion in zeolites. I. Structure of the algorithm

Authors: PAZZONA F. G; DEMONTIS, Pierfranco; SUFFRITTI, Giuseppe Baldovino;

From thermodynamic cell models to partitioning cellular automata for diffusion in zeolites. I. Structure of the algorithm

Abstract

In the study of adsorption of simple adsorbates in microporous materials like zeolites, thermodynamic models of small grand-canonical cells with very local interactions [e.g., see K. G. Ayappa, J. Chem. Phys. 111, 4736 (1999)] have been proven to be able to produce thermodynamic properties in very good agreement with the results of experiments and atomistic simulations. In this paper we present in details the structure and implementation of a thermodynamic partitioning cellular automaton (PCA) devised as a dynamical version of thermodynamic cell models and proposed as an easy environment to perform coarse-grained simulations of adsorption/diffusion of simple interacting molecules in microporous materials. Local evolution rules and memory effects are introduced to make our PCA able to complete the static picture provided by thermodynamic cell models with the simulation of transport properties.

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Italy
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Average
Average
Top 10%
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