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    Determination of absolute adsorption for argon on flat surfaces under sub- and supercritical conditions

    Access Status
    Fulltext not available
    Authors
    Phadungbut, P.
    Fan, Chunyan
    Do, D.
    Nicholson, D.
    Tangsathitkulchai, C.
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Phadungbut, P. and Fan, C. and Do, D. and Nicholson, D. and Tangsathitkulchai, C. 2015. Determination of absolute adsorption for argon on flat surfaces under sub- and supercritical conditions. Colloids and Surfaces A: Physicochemical and Engineering Aspects. 480: pp. 19-27.
    Source Title
    Colloids and Surfaces A: Physicochemical and Engineering Aspects
    DOI
    10.1016/j.colsurfa.2015.04.011
    ISSN
    0927-7757
    School
    Department of Chemical Engineering
    URI
    http://hdl.handle.net/20.500.11937/45712
    Collection
    • Curtin Research Publications
    Abstract

    A new method, employing computer simulation, is proposed for the determination of the location of the interface separating the adsorbed phase from the adjacent gas phase, giving a means to calculate the "absolute" amount adsorbed. The method involves monitoring the fraction of successful insertions of molecules into differential volumes in the simulation box. By applying the concept of equal areas, as implemented in the determination of the Gibbs dividing surface, for the profile of the fraction of success versus distance, we are able to determine the location of the interface bounding the adsorbed phase. This allows us to find (1) the thickness of the adsorbed phase, (2) the absolute surface density (absolute loading) and (3) the volumetric density of the adsorbed phase, as functions of pressure. Knowing the absolute surface density as a function of pressure at different temperatures, we are able to calculate the heat of adsorption as a function of loading, using the Clausius-Clapeyron equation and to show that this is consistent with the heat obtained from the fluctuation formula in grand canonical Monte Carlo simulations.

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