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    Predicting the Adsorption Properties of Carbon Dioxide Corrosion Inhibitors Using a Structure-Activity Relationship

    Access Status
    Fulltext not available
    Authors
    Durnie, William
    De Marco, Roland
    Kinsella, Brian
    Jefferson, Alan
    Pejcic, Bobby
    Date
    2005
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Durnie, William and De Marco, Roland and Kinsella, Brian and Jefferson, Alan and Pejcic, Bobby. 2005. Predicting the Adsorption Properties of Carbon Dioxide Corrosion Inhibitors Using a Structure-Activity Relationship. Journal of the Electrochemical Society 152: B1-B11.
    Source Title
    Journal of the Electrochemical Society
    Additional URLs
    http://www.ecsdl.org/JES/
    Faculty
    Department of Applied Chemistry
    Division of Engineering, Science and Computing
    Faculty of Science
    Remarks

    open access to the full text of this document is available fromm http://www.ecsdl.org/JES/

    URI
    http://hdl.handle.net/20.500.11937/45396
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a fundamental study of the influence of various chemical inhibitors on the corrosion rate of mild steel in brine electrolyte under carbon dioxide conditions. The corrosion inhibitors were fitted to a Temkin adsorption isotherm, and various fundamental constants of adsorption ~i.e., adsorption equilibrium constants and molecular interaction constants! have been obtained. The inhibitor adsorption mechanism has been discussed in terms of thermodynamics ~i.e., DH, DG, and DS), and this revealed that some compounds chemisorb onto the steel electrode. In addition, molecular modeling was undertaken using PCSSPARTAN Plus and HyperChem Professional, and the various molecular parameters have been correlated with the thermodynamic adsorption properties of the inhibitors. Three-parameter and four-parameter fits for both negative and positive models are discussed. Multiple linear regression was performed on various combinations of molecular descriptors to describe the enthalpies and entropies of adsorption. Similarly, principal component analysis ~PCA! was employed to corroborate the scientifically based selection of molecular descriptors used in the multivariate regression models.

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