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    pH mediated facile preparation of hydrotalcite based adsorbent for enhanced arsenite and arsenate removal: Insights on physicochemical properties and adsorption mechanism

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    Authors
    Yadav, M.
    Gupta, A.
    Ghosal, P.
    Mukherjee, Abhijit
    Date
    2017
    Type
    Journal Article
    
    Metadata
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    Citation
    Yadav, M. and Gupta, A. and Ghosal, P. and Mukherjee, A. 2017. pH mediated facile preparation of hydrotalcite based adsorbent for enhanced arsenite and arsenate removal: Insights on physicochemical properties and adsorption mechanism. Journal of Molecular Liquids. 240: pp. 240-252.
    Source Title
    Journal of Molecular Liquids
    DOI
    10.1016/j.molliq.2017.05.082
    ISSN
    0167-7322
    URI
    http://hdl.handle.net/20.500.11937/67310
    Collection
    • Curtin Research Publications
    Abstract

    The preparation process parameters of calcined Mg[sbnd]Fe[sbnd] (CO 3 ) layer double hydroxide, synthesized by a co-precipitation method at a low supersaturation, were optimized targeting the arsenic removal. The multivariate optimization technique was attempted through response surface methodology coupled with a 3 3 factorial design performed on the major influencing parameters of the synthesis process, viz. the molar ratio, formation pH and calcination temperature. The important role of pH in the co-precipitation process at low supersaturation was observed in this study. The molar ratio has a clear antagonistic effect on the removal efficiency of arsenite and arsenate showing a maximum value at 2. The pH and calcination temperature attain the optima around 12 and 300 °C, respectively. The characterization of the material clearly showed that the variation of the process condition has a potential impact on elemental composition, crystallinity, surface morphology and other physico-chemical properties of the adsorbent. The adsorption capacity of this hydrotalcite-based material prepared at the optimum level of the process parameters was found to be 33.90 and 68.03 mg/L for arsenite and arsenate, respectively. The coefficient of regression for Langmuir and Freundlich models for As(III) and As(V) are 0.973, 0.978 and 0.803, 0.822, respectively exhibiting to the fact that fitting of Freundlich model is better for both the arsenic species.

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