Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44657
標題: Novel predicting methods for the removal of divalent metal ions by magnetite/amorphous iron oxide composite systems
作者: Chang, C.M.
張家銘
Wang, Y.J.
Lin, C.
Wang, M.K.
關鍵字: divalent metal ion
magnetite
amorphous iron oxide
linear atomization
energy relationship
density functional theory
decomposing carbon-dioxide
cation distributions
aqueous cr(vi)
simple
spinels
magnetite
energy
reduction
kinetics
approximation
mechanism
期刊/報告no:: Colloids and Surfaces a-Physicochemical and Engineering Aspects, Volume 234, Issue 1-3, Page(s) 1-7.
摘要: Several multi-regressive equations for the quantitative prediction of the percentage of divalent metal ions removal by magnetite/amorphous iron oxide composite systems have been established in the present study. The systems were synthesized from ferrous sulfate mixing with aqueous Mg2+, Ca2+, Mn2+, Zn2+, Cd2+ and Pb2+ nitrate solutions. The results showed that using the thermodynamic properties of divalent metal ions, the predicting model with the best linear regression result (correlation coefficient (r) = 0.9345) can be obtained from: M2+ Removal% = -1.15984 x DeltaGdegrees(solv) + 339.91154 x R-M(2+) - 984.57433. where DeltaGdegrees(solv) and R-M(2+) are the experimental solvation free energies and Shannon-Prewitt ionic radii of divalent metal ions, respectively. Obeying our previous 'linear atomization energy relationships (LAER)', the atomization energies of metal ions (AE(cation)) and metal monoaquo complexes (AE(complex)) can supply well governing parameters for universally predicting various kinds of aqueous reactions of metal ions. The density functional calculated results thus revealed that the atomization energies of metal ions (AE(cation)) and metal monoaquo complexes (AE(complex)), coupling with the interatomic metal-oxide bond lengths (RM-O) in metal monoaquo complexes, can accurately predict the removal percentage of divalent metal ions by magnetite/amorphous iron oxide composite systems (correlation coefficient (r) = 0.9935). The linear predicting model can be expressed as: M2+ Removal% = -2.43375 x AE(cation) + 2.40201 x AE(complex) + 235.44139 x RM-O - 1164.56703. (C) 2003 Elsevier B.V. All rights reserved.
URI: http://hdl.handle.net/11455/44657
ISSN: 0927-7757
文章連結: http://dx.doi.org/10.1016/j.colsurfa.2003.11.018
Appears in Collections:土壤環境科學系

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