Surface Complexation Modeling: Gibbsite Athanasios K Karamalidis

ISBN: 9781280767777

Published: May 10th 2014

ebook

312 pages


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Surface Complexation Modeling: Gibbsite  by  Athanasios K Karamalidis

Surface Complexation Modeling: Gibbsite by Athanasios K Karamalidis
May 10th 2014 | ebook | PDF, EPUB, FB2, DjVu, talking book, mp3, RTF | 312 pages | ISBN: 9781280767777 | 10.52 Mb

This book provides a description of the generalized two layer surface complexation model, data treatment procedures, and thermodynamic constants for sorption of metal cations and anions on gibbsite, the most common form of aluminum oxide found in nature and one of the most abundant minerals in soils, sediments, and natural waters. The book provides a synopsis of aluminum oxide forms and a clearly defined nomenclature.

Compilations of available data for sorption of metal cations and anions on gibbsite are presented, and the results of surface complexation model fitting of these data are given. The consistency of the thermodynamic surface complexation constants extracted from the data is examined through development of linear free energy relationships which are also used to predict thermodynamic constants for ions for which insufficient data are available to extract constants. The book concludes with a comparison of constants extracted from data for sorption on gibbsite with those determined previously for hydrous ferric oxide (HFO), hydrous manganese oxide (HMO), and goethite.

The overall objective of this book is the development and presentation of an internally consistent thermodynamic database for sorption of inorganic cations and anions on gibbsite, an abundant and reactive mineral in soils, sediments, and aquatic systems. Its surface has a high affinity for sorption of metal cations and anions, including radionuclides.

The gibbsite database will enable simulation and prediction of the influence of sorption on the fate of these chemical species in natural systems and treatment processes in which aluminum oxides are abundant. It thus will help to advance the practical application of surface complexation modeling.



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