Open AccessCharacterization and Surface Acidity Modelling of an Iron Oxide-Impregnated Activated Carbon
Author(s) -
Richard Vaughan,
John Yang,
Laura E. LeMire,
Brian E. Reed
Publication year - 2007
Publication title -
adsorption science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.682
H-Index - 36
eISSN - 2048-4038
pISSN - 0263-6174
DOI - 10.1260/026361707783432560
Subject(s) - chemistry , potentiometric titration , adsorption , oxide , activated carbon , titration , acid–base titration , hematite , analytical chemistry (journal) , inorganic chemistry , arsenic , scanning electron microscope , mineralogy , materials science , environmental chemistry , ion , organic chemistry , composite material
The objective of the present research was to characterize the surface of an iron oxide-impregnated activated carbon (FeAC), model the surface acidity of the FeAC and determine the most appropriate acid-base surface-site representation — the foundation for modelling arsenic adsorption in water and wastewater treatment. The FeAC surface was characterized by measuring the surface area, using scanning electron microscopy and electron dispersive spectroscopy to confirm the presence of Fe, and determining the species at the carbon surface [Fe oxides, predominately hematite (α-Fe 2 O 3 )] using X-ray diffraction and differential thermal analysis. Potentiometric titrations of FeAC were performed at three ionic strengths (I) and surface complexation modelling was used to determine the surface-site and electrical double layer (EDL) representations. FeAC was modelled as one component for simplicity, as it was comparable to modelling FeAC as its two separate components (GAC1240 and Fe oxide). The diprotic and two-monoprotic surface-site representations coupled with either the diffuse layer (DLM) or triple layer (TLM) EDL models adequately fitted the data. Parameters for the I = 10 −2 data set were used to predict the acid-base behaviour for I = 1.94 × 10 −3 and 10 −1 . The TLM predicted titration data better than the DLM. Both diprotic and two-monoprotic representations in conjunction with the TLM may be used to predict FeAC surface acidity over a range of I values and can be used as the foundation for arsenic adsorption modelling.