Prediction of the pH effect on arsenic (V) removal by varying catalyst of magnetic xerogel monoliths based on FREN model
Author(s) -
Sasirot Khamkure,
Chidentree Treesatayapun,
Sofía Esperanza Garrido-Hoyos,
Prócoro Gamero-Melo,
Audberto Reyes-Rosas
Publication year - 2020
Publication title -
water science and technology water supply
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 39
eISSN - 1607-0798
pISSN - 1606-9749
DOI - 10.2166/ws.2020.168
Subject(s) - fourier transform infrared spectroscopy , scanning electron microscope , point of zero charge , magnetite , resorcinol , adsorption , catalysis , arsenic , nuclear chemistry , mesoporous material , materials science , analytical chemistry (journal) , isoelectric point , chemistry , chemical engineering , inorganic chemistry , chromatography , composite material , metallurgy , organic chemistry , engineering , enzyme
Magnetic xerogels monoliths (MCs) were simultaneously prepared and formed by the cross-linking polymerization of resorcinol and formaldehyde using the alkaline catalyst and magnetite. The varying of molar ratio of resorcinol and catalyst (R/C) was studied and characterized by isoelectric point (IEP), point of zero charge (pHpzc), scanning electron microscopy–energy dispersive X-ray spectroscopy (SEM-EDX), X-ray diffraction (XRD), N2 adsorption and Fourier transform infrared spectroscopy (FTIR). The result of XRD and EDX confirmed the presence of magnetite into the gel at 1.19% with low molar ratio of magnetite and resorcinol ratio at 0.01. The surface morphology and textural properties of MCs affect directly with SBET, total pore volume and volume of mesopore increase when molar of R/C increases. The behavior of arsenic (As(V)) adsorption by using MCs, was studied in groundwater into the ranges of pH from 2.0 to 7.0. MC50 shows the maximum As(V) uptake and removal were 72 μg/g and 73.5% at pH 5, respectively, while MC100 gave the best performance within the application range of pH both of acidic and neutral region. Furthermore, the prediction technique based on an adaptive fuzzy rules emulated network was utilized for evaluation of the arsenic removal performance.
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