Adsorption of organic and inorganic arsenic from aqueous solution: Optimization, characterization and performance of Fe-Mn-Zr ternary magnetic sorbent.

Arsenic is a highly toxic pollutant and exists in inorganic and organic forms in groundwater and industrial wastewater. It is of great importance to reduce the arsenic content to lower levels in the water (e.g., <10 ppb for drinking) in order to minimize risk to humans. In this study, a Fe-Mn-Zr ternary magnetic sorbent was fabricated via precipitation for removal of inorganic and organic arsenate. The synthesis of sorbent was optimized by Taguchi method, which leads to an adsorbent with higher adsorption capacity. The adsorption of As(V) was pH dependent; the optimal removal was achieved at pH 2 and 5 for inorganic and organic As(V), respectively. Contact time of 25 h was sufficient for complete adsorption of both inorganic and organic As(V). The adsorption isotherm study revealed that the adsorbent performed better in sequestration of inorganic As(V) than that of organic As(V); both adsorption followed the Langmuir isotherm with maximum adsorption capacities of 81.3 and 16.98 mg g-1 for inorganic and organic As(V), respectively. The existence of anions in the water had more profound effect on the adsorption of organic As(V) than the inorganic As(V). The co-existing silicate and phosphate ions caused significantly negative impacts on the adsorption of both As(V). Furthermore, the existence of humic acid caused the deterioration of inorganic As(V) removal but showed insignificant impact on the organic As(V) adsorption. The mechanism study demonstrated that ion exchange and complexation played key roles in arsenic removal. This study provides a promising magnetic adsorptive material for simultaneous removal of inorganic and organic As(V).

Uptake of arsenate by an alginate-encapsulated magnetic sorbent: process performance and characterization of adsorption chemistry.

Arsenate removal by a calcium alginate-encapsulated magnetic sorbent was studied. The morphology, microstructure, and composition properties of the sorbent were explored using X-ray diffraction (XRD) and scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDX). The SEM study demonstrates that there are many protuberances and pores on the sorbent surface; the XRD analysis reveals that the sorbent consists of Fe(3)O(4). The EDX analysis indicates that the adsorption on the surfaces of sorbent is highly location dependent. The interaction characteristics between the arsenic and the functional groups on the sorbent were studied by Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). These studies indicate that the lattice oxygen in magnetite and the oxygen in hydroxyl of the calcium alginate play important roles in the sorption of arsenate ions onto the sorbent. More importantly, the XPS analysis demonstrates that the arsenate is reduced to arsenite after its adsorption onto the sorbent. It is proposed that divalent iron and the alcoholic group in alginate provide electrons to arsenate. A conceptual model for the adsorption is proposed to illustrate the mechanisms.

Characterization of copper adsorption onto an alginate encapsulated magnetic sorbent by a combined FT-IR, XPS, and mathematical modeling study.

Copper adsorption onto calcium alginate encapsulated magnetic sorbent is studied in this paper. The objective of this study was to qualitatively and quantitatively elucidate the copper binding onto the sorbent. The adsorption increases from around 0 to almost 100% as the initial pH is increased from 2 to 5. A maximum adsorption capacity of 0.99 mmol g(-1) is achieved. The FT-IR and XPS studies show that the C-O in carboxyl group of alginate directly attaches to the copper ion that leads to most of the adsorption. A mathematical model is developed, and it includes ion exchange between the calcium and the copper, coordination reaction between the functional group and the copper, as well as surface complex formation between the iron oxide and the copper. The model is capable of describing and predicting effects of various key operational parameters on the adsorption process, such as initial pH, metal concentration, and dosage of sorbent.