Highly selective determination of ultratrace inorganic arsenic species using novel functionalized miniaturized membranes

2017 
Abstract A simple method for highly selective determination of trace and ultratrace arsenic ions, i.e. arsenite and arsenate, was developed. The method is based on new miniaturized membranes, 5 mm diameter and 4.4 mg weight, which are prepared by synthesis of amorphous silica coating on cellulose fibers followed by the modification with (3-mercaptopropyl)-trimethoxysilane. The batch adsorption experiments show that membranes have high selectivity toward arsenite in the presence of heavy metals and anions that usually exist in natural water. Arsenite can be quantitatively adsorbed at pH 1 from 50 mL sample within 60 min using the miniaturized membrane with maximum adsorption capacity of 60 mg g −1 . The excellent adsorptive properties of membranes open the path to simple and selective determination of trace and ultratrace arsenite in water. Moreover, the membranes can be applied in the arsenic speciation due to their selectivity toward arsenite in the presence of arsenate. After adsorption, the arsenite retained onto the membrane is directly measured by energy-dispersive X-ray fluorescence spectrometry, avoiding elution step usually required in other spectroscopy techniques. The method is characterized by excellent enrichment factor of 972, detection limit of 0.045 ng mL −1 and can be successfully applied in analysis of high salinity water, which is difficult to analyze by other spectroscopy techniques. The proposed method is a solvent-free approach based on the use of miniaturized membranes as sorbent followed by the direct measurement using a low-power X-ray spectrometer without either elution step or gas consumption during measurement. It can be considered as environmentally friendly and meets the standards of green analytical chemistry principles.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    58
    References
    12
    Citations
    NaN
    KQI
    []