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Title
Montmorillonite reinforced polystyrene nanocomposite supported on cellulose as a novel layered sorbent for microextraction by packed sorbent for determination of fluoxetine followed by spectrofluorimetry based on multivariate optimisation
Type of Research Article
Keywords
Montmorillonite/ polystyrene/cellulose; microextraction by packed sorbent; multivariate optimisation; fluoxetine; spectrofluorimetry
Abstract
In this work, a novel nanocomposite (NC) by reinforcing montmorillonite into polystyrene (MMT/PS) was prepared and was coated onto cellulose filter paper. The prepared thin film was applied as a novel sorbent for microextraction by packed sorbent (MEPS) in layered configuration. The incorporation of MMT into PS host polymer has lead to higher extraction capability due to the increase of surface multi-functionality and the increase of specific surface area. The elemental analysis of prepared thin layers was conducted by energy dispersive X-ray spectroscopy (EDX) and the surface characteristic and morphology of prepared thin films was studied applying scanning electron microscopy (SEM). The prepared thin films were inserted into MEPS device and were used for extraction of fluoxetine in environmental water samples and wastewater samples continued by spectrofluorimetry (FL) determination. Multivariate optimisation based on central composite design (CCD) was used for evaluating and optimising the effective parameters influencing the MEPS method including MMT doping level, extraction draw-discard cycles, pH, cycles of draw-eject in desorption step and sorbent layers. Based on the method validation, the calibration curve is linear in the concentration range of 7–500 ng mL−1 and the limit of detection (LOD) was 2 ng mL−1. The precision of the developed method (RSD %) by performing three replication of extraction from deionised water (300 ng mL−1) was 6.7%. The developed method was successfully employed for measurement of fluoxetine in wastewater, river and dam water samples; leading to the relative recoveries (RR%) ranged from 76.4 to 107.2%.
Researchers Parastou Matin (First Researcher)، Zahra Ayazi (Second Researcher)، Kazem Jamshidi-Ghaleh (Third Researcher)