Synthesis of oil palm empty fruit bunch-based magnetic-carboxymethyl cellulose nanofiber composite for magnetic solid-phase extraction of organophosphorus pesticides in environmental water samples

This study investigated the development of a magnetic-carboxymethyl cellulose nanofiber (Fe3O4-cmCNF) composite from oil palm empty fruit bunch (OPEFB) as a promising adsorbent for the determination of organophosphorus pesticides (OPPs) in water samples via the magnetic solid-phase extraction (MSPE). The Fe3O4-cmCNF was prepared via the alkaline method and surface-modified using the carboxymethylation and sol–gel technique. The physicochemical characteristics of the Fe3O4-cmCNF were evaluated via FT-IR, VSM, SEM, TEM, XRD, TGA, and BET analysis. Diazinon, fenitrothion, and malathion were selected as the model analytes to examine the feasibility and performance of the Fe3O4-cmCNF-based MSPE to detect OPPs in real environmental water samples using gas chromatography-micro-electron capture detector (GC-µECD). Experimental parameters that affected the extraction efficiency were optimized, including the type and amount of adsorbent, extraction time, pH, type of elution solvent, and ionic strength. Based on the results, the prepared adsorbent showed a high affinity towards OPPs through hydrogen bonding as well as hydrophobic and electrostatic interaction. Under optimum conditions, the linear calibration curves were achieved for diazinon, fenitrothion, and malathion with a concentration range of 0.1–200 ng mL−1 and a suitable determination coefficient (r2) between 0.9962 and 0.9995. Furthermore, acceptable intra- and inter-day precisions were obtained between the range 1.7–4.9 % and 2.0–14.0 %, respectively. The LOD for diazinon, fenitrothion, and malathion was determined at 0.03 ng mL−1, with a satisfactory relative recovery range of 81.8–119.6 %. OPPs residues were detected in river water (0.27–0.39 ng mL−1) and wastewater (0.41–1.31 ng mL−1), indicating that the Fe3O4-cmCNF is a suitable composite for the determination of OPPs in real samples.

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