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Extraction and preconcentration of organophosphorus pesticides from water samples and fruit juices utilizing hydroxyapatite/Fe3O4 nanocomposite
Microchemical Journal ( IF 4.9 ) Pub Date : 2019-01-01 , DOI: 10.1016/j.microc.2018.09.018
Mohammad Chahkandi , Amirhassan Amiri , Seyedeh Roghayyeh Saadatdar Arami

Abstract In this study, we demonstrate the application of newly developed magnetic potassium substituted hydroxyapatite (KHA/Fe3O4) for the extraction of organophosphorus pesticides (OPPs) from water samples. Nanoparticles of KHA were synthesized through an easy alkoxide–based sol–gel technique. The structure of nanocomposite was characterized by X–ray diffraction (XRD), Fourier transform infrared (FTIR) analysis, and energy dispersive X–ray analysis (EDXA). The OPPs were analyzed by gas chromatography–flame ionization detector (GC–FID). Variables affecting the performance of both extraction steps such as pH of the water sample, the sorbent amount, the desorption conditions, and the extraction time solvent were studied and optimized. Under optimum conditions, good linearity was achieved for all analytes in the 0.1–200 ng mL−1 concentration range. The limits of detection (at an S/N ratio of 3) are between 0.03 and 0.22 ng mL−1. The intra-day (n = 5) and inter-day (n = 3) relative standard deviations (RSD%) of three replicates were each demonstrated in the range of 5.2–9.1% and 5.6–9.0%, respectively. The recoveries of OPPs from spiked real water samples and fruit juices are between 89.0 and 99.7%, with relative standard deviations between 5.5 and 10.8%.

中文翻译:

利用羟基磷灰石/Fe3O4纳米复合材料从水样和果汁中提取和预浓缩有机磷农药

摘要 在本研究中,我们展示了新开发的磁性钾取代羟基磷灰石 (KHA/Fe3O4) 在提取水样中的有机磷农药 (OPP) 中的应用。KHA 纳米颗粒是通过一种简单的基于醇盐的溶胶-凝胶技术合成的。通过 X 射线衍射 (XRD)、傅里叶变换红外 (FTIR) 分析和能量色散 X 射线分析 (EDXA) 表征纳米复合材料的结构。通过气相色谱-火焰电离检测器 (GC-FID) 分析 OPP。研究并优化了影响两个萃取步骤性能的变量,例如水样的 pH 值、吸附剂用量、解吸条件和萃取时间溶剂。在最佳条件下,0.1–200 ng mL-1 浓度范围内的所有分析物均获得了良好的线性。检测限(信噪比为 3)介于 0.03 和 0.22 ng mL-1 之间。三个重复的日内 (n = 5) 和日间 (n = 3) 相对标准偏差 (RSD%) 分别显示在 5.2–9.1% 和 5.6–9.0% 的范围内。加标实际水样和果汁中 OPP 的回收率在 89.0% 到 99.7% 之间,相对标准偏差在 5.5% 到 10.8% 之间。
更新日期:2019-01-01
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