• 文献标题:   Woven cotton yarn-graphene oxide-layered double hydroxide composite as a sorbent for thin film microextraction of nonsteroidal anti-inflammatory drugs followed by quantitation through high performance liquid chromatography
  • 文献类型:   Article
  • 作  者:   GHANI M, HAGHDOOSTNEJAD K
  • 作者关键词:   cotton yarn, thin film microextraction, nonsteroidal antiinflammatory drug, hplcuv, experimental design
  • 出版物名称:   ANALYTICA CHIMICA ACTA
  • ISSN:   0003-2670 EI 1873-4324
  • 通讯作者地址:   Univ Mazandaran
  • 被引频次:   3
  • DOI:   10.1016/j.aca.2019.10.066
  • 出版年:   2020

▎ 摘  要

The applicability of a highly flexible and natural cotton yarn-graphene oxide-layered double hydroxide composite (CY-GO-LDH) was introduced for the extraction of the targets in the current study. For increasing the contact area of the analytes and the prepared sorbent, the green substrate was woven and employed as the substrate for the construction of GO layers. It was proved that the prepared CY-GO-LDH film is a reliable sorbent for thin film microextraction (TFME) of the nonsteroidal anti-inflammatory drugs (NSAIDs) including acetylsalicylic acid, naproxen, diclofenac, ibuprofen and mefenamic acid in human urine and plasma. Extraction factors were optimized using multivariate optimization strategy. High adherence of GO-LDH to the natural substrate made this technique more robust for routine analysis. There are two consecutive steps to optimize the parameters influencing the extraction of analytes; First, a Plackett-Burman Design (PBD) was utilized to screen the significant factors. Second, the selected factors were optimized utilizing the Box-Behnken Design (BBD). The extracted NSAIDs were analyzed by HPLC-UV. Under the obtained optimum condition, the linearity of the method was 0.2-200 mu g L-1. Limits of detection, limits of quantification and intra-day as well as inter-day RSDs were lower than 0.25 mu g L-1, 0.72 mu g L-1 and 6.1%, respectively. The method was successfully used to determine NSAIDs in different human biological fluids. (C) 2019 Elsevier B.V. All rights reserved.