4.7 Article

Mesoporous graphitic carbon nitride as an efficient sorbent for extraction of sulfonamides prior to HPLC analysis

期刊

MICROCHIMICA ACTA
卷 186, 期 5, 页码 -

出版社

SPRINGER WIEN
DOI: 10.1007/s00604-019-3394-9

关键词

Column assisted dispersive solid-phase extraction; Sample pretreatment; Mesoporous material; Antibiotic analysis; High performance liquid chromatography; DAD detector; Ultratrace analysis; Sensitive detection; Environmental water; Milk sample

资金

  1. National Nature Science Foundation of China [21477033]
  2. Program for Science & Technology Innovation Talents in Universities of Henan Province [17HASTIT003]
  3. Program for Development in Science and Technology of Henan Province [172102310608]

向作者/读者索取更多资源

Mesoporous graphitic carbon nitride (MCN) is shown to be a viable sorbent for the enrichment of sulfonamides (SAs). To overcome the difficulty of separating the sorbent from the matrix, a novel type kind of column-assisted dispersive solid-phase extraction (CA-dSPE) method was designed. The MCN was characterized by scanning electron microscopy, high-resolution transmission electron microscopy, X-ray diffraction, fourier transform infrared spectroscopy and nitrogen adsorption-desorption measurements. The amount of sorbent, the pH value of the sample, the adsorption time, type and volume of the eluent and desorption time were optimized. The SAs were eluted from the sorbent with elution solvent of methanol containing 10% (v/v) ammonia and then submitted to HPLC analysis. Under the optimized conditions, the limits of detection for the SAs investigated (sulfadiazine, sulfameter, sulfachloropyridazine, sulfabenzamide and sulfadimethoxine) range from 20 to 5pgmL(-1). Satisfactory recoveries were obtained for spiked environmental water (90.1-110.5%) and milk samples (82.3-102.7%), with relative standard deviations of 0.5-3.8% and 1.1-4.4%, respectively. The method is simple, time saving and sensitive.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据