Suppr超能文献

共溶剂增强的磁性天鹅绒状氮化碳吸附用于高效固相萃取

Co-solvent enhanced adsorption with magnetic velvet-like carbon nitride for high efficiency solid phase extraction.

作者信息

Fan Shanshan, Zhu Jun, Ren Lixuan, Wang Man, Bi Wentao, Li Huihui, Huang Xiaohua, Chen David Da Yong

机构信息

Jiangsu Collaborative Innovation Center of Biomedical Functional Materials, Jiangsu Key Laboratory of Biomedical Materials, College of Chemistry and Materials Science, Nanjing Normal University, Nanjing, 210023, China.

Jiangsu Collaborative Innovation Center of Biomedical Functional Materials, Jiangsu Key Laboratory of Biomedical Materials, College of Chemistry and Materials Science, Nanjing Normal University, Nanjing, 210023, China.

出版信息

Anal Chim Acta. 2017 Apr 1;960:63-71. doi: 10.1016/j.aca.2017.01.020. Epub 2017 Jan 25.

Abstract

Magnetic velvet-like graphitic carbon nitride (V-g-CN/FeO) was used for rapid 1 min extraction of flavonoids from different tea extracts by co-solvent enhanced adsorptive magnetic solid phase extraction. The nanocomposite can interact with flavonoids, in which FeO provide hydrogen bond and V-g-CN has hydrophobic and π-π interaction to promote adsorption. The enhanced adsorptive magnetic solid phase extraction method is developed with the addition of a co-solvent (water) to dramatically change the solvent environment, which enhanced the speed of movement of target compounds from the solvent to the sorbent and increase the adsorption capacity. The synergistic effects improved the extraction rate of flavonoids with excellent reproducibility (88.2-107.2%), sensitivity (limits of detection (S/N = 3): 0.075-0.1 μg/mL) and recoveries (88.2-107.2%). This study demonstrated the potential to apply this method for various target analytes from complex sample matrices.

摘要

磁性绒状石墨相氮化碳(V-g-CN/FeO)通过共溶剂增强吸附磁固相萃取法,用于在1分钟内快速从不同茶叶提取物中提取黄酮类化合物。该纳米复合材料可与黄酮类化合物相互作用,其中FeO提供氢键,V-g-CN具有疏水作用和π-π相互作用以促进吸附。通过添加共溶剂(水)显著改变溶剂环境,开发了增强吸附磁固相萃取方法,这提高了目标化合物从溶剂到吸附剂的移动速度并增加了吸附容量。协同效应提高了黄酮类化合物的提取率,具有出色的重现性(88.2-107.2%)、灵敏度(检测限(S/N = 3):0.075-0.1 μg/mL)和回收率(88.2-107.2%)。本研究证明了将该方法应用于复杂样品基质中各种目标分析物的潜力。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验