Knitting Organic Cage into Hyper Cross-Linked Polymers for the Efficient Capture of Fluoroquinolones in Pork Samples

化学 笼子 堆积 吸附 固相萃取 检出限 聚合物 萃取(化学) 多孔性 色谱法 组合化学 有机化学 数学 组合数学
作者
Shuofeng Li,Ying Zhao,Zhi Li,Guoxin Liu,Qianqian Wang,Chun Wang,Qiuhua Wu
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:97 (35): 19321-19330 被引量:8
标识
DOI:10.1021/acs.analchem.5c03564
摘要

Considering the ubiquitous presence and hazardous effects of fluoroquinolone antibiotics (FQs), developing efficient methods for their detection has become imperative to safeguard human health. In this work, we synthesized a porous organic cage-based novel hyper-cross-linked polymer bearing multiple amino moieties (denoted as Cage-HCP-NH2) through knitting and reduction of nitro-functionalized organic cage. The Cage-HCP-NH2 exhibits high porosity (0.258 cm3 g-1) and big specific surface area (255 m2 g-1). Benefiting from unique cage architecture and synergistic interactions of hydrogen bonding, π-π stacking, and F-π and F-O interactions, Cage-HCP-NH2 demonstrated remarkable FQs adsorption capacity (730 mg g-1). Employing Cage-HCP-NH2 as a solid-phase extraction adsorbent coupled with ultrahigh performance liquid chromatography-tandem mass spectrometry, we developed a sensitive method for simultaneous detection of five FQs in pork matrices. Under the optimized conditions, the method achieves ultralow detection limits (0.015-0.060 ng g-1), high precision (relative standard deviation <9%), and good accuracy (method recoveries between 85% and 110%). The integration of cage architectures with functional groups opens new avenues for designing advanced functional materials targeting emerging contaminants.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
1秒前
pancake发布了新的文献求助80
1秒前
1秒前
1秒前
2秒前
henry完成签到,获得积分10
2秒前
天穹雨发布了新的文献求助10
2秒前
3秒前
3秒前
CodeCraft应助等风的人采纳,获得10
3秒前
3秒前
请你走发布了新的文献求助10
4秒前
炙热含之完成签到,获得积分10
5秒前
可爱的函函应助TT采纳,获得10
5秒前
lhz完成签到,获得积分10
5秒前
Ambition完成签到,获得积分10
5秒前
刻苦秋烟发布了新的文献求助10
5秒前
5秒前
昏睡的蟠桃应助闪闪易烟采纳,获得160
6秒前
7秒前
李爱国应助sun采纳,获得10
7秒前
SciGPT应助酷炫贞采纳,获得10
7秒前
7秒前
7秒前
xiaohe发布了新的文献求助10
7秒前
王欧尼完成签到,获得积分10
7秒前
encounter发布了新的文献求助10
8秒前
顾木木发布了新的文献求助10
9秒前
xx-xxx发布了新的文献求助10
9秒前
9秒前
美丽蕨菜子完成签到,获得积分10
10秒前
11秒前
代艳宁发布了新的文献求助10
12秒前
13秒前
14秒前
14秒前
14秒前
14秒前
酷波er应助科研通管家采纳,获得10
14秒前
高分求助中
Adhesion Science: Principles & Practice 1234
Signals, Systems, and Signal Processing 610
Petrology and Plate Tectonics,2025 400
Burger's Medicinal Chemistry and Drug Discovery 400
New directions for experimental lessons in science teaching: Myth, Mystery, Necessity? by Emily K. da Silva Cunha Souto (Author), Flávia Lins Silva (Author) 333
Scientific experimentation in the classroom: Comparison between genetic-Socratic-exemplary teaching and workshop teaching by Ingrid Hofer (Author) 333
Programming for Chemical Engineers Using C, C++, and MATLAB 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6722410
求助须知:如何正确求助?哪些是违规求助? 8458500
关于积分的说明 18058369
捐赠科研通 5975254
什么是DOI,文献DOI怎么找? 2996696
邀请新用户注册赠送积分活动 1972857
关于科研通互助平台的介绍 1926946