Discriminative and quantitative color-coding analysis of fluoroquinolones with dual-emitting lanthanide metal-organic frameworks

判别式 颜色编码 镧系元素 对偶(语法数字) 金属 金属有机骨架 材料科学 计算机科学 人工智能 化学 艺术 文学类 离子 吸附 有机化学
作者
Xingyi Wang,Qiuju Li,Boyang Zong,Xian Fang,Meng Liu,Zhuo Li,Shun Mao,Kostya Ostrikov
出处
期刊:Sensors and Actuators B-chemical [Elsevier BV]
卷期号:373: 132701-132701 被引量:53
标识
DOI:10.1016/j.snb.2022.132701
摘要

Color-coding analysis from chemicals of concern is in great demand, but faces low sensitivity and specificity, low resolution, and complex processing among the many challenges. Here, this work resolves these issues to enable the elusive quantitative detection of a variety of fluoroquinolone (FQ) antibiotics. A fluorescent sensor based on the dual-emitting lanthanide metal-organic frameworks combining Tb 3+ and Eu 3+ as the luminescent center and 1,3,5-benzenetricarboxylic acid as the ligand is constructed. Due to the different sensitization effects to lanthanide metals and different inherent fluorescence emissions of FQs, the sensor exhibits characteristic color variations towards nine FQ and enables the discriminative detection of multiple antibiotics with self-calibrated signals. For the first time, a polynomial surface fitting process is developed to correlate the coordinates of color-coding map and target concentration for quantitative analysis. Moreover, a smartphone-enabled sensing system is demonstrated for on-site imaging analysis of antibiotics. The demonstrated innovative antibiotic detection and color-coding-based signal processing approach will inform the development of cutting-edge analysis systems for public health and environmental monitoring. • Dual-emitting Ln-MOF fluorescent sensor designed for discriminative analysis of structurally similar antibiotics. • Highly sensitive and selective detection of fluoroquinolones antibiotics. • A polynomial surface fitting process is developed to correlate fluorescence color and antibiotic concentration. • Fluorescence color-coding analysis implemented portable device for multi-target detection.
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
说不得大师完成签到,获得积分10
刚刚
科研通AI6.3应助loyo采纳,获得10
1秒前
小新应助对对对采纳,获得10
1秒前
anyuezou完成签到,获得积分10
2秒前
彳亍完成签到,获得积分10
2秒前
2秒前
by发布了新的文献求助20
3秒前
3秒前
3秒前
3秒前
旭向南发布了新的文献求助10
3秒前
大力的灵雁应助yj采纳,获得10
3秒前
Tsugu完成签到,获得积分10
3秒前
李健的小迷弟应助11采纳,获得10
4秒前
辣个男子完成签到,获得积分10
4秒前
4秒前
Xin关闭了Xin文献求助
5秒前
拉稀摆带发布了新的文献求助10
5秒前
华仔应助刘言采纳,获得10
6秒前
PP关闭了PP文献求助
6秒前
lyzzz发布了新的文献求助10
6秒前
欣喜寄云发布了新的文献求助10
8秒前
符昱完成签到,获得积分10
8秒前
嘉心糖应助名字不好起采纳,获得80
9秒前
9秒前
study发布了新的文献求助10
9秒前
HaonanZhang完成签到,获得积分10
10秒前
传奇3应助gill采纳,获得10
10秒前
顾矜应助xxl采纳,获得10
10秒前
hahaha发布了新的文献求助10
10秒前
脑洞疼应助高大橙采纳,获得10
11秒前
FashionBoy应助小樊同学采纳,获得10
11秒前
12秒前
思源应助bxw采纳,获得10
12秒前
mingjingbingying完成签到,获得积分10
13秒前
汉堡包应助动听的笑晴采纳,获得10
13秒前
华仔应助烦恼大海采纳,获得30
14秒前
molihuakai应助烦恼大海采纳,获得10
14秒前
英姑应助烦恼大海采纳,获得10
14秒前
万能图书馆应助烦恼大海采纳,获得10
15秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1500
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Rheumatoid arthritis drugs market analysis North America, Europe, Asia, Rest of world (ROW)-US, UK, Germany, France, China-size and Forecast 2024-2028 500
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 6366041
求助须知:如何正确求助?哪些是违规求助? 8179983
关于积分的说明 17243873
捐赠科研通 5420779
什么是DOI,文献DOI怎么找? 2868231
邀请新用户注册赠送积分活动 1845373
关于科研通互助平台的介绍 1692871