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一种用于比色区分和检测酚类化合物的新型漆酶样铜金属有机框架。

A novel laccase-like Cu-MOF for colorimetric differentiation and detection of phenolic compounds.

作者信息

Gao Ziyi, Guan Jianping, Wang Meng, Liu Shenghong, Chen Kecen, Liu Qi, Chen Xiaoqing

机构信息

College of Chemistry and Chemical Engineering, Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, Central South University, Changsha, 410083, Hunan, China.

College of Chemistry and Chemical Engineering, Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, Central South University, Changsha, 410083, Hunan, China.

出版信息

Talanta. 2024 May 15;272:125840. doi: 10.1016/j.talanta.2024.125840. Epub 2024 Mar 1.

Abstract

The development of convenient, fast, and cost-effective methods for differentiating and detecting common organic pollutant phenols has become increasingly important for environmental and food safety. In this study, a copper metal-organic framework (Cu-MOF) with flower-like morphology was synthesized using 2-methylimidazole (2-MI) as ligands. The Cu-MOF was designed to mimic the natural laccase active site and proved demonstrated excellent mimicry of enzyme-like activity. Leveraging the superior properties of the constructed Cu-MOF, a colorimetric method was developed for analyzing phenolic compounds. This method exhibited a wide linear range from 0.1 to 100 μM with a low limit of detection (LOD) of 0.068 μM. Besides, by employing principal component analysis (PCA), nine kinds of phenols was successfully distinguished and identified. Moreover, the combination of smartphones with RGB profiling enabled real-time, quantitative, and high-throughput detection of phenols. Therefore, this work presents a paradigm and offers guidance for the differentiation and detection of phenolic pollutants in the environment.

摘要

开发便捷、快速且经济高效的方法来区分和检测常见有机污染物酚类,对于环境和食品安全而言变得愈发重要。在本研究中,以2-甲基咪唑(2-MI)为配体合成了具有花状形态的铜金属有机框架(Cu-MOF)。该Cu-MOF被设计用于模拟天然漆酶活性位点,并证明具有出色的类酶活性模拟能力。利用所构建的Cu-MOF的优异性能,开发了一种用于分析酚类化合物的比色法。该方法具有0.1至100 μM的宽线性范围,检测限低至0.068 μM。此外,通过主成分分析(PCA),成功区分并鉴定了九种酚类。而且,智能手机与RGB分析的结合实现了酚类的实时、定量和高通量检测。因此,这项工作为环境中酚类污染物的区分和检测提供了范例和指导。

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