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Hydrogen-bond induced enhanced fluorescence sensing of metal organic frameworks for colorimetric L-glutamic acid and L-aspartic acid detection

文献类型： 外文期刊

作者： Zhang, Zhikun 1 ; Bai, Liwei 1 ; Tian, Heli 1 ; Han, Jilong 1 ; Li, Kunjie 1 ; Li, Zhengjie 1 ; Liu, Qingju 2 ;

作者机构： 1.Hebei Univ Sci & Technol, Sch Chem & Pharmaceut Engn, Shijiazhuang 050018, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Inst Qual Stand & Testing Technol, Beijing 10097, Peoples R China

关键词： Aspartic acid; Fluorescence; Glutamic acid; Metal-organic framework; Paper microsensors

期刊名称：MICROCHEMICAL JOURNAL （ 影响因子：5.1； 五年影响因子：4.7 ）

ISSN： 0026-265X

年卷期： 2025 年 208 卷

页码：

收录情况： SCI

摘要： Fluorescent metal-organic frameworks (FMOFs) hold great promise for detecting biomolecules, but a comprehensive understanding of their response mechanisms is still challenging to guide the design of assays. Herein, we creatively proposed fluorescent analysis via the hydrogen-bond-triggered enhanced fluorescence effect between the oxygen of a carboxyl group of targets and an amino group of ligands in the FMOFs in an aqueous solution. Based on the above strategy, we synthesized the Ru@ZrMOF and constructed a ratiometric fluorescent and colorimetric dual-readout method for a simple and rapid platform to selectively detect amino acids. Taking Lglutamic (L-Glu) and L-aspartic acid (L-Asp) as examples, the introduction of L-Glu and L-Asp sharply increased the fluorescent value at 450 nm through strong hydrogen bonding, leading to a difference in the color from red to blue. Data from computational simulations point to the presence of strong hydrogen bonds between L-Glu/ L-Asp and FMOFs. Dual-mode fluorescent/colorimetric method selectively and accurately detects L-Glu acid and L-Asp within 30 min. The detection range for the two was found to be between 5 mu M and 2 mM, with detection limits of 3 mu M and 2.5 mu M, respectively. In addition, a paper-based platform was fabricated and a logic gate was developed for the rapid and visual measurement of L-Glu and L-Asp. This platform is simple, quick, easy to use, and sensitive, making it a promising tool for future applications in amino acid detection, particularly in biological samples.

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