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A Highly Stable Lanthanide-Organic Framework for Ultrasensitive Dynamic Luminescence Detection of Quercetin
Fujia Ye1, Meiyi Huang1, Ranxi Shi1
1Hubei Key Laboratory of Pollutant Analysis & Reuse Technology, College of Chemistry and Chemical Engineering, Hubei Normal University, Huangshi 435002, P. R. China.
Inorganic Chemistry
|December 11, 2025
Summary
A novel lanthanide-organic framework, HBNU-21-Eu, enables rapid and sensitive detection of quercetin. This advancement offers a promising tool for analyzing quercetin in real-world samples like tea.
Area of Science:
- Materials Science
- Analytical Chemistry
- Nanotechnology
Background:
- Quercetin exhibits significant cardiovascular benefits, but its detection in complex matrices is hindered by weak interactions.
- Developing robust methods for sensitive quercetin detection is crucial for its therapeutic applications.
Purpose of the Study:
- To synthesize a stable lanthanide-organic framework (HBNU-21-Eu) for efficient luminescence-based quercetin detection.
- To establish a rapid and sensitive analytical method for quantifying quercetin in environmental and food samples.
Main Methods:
- Solvothermal synthesis of a mixed ligand lanthanide-organic framework using Eu3+ ions, H4BETC, and H2NDC.
- Characterization of the framework's stability (pH, solvent, thermal) and luminescence properties.
- Development of a dynamic quenching-based luminescence assay for quercetin detection.
Main Results:
- The synthesized HBNU-21-Eu framework demonstrated excellent stability and intense red luminescence (70.2% quantum yield).
- Rapid detection of quercetin was achieved within 30 seconds, with a low detection limit of 0.57 μM.
- The method showed high quenching efficiency (5.08 × 10^4 M^-1) and was successfully applied to tap water and jasmine tea samples with good recovery rates.
Conclusions:
- The HBNU-21-Eu framework provides a highly stable and sensitive platform for luminescence detection of quercetin.
- This method offers a rapid and efficient approach for quercetin analysis in real-world samples, overcoming previous detection challenges.

