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A Novel Fluorescent Biosensor for Detection of Silver Ions Based on Upconversion Nanoparticles
Qian Long1, Yanqing Wen1, Haitao Li1
1Hunan Vocational Institute of Safety Technology, Changsha, 410151, China.
Journal of Fluorescence
|October 25, 2016
Summary
This study introduces a novel fluorescent biosensor for detecting silver ions (Ag+). The method utilizes upconversion nanoparticles (UCNPs) for sensitive and selective silver ion quantification in various samples.
Area of Science:
- Nanomaterials Science
- Analytical Chemistry
- Biomedical Engineering
Background:
- Silver ions (Ag+) are significant environmental pollutants and have biological implications.
- Accurate detection of Ag+ is crucial for environmental monitoring and healthcare.
- Existing detection methods may lack sensitivity, selectivity, or simplicity.
Purpose of the Study:
- To develop a simple, sensitive, and selective fluorescent biosensor for quantitative Ag+ analysis.
- To utilize NaYF4:Yb3+, Tm3+ upconversion nanoparticles (UCNPs) as a core detection platform.
- To demonstrate the potential for real-world sample analysis.
Main Methods:
- Synthesis and characterization of NaYF4:Yb3+, Tm3+ UCNPs.
- Development of a fluorescence quenching assay based on Ag+-mediated oxidation of o-phenylenediamine (OPD).
- Investigation of inner filter effects (IFE) between UCNPs and oxidized OPD (oxOPD).
Main Results:
- The fluorescence intensity of UCNPs was significantly quenched by oxOPD, with quenching efficiency proportional to Ag+ concentration.
- The biosensor achieved quantitative detection of Ag+ in the range of 0 to 0.5 mM.
- A low detection limit of 33 nM for Ag+ was obtained under optimized conditions.
- The method demonstrated high selectivity and sensitivity, with potential for interference-free detection in real samples.
Conclusions:
- A novel and effective fluorescent biosensor for Ag+ detection was successfully developed using UCNPs.
- The proposed method offers a simple, sensitive, and selective approach for Ag+ quantification.
- The biosensor shows promise for practical applications in environmental and biological sample analysis.

