研究目的
Investigating the development of a ratiometric fluorescent and colorimetric dual-signal sensing platform based on N-doped carbon dots for selective and sensitive detection of copper(II) and pyrophosphate ion.
研究成果
The developed dual-signal sensing platform based on N-CDs offers a simple, selective, and sensitive method for detecting Cu2+ and PPi, with potential applications in environmental monitoring and biomedical diagnostics. The study highlights the advantages of using N-CDs for such applications, including their good optical and chemical stabilities.
研究不足
The study focuses on the detection of Cu2+ and PPi, and while it demonstrates good selectivity and sensitivity, the practical application may be limited by the presence of other interfering substances in complex samples. The study also does not explore the long-term stability of the N-CDs under various environmental conditions.
1：Experimental Design and Method Selection:
The N-doped carbon dots (N-CDs) were synthesized via a one-step hydrothermal approach using adenosine as the precursor. The sensing strategy involves the oxidative and chromogenic reaction of o-phenylenediamine (OPD) with Cu2+ to form 2,3-diaminophenazine (oxOPD), which quenches the fluorescence of N-CDs and provides a new emission peak at 553 nm. The presence of PPi inhibits this reaction, protecting the fluorescence of N-CDs.
2：Sample Selection and Data Sources:
The samples included various metal ions and anions to test the selectivity and sensitivity of the sensing system.
3：List of Experimental Equipment and Materials:
UV-2450 spectrophotometer, F-2700 fluorescence spectrophotometer, EscaLab-250 spectrometer, Bruker IFS 113v spectrometer, JEOL-2100 system.
4：Experimental Procedures and Operational Workflow:
The detection of Cu2+ and PPi involved mixing the N-CDs with OPD and Cu2+ or PPi, followed by incubation and measurement of fluorescence and absorption spectra.
5：Data Analysis Methods:
The fluorescence and absorption spectra were analyzed to determine the concentrations of Cu2+ and PPi, with statistical techniques applied to assess sensitivity and selectivity.
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