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Electrochemiluminescence in Graphitic Carbon Nitride Decorated with Silver Nanoparticles for Dopamine Determination
Fang Li1, Hao Peng1, Nuotong Shen1
1Anhui Province Key Laboratory of Value-Added Catalytic Conversion and Reaction Engineering, Anhui Province Engineering Research Center of Flexible and Intelligent Materials, School of Chemistry and Chemical Engineering, Hefei University of Technology, Hefei 230009, Anhui, China.
Researchers developed tunable multicolor electrochemiluminescence (ECL) materials using silver nanoparticle-decorated graphitic carbon nitride (g-CN@Ag). These novel materials enable sensitive dopamine detection and accurate concentration prediction via machine learning.
Area of Science:
- Materials Science
- Nanotechnology
- Analytical Chemistry
Background:
- Wavelength-tunable multicolor electrochemiluminescence (ECL) luminophores are crucial for advanced imaging and multiplexed analyses.
- Developing novel materials with controllable emission properties is essential for expanding ECL applications.
Purpose of the Study:
- To synthesize and characterize silver nanoparticle (Ag NP)-decorated graphitic carbon nitride (g-CN@Ag) nanocomposites for tunable multicolor ECL emissions.
- To fabricate a multicolor ECL detection array using these nanocomposites for sensitive dopamine detection.
- To implement a machine learning-assisted strategy for accurate multiparameter concentration prediction of dopamine.
Main Methods:
- Synthesis of g-CN@Ag nanocomposites with varying Ag NP content.
- Characterization of morphology, composition, and structure using relevant techniques.
- Investigation of ECL properties and emission wavelength tuning from 460 to 565 nm.
- Fabrication of a multicolor ECL detection array and dopamine detection assay.
- Application of deep neural network (DNN) algorithm for machine learning-assisted analysis.
Main Results:
- g-CN@Ag nanocomposites exhibited tunable ECL emissions from blue to yellow (460-565 nm) by modulating Ag NP content.
- A multicolor ECL detection array was successfully fabricated using four distinct g-CN@Ag luminophores.
- Dopamine was detected with a wide linear range (0.1 nM to 1 mM) and a low detection limit of 44 pM.
- Machine learning (DNN) provided accurate multiparameter concentration prediction of dopamine.
Conclusions:
- Metal nanoparticle modification offers a novel strategy to regulate the ECL emission wavelength of g-CN.
- The developed g-CN@Ag nanocomposites are effective multicolor luminophores for ECL applications.
- The combination of multicolor ECL and machine learning presents a powerful approach for sensitive and accurate quantitative detection.
