Electrochemiluminescence in Graphitic Carbon Nitride Decorated with Silver Nanoparticles for Dopamine Determination Using Machine Learning
Fang Li, Hao Peng, Nuotong Shen, Chen Yang, Limin Zhang, Bing Li, Jianbo He
Abstract
Electrochemiluminescence (ECL) luminophores with wavelength-tunable multicolor emissions are essential for multicolor ECL imaging detection and multiplexed analysis. In this work, silver nanoparticle (Ag NP)-decorated graphitic carbon nitride (g-CN@Ag) nanocomposites were synthesized. The morphology, chemical composition, structure, and ECL property of g-CN@Ag were investigated. The prepared g-CN, g-CN@Ag1, g-CN@Ag5, and g-CN@Ag10 can produce blue, blue-green, chartreuse, and yellow colored ECL emissions, respectively, by using K 2 S 2 O 8 as the coreagent. The ECL emission wavelength of g-CN@Ag can be regulated from 460 to 565 nm by modulating the content of the immobilized Ag NPs. Then, a multicolor ECL detection array was fabricated by using g-CN, g-CN@Ag1, g-CN@Ag5, and g-CN@Ag10 as four ECL luminophores. Dopamine was detected based on its inhibition effect on the multicolor ECL emissions. The linear range is from 0.1 nM to 1 mM with the lowest detection limit of 44 pM. Then, machine learning-assisted multiparameter concentration prediction of dopamine was further carried out by combining the deep neural network (DNN) algorithm. This work provides a new avenue to regulate the ECL emission wavelength of g-CN by using the metal nanoparticle modification strategy and presents an effective machine learning-assisted multicolor ECL detection strategy for accurate multiparameter quantitative detection.