Application of CD and Eu3+ Dual Emission MOF Colorimetric Fluorescent Probe Based on Neural Network in Fe3+ Detection

• Published in: Particle & particle systems characterization 2022-10, Vol.39 (10), p.n/a
• Main Authors: Zheng, Yuewei, Wang, Xuan, Guan, Zhihao, Deng, Junjie, Liu, Xinghai, Li, Houbin, Zhao, Pingping
• Format: Article
• Language: English
• Subjects: Aqueous solutions; Back propagation networks; carbon dots; Citric acid; Colorimetry; Computer vision; Emission; Ethylenediamine; Europium; Ferric ions; Fluorescent indicators; ion detection; Metal-organic frameworks; Neural networks
• ISSN: 0934-0866; 1521-4117
• DOI: 10.1002/ppsc.202200124

A new highly fluorescent hybrid material (CD@Eu‐MOF) is synthesized by encapsulating carbon dots (CD) prepared from citric acid and ethylenediamine on the basis of a metal‐organic framework (MOF) prepared from Eu3+ and 1,2,4‐benzenetricarboxylic acid. The prepared composite not only maintains the excellent fluorescence properties of CD and Eu3+, respectively, but also forms a dual‐emission fluorescence system, and the system has good stability in an aqueous solution. It is further used as a novel fluorescent probe for the detection of Fe3+, which can effectively exclude the interference of other metal ions from the detection, and the intensity ratio of IEu/ICD of CD@Eu‐MOF material has a good linear relationship with Fe3+ in the range of 1–200 µm. In this study, computer vision and backpropagation (BP) neural networks are used to train and fit the sample data, and it is verified that the actual fluorescence color of CD has a good linear relationship with Fe3+ concentration. In addition, the BP neural network also verifies that the fluorescence spectrum data of CD@Eu‐MOF also have a good linear relationship with Fe3+ concentration. This study provides a new method for the fabrication of ratio and colorimetric Fe3+ fluorescence sensors. In this study, a novel dual‐emission fluorescent material (CD@Eu‐MOF) is proposed for the detection of Fe3+. At the same time, computer vision and neural networks are used to study the relationship between fluorescence color and Fe3+ concentration. A new method is provided for the fabrication of colorimetric Fe3+ fluorescence sensors.