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Published on: January 12, 2019
An artificial neural network model based on CDs@MOF/COF composites for the ultra-sensitive fluorescence detection of
Yingfei Hui1, Yuli Wei2, Hao Guo1
1Key Lab of Eco-Environments Related Polymer Materials of MOE, Key Lab of Bioelectrochemistry and Environmental Analysis of Gansu Province, College of Chemistry and Chemical Engineering, Northwest Normal University, Gansu International Scientific and Technological Cooperation Base of Water-Retention Chemical Functional Materials, Lanzhou 730070, PR China.
Abstract:
Glyphosate (Gly) overuse threatens public health and ecosystems, necessitating highly sensitive detection. This work developed a novel ratiometric fluorescent probe, CDs@Cu-MOF/TMTA-COF, by integrating carbon dots (CDs), metal organic frameworks (MOFs), and covalent organic frameworks (COFs). We encapsulated CDs within Cu-MOF in-situ grown onto sp2‑carbon conjugated TMTA-COF. Gly binding to Cu2+ causes MOF structural collapse, releasing CDs and enhancing its fluorescence at 425 nm. Simultaneously, Gly inhibits competitive absorption and acceptor-photoinduced electron transfer process between TMTA-COF and Cu-MOF, increasing COF fluorescence at 550 nm. This dual-signal change enables ratiometric Gly detection from 0.2 to 40 μM, with a detection limit of 0.07 μM. This probe was successfully used to measure Gly in soil, Yellow River water and soybean (recoveries: 95.3-104.1 %). Additionally, the back propagation neural network further enables fast detection of Gly. This work demonstrates the potential of MOF/COF hybrids for fluorescence sensing for pesticide analysis and paves the way for intelligent sensing platforms.
