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Updated: Feb 3, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
Published on: September 25, 2021
Imidazole functionalized nanozyme for deep detoxification and machine-learning-assisted intelligent sensing of
Xiaochen Liao1, Bai Li1, Xiao Wang1
1State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China.
Abstract:
The excessive use of profenofos (PF) poses a significant threat to ecosystems and human health, making efficient degradation and detection technologies imperative. Despite the evident promise of nanozyme-based cascade catalytic systems, integrating such systems into a single nanozyme with high reactivity remains challenging. In this study, we have developed a luminescent nanozyme, Zn/MOF-808@Im/Cu-MOF, which possesses dual phosphatase and laccase activities for PF detoxification and intelligent sensing. Zn2+ doping enhances the Lewis acidity of MOF-808, promoting the hydrolysis of PF to 4-bromo-2-chlorophenol (BCP). Imidazole (Im) functionalization bridges MOFs and mimics the coordination environment of natural enzymes to accelerate proton and electron transfer, thereby boosting both hydrolytic and oxidative activities of the nanozyme and driving the oxidation of BCP to quinone intermediates. The quinone intermediates with 4-aminoantipyrine (4-AP) to generate a red product with a characteristic absorption peak and quench the fluorescence of Zn/MOF-808@Im/Cu-MOF, achieving colorimetric and fluorometric detection of PF. Utilizing the Residual Network 50 with Convolutional Block Attention Module (ResNet50-CBAM) neural network model, a machine learning-assisted intelligent sensing platform was further developed to enhance the practicability of PF detection. This study presents a novel approach for the detoxification and intelligent sensing of organophosphorus pesticides (OPs).
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