Pentagonal Porphyrin-Based Covalent Organic Framework with Switchable Dual-Enzyme Activity for Adaptive Catechol...

Area of Science:

• Materials Science
• Nanotechnology
• Analytical Chemistry

Background:

• Intelligent pollutant monitoring requires advanced nanozymes with stable activity under diverse environmental conditions.
• Conventional 2D covalent organic frameworks (COFs) face symmetry limitations, hindering optimal enzyme-mimicking microenvironments.

Purpose of the Study:

• To construct a novel pentagonal 2D porphyrin-based COF (NiPor-BATA-COF) with tunable anisotropy and enhanced adaptive nanozyme properties.
• To develop an adaptive colorimetric sensor for catechol (CC) detection utilizing the nanozyme's switchable dual-enzyme activity.
• To create a portable platform with a deep learning model for real-time, automated CC quantification.

Main Methods:

• Synthesis of a pentagonal 2D NiPor-BATA-COF via Schiff-base reaction.
• Characterization of the COF's hierarchical pore architecture and tunable anisotropy.
• Evaluation of switchable peroxidase (POD)-like and catalase (CAT)-like activities under varying pH and temperature.
• Development of a colorimetric sensor for CC detection and a cascade molecular logic system.
• Integration with a YOLO v5-CC deep learning model for a portable, automated sensing platform.

Main Results:

• The NiPor-BATA-COF nanozyme demonstrated switchable "seesaw" dual-enzyme activity, favoring POD under acidic/cold conditions and CAT under alkaline/hot conditions.
• An adaptive colorimetric sensor for CC achieved a wide linear range (2-300 μM) and low detection limits (0.16-0.23 μM) across broad pH/temperature windows.
• A portable platform with a YOLO v5-CC model enabled automated CC quantification from real samples, achieving an integrated "sample-in-result-out" workflow.

Conclusions:

• The study presents an adaptive design paradigm for pentagonal COF nanozymes with switchable dual-enzyme characteristics.
• This work offers a promising strategy for sensitive and adaptive phenolic compound sensing in point-of-care testing applications.
• The developed platform demonstrates the potential for intelligent environmental monitoring and diagnostics.