Open Metal Sites in Metal−Organic Frameworks Enhance Electrochemiluminescence for Perfluorooctanoic Acid Imaging Analysis

Abstract Metal−organic frameworks (MOFs) are attractive for electrochemiluminescence (ECL) due to tunable structures and abundant luminescent centers, but their insufficient electron transfer capacity hinders the activation of co-reactants. Herein, we reported a class of Cu-based metal−organic frameworks (CuMOFs) that can self-activate the co-reactant for greatly boosting ECL performance by the use of Cu clusters bearing open metal sites (OMS) as the catalytic secondary building units and 1,3,6,8-tetrakis(4-carboxyphenyl)pyrene (H4TBAPy) as the emitter. Experimental results and theoretical calculation results indicated that the axial OMS exposed on Cu clusters promoted the generation of TEA• from triethylamine (TEA), leading to a substantial enhancement of the ECL signal. On this basis, an “off-on” biosensor for perfluorooctanoic acid (PFOA) was developed by integrating ECL resonance energy transfer (ECL-RET) with a DNAzyme cyclic amplification strategy. The smartphone-based ECL imaging intensity exhibited a favorable linear relationship with logarithm of PFOA concentration in the range of 0.01−1.0 × 104 nmol/L, and the limit of detection was 6.7 × 10−3 nmol/L. Notably, a convolutional neural network (CNN) was employed to analyze the ECL images and facilitated automated preliminary classification of PFOA samples into high- and low-risk groups. This work integrated co-reactant self-activation-enhanced ECL with machine learning image recognition, offering a promising solution for detection and early risk warning of emerging contaminants.