Laccase- and peroxidase-like synergy-driven Cu-MOF nanozyme sensor array for high-throughput screening of phenolic...

Phenols are widespread high-risk environmental contaminants that exhibit structural diversity and vary in toxicity and environmental risk with substituent changes. Therefore, efficient methods for screening multiple phenols are urgently needed. Here, two nanozymes (Cu-MB and Cu-TB) with dual laccase-like and peroxidase-like activities were synthesized via a hydrothermal method using copper nitrate, 2-aminoterephthalic acid, and ligands (2-methylimidazole or tryptophan). The laccase-like activity oxidizes different phenols into structurally distinct quinone products, generating diverse absorption signals at 410-520 nm; meanwhile, the different reductive properties of phenols lead to differential inhibitory effects on the peroxidase-mediated 3,3',5,5'-tetramethylbenzidine (TMB)-H₂O₂ chromogenic reaction at 652 nm. This catalytic-inhibitory dual-response synergistic mechanism endows phenolic compounds with abundant cross-reactivity. Based on this, a six-channel sensor array was constructed by combining the two nanozymes with three characteristic absorption channels (420 nm, 510 nm, 652 nm), enabling rapid discrimination and sensitive quantification of 11 common phenols with a detection limit of 0.017 μM. Nine mainstream machine learning algorithms were further evaluated, and linear discriminant analysis (LDA) was identified as the optimal classification model, achieving 97.27% recognition accuracy for mixed-concentration samples. In addition, a binary classifier for rapidly distinguishing phenolic pollutants was developed, achieving 98.6% accuracy. The proposed phenol screening strategy significantly improves discrimination coverage and quantification accuracy, showing great potential for environmental monitoring and rapid risk assessment.