Fe-Mo atomic-scale synergy enables trace detection and multidimensional fingerprinting of phenolic pollutants.

Phenolic pollutants, originating from petrochemical production, dyeing and domestic wastewater, are among the most persistent and toxic organic contaminants in aquatic systems. Their structural diversity and similar physicochemical properties often lead to overlapping signals in traditional detection methods, hindering accurate identification and quantification in real-world samples. Conventional spectroscopic, electrochemical, and colorimetric techniques offer fast response and high sensitivity but often struggle to distinguish structurally similar phenolics in complex mixtures. Therefore, developing sensing strategies that generate rich feature information and enable multidimensional recognition have become increasingly essential. Herein, we developed a fingerprinting sensing strategy based on an ultralow-loading Fe-Mo double-atomic site catalyst (DAC) coupled with H2O2 and TMB. Compared with isolated Fe or Mo single-atomic sites, the Fe-Mo DAC exhibited markedly enhanced peroxidase-like activity, which is attributed to Mo-induced modulation of the electronic structure and oxidation state of Fe centers. This catalytic synergy enables broad detection ranges, high sensitivity, and ultralow limits of detection (LOD, 0.76 μM) for five representative phenolic compounds and their binary mixtures. Experimental and theoretical investigations reveal that the variation in LOD is governed by the phenols' intrinsic oxidation energy barriers and reducing abilities. By integrating multiple linear and nonlinear spectral descriptors into a four-dimensional feature matrix, followed by PCA and LDA, the system achieved accurate high-dimensional discrimination of phenolic species. This work offers a rapid, sensitive, and robust method for identifying phenolic pollutants, enabling mixture discrimination, source tracing, and environmental monitoring. The approach also provides a generalizable framework for expanding nanozyme-based sensing toward complex chemical mixtures.