Engineering a Metal-Organic Framework-Dominated Bioinspired Multienzymatic Sensor Array for Portable Detection of Perfluoroalkyl Substances.

Abstract

Accurate identification of perfluoroalkyl substances (PFASs) is essential for environmental regulation and public health protection. However, current analytical techniques struggle to differentiate PFASs due to their structural similarity. Herein, we report a novel multienzymatic activity sensor array based on a cerium-based metal-organic framework (Ce-MOF) capable of discriminating a wide range of PFASs in complex matrices. The Ce-MOF was engineered to exhibit triple enzyme-mimicking activities: oxidase, laccase, and superoxide dismutase. PFASs modulate these activities via electrostatic interactions and structural distortion, as supported by density functional theory calculations, producing three distinct signal outputs. By employing various machine learning algorithms, an optimized classification model was established that accurately identifies nine different PFASs with 100% prediction accuracy. The sensor array further enables reliable detection across a range of concentrations and in binary or ternary mixtures. The sensor array demonstrated robust performance in real-world samples including seawater, shrimp, and codfish. Additionally, a portable hydrogel-based kit was developed for onsite PFAS differentiation. This study presents the first demonstration of PFAS-regulated multienzymatic activity in Ce-MOF and offers a cost-effective, and practical strategy for PFAS detection with significant implications for environmental monitoring and public health.