Ligand Microenvironment-Regulated Nanozymes Enabled Machine Learning-Assisted Sensor Array for Simultaneous Identification of Phenolic Pollutants

作者：Dali Wei, Mengfan Li, Yudi Yang, Chunmeng Deng, Fang Zhu, Ming Li, Yibin Deng, Zhen Zhang · 发表于：ACS Sensors · 年份：2025 · DOI：10.1021/acssensors.5c01499 · 被引用次数：24 · 研究领域：Advanced Nanomaterials in Catalysis、Electrochemical sensors and biosensors、Advanced biosensing and bioanalysis techniques

Phenolic pollutants pose a great threat to human health due to high toxicity, whereas existing methods are difficult to achieve the rapid recognition of multiple phenolic pollutants. In this study, we developed a novel machine learning-assisted sensor array based on ligand microenvironment-regulated Pt nanozymes for the simultaneous differentiation of five phenolic pollutants (phenol, 2,4-DCP, p -chlorophenol, o -chlorophenol, and m -chlorophenol), wherein four cellulose ligands (carboxymethylcellulose, CMC; methylcellulose, MC; hydroxyethyl cellulose, HC; and hydroxypropyl methyl cellulose, HPMC)-regulated Pt nanozymes (Pt@CMC, Pt@MC, Pt@HC, and Pt@HPMC) with considerable laccase-mimicking activity were designed, and the Pt@CMC nanozyme exhibited the highest catalytic activity, which was about 7.5-folds than that of natural laccase. The calculation of density functional theory revealed that Pt@CMC had a stronger ability for capturing 2,4-DCP molecules, showing higher laccase-like activity. More importantly, the different cellulose ligands endowed four Pt nanozymes with laccase-like activity diverse recognition capability to phenolic compounds; thus, a nanozyme sensor array was developed for the differentiation of five phenolic pollutants. Moreover, the integration of a machine learning algorithm and the nanozyme sensor array successfully achieved accurate identification and prediction of the five phenolic pollutants in real water samples. Therefore, this study provided an em...