Machine learning-assisted laccase-like Pt@GA-Cu Nanozyme for phenolic compound discrimination and detection

Pt NPs was loaded to enhance the laccase-like activity of Cu-based nanozyme. • Design of a three-channel colorimetric sensing array based on the nanozyme. • The array enabled the simultaneous identification and detection of multiple phenol. • The developed sensor array showed superior analytical ability for phenol detection. Phenolic compounds represent a class of hazardous environmental pollutants characterized by high toxicity, low biodegradability, and a strong tendency to bioaccumulate, leading to considerable risks for ecological safety and human health. In this work, a novel bimetallic nanozyme Pt@GA-Cu was synthesized by loading platinum nanoparticles (Pt NPs) onto a copper-based laccase-like material. The synergistic interaction between Pt and Cu significantly enhanced the electron transfer rate and catalytic efficiency, resulting in better laccase-like performance than that of the individual materials. By this enhanced performance, a time-dependent three-channel colorimetric sensor array was constructed. With the integration of machine learning (ML), specifically linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA), the as-developed sensor successfully not only distinguished four distinct phenolic compounds (2,4-DP, 4-cP, CAT, and 2,6-DMP) with high accuracy, but also exhibited exceptional discriminative capabilities even when deployed within intricate multi-component phenolic solutions. Furthermore, to evaluate the functionality of the sensor in practical condition, real water samples were tested using standard addition methods, and satisfactory recovery rates (93.15–112.5%) and low relative standard deviations (<2.2%) were obtained. This study provides an efficient and reliable strategy to rapidly detect and differentiate among various phenolic pollutants, offering considerable potential for environmental monitoring applications. A Pt-loaded Cu nanozyme-based time-dependent colorimetric array with machine learning simultaneously discriminates and detects four phenolic pollutants. Download: Download high-res image (179KB) Download: Download full-size image