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Ligand Microenvironment-Regulated Nanozymes Enabled Machine Learning-Assisted Sensor Array for Simultaneous
Dali Wei1, Mengfan Li1, Yudi Yang1
1School of the Environment and Safety Engineering, School of the Emergency Management, Jiangsu University, Zhenjiang 212013, China.
ACS Sensors
|July 15, 2025
Summary
This study introduces a novel sensor array using machine learning and specialized nanozymes to rapidly identify multiple toxic phenolic pollutants in water. This method offers a promising advancement for environmental monitoring and water safety.
Area of Science:
- Environmental Chemistry
- Materials Science
- Analytical Chemistry
Background:
- Phenolic pollutants are toxic and pose significant health risks.
- Current methods struggle with rapid, simultaneous detection of multiple phenolic compounds.
Purpose of the Study:
- To develop a machine learning-assisted sensor array for differentiating five phenolic pollutants.
- To design novel ligand-microenvironment-regulated platinum (Pt) nanozymes with enhanced laccase-mimicking activity.
Main Methods:
- Fabrication of four cellulose ligand-regulated Pt nanozymes (Pt@CMC, Pt@MC, Pt@HC, Pt@HPMC).
- Evaluation of laccase-mimicking activity and recognition capabilities of the nanozymes.
- Development of a nanozyme sensor array integrated with a machine learning algorithm.
Main Results:
- Pt@CMC nanozyme showed the highest catalytic activity, 7.5-fold higher than natural laccase.
- Density functional theory confirmed Pt@CMC's strong binding affinity for 2,4-DCP.
- The sensor array successfully differentiated five phenolic pollutants with high accuracy in real water samples.
Conclusions:
- The developed nanozyme sensor array, aided by machine learning, provides an effective strategy for simultaneous phenolic pollutant identification.
- This approach offers a promising pathway for advanced environmental monitoring applications.
