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Machine Learning-Enhanced Nanozyme Sensor Array for Accurate Multiple Quinolone Antibiotics Recognition.

Qihao Shi1, Ziyuan Li1, Yu Wang1

  • 1Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, College of Biotechnology, Tianjin University of Science and Technology, No. 29 of 13th Street, TEDA, Tianjin 300457, P. R. China.

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Summary
This summary is machine-generated.

A novel nanozyme sensor array utilizing copper dihydroxosulfate (Cu2(OH)2SO4) nanosheets effectively detects quinolone antibiotics (QNs). This method uses distinct reaction dynamics and machine learning for high-precision, concentration-independent identification.

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Materials Science

Background:

  • Quinolone antibiotics (QNs) overuse poses significant risks to human health and ecosystems.
  • Developing sensitive and selective methods for QN detection is crucial for environmental monitoring and public health.

Purpose of the Study:

  • To develop a nanozyme-based sensing array for the identification of eight different quinolone antibiotics (QNs).
  • To leverage the distinct interactions of QNs with copper dihydroxosulfate (Cu2(OH)2SO4) nanosheets' peroxidase-like (POD) and laccase-like (LAC) activities for sensing.
  • To enhance detection accuracy using machine learning (ML) and a self-calibrating sensor array.

Main Methods:

  • Synthesis of copper dihydroxosulfate (Cu2(OH)2SO4) nanosheets in basic deep eutectic solvents (DES).
  • Exploitation of QNs' differential effects on Cu2(OH)2SO4 POD (enhancement) and LAC (inhibition) activities over time.
  • Development of a nanozyme sensing array with self-calibration capabilities and optimization using machine learning algorithms.

Main Results:

  • Cu2(OH)2SO4 nanosheets exhibited tunable POD and LAC activities influenced by QNs.
  • A nanozyme sensing array demonstrated distinct reaction dynamics for identifying eight QNs.
  • Machine learning optimization significantly improved the concentration-independent recognition model's precision from 39.08% to 91.95%.

Conclusions:

  • The developed nanozyme sensing array offers a promising approach for sensitive and selective detection of QNs in complex samples.
  • The combination of reaction dynamics, self-calibration, and machine learning provides a robust platform for antibiotic residue analysis.
  • This study highlights the potential of engineered nanozymes for environmental monitoring and safeguarding public health against antibiotic pollution.