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Related Experiment Video

Updated: Jun 29, 2025

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Machine learning-enhanced drug testing for simultaneous morphine and methadone detection in urinary biofluids.

Mohammad Mehdi Habibi1, Mitra Mousavi1, Maryam Shekofteh-Gohari1

  • 1School of Chemistry, University College of Science, University of Tehran, P.O. Box 14155-6455, Tehran, Iran.

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|April 6, 2024
PubMed
Summary

This study presents a novel electrochemical sensor for simultaneous drug detection in urine. The g-C3N4-CNT sensor accurately measures morphine, methadone, and uric acid using machine learning, offering a reliable tool for clinical drug analysis.

Keywords:
Drug analysisFFT-voltametryPLS methodg-C3N4-CNT nanocomposite

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Area of Science:

  • Electrochemistry
  • Materials Science
  • Analytical Chemistry

Background:

  • Simultaneous drug identification is challenging due to complex biological matrices and analyte interactions.
  • Existing methods often struggle with precision and sensitivity in real-world samples.

Purpose of the Study:

  • To develop an innovative electrochemical sensor for the precise and simultaneous determination of morphine (MOR), methadone (MET), and uric acid (UA) in urine.
  • To overcome the limitations of current analytical techniques in complex biological samples.

Main Methods:

  • Fabrication of a novel electrochemical sensor using carbon nanotubes (CNT) modified with graphitic carbon nitride (g-C3N4) nanosheets.
  • Utilized fast Fourier transform (FFT) voltammetry for quantitative measurements.
  • Employed partial least squares (PLS) machine learning for predictive modeling and validation.

Main Results:

  • The sensor achieved high sensitivity and precision for simultaneous MOR, MET, and UA detection.
  • Low RMSECV and RMSEP values demonstrated the model's accuracy (e.g., MOR RMSECV: 0.1827 µM, RMSEP: 0.1925 µM).
  • Excellent performance in real urine samples with low RSD (3.71-5.26%) and high recovery (96-106%).

Conclusions:

  • The developed g-C3N4-CNT electrochemical sensor offers a robust and reliable platform for simultaneous drug analysis in complex biological matrices.
  • The integration of advanced materials and machine learning signifies a significant advancement in electrochemical sensing for clinical and practical applications.
  • This technology holds promise for transforming drug analysis, enabling more accurate and efficient diagnostics.