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Related Experiment Video

Updated: Jun 20, 2026

Fabrication of Carbon Nanotube High-Frequency Nanoelectronic Biosensor for Sensing in High Ionic Strength Solutions
12:20

Fabrication of Carbon Nanotube High-Frequency Nanoelectronic Biosensor for Sensing in High Ionic Strength Solutions

Published on: July 22, 2013

Nanoelectronic Detection of Opioids: Machine Learning-Powered Screening With Carbon Nanotube Field-Effect Transistor

Zhengru Liu1, Mehdy Dousty2, Wenting Shao1

  • 1Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

Small (Weinheim an Der Bergstrasse, Germany)
|June 19, 2026
PubMed
Summary

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A new nanoelectronic detection method using machine learning accurately identifies fentanyl and other opioids. This rapid screening technique shows promise for combating the opioid crisis.

Area of Science:

  • Nanoelectronic chemical sensing
  • Machine learning applications in toxicology
  • Opioid detection technologies

Background:

  • The United States faces a severe opioid crisis, largely fueled by illicit fentanyl.
  • There is a critical need for fast, affordable, and portable methods for screening opioids.

Purpose of the Study:

  • To develop a nanoelectronic detection method for identifying opioids using machine learning.
  • To evaluate the performance of field-effect transistor (FET) sensor arrays combined with machine learning for opioid classification.

Main Methods:

  • Utilized a field-effect transistor (FET) sensor array of single-walled carbon nanotubes (SWCNTs) functionalized with gold and platinum nanoparticles.
  • Tested fentanyl, codeine, hydrocodone, and morphine using manual and automated electrolyte-gated FET systems.
Keywords:
carbon nanotubefield‐effect transistorlaboratory automationmachine learningopioid sensing

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Engineering Molecular Recognition with Bio-mimetic Polymers on Single Walled Carbon Nanotubes
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Engineering Molecular Recognition with Bio-mimetic Polymers on Single Walled Carbon Nanotubes

Published on: January 10, 2017

Related Experiment Videos

Last Updated: Jun 20, 2026

Fabrication of Carbon Nanotube High-Frequency Nanoelectronic Biosensor for Sensing in High Ionic Strength Solutions
12:20

Fabrication of Carbon Nanotube High-Frequency Nanoelectronic Biosensor for Sensing in High Ionic Strength Solutions

Published on: July 22, 2013

Engineering Molecular Recognition with Bio-mimetic Polymers on Single Walled Carbon Nanotubes
09:28

Engineering Molecular Recognition with Bio-mimetic Polymers on Single Walled Carbon Nanotubes

Published on: January 10, 2017

  • Applied conventional supervised machine learning (LDA, RF) and deep learning (CNN) models to FET data for opioid classification.

    • Main Results:

    • Conventional machine learning models (LDA, RF) achieved 82.6% accuracy in opioid classification.
    • A convolutional neural network (CNN) reached 98.3% accuracy, highlighting the importance of gate-source current.
    • The study revealed novel insights into molecular sensing mechanisms with liquid-gated FETs.

    Conclusions:

    • Combining nanoelectronics with machine learning offers a powerful approach for chemical detection, particularly for opioids.
    • The developed method demonstrates potential for rapid, accurate, and portable opioid screening.
    • Further research can refine these techniques for broader applications in drug detection and public health.