Machine Learning Discrimination and Ultrasensitive Detection of Fentanyl Using Gold Nanoparticle-Decorated Carbon Nanotube-Based Field-Effect Transistor Sensors

The opioid overdose crisis is a global health challenge. Fentanyl, an exceedingly potent synthetic opioid, has emerged as a leading contributor to the surge in opioid-related overdose deaths. The surge in overdose fatalities, particularly due to illicitly manufactured fentanyl and its contamination of street drugs, emphasizes the urgency for drug-testing technologies that can quickly and accurately identify fentanyl from other drugs and quantify trace amounts of fentanyl. In this paper, gold nanoparticle (AuNP)-decorated single-walled carbon nanotube (SWCNT)-based field-effect transistors (FETs) are utilized for machine learning-assisted identification of fentanyl from codeine, hydrocodone, and morphine. The unique sensing performance of fentanyl led to use machine learning approaches for accurate identification of fentanyl. Employing linear discriminant analysis (LDA) with a leave-one-out cross-validation approach, a validation accuracy of 91.2% is achieved. Meanwhile, density functional theory (DFT) calculations reveal the factors that contributed to the enhanced sensitivity of the Au-SWCNT FET sensor toward fentanyl as well as the underlying sensing mechanism. Finally, fentanyl antibodies are introduced to the Au-SWCNT FET sensor as specific receptors, expanding the linear range of the sensor in the lower concentration range, and enabling ultrasensitive detection of fentanyl with a limit of detection at 10.8 fg mL-1. Gold nanoparticle-decorated single-walled carbon nanotube-based field-effect transistors are utilized for machine learning-assisted identification of fentanyl from other opioids. The sensing mechanism behind the unique sensing performance of fentanyl is revealed through theoretical studies. Additionally, ultrasensitive detection of fentanyl with a limit of detection of 10.8 fg mL-1 is achieved by introducing fentanyl antibodies on the sensor. image