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Fentanyl, an exceedingly potent synthetic opioid, has emerged as a leading contributor to the surge in opioid\u2010related overdose deaths. The surge in overdose fatalities, particularly due to illicitly manufactured fentanyl and its contamination of street drugs, emphasizes the urgency for drug\u2010testing technologies that can quickly and accurately identify fentanyl from other drugs and quantify trace amounts of fentanyl. In this paper, gold nanoparticle (AuNP)\u2010decorated single\u2010walled carbon nanotube (SWCNT)\u2010based field\u2010effect transistors (FETs) are utilized for machine learning\u2010assisted 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\u2010one\u2010out cross\u2010validation 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\u2010SWCNT FET sensor toward fentanyl as well as the underlying sensing mechanism. Finally, fentanyl antibodies are introduced to the Au\u2010SWCNT 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\u00a0fg\u00a0mL<jats:sup>\u22121<\/jats:sup>.<\/jats:p>","DOI":"10.1002\/smll.202311835","type":"journal-article","created":{"date-parts":[[2024,4,28]],"date-time":"2024-04-28T23:25:15Z","timestamp":1714346715000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Machine Learning Discrimination and Ultrasensitive Detection of Fentanyl Using Gold Nanoparticle\u2010Decorated Carbon Nanotube\u2010Based Field\u2010Effect Transistor Sensors"],"prefix":"10.1002","volume":"20","author":[{"given":"Wenting","family":"Shao","sequence":"first","affiliation":[{"name":"Department of Chemistry University of Pittsburgh  Pittsburgh Pennsylvania 15260 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan C.","family":"Sorescu","sequence":"additional","affiliation":[{"name":"United States Department of Energy National Energy Technology Laboratory  Pittsburgh Pennsylvania 15236 USA"},{"name":"Department of Chemical &amp; 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