1. Tetrahydrocannabinol Detection Using Semiconductor-Enriched Single-Walled Carbon Nanotube Chemiresistors
- Author
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Sean I. Hwang, Ervin Sejdic, Michael A. Rothfuss, Miranda L. Vinay, David L. White, Nicholas G. Franconi, Brett J. Sopher, Raymond W. Euler, Long Bian, Seth C. Burkert, Alexander Star, and Kara N. Bocan
- Subjects
business.product_category ,Materials science ,Steel wool ,Bioengineering ,Biosensing Techniques ,02 engineering and technology ,Carbon nanotube ,Mass spectrometry ,01 natural sciences ,law.invention ,Machine Learning ,chemistry.chemical_compound ,law ,mental disorders ,Acetone ,Humans ,Dronabinol ,Instrumentation ,Breathalyzer ,Fluid Flow and Transfer Processes ,Chemiresistor ,Chromatography ,Molecular Structure ,Nanotubes, Carbon ,organic chemicals ,Process Chemistry and Technology ,010401 analytical chemistry ,Electrochemical Techniques ,021001 nanoscience & nanotechnology ,0104 chemical sciences ,Breath Tests ,Semiconductors ,chemistry ,Breath gas analysis ,Methanol ,0210 nano-technology ,business - Abstract
Semiconductor-enriched single-walled carbon nanotubes (s-SWCNTs) have potential for application as a chemiresistor for the detection of breath compounds, including tetrahydrocannabinol (THC), the main psychoactive compound found in the marijuana plant. Herein we show that chemiresistor devices fabricated from s-SWCNT ink using dielectrophoresis can be incorporated into a hand-held breathalyzer with sensitivity toward THC generated from a bubbler containing analytical standard in ethanol and a heated sample evaporator that releases compounds from steel wool. The steel wool was used to capture THC from exhaled marijuana smoke. The generation of the THC from the bubbler and heated breath sample chamber was confirmed using ultraviolet-visible absorption spectroscopy and mass spectrometry, respectively. Enhanced selectivity toward THC over more volatile breath components such as CO2, water, ethanol, methanol, and acetone was achieved by delaying the sensor reading to allow for the desorption of these compounds from the chemiresistor surface. Additionally, machine learning algorithms were utilized to improve the selective detection of THC with better accuracy at increasing quantities of THC delivered to the chemiresistor.
- Published
- 2019