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Electronic Nose Development and Preliminary Human Breath Testing for Rapid, Non-Invasive COVID-19 DetectionWe adapted an existing, spaceflight-proven, robust “electronic nose” (E-Nose) that uses an array of electrical resistivity-based nanosensors mimicking aspects of mammalian olfaction to conduct on-site, rapid screening for COVID-19 infection by measuring the pattern of sensor responses to volatile organic compounds (VOCs) in exhaled human breath. We built and tested multiple copies of a hand-held prototype E-Nose sensor system, composed of 64 chemically sensitive nanomaterial sensing elements tailored to COVID-19 VOC detection; data acquisition electronics; a smart tablet with software (App) for sensor control, data acquisition and display; and a sampling fixture to capture exhaled breath samples and deliver them to the sensor array inside the E-Nose. The sensing elements detect the combination of VOCs typical in breath at parts-per-billion (ppb) levels, with repeatability of 0.02% and reproducibility of 1.2%; the measurement electronics in the E-Nose provide measurement accuracy and signal-to-noise ratios comparable to benchtop instrumentation. Preliminary clinical testing at Stanford Medicine with 63 participants, their COVID-19-positive or COVID-19-negative status determined by concomitant RT-PCR, discriminated between these two categories of human breath with a 79% correct identification rate using “leave-one-out” training-and-analysis methods. Analyzing the E-Nose response in conjunction with body temperature and other non-invasive symptom screening using advanced machine learning methods, with a much larger database of responses from a wider swath of the population, is expected to provide more accurate on-the-spot answers. Additional clinical testing, design refinement, and a mass manufacturing approach are the main steps toward deploying this technology to rapidly screen for active infection in clinics and hospitals, public and commercial venues, or at home.
Document ID
20230002401
Acquisition Source
Ames Research Center
Document Type
Accepted Manuscript (Version with final changes)
Authors
Jing Li ORCID
(Ames Research Center Mountain View, California, United States)
Ami Hannon
(Ames Research Center Mountain View, California, United States)
George Yu
(Variable, Inc.)
Luke A. Idziak
(Ames Research Center Mountain View, California, United States)
Adwait Sahasrabhojanee
(Ames Research Center Mountain View, California, United States)
Prasanthi Govindarajan
(Stanford University Stanford, California, United States)
Yvonne A. Maldonado
(Stanford University Stanford, California, United States)
Khoa Ngo
(Ames Research Center Mountain View, California, United States)
John P. Abdou
(Ames Research Center Mountain View, California, United States)
Nghia Mai
(Ames Research Center Mountain View, California, United States)
Antonio J. Ricco ORCID
(Stanford University Stanford, California, United States)
Date Acquired
February 21, 2023
Publication Date
May 24, 2023
Publication Information
Publication: ACS Sensors
Publisher: American Chemical Society
Volume: 8
Issue: 6
Issue Publication Date: June 23, 2023
e-ISSN: 2379-3694
Subject Category
Life Sciences (General)
Funding Number(s)
INTERAGENCY: SAA5-2020-4-I32973
CONTRACT_GRANT: NNA14AB82C
CONTRACT_GRANT: 80ARC021D0001
CONTRACT_GRANT: NNA16BD14C
INTERAGENCY: IPA53-NA
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
Single Expert
Keywords
COVID-19
electronic nose
sensor array
nanomaterial
infection detection
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