Biosensors and Bioelectronics: X (Sep 2023)
Novel, accurate pathogen sensors for fast detection of SARS-CoV-2 in the aerosol in seconds for a breathalyzer platform
- Xiaoling Shi,
- Pardis Sadeghi,
- Nader Lobandi,
- Shadi Emam,
- Seyed Mahdi Seyed Abrishami,
- Isabel Martos-Repath,
- Natesan Mani,
- Mehdi Nasrollahpour,
- William Sun,
- Stav Rones,
- Joshua Kwok,
- Harsh Shah,
- Joseph Charles,
- Zulqarnain Khan,
- Sheree Pagsuyoin,
- Akarapan Rojjanapinun,
- Ping Liu,
- Jeongmin Chae,
- Maxime Ferreira Da Costa,
- Jianxiu Li,
- Xin Sun,
- Mengdi Yang,
- Jiahe Li,
- Jennifer Dy,
- Jennifer Wang,
- Jeremy Luban,
- ChingWen Chang,
- Robert Finberg,
- Urbashi Mitra,
- Sydney Cash,
- Gregory Robbins,
- Cole Hodys,
- Hui Lu,
- Patrick Wiegand,
- Robert Rieger,
- Nian X. Sun
Affiliations
- Xiaoling Shi
- Winchester Technologies, LLC, Burlington, MA, USA
- Pardis Sadeghi
- Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA
- Nader Lobandi
- Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA
- Shadi Emam
- Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA
- Seyed Mahdi Seyed Abrishami
- Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA
- Isabel Martos-Repath
- Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA
- Natesan Mani
- Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA
- Mehdi Nasrollahpour
- Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA
- William Sun
- Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA
- Stav Rones
- Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA
- Joshua Kwok
- Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA
- Harsh Shah
- Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA
- Joseph Charles
- Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA
- Zulqarnain Khan
- Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA
- Sheree Pagsuyoin
- Civil and Environmental Engineering, Center for Pathogen Research & Training (CPRT), U. of Massachusetts, Lowell, MA, USA
- Akarapan Rojjanapinun
- Civil and Environmental Engineering, Center for Pathogen Research & Training (CPRT), U. of Massachusetts, Lowell, MA, USA
- Ping Liu
- University of Massachusetts Medical School, Worcester, MA, USA
- Jeongmin Chae
- Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
- Maxime Ferreira Da Costa
- Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
- Jianxiu Li
- Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
- Xin Sun
- Bioengineering, Northeastern University, Boston, MA, USA
- Mengdi Yang
- Bioengineering, Northeastern University, Boston, MA, USA
- Jiahe Li
- Bioengineering, Northeastern University, Boston, MA, USA
- Jennifer Dy
- Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA
- Jennifer Wang
- University of Massachusetts Medical School, Worcester, MA, USA
- Jeremy Luban
- University of Massachusetts Medical School, Worcester, MA, USA
- ChingWen Chang
- University of Massachusetts Medical School, Worcester, MA, USA
- Robert Finberg
- University of Massachusetts Medical School, Worcester, MA, USA
- Urbashi Mitra
- Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
- Sydney Cash
- Massachusetts General Hospital, Boston, MA, USA
- Gregory Robbins
- Massachusetts General Hospital, Boston, MA, USA
- Cole Hodys
- Winchester Technologies, LLC, Burlington, MA, USA
- Hui Lu
- Winchester Technologies, LLC, Burlington, MA, USA
- Patrick Wiegand
- Networked Electronic Systems, University of Kiel, Kiel, Germany
- Robert Rieger
- Networked Electronic Systems, University of Kiel, Kiel, Germany
- Nian X. Sun
- Winchester Technologies, LLC, Burlington, MA, USA; Electrical & Computer Engineering, Bioengineering & Chemical Engineering, W.M. Keck Laboratory for Integrated Ferroics, Northeastern University, Boston, MA, USA; Corresponding author. Winchester Technologies, LLC, Burlington, MA, USA.
- Journal volume & issue
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Vol. 14
p. 100369
Abstract
Rapid and accurate detection of the pathogens, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for COVID-19, is critical for mitigating the COVID-19 pandemic. Current state-of-the-art pathogen tests for COVID-19 diagnosis are done in a liquid medium and take 10–30 min for rapid antigen tests and hours to days for polymerase chain reaction (PCR) tests. Herein we report novel accurate pathogen sensors, a new test method, and machine-learning algorithms for a breathalyzer platform for fast detection of SARS-CoV-2 virion particles in the aerosol in 30 s. The pathogen sensors are based on a functionalized molecularly-imprinted polymer, with the template molecules being the receptor binding domain spike proteins for different variants of SARS-CoV-2. Sensors are tested in the air and exposed for 10 s to the aerosols of various types of pathogens, including wild-type, D614G, alpha, delta, and omicron variant SARS-CoV-2, BSA (Bovine serum albumin), Middle East respiratory syndrome–related coronavirus (MERS-CoV), influenza, and wastewater samples from local sewage. Our low-cost, fast-responsive pathogen sensors yield accuracy above 99% with a limit-of-detection (LOD) better than 1 copy/μL for detecting the SARS-CoV-2 virus from the aerosol. The machine-learning algorithm supporting these sensors can accurately detect the pathogens, thereby enabling a new and unique breathalyzer platform for rapid COVID-19 tests with unprecedented speeds.
