An MXene-graphene field-effect transistor (FET) sensor for both influenza virus and 2019-nCoV sensing was developed and characterized. The developed sensor combines the high chemical sensitivity of MXene and the continuity of large-area high-quality graphene to form an ultra-sensitive virus-sensing transduction material (VSTM). Through polymer linking, we are able to utilize antibody-antigen binding to achieve electrochemical signal transduction when viruses are deposited onto the VSTM surface. The MXene-graphene VSTM was integrated into a microfluidic channel that can directly receive viruses in solution. The developed sensor was tested with various concentrations of antigens from two viruses: inactivated influenza A (H1N1) HA virus ranging from 125 to 250,000 copies/mL and a recombinant 2019-nCoV spike protein ranging from 1 fg/mL to 10 pg/mL. The average response time was about ∼50 ms, which is significantly faster than the existing real-time reverse transcription-polymerase chain reaction method ( > 3 h). The low limit of detection (125 copies/mL for the influenza virus and 1 fg/mL for the recombinant 2019-nCoV spike protein) has demonstrated the sensitivity of the MXene-graphene VSTM on the FET platform to virus sensing. Especially, the high signal-to-viral load ratio (∼10% change in source-drain current and gate voltage) also demonstrates the ultra-sensitivity of the developed MXene-graphene FET sensor. In addition, the specificity of the sensor was also demonstrated by depositing the inactivated influenza A (H1N1) HA virus and the recombinant 2019-nCoV spike protein onto microfluidic channels with opposite antibodies, producing signal differences that are about 10 times lower. Thus, we have successfully fabricated a relatively low-cost, ultrasensitive, fast-responding, and specific inactivated influenza A (H1N1) and 2019-nCoV sensor with the MXene-graphene VSTM.


Civil, Architectural and Environmental Engineering

Second Department

Mechanical and Aerospace Engineering

Third Department

Biological Sciences

Fourth Department

Electrical and Computer Engineering

Research Center/Lab(s)

Center for High Performance Computing Research


Yanxiao Li, Congjie Wei, Xiangyang Dong, and Chenglin Wu gratefully acknowledge financial support of this work by the National Science Foundation through grant no. CMMI-1930881. These authors also acknowledge funding support from Mid-America Transportation Center and Missouri Department of Transportation.

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Publication Date

16 Mar 2021

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Biology Commons