Designing tunable carbon nanotube-based nanomechanical biosensor for SARS-CoV-2 detection: A stochastic finite element analysis.

Background: COVID-19 is a novel infection in humans that is triggered by a virus called SARS-CoV-2, which affects the respiratory system severely. The transmission rate and mortality of COVID-19 can be reduced by early diagnosis. COVID-19 is confirmed by PCR-based tests, which are the standard method, but biosensing platform for SARS-CoV-2 detection also have an essential role in diagnosing COVID-19 and are widely used. This paper presents a CNT-based biosensor for SARS-CoV-2 detection, which is a nanomechanical sensor that operates by altering in the vibrational response(frequency shift) of the system upon the addition of a external object.

Method: The finite element method (FEM) is used for the design and analysis and the tensile axial strain has been utilized to tune the biosensor. The sensor was modeled by coating a carbon nanotube (CNT) with a specific antibody against the SARS-CoV-2 Spike S1 antigen. Then, the SARS-CoV-2 viruses are randomly distributed on the CNT surface with Poisson point function, and crucial design factors of the biosensor, including resonance frequency, frequency shift, relative frequency shift, and sensor sensitivity are estimated. The effect of the CNT geometry, axial strain, boundary conditions, and the number of virus variations on the on biosensor characteristics are investigated.

Results: The results show that, By selecting CNTs with the appropriate geometry (Diameter: 10-40 nm, Length: 10-1000μm), an excellent sensitivity index for SARS-CoV-2 detection is attainable even with less than 100 viruses per test. Finally, based on the simulation results, an analytical model is introduced to estimate the sensor's limit of detection (LOD) with the necessary sensitivity index.

Conclusions: With the outbreak of new deadly viruses such as SARS-CoV-2, the design of biosensors for them seems necessary. The results can assist in designing and fabricating specific CNT-based biosensors for SARS-CoV-2 detection. Also, the proposed method can facilitate the design of biosensors for other potential viruses in the future.