Computational Method‐Based Optimization of Carbon Nanotube Thin‐Film Immunosensor for Rapid Detection of SARS‐CoV‐2 Virus

Abstract

The recent global spread of COVID‐19 stresses the importance of developing diagnostic testing that is rapid and does not require specialized laboratories. In this regard, nanomaterial thin‐film‐based immunosensors fabricated via solution processing are promising, potentially due to their mass manufacturability, on‐site detection, and high sensitivity that enable direct detection of virus without the need for molecular amplification. However, thus far, thin‐film‐based biosensors have been fabricated without properly analyzing how the thin‐film properties are correlated with the biosensor performance, limiting the understanding of property−performance relationships and the optimization process. Herein, the correlations between various thin‐film properties and the sensitivity of carbon nanotube thin‐film‐based immunosensors are systematically analyzed, through which optimal sensitivity is attained. Sensitivities toward SARS‐CoV‐2 nucleocapsid protein in buffer solution and in the lysed virus are 0.024 [fg/mL]−1 and 0.048 [copies/mL]−1, respectively, which are sufficient for diagnosing patients in the early stages of COVID‐19. The technique, therefore, can potentially elucidate complex relationships between properties and performance of biosensors, thereby enabling systematic optimization to further advance the applicability of biosensors for accurate and rapid point‐of‐care (POC) diagnosis.

Keywords

• biosensors
• carbon nanotubes
• machine learning
• SARS-CoV-2
• solution shearing