Computational Method‐Based Optimization of Carbon Nanotube Thin‐Film Immunosensor for Rapid Detection of SARS‐CoV‐2 Virus
- Authors
- Kim, Su Yeong; Lee, Jeong-Chan; Seo, Giwan; Woo, Jun Hee; Lee, Minho; Nam, Jaewook; Sim, Joo Yong; Kim, Hyung-Ryong; Park, Edmond Changkyun; Park, Steve
- Issue Date
- Feb-2022
- Publisher
- Wiley
- Keywords
- biosensors; carbon nanotubes; machine learning; SARS-CoV-2; solution shearing
- Citation
- Small Science, v.2, no.2, pp 1 - 9
- Pages
- 9
- Journal Title
- Small Science
- Volume
- 2
- Number
- 2
- Start Page
- 1
- End Page
- 9
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/151364
- DOI
- 10.1002/smsc.202100111
- ISSN
- 2688-4046
- 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.
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