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Kim SY, Lee JC, Seo G, et al. Computational Method-Based Optimization of Carbon Nanotube Thin-Film Immunosensor for Rapid Detection of SARS-CoV-2 Virus. Small Sci. 2021;2100111doi: 10.1002/smsc.202100111.
Kim, S. Y., Lee, J. C., Seo, G., Woo, J. H., Lee, M., Nam, J., Sim, J. Y., Kim, H. R., Park, E. C., & Park, S. (2021). Computational Method-Based Optimization of Carbon Nanotube Thin-Film Immunosensor for Rapid Detection of SARS-CoV-2 Virus. Small science, 2100111. https://doi.org/10.1002/smsc.202100111
Kim, Su Yeong, et al. "Computational Method-Based Optimization of Carbon Nanotube Thin-Film Immunosensor for Rapid Detection of SARS-CoV-2 Virus." Small science vol. (2021): 2100111. doi: https://doi.org/10.1002/smsc.202100111
Kim SY, Lee JC, Seo G, Woo JH, Lee M, Nam J, Sim JY, Kim HR, Park EC, Park S. Computational Method-Based Optimization of Carbon Nanotube Thin-Film Immunosensor for Rapid Detection of SARS-CoV-2 Virus. Small Sci. 2021 Nov 16;2100111. doi: 10.1002/smsc.202100111. Epub 2021 Nov 16. PMID: 34901932; PMCID: PMC8646396.
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Small Sci. 2021 Nov 16;2100111. doi: 10.1002/smsc.202100111. Epub 2021 Nov 16.

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

Small science

Su Yeong Kim, Jeong-Chan Lee, Giwan Seo, Jun Hee Woo, Minho Lee, Jaewook Nam, Joo Yong Sim, Hyung-Ryong Kim, Edmond Changkyun Park, Steve Park

Affiliations

  1. Organic and nano electronics laboratory KI for Health Science and Technology Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea.
  2. Research Center for Bioconvergence Analysis Korea Basic Science Institute Cheongju 28119 Republic of Korea.
  3. Center for Convergent Research of Emerging Virus Infection Korea Research Institute of Chemical Technology Daejeon 34114 Republic of Korea.
  4. School of Chemical and Biological Engineering and Institute of Chemical Process Seoul National University Seoul 08826 Republic of Korea.
  5. Department of Mechanical Systems Engineering Sookmyung Women's University Seoul 04310 Republic of Korea.
  6. Department of Pharmacology College of Dentistry Jeonbuk National University Jeonju 54896 Republic of Korea.

PMID: 34901932 PMCID: PMC8646396 DOI: 10.1002/smsc.202100111

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]

© 2021 The Authors. Small Science published by Wiley‐VCH GmbH.

Keywords: SARS-CoV-2; biosensors; carbon nanotubes; machine learning; solution shearing

Conflict of interest statement

The authors declare no conflict of interest.

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