Computational Method‐Based Optimization of Carbon Nanotube Thin‐Film Immunosensor for Rapid Detection of SARS‐CoV‐2 Virus
2021 · Small Science
저자
Su Yeong Kim, Jeong‐Chan Lee, Giwan Seo, Jun Hee Woo, Min Ho Lee, 남재욱 (Jaewook Nam), Joo Yong Sim, Hyung‐Ryong Kim, Edmond Changkyun Park, Steve Park
연구주제
Electrochemical sensors and biosensors
NTIS 과학기술표준분류
가장 가까운 분류(보건의료 > 의료기기)가 화학·화공 범위 밖이다. 배정 근거: 지원 과제·키워드 근거가 없어 AI가 논문 제목·초록을 읽고 고른 분류.
AI 연구 키워드·방법
carbon nanotube thin film immunosensor sars cov 2 detection
computational optimization
키워드 (OpenAlex)
carbon nanotube films immunosensor COVID-19 detection COVID-19 diagnosis nucleocapsid protein point-of-care testing biosensor sensitivity rapid virus detection
초록
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 a…
이 논문을 지원한 국가R&D 과제 (NTIS 성과)
NTIS 성과DB(2020–24)에서 DOI 매칭 없음
인접 그래프
노드 hover = 이웃 강조 · 1회 클릭 = 고정 · 고정된 노드 다시 클릭 = 페이지 이동