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A chemodosimeter-modified carbon nanotube-field effect transistor: toward a highly selective and sensitive electrical sensing platform.
Lee, Chang-Seuk; Kim, Jong Seung; Kim, Tae Hyun.
Afiliação
  • Lee CS; Department of Chemistry, Soonchunhyang University Republic of Korea thkim@sch.ac.kr +82-41-530-4722.
  • Kim JS; Department of Chemistry, Korea University Republic of Korea jonskim@korea.ac.kr +82-2-3290-3183.
  • Kim TH; Department of Chemistry, Soonchunhyang University Republic of Korea thkim@sch.ac.kr +82-41-530-4722.
RSC Adv ; 9(49): 28414-28420, 2019 Sep 09.
Article em En | MEDLINE | ID: mdl-35529645
ABSTRACT
We present a carbon nanotube-field effect transistor (CNT-FET) biosensor which first implements the chemodosimeter sensing principle in CNT nanoelectronics. We experimentally illustrate the specific molecular interplay that the cysteine-selective chemodosimeter immobilized on the CNT surface can specifically interact with cysteine, which leads to the chemical transformation of the chemodosimeter. Since the chemical transformation of the chemodosimeter can disrupt the charge distribution in the vicinity of the CNT surface, the carrier equilibrium in CNT might be altered, and manifested by the conductivity change of CNT-FET. The real-time conductance measurements show our biosensor is capable of label-free, rapid, highly selective and ultrasensitive detection of cysteine with a detection limit down to 0.45 fM. These results first verify the signaling principle competency of chemical transformation of the chemodosimeter in CNT electronic sensors. Combined with the advantages of the highly selective chemodosimeter and sensitive CNT-FET, the excellent performance of our sensor indicates its promising prospect as a valuable tool for developing highly sensitive and selective sensing platforms in practical application.

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article