Evidence map›Paper›PMID 35529645›Full record

ArticleRSC advances2019

A chemodosimeter-modified carbon nanotube-field effect transistor: toward a highly selective and sensitive electrical sensing platform.

Chang-Seuk Lee, Jong Seung Kim, Tae Hyun Kim

Open access · goldAbstract read
In one paragraph

Article in RSC advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 40% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Fabrication and Functionalisation of Nanocarbon-Based Field-Effect Transistor Biosensors.Chembiochem : a European journal of chemical biology · 2022
    Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Chang-Seuk LeeDepartment of Chemistry, Soonchunhyang University Republic of Korea thkim@sch.ac.kr +82-41-530-4722.
Jong Seung KimDepartment of Chemistry, Korea University Republic of Korea jonskim@korea.ac.kr +82-2-3290-3183.ORCID https://orcid.org/0000-0003-3477-1172
Tae Hyun KimDepartment of Chemistry, Soonchunhyang University Republic of Korea thkim@sch.ac.kr +82-41-530-4722.ORCID https://orcid.org/0000-0002-0154-518X
Soonchunhyang University · KRKorea University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID35529645
PMCPMC9071199
OpenAlexW2972599022

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.