Evidence map›Paper›PMID 35892456›Full record

ArticleBiosensors2022

Thermally Drawn CNT-Based Hybrid Nanocomposite Fiber for Electrochemical Sensing.

Rino Nishimoto, Yuichi Sato, Jingxuan Wu, Tomoki Saizaki, Mahiro Kubo, Mengyun Wang, Hiroya Abe, Inès Richard, Tatsuo Yoshinobu, Fabien Sorin and 1 more

Open access · goldAbstract read
In one paragraph

Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 24% 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

3 citing papers in PubMed, 16 citations in OpenAlex.

  1. Review
  2. Article
  3. Microelectronic fibers for multiplexed sweat sensing.Analytical and bioanalytical chemistry · 2023
    Article
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

11 authors at 2 institutions in 2 countries.

Rino NishimotoGraduate School of Engineering, Tohoku University, Sendai 980-8579, Japan.
Yuichi SatoFrontier Research Institute for Interdisciplinary Sciences (FRIS), Tohoku University, Sendai 980-0845, Japan.
Jingxuan WuGraduate School of Engineering, Tohoku University, Sendai 980-8579, Japan.ORCID 0000-0003-0073-8809
Tomoki SaizakiSchool of Engineering, Tohoku University, Sendai 980-8579, Japan.ORCID 0000-0003-4954-1222
Mahiro KuboSchool of Engineering, Tohoku University, Sendai 980-8579, Japan.
Mengyun WangGraduate School of Engineering, Tohoku University, Sendai 980-8579, Japan.ORCID 0000-0002-1978-7195
Hiroya AbeGraduate School of Engineering, Tohoku University, Sendai 980-8579, Japan.ORCID 0000-0002-8847-1581
Inès RichardInstitute of Materials, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.ORCID 0000-0001-7918-1913
Tatsuo YoshinobuGraduate School of Engineering, Tohoku University, Sendai 980-8579, Japan.ORCID 0000-0001-7993-716X
Fabien SorinInstitute of Materials, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.ORCID 0000-0003-1019-6484
Yuanyuan GuoFrontier Research Institute for Interdisciplinary Sciences (FRIS), Tohoku University, Sendai 980-0845, Japan.ORCID 0000-0002-9618-7919
Tohoku University · JPÉcole Polytechnique Fédérale de Lausanne · CH

Funding

Japan Science and Technology Agency JPMJCE1303Japan Science and Technology Agency JP-MJFR205D
6 · The paper itself

Abstract

Nowadays, bioelectronic devices are evolving from rigid to flexible materials and substrates, among which thermally-drawn-fiber-based bioelectronics represent promising technologies thanks to their inherent flexibility and seamless integration of multi-functionalities. However, electrochemical sensing within fibers remains a poorly explored area, as it imposes new demands for material properties-both the electrochemical sensitivity and the thermomechanical compatibility with the fiber drawing process. Here, we designed and fabricated microelectrode fibers made of carbon nanotube (CNT)-based hybrid nanocomposites and further evaluated their detailed electrochemical sensing performances. Carbon-black-impregnated polyethylene (CB-CPE) was chosen as the base material, into which CNT was loaded homogeneously in a concentration range of 3.8 to 10 wt%. First, electrical impedance characterization of CNT nanocomposites showed a remarkable decrease of the resistance with the increase in CNT loading ratio, suggesting that CNTs notably increased the effective electrical current pathways inside the composites. In addition, the proof-of-principle performance of fiber-based microelectrodes was characterized for the detection of ferrocenemethanol (FcMeOH) and dopamine (DA), exhibiting an ultra-high sensitivity. Additionally, we further examined the long-term stability of such composite-based electrode in exposure to the aqueous environment, mimicking the in vivo or in vitro settings. Later, we functionalized the surface of the microelectrode fiber with ion-sensitive membranes (ISM) for the selective sensing of Na+ ions. The miniature fiber-based electrochemical sensor developed here holds great potential for standalone point-of-care sensing applications. In the future, taking full advantage of the thermal drawing process, the electrical, optical, chemical, and electrochemical modalities can be all integrated together within a thin strand of fiber. This single fiber can be useful for fundamental multi-mechanistic studies for biological applications and the weaved fibers can be further applied for daily health monitoring as functional textiles.

Indexed as

NanocompositesNanotubes, CarbonCarbon FiberDopamineMicroelectrodesCarbon FiberDopamineNanotubes, Carboncarbon nanotube (CNT)electrochemical sensingfiberspolymer compositethermal drawing

Identifiers

PMID35892456
PMCPMC9394265
OpenAlexW4287009242

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.