Evidence map›Paper›PMID 33578782›Full record

ReviewSensors (Basel, Switzerland)2021

Multi-Walled Carbon Nanotubes-Based Sensors for Strain Sensing Applications.

Anindya Nag, Md Eshrat E Alahi, Subhas Chandra Mukhopadhyay, Zhi Liu

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
6.8field-weighted citation impact, top 2% 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

24 citing papers in PubMed, 124 citations in OpenAlex.

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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

4 authors at 3 institutions in 2 countries.

Anindya NagSchool of Information Science and Engineering, Shandong University, Jinan 251600, China.ORCID 0000-0002-2721-5146
Md Eshrat E AlahiThe Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Subhas Chandra MukhopadhyaySchool of Engineering, Macquarie University, Sydney 2109, Australia.ORCID 0000-0002-8600-5907
Zhi LiuSchool of Information Science and Engineering, Shandong University, Jinan 251600, China.
Shandong University · CNChinese Academy of Sciences · CNMacquarie University · AU

Funding

CAS President International Fellowship Initiative 2019PT0008National Natural Science Foundation of China 61950410613
6 · The paper itself

Abstract

The use of multi-walled carbon nanotube (MWCNT)-based sensors for strain-strain applications is showcased in this paper. Extensive use of MWCNTs has been done for the fabrication and implementation of flexible sensors due to their enhanced electrical, mechanical, and thermal properties. These nanotubes have been deployed both in pure and composite forms for obtaining highly efficient sensors in terms of sensitivity, robustness, and longevity. Among the wide range of applications that MWCNTs have been exploited for, strain-sensing has been one of the most popular ones due to the high mechanical flexibility of these carbon allotropes. The MWCNT-based sensors have been able to deduce a broad spectrum of macro- and micro-scaled tensions through structural changes. This paper highlights some of the well-approved conjugations of MWCNTs with different kinds of polymers and other conductive nanomaterials to form the electrodes of the strain sensors. It also underlines some of the measures that can be taken in the future to improve the quality of these MWCNT-based sensors for strain-related applications.

Indexed as

carbon nanotubesflexiblemulti-walledsensorsstrain

Identifiers

PMID33578782
PMCPMC7916448
OpenAlexW3128494189

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.