Evidence map›Paper›PMID 33092170›Full record

ArticleMolecules (Basel, Switzerland)2020

Electrical Properties Enhancement of Carbon Nanotube Yarns by Cyclic Loading.

Orli Weizman, Joey Mead, Hanna Dodiuk, Samuel Kenig

Open access · goldAbstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.6field-weighted citation impact, top 36% 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

0 citing papers in PubMed, 8 citations in OpenAlex.

No citing paper in PubMed yet.

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 2 institutions in 2 countries.

Orli WeizmanDepartment of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA.
Joey MeadDepartment of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA.
Hanna DodiukDepartment of Polymer Materials Engineering, Shenkar College of Engineering and Design, Ramat Gan 52526, Israel.
Samuel KenigDepartment of Polymer Materials Engineering, Shenkar College of Engineering and Design, Ramat Gan 52526, Israel.ORCID 0000-0003-1547-9125
Shenkar College of Engineering and Design · ILUniversity of Massachusetts Lowell · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Carbon nanotube yarns (CNTYs) possess low density, high conductivity, high strength, and moderate flexibility. These intrinsic properties allow them to be a preferred choice for use as conductive elements in high-performance composites. To fully exploit their potential as conductive reinforcing elements, further improvement in their electrical conductivity is needed. This study demonstrates that tensile cyclic loading under ambient conditions improves the electrical conductivity of two types of CNTYs. The results showed that the electrical resistance of untreated CNTYs was reduced by 80% using cyclic loading, reaching the resistance value of the drawn acid-treated CNTYs. Scanning electron microscopy showed that cyclic loading caused orientation and compaction of the CNT bundles that make up the CNTYs, resulting in significantly improved electrical conductivity of the CNTYs. Furthermore, the elastic modulus was increased by 20% while preserving the tensile strength. This approach has the potential to replace the environmentally unfriendly acid treatment currently used to enhance the conductivity of CNTYs.

Indexed as

Electric ConductivityNanotechnologyElastic ModulusMaterials TestingMicroscopy, Electron, ScanningNanotubes, CarbonTensile StrengthNanotubes, Carboncarbon nanotube yarnscyclic loadingelectrical conductivity

Identifiers

PMID33092170
PMCPMC7587937
OpenAlexW3093625239

What OpenQuestion holds

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LicenceCC BY
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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.