Evidence map›Paper›PMID 42393062›Full record

ArticleNature communications2026

Improvement of the tensile strength of carbon nanotube fibers to 12.5 GPa by fluidics-induced alignment and densification.

Jian Nong Wang, Yu Ting Chen, Heng Zhang, Hang Zhan, Qing Xuan Wang, Jun Cao, Xi Liu, Jian Feng Zheng, Kang Fei Liu

Abstract read
In one paragraph

Article in Nature communications, 2026. 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
–field-weighted citation impact
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.

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

9 authors.

Jian Nong WangSchool of Mechanical and Power Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China. jnwang@ecust.edu.cn.ORCID http://orcid.org/0000-0002-2766-1904
Yu Ting ChenSchool of Materials Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China.
Heng ZhangSchool of Materials Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China.
Hang ZhanSchool of Mechanical and Power Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China.
Qing Xuan WangSchool of Mechanical and Power Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China.
Jun CaoSchool of Mechanical and Power Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China.
Xi LiuSchool of Mechanical and Power Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China.
Jian Feng ZhengSchool of Mechanical and Power Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China.
Kang Fei LiuSchool of Mechanical and Power Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 52172089
6 · The paper itself

Abstract

Achieving the superior tensile strength of individual carbon nanotubes (CNTs) in macroscopic fibers depends on how CNTs are assembled. This study reports a fluidics approach: spraying high-velocity ethanol to scour and shrink pre-made CNT fibers. Experimental measurements show that the continuous scouring and shrinking induce significant improvements of the CNT alignment and packing density, leading to large increases of the van der Waals forces between CNTs and thus the tensile strength of the fibers. Under optimal conditions, the new CNT fibers achieve a specific tensile strength of 7.5×10

Identifiers

PMID42393062
PMCPMC13462575

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Registered trials

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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.