Evidence map›Paper›PMID 40263355›Full record

ArticleNature communications2025

Shape morphing of soft robotics by pneumatic torsion strip braiding.

Changchun Wu, Hao Liu, Senyuan Lin, James Lam, Ning Xi, Yonghua Chen

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Changchun WuDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
Hao LiuDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong, Hong Kong.ORCID http://orcid.org/0000-0002-0110-1693
Senyuan LinDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
James LamDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
Ning XiDepartment of Data and Systems Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
Yonghua ChenDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong, Hong Kong. yhchen@hku.hk.ORCID http://orcid.org/0000-0003-4020-1977

Funding

Research Grants Council, University Grants Committee (RGC, UGC) STG1/E-401/23-NResearch Grants Council, University Grants Committee (RGC, UGC) T42-717/20-R
6 · The paper itself

Abstract

Shape morphing technologies are significant in soft robotic applications. To this end, we introduce a new shape morphing approach using pneumatic torsion strips, inspired by the shape of a Möbius strip. A pneumatic torsion strip is simply formed by bending and twisting a ribbon of bladder. When locating a pneumatic torsion strip on a braided soft body, its intrinsic elastic energy always tends to bend the soft body. Meanwhile, its elastic energy is adjustable and correlated with the geometry and internal-pressure dependent material properties. Compared with common strain-mismatch based morphing methods, pneumatic torsion strips directly exert bending torque to the soft body without generating in-plane strain and affecting rigidity. As such, the local bending of a soft body over a large curvature range at almost any position can be realized through pneumatic torsion strips. A mathematical model describing the geometry and elastic energy of a pneumatic torsion strip is also established to explain its basic shape morphing mechanism. Finally, we provide several case studies to illustrate their performance and advantages in practical shape morphing applications, such as a 2 kg meter-scale transformable carpet that can curl like plant tendrils.

Identifiers

PMID40263355
PMCPMC12015459

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LicenceCC BY-NC-ND
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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.