Evidence map›Paper›PMID 36135905›Full record

ArticleMembranes2022

Study on the Shear Behaviour and Fracture Characteristic of Graphene Kirigami Membranes via Molecular Dynamics Simulation.

Yuan Gao, Shuaijie Lu, Weiqiang Chen, Ziyu Zhang, Chen Gong

Abstract read
In one paragraph

Article in Membranes, 2022. 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. 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

5 authors.

Yuan GaoSchool of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.
Shuaijie LuSchool of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.
Weiqiang ChenDepartment of Mechanical, Aerospace and Civil Engineering, School of Engineering, The University of Manchester, Manchester M13 9PL, UK.ORCID 0000-0002-5355-6710
Ziyu ZhangDepartment of Chemical Engineering and Analytical Sciences, The University of Manchester, Manchester M13 9PL, UK.
Chen GongSchool of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.

Funding

National Natural Science Foundation of China 51734009
6 · The paper itself

Abstract

In this study, we aimed to provide systematic and critical research to investigate the shear performance and reveal the corresponding structural response and fracture characteristics of the monolayer GK membrane. The results demonstrate that the kirigami structure significant alters the shear performance of graphene-based sheets. Tuning the porosity by controlling the incision size, pore distribution, and incision direction can effectively adjust the shear strength and elastic modulus of GK membranes. The trade-off of the stress and strain of the GK membrane is critical to its shear behaviour. The microstructural damage processes and failure characteristics further reveal that making more carbon atoms on the GK structure sharing the strain energy is the key to reinforcing the shear performance of membranes. Based on this, we found that adding the shear loading in the direction of perpendicular to the incisions on the GK membrane can significantly improve the shear strength and stiffness of the membrane by 26.2-32.1% and 50.2-75.3% compared to applying shear force parallel to GK incisions. This research not only broadens the understanding of shear properties of monolayer GO membrane but also provides more reference on the fracture characteristics of GK membranes for future manufacturing and applications.

Indexed as

graphene kirigamiMD simulationmembrane scienceporosityshear performance

Identifiers

PMID36135905
PMCPMC9501511

What OpenQuestion holds

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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.