Evidence map›Paper›PMID 36363683›Full record

ArticleMembranes2022

Controlling the Thermal Conductivity of Monolayer Graphene with Kirigami Structure.

Yuan Gao, Shuaijie Lu, Weiqiang Chen, Jinyuan Zhang, Chundi Feng, Yanming Liu

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. 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

6 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
Jinyuan ZhangSchool of Life Sciences, Nantong University, Nantong 226019, China.
Chundi FengDepartment of Mechanical, Aerospace and Civil Engineering, School of Engineering, The University of Manchester, Manchester M13 9PL, UK.
Yanming LiuSchool of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.ORCID 0000-0001-8599-7118

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this work, the thermal conductivity performance of graphene kirigami (GK) was systematically investigated via molecular dynamics (MD) simulations. The results indicate that the degree of defects (DD) on GK has a significant influence on thermal conductivity. Reducing the DD is the most effective way to decrease the thermal conductivity of GK. For zigzag-incised GK sheets, the change rate of thermal conductivity can reach up to 1.86 W/mK per 1% change in DD by tuning the incision length. The rate of changing thermal conductivity with DD can be slowed down by changing the width among incisions. Compared with the zigzag-incised GK sheets, heat transfer across the armchair-incised GK comes out more evenly, without significant steep and gentle stages along the heat transfer routes. More importantly, the GK structure can adjust the thermal conductivity by stretching, which the previously reported nanoporous graphene does not have. The change rate of thermal conductivity achieves about 0.17 W/mK with 1% stretching strain for simulated GK and can be further reduced at high tensile strain rates, benefiting the precise and variable control of the thermal conductivity of the monolayer graphene.

Indexed as

adjustabilitygraphene-based membranekirigami structuremolecular dynamics simulationthermal conductivity

Identifiers

PMID36363683
PMCPMC9694673

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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.