Evidence map›Paper›PMID 31906508›Full record

ArticlePolymers2020

Highly Sensitive Graphene/Polydimethylsiloxane Composite Films near the Threshold Concentration with Biaxial Stretching.

Anqi Liu, Zhengji Ni, Juan Chen, Yuanshen Huang

Abstract read
In one paragraph

Article in Polymers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Anqi LiuSchool of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Zhengji NiSchool of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Juan ChenSchool of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Yuanshen HuangSchool of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Funding

National Key R&D Program of China 2016YFF0101904National Natural Science Foundation of China 11105149Natural Science Foundation of Shanghai 19ZR1436100
6 · The paper itself

Abstract

Uniformly dispersed graphene effectively improves the strain-sensing capability of the composite film under a low graphene load in nanocomposites prepared with polydimethylsiloxane (PDMS) and graphene (GNP) monolayer powder. The threshold concentration of graphene was determined by loading nanocomposites at different temperatures. For different concentrations, when using traditional uniaxial stretching, the rate of resistance change of films near the threshold concentration is five times higher than the rate of films with a high concentration. Compared with traditional uniaxial stretching, the biaxial stretching we introduced can effectively improve the sensitivity of the film by an order of magnitude. The change in the resistance of the film near the threshold concentration is due to the change of the tunnel length and the cross-section of the tunnel, whereas the high concentration of the film is due to the change of the conductive path inside the film. Biaxial stretching has different effects on films with different concentrations, but the final effect of increasing sensitivity is the same. This study provides guidance for improving the strain-sensing sensitivity of GNP/PDMS composite films and the application of biaxial tension in detecting human motions.

Indexed as

biaxial tensiongraphenePDMSthreshold concentration

Identifiers

PMID31906508
PMCPMC7023675

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.