Evidence map›Paper›PMID 36145910›Full record

ArticlePolymers2022

Stretchable and Self-Healable Graphene-Polymer Conductive Composite for Wearable EMG Sensor.

Jihyang Song, Yewon Kim, Kyumin Kang, Sangkyu Lee, Mikyung Shin, Donghee Son

Abstract read
In one paragraph

Article in Polymers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
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  5. Review
  6. 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

6 authors.

Jihyang SongDepartment of Superintelligence Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Korea.
Yewon KimCenter for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, Korea.
Kyumin KangCenter for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, Korea.ORCID 0000-0003-0273-9231
Sangkyu LeeCenter for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, Korea.ORCID 0000-0003-0613-4846
Mikyung ShinCenter for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, Korea.ORCID 0000-0002-7990-5210
Donghee SonDepartment of Superintelligence Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Korea.ORCID 0000-0002-3772-8009

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In bioelectronics, stretchable and self-healable electrodes can reliably measure electrophysiological signals from the human body because they have good modulus matching with tissue and high durability. In particular, the polymer-graphene composite has advantages when it is used as an electrode for bioelectronic sensor devices. However, it has previously been reported that external stimuli such as heat or light are required for the self-healing process of polymer/graphene composites. In this study, we optimized a conducting composite by mixing a self-healing polymer (SHP) and graphene. The composite materials can not only self-heal without external stimulation but also have rapid electrical recovery from repeated mechanical damage such as scratches. In addition, they had stable electrical endurance even when the cyclic test was performed over 200 cycles at 50% strain, so they can be useful for a bioelectronic sensor device with high durability. Finally, we measured the electromyogram signals caused by the movement of arm muscles using our composite, and the measured data were transmitted to a microcontroller to successfully control the movement of the robot's hand.

Indexed as

compositeelectromyogramgraphenehuman–robot interfaceself-healingstretchability

Identifiers

PMID36145910
PMCPMC9505217

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.