Evidence map›Paper›PMID 36234394›Full record

ArticleNanomaterials (Basel, Switzerland)2022

Bioinspired Spinosum Capacitive Pressure Sensor Based on CNT/PDMS Nanocomposites for Broad Range and High Sensitivity.

Yanhao Duan, Jian Wu, Shixue He, Benlong Su, Zhe Li, Youshan Wang

Abstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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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.

Yanhao DuanNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Harbin Institute of Technology, Harbin 150090, China.
Jian WuNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Harbin Institute of Technology, Harbin 150090, China.ORCID 0000-0003-3007-2972
Shixue HeNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Harbin Institute of Technology, Harbin 150090, China.
Benlong SuNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Harbin Institute of Technology, Harbin 150090, China.ORCID 0000-0003-1319-2673
Zhe LiCenter for Rubber Composite Materials and Structures, Harbin Institute of Technology, Weihai 264209, China.
Youshan WangNational Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Harbin Institute of Technology, Harbin 150090, China.

Funding

National Natural Science Foundation of China 52075119
6 · The paper itself

Abstract

Flexible pressure sensors have garnered much attention recently owing to their prospective applications in fields such as structural health monitoring. Capacitive pressure sensors have been extensively researched due to their exceptional features, such as a simple structure, strong repeatability, minimal loss and temperature independence. Inspired by the skin epidermis, we report a high-sensitivity flexible capacitive pressure sensor with a broad detection range comprising a bioinspired spinosum dielectric layer. Using an abrasive paper template, the bioinspired spinosum was fabricated using carbon nanotube/polydimethylsiloxane (CNT/PDMS) composites. It was observed that nanocomposites comprising 1 wt% CNTs had excellent sensing properties. These capacitive pressure sensors allowed them to function at a wider pressure range (~500 kPa) while maintaining sensitivity (0.25 kPa

Indexed as

bioinspired spinosumCNT/PDMS nanocompositeflexible capacitive sensorstires

Identifiers

PMID36234394
PMCPMC9565558

What OpenQuestion holds

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LicenceCC BY
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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.