Evidence map›Paper›PMID 36536055›Full record

ArticleScientific reports2022

An angle-compensating colorimetric strain sensor with wide working range and its fabrication method.

Nguyen Hoang Minh, Kwanoh Kim, Do Hyun Kang, Yeong-Eun Yoo, Jae Sung Yoon

Abstract read
In one paragraph

Article in Scientific reports, 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.

Nguyen Hoang MinhDepartment of Nano Manufacturing Technology, Korea Institute of Machinery and Materials (KIMM), Daejeon, South Korea.
Kwanoh KimDepartment of Nano Manufacturing Technology, Korea Institute of Machinery and Materials (KIMM), Daejeon, South Korea.
Do Hyun KangDepartment of Nano Manufacturing Technology, Korea Institute of Machinery and Materials (KIMM), Daejeon, South Korea.
Yeong-Eun YooDepartment of Nano Manufacturing Technology, Korea Institute of Machinery and Materials (KIMM), Daejeon, South Korea.
Jae Sung YoonDepartment of Nano Manufacturing Technology, Korea Institute of Machinery and Materials (KIMM), Daejeon, South Korea. jaesyoon@kimm.re.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The visual response is one of the most intuitive principles of sensors. Therefore, emission and change of the colors are widely studied for development of chemical, thermal and mechanical sensors. And it is still a challenging issue to fabricate them with a simple working mechanism, high sensitivity, good reliability, and a cost-effective fabrication process. In this study, we propose a mechanical strain sensor, which has 2D photonic crystal structures in nanoscale on stretchable polydimethylsiloxane (PDMS) substrate. Due to the periodic nanostructures, the surface of the sensor produces structural colors. And when it is stretched, the periodicity of the nanostructures changes, which results in the shift of the colors. Multiple nanostructures with different periodicities are integrated on the sensor in order to extend the working range up to 150% with high sensitivity. In addition, reusable and robust molds, which are fabricated by self-assembly of nanoparticles, are used for multiple replications of sensor substrates. Thus, the fabrication process of this study is believed to be potential for possible industrial manufacturing. This study is expected to contribute to strain sensors in the future for the applications of health care, infrastructure monitoring, soft robotics, and wearable devices.

Indexed as

NanoparticlesNanostructuresWearable Electronic DevicesColorimetryReproducibility of Results

Identifiers

PMID36536055
PMCPMC9763495

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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.