Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 253

The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.

A stretchable and transparent strain sensor based on sandwich-like PDMS/CNTs/PDMS composite containing an ultrathin conductive CNT layer

Chen, Jianwen, Zhu, Yutian, Jiang, Wei·2020·Composites Science and Technology

Abstract

Abstract Stretchable and transparent strain sensors based on conductive polymer composites (CPCs) possess potential applications in a variety of fields, including electronic skin, wearable electronics, and human motion detection. However, it is still a challenge to prepare CPC-based strain sensors with both good transparency and broad sensing range by a low-cost, facile, and scalable fabrication method. Herein, we report a simple spray deposition and transfer method to fabricate a stretchable and transparent strain sensor based on the sandwich-like polydimethylsiloxane (PDMS)/carbon nanotubes (CNTs)/PDMS composite containing with an ultrathin and uniform conductive layer. The fabricated strain sensors exhibit superb stretchability, good optical transparency, and excellent sensing performances, which can detect both the subtle and large strains with outstanding stability and repeatability. For example, the strain sensor based on PDMS/CNTs/PDMS composite (containing 0.16 mg/cm2 CNTs on the conductive layer) presents a good optical transmittance of 53.1% at 550 nm, broad sensing range of over 130%, and the ability to monitor both the subtle motions of facial expressions and the large motions of human joints, which has great potential applications in human motion detection, wearable electronics, and electronic skin.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.compscitech.2019.107938

Cites

38 of 38 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 253

Metadata

Authors
Chen, J., Zhu, Y., & Jiang, W.
Venue
Composites Science and Technology
Publisher
Elsevier BV
Published
2020-01
Volume
186
Pages
107938
ISSN
0266-3538 (ISSN-L)
OpenAlex
W2993470950
Cited by
253
References
38
FWCI
9.70
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.