Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 293

The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.

Polyurethane/Cotton/Carbon Nanotubes Core-Spun Yarn as High Reliability Stretchable Strain Sensor for Human Motion Detection

Wang, Zifeng, Huang, Yan, Sun, Jinfeng, Huang, Yang, Hu, Hong, Jiang, Ruijuan, Gai, Weiming, Li, Guangming, Zhi, Chunyi·2016·ACS Applied Materials & Interfaces

Abstract

Smart yarns and textiles are an active field of researches nowadays due to their potential applications in flexible and stretchable electronics, wearable devices, and electronic sensors. Integration of ordinary yarns with conductive fillers renders the composite yarns with new intriguing functions, such as sensation and monitoring of strain and stress. Here we report a low cost scalable fabrication for highly reliable, stretchable, and conductive composite yarn as effective strain sensing material for human motion monitoring. By incorporating highly conductive single-wall carbon nanotubes (SWCNTs) into the elastic cotton/polyurethane (PU) core-spun yarn through a self-designed coating approach, we demonstrated that the yarn is able to detect and monitor the movement of human limbs, such as finger and elbow, and even the wink of eyes. By virtue of the covered structure of the cotton/PU yarn and the reinforcement effect of SWCNTs, the composite yarn can bear up to 300% strain and could be cycled nearly 300,000 times under 40% strain without noticeable breakage. It is promising that this kind of conductive yarn can be integrated into various fabrics and used in future wearable devices and electronic skins.

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 · 10.1021/acsami.6b08207

Cites

29 of 29 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 293

Metadata

Authors
Wang, Z., Huang, Y., Sun, J., Huang, Y., Hu, H., Jiang, R., Gai, W., Li, G., & Zhi, C.
Venue
ACS Applied Materials & Interfaces
Published
2016-09-06
Volume
8
Issue
37
Pages
24837-24843
ISSN
  • 1944-8244 (ISSN-L)
  • 1944-8252
OpenAlex
W2515567869
Cited by
293
References
29
FWCI
16.41
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.