Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 411

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

Scalable fabrication of high-performance and flexible graphene strain sensors

Tian, He, Shu, Yi, Cui, Ya-Long, Mi, Wen-Tian, Yang, Yi, Xie, Dan, Ren, Tian-Ling·2014·Nanoscale

Abstract

Graphene strain sensors have promising prospects of applications in detecting human motion. However, the shortage of graphene growth and patterning techniques has become a challenging issue hindering the application of graphene strain sensors. Therefore, we propose wafer-scale flexible strain sensors with high-performance, which can be fabricated in one-step laser scribing. The graphene films could be obtained by directly reducing graphene oxide film in a Light-Scribe DVD burner. The gauge factor (GF) of the graphene strain sensor (10 mm × 10 mm square) is 0.11. In order to enhance the GF further, graphene micro-ribbons (20 μm width, 0.6 mm long) has been used as strain sensors, of which the GF is up to 9.49. The devices may conform to various application requirements, such as high GF for low-strain applications and low GF for high deformation applications. The work indicates that laser scribed flexible graphene strain sensors could be widely used in medical-sensing, bio-sensing, artificial skin and many other areas.

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 · 10.1039/c3nr04521h

Cites

24 of 24 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

Show all 24 references

Cited by

See all 411

Metadata

Authors
Tian, H., Shu, Y., Cui, Y.-L., Mi, W.-T., Yang, Y., Xie, D., & Ren, T.-L.
Venue
Nanoscale
Published
2014
Volume
6
Issue
2
Pages
699-705
ISSN
  • 2040-3364 (ISSN-L)
  • 2040-3372
OpenAlex
W2025487006
Cited by
411
References
24
FWCI
12.40
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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Scalable fabrication of high-performance and flexible graphene strain sensors. https://app.cochat.ai/papers/10.1039/c3nr04521h
The paper
Tian, H., Shu, Y., Cui, Y.-L., Mi, W.-T., Yang, Y., Xie, D., & Ren, T.-L. (2014). Scalable fabrication of high-performance and flexible graphene strain sensors. Nanoscale. https://doi.org/10.1039/c3nr04521h

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.