Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 381

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Graphene Reinforced Carbon Nanotube Networks for Wearable Strain Sensors

Shi, Jidong, Li, Xinming, Cheng, Huanyu, Liu, Zhuangjian, Zhao, Lingyu, Yang, Tingting, Dai, Zhaohe, Cheng, Zengguang, Shi, Enzheng, Yang, Long, Zhang, Zhong, Cao, Anyuan, Zhu, Hongwei, Fang, Ying·2016·Advanced Functional Materials

Abstract

Transparent, stretchable films of carbon nanotubes (CNTs) have attracted significant attention for applications in flexible electronics, while the lack of structural strength in CNT networks leads to deformation and failure under high mechanical load. In this work, enhancement of the strength and load transfer capabilities of CNT networks by chemical vapor deposition of graphene in the nanotube voids is proposed. The graphene hybridization significantly strengthens the CNT networks, especially at nanotube joints, and enhances their resistance to buckling and bundling under large cyclic strain up to 20%. The hybridized films show linear and reproducible responses to tensile strains, which have been applied in strain sensors to detect human motions with fast response, high sensitivity, and durability.

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 · 10.1002/adfm.201504804

Cites

2 of 2 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 2 references

Cited by

See all 381

Metadata

Authors
Shi, J., Li, X., Cheng, H., Liu, Z., Zhao, L., Yang, T., Dai, Z., Cheng, Z., Shi, E., Yang, L., Zhang, Z., Cao, A., Zhu, H., & Fang, Y.
Venue
Advanced Functional Materials
Publisher
Wiley
Published
2016-02-05
Volume
26
Issue
13
Pages
2078-2084
ISSN
  • 1616-301X (ISSN-L)
  • 1616-3028
OpenAlex
W2297109848
Cited by
381
References
2
FWCI
25.84
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: Graphene Reinforced Carbon Nanotube Networks for Wearable Strain Sensors. https://app.cochat.ai/papers/10.1002/adfm.201504804
The paper
Shi, J., Li, X., Cheng, H., Liu, Z., Zhao, L., Yang, T., Dai, Z., Cheng, Z., Shi, E., Yang, L., Zhang, Z., Cao, A., Zhu, H., & Fang, Y. (2016). Graphene Reinforced Carbon Nanotube Networks for Wearable Strain Sensors. Advanced Functional Materials. https://doi.org/10.1002/adfm.201504804

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.