Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 261

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Highly sensitive electronic whiskers based on patterned carbon nanotube and silver nanoparticle composite films

Open access
Takei, Kuniharu, Yu, Zhibin, Zheng, Maxwell, Ota, Hiroki, Takahashi, Toshitake, Javey, Ali·2014·Proceedings of the National Academy of Sciences

Abstract

Significance: Whiskers are hairlike tactile sensors used by certain mammals and insects to monitor wind and navigate around local obstacles. Here, we demonstrate artificial electronic whiskers that can respond to pressures as low as 1 Pa with high sensitivity. The active component is based on composites of carbon nanotubes and silver nanoparticles that are painted on high-aspect-ratio fibers. The resistivity of the composite films is highly sensitive to mechanical strain and can be readily tuned by changing the composition ratio of the components. As a proof of concept, arrays of electronic whiskers are fabricated for real-time two- and three-dimensional gas-flow mapping with high accuracy. This work may enable a wide range of applications in advanced robotics and human–machine interfacing.

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 · 10.1073/pnas.1317920111

Cites

4 of 4 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 4 references

Cited by

See all 261

Metadata

Authors
Takei, K., Yu, Z., Zheng, M., Ota, H., Takahashi, T., & Javey, A.
Venue
Proceedings of the National Academy of Sciences
Published
2014-01-21
Volume
111
Issue
5
Pages
1703-1707
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W1964390379
Cited by
261
References
4
FWCI
13.41
Type
journal article
Language
en
Open access
green

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: Highly sensitive electronic whiskers based on patterned carbon nanotube and silver nanoparticle composite films. https://app.cochat.ai/papers/10.1073/pnas.1317920111
The paper
Takei, K., Yu, Z., Zheng, M., Ota, H., Takahashi, T., & Javey, A. (2014). Highly sensitive electronic whiskers based on patterned carbon nanotube and silver nanoparticle composite films. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1317920111

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.