Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 241

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Soft, Highly Conductive Nanotube Sponges and Composites with Controlled Compressibility

Gui, Xuchun, Cao, Anyuan, Wei, Jinquan, Li, Hongbian, Jia, Yi, Li, Zhen, Fan, Lili, Wang, Kunlin, Zhu, Hongwei, Wu, Dehai·2010·ACS Nano

Abstract

Porous carbon nanotube networks represent a type of material that can achieve both structural robustness and high flexibility. We demonstrate here controlled synthesis of soft to hard sponges with densities ranging from 5 to 25 mg/cm(3), while retaining a porosity of >99%. The stable sponge-like structure allows excellent compressibility tunable up to 90% volume shrinkage, and the ability to recover most of volume by free expansion. Electrical resistivity of the sponges changes linearly and reversibly after 300 cycles of large-strain compression. Nanotubes forming the three-dimensional scaffold maintain good contact and percolation during large-strain deformation, polymer infiltration, and cross-linking process, suggesting potential applications as strain sensors and conductive nanocomposites.

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/nn100114d

Cites

1 of 1 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 241

Metadata

Authors
Gui, X., Cao, A., Wei, J., Li, H., Jia, Y., Li, Z., Fan, L., Wang, K., Zhu, H., & Wu, D.
Venue
ACS Nano
Published
2010-04-01
Volume
4
Issue
4
Pages
2320-2326
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2054258756
Cited by
241
References
1
FWCI
7.72
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.