Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 276

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

Highly Thermal Conductive Copper Nanowire Composites with Ultralow Loading: Toward Applications as Thermal Interface Materials

Wang, Shouling, Cheng, Yin, Wang, Ranran, Sun, Jing, Gao, Lian·2014·ACS Applied Materials & Interfaces

Abstract

Thermal interface materials (TIMs) are of ever-rising importance with the development of modern microelectronic devices. However, traditional TIMs exhibit low thermal conductivity even at high loading fractions. The use of high-aspect-ratio material is beneficial to achieve low percolation threshold for nanocomposites. In this work, single crystalline copper nanowires with large aspect ratio were used as filling materials for the first time. A thermal conductivity of 2.46 W/mK was obtained at an ultralow loading fraction, ∼0.9 vol %, which was enhanced by 1350% compared with plain matrix. Such an excellent performance makes copper nanowires attractive fillers for high-performance TIMs.

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/am500009p

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 276

Metadata

Authors
Wang, S., Cheng, Y., Wang, R., Sun, J., & Gao, L.
Venue
ACS Applied Materials & Interfaces
Published
2014-04-21
Volume
6
Issue
9
Pages
6481-6486
ISSN
  • 1944-8244 (ISSN-L)
  • 1944-8252
OpenAlex
W2319222007
Cited by
276
References
2
FWCI
8.49
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: Highly Thermal Conductive Copper Nanowire Composites with Ultralow Loading: Toward Applications as Thermal Interface Materials. https://app.cochat.ai/papers/10.1021/am500009p
The paper
Wang, S., Cheng, Y., Wang, R., Sun, J., & Gao, L. (2014). Highly Thermal Conductive Copper Nanowire Composites with Ultralow Loading: Toward Applications as Thermal Interface Materials. ACS Applied Materials & Interfaces. https://doi.org/10.1021/am500009p

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.