Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 606

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Thermally Conductive Graphene-Polymer Composites: Size, Percolation, and Synergy Effects

Shtein, Michael, Nadiv, Roey, Buzaglo, Matat, Kahil, Keren, Regev, Oren·2015·Chemistry of Materials

Abstract

The rapidly increasing device densities in electronics dictate the need for efficient thermal management. If successfully exploited, graphene, which possesses extraordinary thermal properties, can be commercially utilized in polymer composites with ultrahigh thermal conductivity (TC). The total potential of graphene to enhance TC, however, is restricted by the large interfacial thermal resistance between the polymer mediated graphene boundaries. We report a facile and scalable dispersion of commercially available graphene nanoplatelets (GnPs) in a polymer matrix, which formed composite with an ultrahigh TC of 12.4 W/m K (vs 0.2 W/m K for neat polymer). This ultrahigh TC was achieved by applying high compression forces during the dispersion that resulted in the closure of gaps between adjacent GnPs with large lateral dimensions and low defect densities. We also found strong evidence for the existence of a thermal percolation threshold. Finally, the addition of electrically insulating boron-nitride nanoparticles to the thermally conductive GnP-polymer composite significantly reduces its electrical conductivity (to avoid short circuit) and synergistically increases the TC. The efficient dispersion of commercially available GnPs in polymer matrix provides the ideal framework for substantial progress toward the large-scale production and commercialization of GnP-based thermally conductive composites.

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/cm504550e

Cites

3 of 3 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 3 references

Cited by

Metadata

Authors
Shtein, M., Nadiv, R., Buzaglo, M., Kahil, K., & Regev, O.
Venue
Chemistry of Materials
Published
2015-03-10
Volume
27
Issue
6
Pages
2100-2106
ISSN
  • 0897-4756 (ISSN-L)
  • 1520-5002
OpenAlex
W2322572553
Cited by
606
References
3
FWCI
21.32
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.