The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The Classical Nature of Thermal Conduction in Nanofluids
Open accessAbstract
We show that a large set of nanofluid thermal conductivity data falls within the upper and lower Maxwell bounds for homogeneous systems. This indicates that the thermal conductivity of nanofluids is largely dependent on whether the nanoparticles stay dispersed in the base fluid, form large aggregates, or assume a percolating fractal configuration. The experimental data, which are strikingly analogous to those in most solid composites and liquid mixtures, provide strong evidence for the classical nature of thermal conduction in nanofluids.
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.
Integrity signals
Registry
Cites
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.
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
Thermal Conductance of Hydrophilic and Hydrophobic Interfaces
Ge, Z., Cahill, D. G., & Braun, P. V. (2006) · Physical Review Letters · doi:10.1103/physrevlett.96.186101 · Cited by 455
Cited by
Qiu, L., Zhu, N., Feng, Y., Michaelides, E. E., Żyła, G., Jing, D., Zhang, X., Norris, P. M., Markides, C. N., & Mahian, O. (2020) · Physics Reports · doi:10.1016/j.physrep.2019.12.001 · Cited by 523
Heat transfer enhancement by magnetic nanofluids—A review
Nkurikiyimfura, I., Wang, Y., & Pan, Z. (2013) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.12.039 · Cited by 422
Thermal properties of nanofluids
Philip, J., & Shima, P. D. (2012) · Advances in Colloid and Interface Science · doi:10.1016/j.cis.2012.08.001 · Cited by 298
Nanoscale heat transfer – from computation to experiment
Luo, T., & Chen, G. (2013) · Physical Chemistry Chemical Physics · doi:10.1039/c2cp43771f · Cited by 273
Metadata
- Authors
- Eapen, J., Rusconi, R., Piazza, R., & Yip, S.
- Venue
- Journal of Heat Transfer
- Publisher
- ASME International
- Published
- 2010-07-23
- Volume
- 132
- Issue
- 10
- ISSN
- 0022-1481 (ISSN-L)
- 1528-8943
- OpenAlex
- W2094026710
- Cited by
- 246
- References
- 1
- FWCI
- 12.01
- 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: The Classical Nature of Thermal Conduction in Nanofluids. https://app.cochat.ai/papers/10.1115/1.4001304
- The paper
- Eapen, J., Rusconi, R., Piazza, R., & Yip, S. (2010). The Classical Nature of Thermal Conduction in Nanofluids. Journal of Heat Transfer. https://doi.org/10.1115/1.4001304
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.
