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Enhanced Thermal Conductivity through the Development of Nanofluids

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Eastman, J. A., Choi, U. S., Li, S., Thompson, L. J., Lee, S.·1996·MRS Proceedings

Abstract

Low thermal conductivity is a primary limitation in the development of energy-efficient heat transfer fluids required in many industrial applications. To overcome this limitation, a new class of heat transfer fluids is being developed by suspending nanocry stalline particles in liquids such as water or oil. The resulting “nanofluids” possess extremely high thermal conductivities compared to the liquids without dispersed nanocrystalline particles. For example, 5 volume % of nanocrystalline copper oxide particles suspended in water results in an improvement in thermal conductivity of almost 60% compared to water without nanoparticles. Excellent suspension properties are also observed, with no significant settling of nanocrystalline oxide particles occurring in stationary fluids over time periods longer than several days. Direct evaporation of Cu nano-particles into pump oil results in similar improvements in thermal conductivity compared to oxide-in-water systems, but importantly, requires far smaller concentrations of dispersed nanocrystalline powder.

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Metadata

Authors
Eastman, J. A., Choi, U. S., Li, S., Thompson, L. J., & Lee, S.
Venue
MRS Proceedings
Published
1996
Volume
457
ISSN
  • 0272-9172 (ISSN-L)
  • 1946-4274
OpenAlex
W2098215695
Cited by
1,140
References
0
FWCI
1.62
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Enhanced Thermal Conductivity through the Development of Nanofluids. https://app.cochat.ai/papers/10.1557/proc-457-3
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Eastman, J. A., Choi, U. S., Li, S., Thompson, L. J., & Lee, S. (1996). Enhanced Thermal Conductivity through the Development of Nanofluids. MRS Proceedings. https://doi.org/10.1557/proc-457-3

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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.