Extension of the two-layer model to heat transfer coefficient predictions of nanoporous Si thin films
Affiliation
Department of Aerospace and Mechanical Engineering, University of ArizonaIssue Date
2022-07-04
Metadata
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AIP PublishingCitation
Wang, S., Chen, Q., & Hao, Q. (2022). Extension of the two-layer model to heat transfer coefficient predictions of nanoporous Si thin films. Applied Physics Letters, 121(1).Journal
Applied Physics LettersRights
© 2022 Author(s). Published under an exclusive license by AIP Publishing.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Heat exchange between a solid material and the gas environment is critical for the heat dissipation of miniature electronic devices. In this aspect, existing experimental studies focus on non-porous structures such as solid thin films, nanotubes, and wires. In this work, the proposed two-layer model for the heat transfer coefficient (HTC) between a solid sample and the surrounding air is extended to 70-nm-thick nanoporous Si thin films that are patterned with periodic rectangular nanopores having feature sizes of 100-400 nm. The HTC values are extracted using the 3 ω method based on AC self-heating of a suspended sample with better accuracy than steady-state measurements in some studies. The dominance of air conduction in the measured HTCs is confirmed by comparing measurements with varied sample orientations. The two-layer model, developed for nanotubes, is still found to be accurate when the nanoporous film is simply treated as a solid film in the HTC evaluation along with the radiative mean beam length as the characteristic length of the nanoporous film. This finding indicates the potential of increasing HTC by introducing ultra-fine nanoporous patterns, as guided by the two-layer model.Note
12 month embargo; published online: 06 July 2022ISSN
0003-6951EISSN
1077-3118Version
Final published versionSponsors
National Science Foundationae974a485f413a2113503eed53cd6c53
10.1063/5.0099312
