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We demonstrate, both by experiments and by data informatics, an alternative strategy to achieve ultralow thermal conductivity in a dense solid. The interfacial thermal resistance (ITR) prediction of the machine learning model is implemented in a nanoscale field. The size dependence on ITR is considered and applied to the interface design of nanostructuring. The Bi/Si system was selected from 2025 kinds of interfaces through the interface thermal resistance prediction model by machine learning. The BiSi nanocomposite, which was composed of crystallized Bi and amorphous Si, was designed with various parameters by a laboratory-built combinatorial sputtering system. Electrically conductive, thermally insulating BiSi nanocomposites were reported for the first time and have a thermal conductivity as low as 0.16 W m–1 K–1. The ultralow thermal conductivity is attributed to the high ratio between the interfacial surface area and the volume because of the small Bi particle size and high Si/Bi atomic ratio. By introducing the informatics method, the potential candidates can be discovered and realized for thermoelectric applications.

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• In Copyright
  This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS APPLIED NANO MATERIALS, copyright © 2018 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acsanm.8b00575.

Keyword: thermal insulating thin film, interface thermal resistance, interface design, nanostructuring, nanocomposite, machine learning

Date published: 2018-07-27

Publisher: American Chemical Society (ACS)

Journal:

• ACS APPLIED NANO MATERIALS (ISSN: 25740970) vol. 1 issue. 7 p. 3355-3363

Funding:

Manuscript type: Author's version (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.4194

First published URL: https://doi.org/10.1021/acsanm.8b00575

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Updated at: 2024-01-05 22:12:22 +0900

Published on MDR: 2023-06-26 20:15:56 +0900