Quantifying the Effect of Nanofilms on Near-Field Radiative Heat Transfer

Abstract

Recent measurements of near-field radiative heat transfer (NFRHT) between objects separated by nanometer-sized vacuum gaps have revealed that thermal radiation at the nanoscale is remarkably distinct from far-field thermal radiation and can exceed the blackbody radiation limit by orders of magnitude. Given the technological relevance of thin films, there remains a significant need to experimentally explore how such films influence NFRHT. Here, we report direct measurements of the thickness-dependence of NFRHT between planar nanofilms of magnesium fluoride (thickness ranging from 20 to 500 nm) performed using microfabricated devices and a custom-developed nanopositioner. These results directly demonstrate for the first time that nanofilms can enhance thermal radiation up to 800-fold above the blackbody limit and are as effective as bulk materials when nanoscale gaps have dimensions smaller than the film thickness. Finally, calculations based on fluctuational electrodynamics show good agreement with the measured gap-size dependence of the heat transfer coefficient for films of all thicknesses and provide physical insight into the observed dependence. The experimental techniques and insights reported here pave the way for systematically exploring novel thin films for near-field thermal and energy systems.

Generative adversarial networks for data-scarce radiative heat transfer applications

“…At such near-field distances, the heat transfer process is enhanced through evanescent waves (photon tunneling), an effect not encompassed by Stefan-Boltzmann's conventional framework. This mechanism governs the near-field radiative heat transfer (NFRHT) when the intervening separations are exceedingly narrow [49][50][51][52]. To further augment NFRHT, numerous strategies have been introduced, with a particular emphasis on exploiting surface modes found within multilayered structures.…”

“…In this work we leverage the robust theoretical models of radiative heat transfer, which have been validated by experimental data across various studies (see, for instance [49][50][51][52]). The fidelity of these models provides a solid foundation for the datasets used to train our GANs, underscoring the reliability of our generated data.…”