Mechanisms for enhancing interfacial phonon thermal transport by large-size nanostructures.

Employing nanostructures has been experimentally demonstrated to be an effective way of enhancing the phonon thermal transport across solid-solid interfaces, whereas the strengthening mechanism by large-size nanostructures is still unclear. In this paper, a novel theoretical method for simulating the heat transfer characteristics of the solid-solid contact interface containing large-size nanostructures is developed by combining the lattice Boltzmann method and molecular dynamics. The phonon transport features of the planar interface and the nanostructured ones are compared. The effects of the nanostructure shape and size on the interfacial phonon thermal transport are investigated, and mechanisms for enhancing interfacial phonon thermal transport by large-size nanostructures are revealed. The results show that the phonon transport at the large-size nanostructured interface is distributed regionally and has a pronounced directionality. The thermal transport enhancement of the large-size nanostructured interface is primarily achieved by increasing the interfacial contact area with respect to the planar interface, which increases the probability of phonon scattering at the interface and forms a thermal conduction pathway. The interfacial thermal transfer enhancement of large-size nanostructures is also influenced by the interfacial shape and the ballistic transport effect. There exist the optimal shape and size of the nanostructures to maximize the thermal transport across the solid-solid contact interface.

Daytime radiative cooling multilayer films designed by a machine learning method and genetic algorithm.

Recently, there has been growing interest and attention towards daytime radiative cooling. This cooling technology is considered a potentially significant alternative to traditional cooling methods because of its neither energy consumption nor harmful gas emission during operation. In this paper, a daytime radiative cooling emitter (DRCE) consisting of polydimethylsiloxane, silicon dioxide, and aluminum nitride from top to bottom on a silver-silicon substrate was designed by a machine learning method (MLM) and genetic algorithm to achieve daytime radiative cooling. The optimal DRCE had 94.43% average total hemispherical emissivity in the atmospheric window wavelength band and 98.25% average total hemispherical reflectivity in the solar radiation wavelength band. When the ambient temperature was 30°C, and the power of solar radiation was about 900W/m 2, the net cooling power of the optimal DRCE could achieve 140.38W/m 2. The steady-state temperature of that could be approximately 9.08°C lower than the ambient temperature. This paper provides a general research strategy for MLM-driven design of DRCE.