Daytime
radiative coolers are used to pump excess heat from a target object
into a cold exterior space without energy consumption. Radiative coolers
have become attractive cooling options. In this study, a daytime radiative
cooler was designed to have a selective emissive property of electromagnetic
waves in the atmospheric transparency window of 8–13 μm
and preserve low solar absorption for enhancing radiative cooling
performance. The proposed daytime radiative cooler has a simple multilayer
structure of inorganic materials, namely, Al2O3, Si3N4, and SiO2, and exhibits
high emission in the 8–13 μm region. Through a particle
swarm optimization method, which is based on an evolutionary algorithm,
the stacking sequence and thickness of each layer were optimized to
maximize emissions in the 8–13 μm region and minimize
the cooling temperature. The average value of emissivity of the fabricated
inorganic radiative cooler in the 8–13 μm range was 87%,
and its average absorptivity in the solar spectral region (0.3–2.5
μm) was 5.2%. The fabricated inorganic radiative cooler was
experimentally applied for daytime radiative cooling. The inorganic
radiative cooler can reduce the temperature by up to 8.2 °C compared
to the inner ambient temperature during the daytime under direct sunlight.
Anisotropic small structures found throughout living
nature have unique functionalities as seen by Gecko lizards. Here,
we present a simple yet programmable method for fabricating anisotropic,
submicrometer-sized bent pillar structures using photoreconfiguration
of an azopolymer. A slant irradiation of a p-polarized light on the
pillar structure of an azopolymer simply results in a bent pillar
structure. By combining the field-gradient effect and directionality
of photofluidization, control of the bending shape and the curvature
is achieved. With the bent pillar patterned surface, anisotropic wetting
and directional adhesion are demonstrated. Moreover, the bent pillar
structures can be transferred to other polymers, highlighting the
practical importance of this method. We believe that this pragmatic
method to fabricate bent pillars can be used in a reliable manner
for many applications requiring the systematic variation of a bent
pillar structure.
In using nanostructures to design solar thermal absorbers, computational methods, such as rigorous coupled-wave analysis and the finite-difference time-domain method, are often employed to simulate light-structure interactions in the solar spectrum. However, those methods require heavy computational resources and CPU time. In this study, using a state-of-the-art modeling technique, i.e., deep learning, we demonstrate significant reduction of computational costs during the optimization processes. To minimize the number of samples obtained by actual simulation, only regulated amounts are prepared and used as a data set to train the deep neural network (DNN) model. Convergence of the constructed DNN model is carefully examined. Moreover, several analyses utilizing an evolutionary algorithm, which require a remarkable number of performance calculations, are performed using the trained DNN model. We show that deep learning effectively reduces the actual simulation counts compared to the case of a design process without a neural network model. Finally, the proposed solar thermal absorber is fabricated and its absorption performance is characterized.
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