Polarization-driven thermal emission regulator based on self-aligned GST nanocolumns

Dynamically manipulating long-wave infrared polarized thermal radiation by a vanadium dioxide metasurface

Dynamically manipulating the spectra and polarization properties of thermal radiation is the key to counter an infrared polarization imaging system (IPIS) under the different background environments. In this Letter, we propose a phase-change metasurface thermal emitter (PCMTE) composed of vanadium dioxide (VO2) dipole antenna arrays to dynamically manipulate polarized radiation spectra in the long-wave infrared (LWIR) region of 8-14 µm. During the thermally induced and reversible insulator-to-metal transition (IMT) in VO2, by simulating the LWIR images at different polarization angles for the PCMTE and background plates, the PCMTE can realize dynamically tunable LWIR camouflage; then, their degree of linear polarization (DoLP) can be calculated, which can demonstrate that the PCMTE can also achieve dynamically tunable LWIR polarization camouflage at the specific radiation angles and backgrounds. Our proposed PCMTE provides an effective scheme for adaptive IR polarization camouflage.

Materials in Radiative Cooling Technologies

Radiative cooling (RC) is a carbon-neutral cooling technology that utilizes thermal radiation to dissipate heat from the Earth's surface to the cold outer space. Research in the field of RC has garnered increasing interest from both academia and industry due to its potential to drive sustainable economic and environmental benefits to human society by reducing energy consumption and greenhouse gas emissions from conventional cooling systems. Materials innovation is the key to fully exploit the potential of RC. This review aims to elucidate the materials development with a focus on the design strategy including their intrinsic properties, structural formations, and performance improvement. The main types of RC materials, i.e., static-homogeneous, static-composite, dynamic, and multifunctional materials, are systematically overviewed. Future trends, possible challenges, and potential solutions are presented with perspectives in the concluding part, aiming to provide a roadmap for the future development of advanced RC materials.

Laser Dynamic Control of the Thermal Emissivity of a Planar Cavity Structure Based on a Phase-Change Material

Tailoring the thermal emission of a material in the long-wave infrared (IR) range of 8-13 μm is crucial for many IR-adaptive applications, including personal thermal management, IR camouflage, and radiative cooling. Although various materials and surface structures have been proposed for these purposes, space-selective and dynamic control of their emissivity is challenging. In this study, we present a planar surface cavity structure consisting of a Ge2Sb2Te5 (GST) film on top of a thin metal reflector to modulate its emissivity by using an ultraviolet laser beam. A laser-induced phase change in GST allowed for the local control of emissivity. The average emissivity in the long-wave IR range was tunable from 0.15 to 0.77 simply by changing the laser energy deposited on the GST film. This enabled the laser printing of high-contrast emissivity patterns, which were erasable by subsequent thermal annealing. Emissivity-modulated GST cavities could be fabricated on not only rigid substrates but also flexible plastic substrates such as polyimide. The GST surface cavity was highly flexible and remained stable upon repeated bending to a curvature radius of 0.5 cm. This study provides a promising route for realizing scalable and flexible thermal emitters with tunable surface emissivity.

Full-Control and Switching of Optical Fano Resonance by Continuum State Engineering

Fano resonance, known for its unique asymmetric line shape, has gained significant attention in photonics, particularly in sensing applications. However, it remains difficult to achieve controllable Fano parameters with a simple geometric structure. Here, a novel approach of using a thin-film optical Fano resonator with a porous layer to generate entire spectral shapes from quasi-Lorentzian to Lorentzian to Fano is proposed and experimentally demonstrated. The glancing angle deposition technique is utilized to create a polarization-dependent Fano resonator. By altering the linear polarization between s- and p-polarization, a switchable Fano device between quasi-Lorentz state and negative Fano state is demonstrated. This change in spectral shape is advantageous for detecting materials with a low-refractive index. A bio-particle sensing experiment is conducted that demonstrates an enhanced signal-to-noise ratio and prediction accuracy. Finally, the challenge of optimizing the film-based Fano resonator due to intricate interplay among numerous parameters, including layer thicknesses, porosity, and materials selection, is addressed. The inverse design tool is developed based on a multilayer perceptron model that allows fast computation for all ranges of Fano parameters. The method provides improved accuracy of the mean validation factor (MVF = 0.07, q-q') compared to the conventional exhaustive enumeration method (MVF = 0.37).

Machine learning enabled rational design for dynamic thermal emitters with phase change materials

Dynamic thermal emitters have attracted considerable attention due to their potential in widespread applications such as radiative cooling, thermal switching, and adaptive camouflage. However, the state-of-art performances of dynamic emitters are still far below expectations. Here, customized to the special and stringent requirement of dynamic emitters, a neural network model is developed to effectively bridge the structural and spectral spaces and further realizes the inverse design with coupling to genetic algorithms, which considers the broadband spectral responses in different phase-states and utilizes comprehensive measures to ensure the modeling accuracy and computational speed. Besides achieving an outstanding emittance tunability of 0.8, the physics and empirical rules have also been mined qualitatively through decision trees and gradient analyses. The study demonstrates the feasibility of using machine learning to obtain the near-perfect performance of dynamic emitters, as well as guiding the design of other thermal and photonic nanostructures with multifunctions.