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Simulation, Fabrication and Characterization of THz Metamaterial Absorbers
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Deep Learning-Assisted Active Metamaterials with Heat-Enhanced Thermal Transport
Peng Jin1, Liujun Xu2,3, Guoqiang Xu2
1Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory of Micro and Nano Photonic Structures (MOE), Fudan University, Shanghai, 200438, China.
Advanced Materials (Deerfield Beach, Fla.)
|October 23, 2023
Summary
Deep learning powers new heat-enhanced thermal diffusion metamaterials. These adaptive metamaterials automatically adjust thermal functions for advanced applications, overcoming limitations of current self-adaptive materials.
Area of Science:
- Materials Science
- Nanotechnology
- Artificial Intelligence
Background:
- Effective heat management is vital for advanced technologies like radiative cooling and smart wearables.
- Current adaptive metamaterials face challenges including narrow temperature ranges, anisotropy, and manual control.
Purpose of the Study:
- To introduce deep learning-powered, heat-enhanced thermal diffusion metamaterials for self-adaptive thermal management.
- To overcome the limitations of existing adaptive metamaterials, enabling spontaneous and tunable thermal responses.
Main Methods:
- Development of heat-enhanced thermal diffusion metamaterials utilizing deep learning algorithms.
- Simulation and experimental validation of the metamaterials' thermal performance and adaptability.
- Design of two metadevices showcasing on-demand adaptability with isotropic materials.
Main Results:
- Demonstrated spontaneous and continuous adjustment of thermal functions based on ambient temperature.
- Achieved robust thermal performance irrespective of external thermal field direction.
- Validated exceptional performance through both simulations and experiments.
Conclusions:
- The developed metamaterials offer a framework for intelligent thermal diffusion with on-demand adaptability.
- The approach enables isotropic materials with wide working temperatures and spontaneous response.
- This technology has potential for broader applications in dynamic and complex environments.
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