Advancing thermal radiation efficiency with hybrid-patterned metasurface emitters in thermophotovoltaic systems

Abstract

In thermophotovoltaic systems, it is essential for emitters to effectively convert thermal energy into thermal radiation photons within the useable wavelengths for photovoltaic cells. Metasurface emitters enable broadband emission to efficiently and accurately match photovoltaic cells because of their complex optical properties and adjustable resonance behavior. In this study, a hybrid-patterned metasurface emitter was designed using an inverse neural network and optimized by a genetic algorithm to efficiently match InGaAs cells with different bandgaps. The designed hybrid-patterned metasurface emitter exhibits 90% high in-band emission with near-zero out-of-band emission, and it is angle insensitive and polarization independent. The optical performance of the emitter and the output performance of the radioisotope thermophotovoltaic system were studied at temperatures ranging from 700 K to 1300 K. At 1300 K, the emitter demonstrates an effective spectral efficiency of 69.84%. The radioisotope thermophotovoltaic system with a 0.56 eV InGaAs cell has a maximum output power density of 1.16 W/cm² and an energy conversion efficiency of 20.77%. The mechanism of selective emission was elucidated. Finally, the hybrid-patterned metasurface emitter was fabricated by micro-nano fabrication technology, and its performance was studied. The optimization and fabrication of the hybrid-patterned metasurface emitter provide an important theoretical foundation and practical guidance for enhancing thermal radiation efficiency.