Efficient and Scalable Thermal Radiation Management for Large-Scale Thermophotovoltaic Applications.

Abstract

Thermophotovoltaics (TPVs) convert radiative heat to electricity and are promising in applications such as waste heat recovery and grid-scale energy storage. State-of-the-art TPV energy conversion promoting devices, back surface reflectors, improve the efficiency in the laboratory but experience significant efficiency drop due to out-of-band (OOB) parasitic absorptions in cell arrays. Here, we design spectral management prior to the cell, which has exceptional tolerance to the OOB absorptions, offering significant advantages for large-scale TPV applications. We refresh the spectral management design using machine learning, introducing an efficient and scalable filter with only 6 lithography-free layers. The device experimentally promotes the commercial GaSb cell efficiency to 20.6 ± 0.13% at 1441 °C with an estimated efficiency of 32.99% when incorporated with the GaInAs cell. Besides, the trade-off among efficiency, scalability, and large-scale compatibility distinguishes this device from previous ones; thus, it is expected to expedite large-scale applications of TPV technology.