The efficiency of traditional solar cells is constrained due to the Shockley-Queisser limit, to circumvent this theoretical limit, the concept of solar thermophotovoltaics (STPVs) has been introduced. The typical design...
A perfect absorber in the visible-infrared regime maintaining its performance at elevated temperatures and under a harsh environment is needed for energy harvesting using solar-thermophotovoltaic (STPV) systems. A near-perfect metasurface absorber based on lossy refractory metal nitride, zirconium-nitride (ZrN), having a melting-point of 2,980°C, is presented. The numerically proposed design with metal-insulator-metal configuration exhibits an average of > 95% for 400-800 nm and 86% for 280-2200 nm. High absorption is attributed to magnetic resonance leading to free-space impedance matching. The subwavelength structure is polarization- and angle-insensitive and is highly tolerant to fabrication imperfections. An emitter is optimized for bandgap energy ranging from 0.7 eV-1.9 eV.
Flat optics have become capable of achieving unprecedented functionalities through electromagnetic (EM) wave manipulation by employing the metasurfaces. The most crucial part in the design of metasurface is the selection the constitutive component i.e. the meta-atom's material and structure so that it exhibits the precise operation as per the desired application. The unit-cell design calls for an iterative loop of simulations in order to explore the EM responses for intended operation. In this work, we have studied the absorption response of refractory materials under visible light radiations for their utilization in energy harvesting applications. The absorption response estimation using machine-learning techniques for the materials having very high melting-points, mechanical stabilities and inertness to the atmosphere has been carried out to investigate their performance in the broadband range. The presented regression models incorporate hybrid data format i.e. they simultaneously contend with 3-D and 1-D properties of various shapes of nano-resonators. The images' feature extraction is carried out by employing Singular Value Decomposition. The trained models are potent enough to bypass the repetitive sequence of optimization involved in conventional EM solvers. Additionally, the models are capable of predicting the optimum shape along with structural dimensions of unit-cell. For forward model, the MSEs for training and testing are 1.302×10 -2 and 3.269×10 -2 while R 2 scores are 0.9804 and 0.8764, respectively. The approach applied is so robust that, irrespective of complexity of unit-cell structure is, it serves the purpose of predicting the distinct structure with highest performance while bypassing the time-and computationally-intensive EM simulations.
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