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Optimization of Broadband Solar Metamaterial Absorber Based on Deep Neural Network

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Abstract

We propose a broadband solar metamaterial absorber based on a Ti-SiO2-Ti-SiO2-Fe2O3 five-layer configuration. The optimized parameters of the configuration are obtained from the deep neural network with Keras in Python. The average absorptance of the absorber can achieve 98.70% in the wavelength range from 170 nm to 900 nm under the normal incidence. The absorber is not sensitive to the incident angle of the polarized light. When the incident angle of light is not greater than 40°, the average absorptance of the absorber can reach over 90% for both x- and y- polarized lights. In addition, different polarization angles in the range of 0–90° have almost no effect on the absorptance of the absorber. The proposed absorber has potential applications in many fields, such as solar energy absorption, ultraviolet protection and near-infrared detection.

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Data Availability

The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. School of Electronic Engineering, Heilongjiang University, No. 74 Xuefu Road, Harbin, 150080, China

    Yongxin Gai, Sheng Zhou & Guoqiang Lan

  2. Heilongjiang Provincial Key Laboratory of Micro-nano Sensitive Devices and Systems, Heilongjiang University, Harbin, 150080, China

    Guoqiang Lan

Authors
  1. Yongxin Gai
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  2. Sheng Zhou
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Contributions

Yongxin Gai performed the required simulations of the metamaterial absorber structure with associated potentials and wrote the original manuscript. Sheng Zhou Contributed to the verification results. Guoqiang Lan reviewed and edited the manuscript. All authors read and agreed to the final version of the manuscript.

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Correspondence to Guoqiang Lan.

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Gai, Y., Zhou, S. & Lan, G. Optimization of Broadband Solar Metamaterial Absorber Based on Deep Neural Network. Plasmonics (2024). https://doi.org/10.1007/s11468-024-02371-9

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