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Updated: May 8, 2026

Simulation, Fabrication and Characterization of THz Metamaterial Absorbers

Simulation, Fabrication and Characterization of THz Metamaterial Absorbers

Published on: December 27, 2012

Graphene-based metasurface solar absorber design with absorption prediction using machine learning.

Juveriya Parmar¹, Shobhit K Patel^2,3, Vijay Katkar⁴

¹Department of Electronics and Communication, Marwadi University, Rajkot, 360003, India.

Scientific Reports

|February 17, 2022

Summary

This study introduces a graphene-based solar absorber with O-shape metasurfaces, achieving high absorption across ultraviolet and visible light. Machine learning accurately predicted performance, paving the way for efficient green energy applications.

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Area of Science:

Materials Science
Nanotechnology
Renewable Energy

Background:

Solar absorbers are crucial for converting solar energy.
Improving solar absorber efficiency requires optimizing light absorption across the solar spectrum.
Graphene-based metasurfaces offer tunable optical properties for advanced absorber designs.

Purpose of the Study:

To design and evaluate a novel graphene-based solar absorber utilizing distinct metasurface geometries.
To enhance solar absorption efficiency by comparing O-shape and L-shape metasurfaces.
To investigate the impact of structural parameters and angle of incidence on absorber performance.

Main Methods:

Fabrication and optical characterization of graphene-based solar absorbers with O-shape and L-shape metasurfaces.
Comparison of absorption spectra against the standard AM 1.5 solar spectral irradiance.
Systematic variation of resonator and substrate thicknesses to optimize absorption.
Development of a 1D-Convolutional Neural Network Regression model for predicting absorption performance.
Analysis of absorption across a wide range of incidence angles.

Main Results:

The O-shape metasurface design demonstrated superior absorption performance compared to the L-shape design.
Maximum absorption was achieved in the ultraviolet and visible spectral ranges.
The absorber exhibited high and stable absorption across a broad range of incidence angles.
Machine learning models accurately predicted absorption values for varied parameters and angles.
Optimized designs showed enhanced absorption by adjusting resonator and substrate thicknesses.

Conclusions:

The proposed graphene-based solar absorber with O-shape metasurfaces is highly effective for solar energy harvesting.
The study validates the use of machine learning for predicting and optimizing solar absorber performance.
This advanced solar absorber design holds significant potential for green energy applications.