Machine learning-optimized graphene-integrated refractory metasurface for broadband solar energy harvesting

Hussein A. Elsayed; Emad Solouma; Jacob Wekalao; Ahmed Mehaney; Mohammed Messaoudi; Amuthakkannan Rajakannu; Abdelrahman M. Rabie; Wail Al Zoubi

doi:10.1038/s41598-026-51399-w

May 4, 2026Scientific ReportsOpen Access

Machine learning-optimized graphene-integrated refractory metasurface for broadband solar energy harvesting

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Key Points

The aim is to develop a metasurface optimized for solar energy harvesting using machine learning techniques.
Optimization of a graphene-integrated refractory metasurface configuration using machine learning algorithms.
Evaluation of the metasurface's absorptance and spectral performance under near normal incidence illumination.
Achieved a peak absorptance of 99.99% in the optimized configuration.
Attained a broadband solar-weighted average absorptance of 98.6%.

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Implication

This research demonstrates enhanced absorptance in a broadband graphene metasurface, suggesting improved solar energy harvesting efficiency.

Abstract

approximately 0.63 near normal incidence, where overlapping resonances increase spectral complexity. The optimised configuration achieves a peak absorptance of 99.99% and a broadband solar-weighted average of 98.6%.

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Elsayed et al. (2026) studied this question.

synapsesocial.com/papers/69fbe2f2164b5133a91a2424 https://doi.org/10.1038/s41598-026-51399-w

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Machine learning-optimized graphene-integrated refractory metasurface for broadband solar energy harvesting

Key Points

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Implication

Abstract

Discussion

Cite This Study

Machine learning-optimized graphene-integrated refractory metasurface for broadband solar energy harvesting

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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