What does this research mean for the field?

A machine learning-optimized, hierarchically nested graphene-integrated refractory metasurface achieves an average broadband solar absorptance of 98.58% across the 0.20–3.0 μm spectrum, driven primarily by impedance matching. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Design and optimize a hierarchically nested graphene-integrated refractory metasurface absorber for broadband solar-thermal energy conversion across the 0.20–3.0 μm spectral range.

September 14, 2026Energy ReportsOpen Access

Machine learning-optimized graphene-integrated refractory metasurface based absorber for broadband solar thermal energy conversion

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Authors

AAAmmar ArmghanJouf University KAKhaled AliqabJouf University MAMeshari AlsharariJouf University

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Implication

Simulation study demonstrates 98.58% broadband solar absorption in a refractory graphene metasurface, suggesting high utility for concentrated solar power.

Key Points

Design and optimize a hierarchically nested graphene-integrated refractory metasurface absorber for broadband solar-thermal energy conversion across the 0.20–3.0 μm spectral range.
Simulated a five-tier concentric resonator structure (strontium, caesium, GaAs/InAs, tungsten, and Fe₃O₄) on a gold ground plane with a 0.34-nm monolayer graphene interface using COMSOL Multiphysics v6.2.
Verified impedance matching via the Nicholson–Ross–Weir method and trained a Random Forest surrogate model on 680 full-wave simulations using an 80/20 train-test split and 5-fold cross-validation.
Achieved an average absorptance of 98.58% across the 0.20–3.0 μm range, with peak absorption exceeding 99.99% at 240 nm, 1267 nm, and 2897 nm.
Maintained wide-angle stability with TM absorption exceeding 96.99% at 50° and 89.41% at 60°, and TE absorption remaining above 99.48% up to 30°.
Demonstrated that the Random Forest model predicted full 512-point absorption spectra with an R² > 0.95 based on graphene thickness and incidence angle, outperforming Gradient Boosting (0.961), ANN (0.943), SVR (0.912), and Linear Regression (0.731).

Cite This Study

Armghan et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b3fc0926e14a848b359c https://doi.org/10.1016/j.egyr.2026.109660

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Implication

Simulation study demonstrates 98.58% broadband solar absorption in a refractory graphene metasurface, suggesting high utility for concentrated solar power.

Machine learning-optimized graphene-integrated refractory metasurface based absorber for broadband solar thermal energy conversion

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Implication

Key Points

Cite This Study

Machine learning-optimized graphene-integrated refractory metasurface based absorber for broadband solar thermal energy conversion

Discussion

Member takes

Implication

Key Points

Cite This Study

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