ArticleScientific reports2026
Machine learning-optimized graphene-integrated refractory metasurface for broadband solar energy harvesting.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
A graphene-integrated refractory metasurface absorber is proposed for broadband solar thermal energy conversion. Near-unity broadband absorptance across 300-2500 nm is achieved through three concurrent mechanisms: free-space impedance matching, ground-plane-mediated transmission suppression, and multimodal electromagnetic energy dissipation distributed across spectrally coupled plasmonic and dielectric resonant channels. Spectral tunability without structural reconfiguration is demonstrated through electrostatic modulation of the graphene Fermi level, which enables reversible control of the optical sheet conductivity. Alternative material configurations incorporating caesium, gallium arsenide, copper, and strontium are evaluated through full-wave COMSOL Multiphysics simulations under periodic boundary conditions, with assessment of spectral bandwidth, resonance behaviour, and thermal robustness at elevated temperatures. A dielectric substrate thickness of approximately 4.1 μm satisfies the quarter-wavelength Fabry-Perot cavity resonance condition, suppressing mid-infrared radiative emission and reducing parasitic thermal losses. A random forest regression surrogate model trained on 1,200 finite-element simulation samples, with five geometric and material input parameters and 500 estimators, maps the design space with R
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