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◆ Ain Shams Engineering Journal2026-03-09· Materials science

Design of ultra-broadband metamaterial solar thermal absorber structure based on Ti-MXene-SiO2-Ag material for heating application with machine learning approach

Meshari Alsharari, Raj Agravat, Khaled Aliqab, Ammar Armghan, S. K. Patel

原始摘要(英文原文)· Original abstract
Renewable energy uses are increasing day by day with various approaches. To collect renewable solar energy using a solar absorber device that is created with various materials with various designs. This study shows an MXene Layer-based Hexagonal Ring-shaped Metamaterial Solar Absorber (MLHRMSA). MLHRMSA is made with MXene and Ti, which is utilised as a resonator. And SiO 2 with Ag was utilised as MLHRMSA substrate and back layer. MXene is a 2-D transition-metal carbide composed of titanium and silver, enabling robust surface plasmon resonance along with extensive absorption over the ultraviolet to far-infrared spectrum. MXene has excellent electrical conductivity and customizable optical characteristics, enabling effective light-matter coupling. The addition of a Ti-based hexagonal resonator adds geometric variation and sharp-edge phenomena that facilitate multi-modal plasmonic resonance. These design characteristics together differentiate this approach from conventional dielectric-metal absorbers and highlight the possibility for incorporation into sophisticated solar-thermal structures. This polarisation-insensitive MLHRMSA’s ultra-wideband absorber has an extensive bandwidth of 440 & 1210 nm. The absorber’s reliability and absorptance were enhanced by the negative metamaterial impact of the MLHRMSA. To effectively optimise the MLHRMSA’s structure and different parameters. Furthermore, Machine Learning (ML) is applied to estimate the absorptance of the MLHRMSA, achieving an optimal R 2 value of 0.99181. This MLHRMSA is used in industrial heaters because of its outstanding absorptance.
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Design of ultra-broadband metamaterial solar thermal absorber structure based on Ti-MXene-SiO2-Ag material for heating application with machine learning approach — 科研速览 Science Skim