Graphene metamaterial solar absorber using Al-TiN-Fe for efficient solar thermal energy conversion and optimization...

Area of Science:

• Materials Science
• Nanotechnology
• Renewable Energy

Background:

• Developing efficient solar absorbers is crucial for renewable energy applications.
• Graphene-based metamaterials offer promising properties for light absorption.

Purpose of the Study:

• To design and analyze a novel graphene-based solar absorber with enhanced absorption capabilities.
• To investigate the effect of material displacement and wavelength configuration on absorption efficiency.
• To explore the potential of machine learning (ML) for accelerating absorber design.

Main Methods:

• Fabrication of a multi-layered absorber using graphene, Aluminum (Al), Titanium Nitride (TiN), and Iron (Fe).
• Simulation and analysis of absorption spectra across ultraviolet (UV), visible (Vis), and near-infrared (NIR) regions.
• Parametric studies and radiation analysis to optimize absorber performance.
• Application of a machine learning (ML) approach to expedite the design process.

Main Results:

• Achieved over 97% absorption in the 1.5-2.5 µm wavelength range (1000 nm bandwidth).
• Demonstrated absorption rates above 95% for a 2000 nm bandwidth (0.5-2.5 µm) and 92.42% for a 2800 nm bandwidth (0.2-3 µm).
• Validated absorption across UV, Vis, and NIR spectral regions.

Conclusions:

• The proposed graphene-based absorber design exhibits high absorption efficiency across a broad spectrum.
• The design is suitable for various multi-solar applications, including water heating, lighting, and electric vehicle charging.
• The integration of ML significantly reduces the time required for absorber development.