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Machine learning optimized efficient graphene-based ultra-broadband solar absorber for solar thermal applications
Meshari Alsharari1, Bo Bo Han2, Shobhit K Patel3
1Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia.
Scientific Reports
|December 3, 2024
Summary
This study presents an ultra-broadband graphene absorber achieving over 98% absorption across a 1500 nm bandwidth. The novel design, optimized with machine learning, efficiently captures solar energy in visible, near-infrared, and ultraviolet spectrums.
Area of Science:
- Materials Science
- Nanotechnology
- Optics
Background:
- Graphene's unique properties make it a promising material for advanced optical applications.
- Developing efficient broadband absorbers is crucial for solar energy harvesting and optoelectronic devices.
Purpose of the Study:
- To design and investigate an ultra-broadband graphene absorber structure.
- To enhance solar absorption across visible, near-infrared, and ultraviolet regions.
- To optimize the absorber design using machine learning algorithms.
Main Methods:
- A resonator design based on the Al-AlSb-Cr structure with an inserted graphene layer was developed.
- The structure's absorption performance was analyzed across a wide spectral range.
- Machine learning algorithms were employed for design optimization.
Main Results:
- The graphene absorber achieved over 98% absorption for a 1500 nm bandwidth.
- An overall absorption of 93.68% was recorded across a 2800 nm bandwidth.
- The optimized design demonstrated superior performance compared to existing systems.
Conclusions:
- The novel graphene absorber exhibits excellent broadband absorption capabilities.
- The design is suitable for applications in solar energy harvesting and photovoltaic devices.
- The integration of machine learning significantly improved the absorber's performance.
