Graphene metamaterial solar absorber using Al-TiN-Fe for efficient solar thermal energy conversion and optimization using machine learning
Abstract The contributed absorber design in graphene addition with the displacement of three materials for resonator design in Aluminum (Al), the middle substrate position with Titanium nitride (TiN), and the ground layer deposition by Iron (Fe) respectively. For the absorption validation highlight,...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80485-0 |
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| author | Khaled Aliqab Bo Bo Han Om Prakash Kumar Meshari Alsharari Ammar Armghan Shobhit K. Patel |
| author_facet | Khaled Aliqab Bo Bo Han Om Prakash Kumar Meshari Alsharari Ammar Armghan Shobhit K. Patel |
| author_sort | Khaled Aliqab |
| collection | DOAJ |
| description | Abstract The contributed absorber design in graphene addition with the displacement of three materials for resonator design in Aluminum (Al), the middle substrate position with Titanium nitride (TiN), and the ground layer deposition by Iron (Fe) respectively. For the absorption validation highlight, the best four absorption wavelengths (µm) of 0.29, 0.58, 1, and 2 are also selected to indicate the changes in radiation outputs for every observation. With the displacement of wavelength and bandwidth configuration, the current absorber is observed at 97.32% (more than 97%) for 1.5–2.5 µm wavelength range (1000 nm bandwidth) and above 95% rate (95.38%) is displayed by the 2000 nm bandwidth due to 0.5 and 2.5 µm wavelength separation. The 2800 nm band rate demonstration by 0.2–3 µm wavelength separation verifies 92.42%. For every analysis in this work, the output radiation is shown in ultraviolet region (UV), visible spectrum (Vis), and near-infrared (NIR) area respectively. In the following distribution of the current absorber, the design development in lithography and step-by-step, parametric assignment and AM configuration, radiation analysis for each parameter changes in graph presentation and conclusion. Additionally, the ML approach is applied to reduce the time required in the study. The current solar absorber in the new design can be generated for the multi-solar purposes of water heating, lighting, ventilation, charging for electronic devices, and electric vehicle transportation. |
| format | Article |
| id | doaj-art-d065b9e948214de88af3533861f32d1f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-d065b9e948214de88af3533861f32d1f2025-01-05T12:28:11ZengNature PortfolioScientific Reports2045-23222024-12-0114111410.1038/s41598-024-80485-0Graphene metamaterial solar absorber using Al-TiN-Fe for efficient solar thermal energy conversion and optimization using machine learningKhaled Aliqab0Bo Bo Han1Om Prakash Kumar2Meshari Alsharari3Ammar Armghan4Shobhit K. Patel5Department of Electrical Engineering, College of Engineering, Jouf UniversityDepartment of Information and Communication Technology, Marwadi UniversityDepartment of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationDepartment of Electrical Engineering, College of Engineering, Jouf UniversityDepartment of Electrical Engineering, College of Engineering, Jouf UniversityDepartment of Computer Engineering, Marwadi UniversityAbstract The contributed absorber design in graphene addition with the displacement of three materials for resonator design in Aluminum (Al), the middle substrate position with Titanium nitride (TiN), and the ground layer deposition by Iron (Fe) respectively. For the absorption validation highlight, the best four absorption wavelengths (µm) of 0.29, 0.58, 1, and 2 are also selected to indicate the changes in radiation outputs for every observation. With the displacement of wavelength and bandwidth configuration, the current absorber is observed at 97.32% (more than 97%) for 1.5–2.5 µm wavelength range (1000 nm bandwidth) and above 95% rate (95.38%) is displayed by the 2000 nm bandwidth due to 0.5 and 2.5 µm wavelength separation. The 2800 nm band rate demonstration by 0.2–3 µm wavelength separation verifies 92.42%. For every analysis in this work, the output radiation is shown in ultraviolet region (UV), visible spectrum (Vis), and near-infrared (NIR) area respectively. In the following distribution of the current absorber, the design development in lithography and step-by-step, parametric assignment and AM configuration, radiation analysis for each parameter changes in graph presentation and conclusion. Additionally, the ML approach is applied to reduce the time required in the study. The current solar absorber in the new design can be generated for the multi-solar purposes of water heating, lighting, ventilation, charging for electronic devices, and electric vehicle transportation.https://doi.org/10.1038/s41598-024-80485-0Surface plasmon resonancePlasmonicSolar absorberMachine learningGrapheneHeating |
| spellingShingle | Khaled Aliqab Bo Bo Han Om Prakash Kumar Meshari Alsharari Ammar Armghan Shobhit K. Patel Graphene metamaterial solar absorber using Al-TiN-Fe for efficient solar thermal energy conversion and optimization using machine learning Scientific Reports Surface plasmon resonance Plasmonic Solar absorber Machine learning Graphene Heating |
| title | Graphene metamaterial solar absorber using Al-TiN-Fe for efficient solar thermal energy conversion and optimization using machine learning |
| title_full | Graphene metamaterial solar absorber using Al-TiN-Fe for efficient solar thermal energy conversion and optimization using machine learning |
| title_fullStr | Graphene metamaterial solar absorber using Al-TiN-Fe for efficient solar thermal energy conversion and optimization using machine learning |
| title_full_unstemmed | Graphene metamaterial solar absorber using Al-TiN-Fe for efficient solar thermal energy conversion and optimization using machine learning |
| title_short | Graphene metamaterial solar absorber using Al-TiN-Fe for efficient solar thermal energy conversion and optimization using machine learning |
| title_sort | graphene metamaterial solar absorber using al tin fe for efficient solar thermal energy conversion and optimization using machine learning |
| topic | Surface plasmon resonance Plasmonic Solar absorber Machine learning Graphene Heating |
| url | https://doi.org/10.1038/s41598-024-80485-0 |
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