Aliqab Khaled, Agravat Dhruvik, Patel Shobhit K, Armghan Ammar, Ali Naim Ben, Alsharari Meshari
Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia.
Department of Physics, Marwadi University, Rajkot, 360003, Gujarat, India.
Sci Rep. 2025 Feb 7;15(1):4599. doi: 10.1038/s41598-024-83486-1.
Because energy interest demands clean and sustainability in the last ten years. Solar thermal energy conversion, where sunlight can be absorbed to convert it into heat can stand as an alternative for this purpose. Graphene dispersed with different substrates enables us to get torsion control over light absorption and heat transport. This work discusses the optothermal properties of graphene-based coatings on different substrates such as CuO, MAPBI3, Fe, etc. The optothermal properties of such CuO-graphene, MAPBI3-graphene, and Fe-graphene combinations display the highest average absorptance of 96.8% across the solar spectrum between 0.2 and 2.5 μm followed by 86.7% by MAPBI3-graphene. However, Fe-graphene depicts a significantly lower value of 24.3%. A critical inspection of these optothermal properties would enrich one with critical knowledge of design optimisation in graphene-coated solar absorbers. Thus, the data collection time is greatly reduced using ML compared to running simulations which have a step size of about 8 h per change. Where the machine learning efficacy is 98% for the thickness optimization of Fe, CuO, and MAPBI3 with 25% test data. Of much potential interest are the solar absorbers developed using these materials in fields such as solar thermal energy harvesting, air/water heaters, and industrial heating systems.
在过去十年中，由于能源需求要求清洁和可持续性。太阳能热转换，即吸收阳光将其转化为热量，可以作为实现这一目的的一种替代方案。与不同基底分散的石墨烯使我们能够对光吸收和热传输进行扭转控制。这项工作讨论了基于石墨烯的涂层在不同基底（如CuO、MAPBI3、Fe等）上的光热特性。这种CuO-石墨烯、MAPBI3-石墨烯和Fe-石墨烯组合的光热特性在0.2至2.5μm的太阳光谱范围内显示出最高平均吸收率为96.8%，其次是MAPBI3-石墨烯的86.7%。然而，Fe-石墨烯的吸收率显著较低，为24.3%。对这些光热特性的严格检查将丰富人们对石墨烯涂层太阳能吸收器设计优化的关键知识。因此，与运行步长约为每次变化8小时的模拟相比，使用机器学习大大减少了数据收集时间。对于Fe、CuO和MAPBI3的厚度优化，机器学习效率为98%，测试数据为25%。在太阳能热收集、空气/水加热器和工业加热系统等领域使用这些材料开发的太阳能吸收器具有很大的潜在兴趣。
