The latest advancements in solar technology have brought forth a major breakthrough in the production of perovskite solar cells, aiming to make them more efficient and reliable. These tandem solar cells have proven to outperform traditional silicon solar cells in terms of efficiency. However, concerns about stability and manufacturing processes have hindered their widespread adoption.
Researchers from the Karlsruhe Institute of Technology (KIT), in collaboration with Helmholtz Imaging at the German Cancer Research Center (DKFZ) and Helmholtz AI, have made substantial progress in utilizing artificial intelligence (AI) and machine learning to enhance the quality of perovskite layers during the manufacturing process.
By harnessing the power of machine learning and innovative AI methods, they can now assess the quality of these layers by analyzing variations in light emission during production. This has led to a comprehensive understanding of the factors influencing the production of high-grade solar cells, enabling a more targeted and strategic approach to experiments.
The use of AI has been a paradigm shift in materials science, offering systematic insights that go beyond traditional approaches. The researchers have been able to predict the efficiency levels of each solar cell based on the observed light emission during the manufacturing process, opening the door to more targeted and effective experiments.
The significance of this development is underscored by the fact that these cells hold promise for widespread adoption, with an efficiency exceeding 33% and the use of cost-effective raw materials. The findings of the study have been published in the journal Advanced Materials, marking a major milestone in the field of solar energy technology.
Overall, this collaborative effort represents a significant step towards the industrial-scale production of highly efficient solar cells, which is crucial for the ongoing energy transition. With this groundbreaking research, the future of perovskite solar cells looks brighter than ever. ⓒ 2023 TECHTIMES.com All rights reserved. Do not reproduce without permission.
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