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UNIST and Sungkyunkwan University Develop High-Efficiency Organic Solar Cells That Surpassed AI Predictions
Recorded 19.67% efficiency in eco-friendly process, confirmed molecular aggregation effect missed by AI
Professor Chang-Duck Yang's team from the Department of Energy and Chemical Engineering at UNIST and Professor Doo-Hyun Ko's team from Sungkyunkwan University disclosed the research results on the 21st. The research achievement was published in the international academic journal 'Advanced Energy Materials' in the field of energy materials on April 20.
The research team newly designed an electron acceptor molecule called 'YBOV'. YBOV exhibits the characteristic of aggregating molecules in a solvent, and it was analyzed that this aggregation organizes the molecular arrangement of the photoactive layer during the thin film formation process, thereby facilitating charge transfer.
YBOV-based organic solar cells recorded a maximum photoelectric conversion efficiency of 19.67% even under conditions using orthoxylene, an eco-friendly solvent, instead of toxic chlorine-based solvents. In other electron donor-electron acceptor combinations as well, efficiency was improved compared to the control group when a small amount of YBOV was added.
This result also demonstrated the limitations of existing AI prediction models. The AI model trained by the research team on 750 organic solar cell data points predicted the open-circuit voltage of YBOV-based devices to be lower than the actual value. This is explained by the fact that the calculation method, which focuses on the structure of a single molecule, failed to sufficiently reflect the collective behavior of molecules in solution.
The joint research team stated that this study demonstrates that in the design of organic solar cells, not only the molecular structure but also the aggregation behavior in solution must be considered. Organic solar cells are lightweight and flexible, so their potential applications in building facades, windows, and wearable devices are being explored.
UNIST researchers Seok-Hwan Jeong, Dong-Hoo Won, and Zhu Sun participated as co-first authors in this study. The research was conducted with support from the Ministry of Science and ICT, the National Research Foundation of Korea, and the InnoCore Project.
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