Evolutionary theory inspires more efficient solar cell design

Solar energy is one of the most abundant and sustainable resources available, yet converting it into electricity using traditional crystalline silicon solar cells remains costly—often ten times more expensive than coal-based power generation. This has led scientists to explore alternative solutions, such as organic solar cells, which are made from polymers and offer potential for lower production costs. However, their electrical performance is still suboptimal, and the overall design of these cells needs significant improvement. Researchers at Northwestern University in the United States are working on a breakthrough approach to enhance the efficiency and affordability of organic solar cells. Their focus is on optimizing the structure of the scattering layer, a key component that helps trap and guide light within the cell. Unlike previous methods that focused on adjusting the thickness of the polymer layer, the team aimed to maximize light absorption through clever geometric design. By applying mathematical algorithms inspired by natural evolution, the researchers developed precise patterns that can effectively capture and store sunlight within thin organic layers. This innovative method has resulted in a design that exceeds the Yablonovitch Limit—a theoretical maximum efficiency limit for solar cells established in the 1980s—by three times. In this new design, sunlight first enters a 100-nanometer-thick scattering layer, which is patterned to optimally direct light toward the active layer where the conversion to electricity takes place. The use of advanced computational techniques, such as genetic algorithms, allowed the team to simulate and refine thousands of possible structures over multiple generations, ultimately arriving at an optimized solution. Cheng Sun, an assistant professor at the McCormick School of Engineering and Applied Science at Northwestern, emphasized the importance of carefully designing the scattering layer to ensure peak performance. He noted that while the task was complex, using evolutionary-inspired algorithms helped overcome the challenges of trial and error. Professor Wei Chen, another researcher involved in the project, explained that genetic algorithms mimic biological processes, allowing the system to evolve toward the most efficient designs. “Given the nonlinear and unpredictable nature of the system, we needed an intelligent approach,” he said. “Our process mirrors the survival of the fittest in nature.” The research team started with dozens of random patterns, "mated" them, and tested the resulting offspring to evaluate their ability to trap light. This iterative process continued over 20 generations, leading to a final design that significantly outperformed conventional models. This groundbreaking technology is now being manufactured by the Argonne National Laboratory, bringing us one step closer to more efficient and affordable solar energy solutions. (Translated by Krystal)

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