Researchers at Osaka University have revolutionized the hunt for efficient solar materials with a groundbreaking automated system. Published in JACS Au, their study introduces a robotic solution that accelerates the exploration of alternative solar cell materials.

While silicon dominates current solar cells, diversifying materials is crucial for enhanced efficiency. However, these materials must meet specific criteria: high efficiency, use of common elements, and low toxicity. Few options fulfill all these requirements.

Traditionally, discovering new materials is slow and expensive. To fast-track this process, the team developed a robotic setup capable of conducting various analyses, including photoabsorption spectroscopy and conductivity measurements. They evaluated 576 semiconductor samples, primarily composed of cesium, bismuth, tin, and iodine, treated under different conditions.

Lead author Chisato Nishikawa emphasizes the need to move beyond toxic components like lead found in efficient perovskite solar cells. The team utilized machine learning alongside automated experiments to streamline data analysis and material characterization.

According to senior author Akinori Saeki, combining automated experiments with machine learning proves ideal for material property exploration, especially in areas lacking existing data.

Their robotic system has shown promising outcomes, slashing research time by one-sixth while maintaining accuracy. This breakthrough significantly simplifies the quest for efficient and non-toxic solar materials. With robots and AI driving the search, the future of solar energy appears closer than ever.

Based on: https://www.sciencedaily.com
Materials provided by:  Osaka University