Solar energy holds tremendous promise for powering the world of the future. However, in order to maximize the efficiency of solar cells, researchers must explore new materials that offer improved performance. Addressing this challenge, scientists at Osaka University have developed an innovative system that automates key experimental and analytical processes, significantly accelerating research on solar materials.
While silicon is commonly used in solar cells, alternative materials may offer even greater efficiency. However, for widespread use, these materials must meet certain requirements: high efficiency, composition from readily available elements, and low toxicity.
Thus far, it has been challenging to find materials that meet all three criteria. Moreover, the manual nature of material exploration is both time-consuming and costly. To expedite the discovery process, the Osaka University researchers have devised a unique robotic measurement system. This system can perform photoabsorption spectroscopy, optical microscopy, and time-resolved microwave conductivity analyses. With the aid of this robot, the team evaluated a staggering 576 thin-film semiconductor samples.
Lead author Chisato Nishikawa explains, “Current solar cells are made of inorganic semiconductors containing silicon and gallium, but next-generation solar cells need to reduce both cost and weight. Safety is also a concern; perovskite solar cells are efficient enough to rival silicon solar cells, but they contain toxic lead.”
The research team employed samples made from varying compositions of cesium, bismuth, tin, and iodine. These samples underwent annealing at different temperatures and were treated with various organic salt additives. To streamline the experimental process and thoroughly analyze the material properties, the researchers utilized artificial intelligence, specifically machine learning.
Over time, the researchers aim to automate additional steps, allowing for the efficient investigation of entirely new materials. This approach is particularly useful when exploring uncharted territories. As Nishikawa notes, “This method is ideal for exploring areas where there’s no existing data.”
By harnessing the capabilities of robots and artificial intelligence, the solar material discovery process becomes more efficient and accessible. The automated system not only accelerates research but also ensures high accuracy. Impressively, the work can be completed in just one-sixth of the time typically required.
With this groundbreaking technology, the future of solar energy is closer than ever before. By leveraging robotics and AI, researchers are paving the way for more efficient and non-toxic solar materials, offering immense potential for a sustainable energy future.
What are the requirements for solar materials?
Solar materials need to be highly efficient, composed of common elements, and have low toxicity.
What challenges are associated with finding new solar materials?
Finding new solar materials is challenging due to the vast number of potential candidates and the need for extensive manual experimentation, which is both time-consuming and expensive.
How does the robotic measurement system assist in the discovery of solar materials?
The robotic system automates key experimental and analytical processes, such as photoabsorption spectroscopy, optical microscopy, and time-resolved microwave conductivity analyses. It accelerates the research process and allows for the evaluation of a large number of samples, enabling the discovery of promising solar materials.
How does artificial intelligence contribute to the research?
Artificial intelligence, specifically machine learning, is used to analyze the data obtained from the experiments. It helps researchers gain insights into material properties and streamline the discovery process.