According to the team, solution-processed inorganic solar cells, with less toxic and earth-abundant elements, are emerging as viable alternatives to lead-halide perovskite solar cells, but the range of elements and process parameters impede their rapid development.
To speed up the discovery of these materials, the researchers created a robotic measurement system that can perform photoabsorption spectroscopy, optical microscopy, and time-resolved microwave conductivity analyses. They then used the robot to evaluate 576 different thin-film semiconductor samples. The team’s research is detailed in JACS Au.
“Current solar cells are made of inorganic semiconductors containing silicon and gallium, but next-generation solar cells need to reduce both cost and weight," said lead author Chisato Nishikawa. "Safety is also a concern; perovskite solar cells are efficient enough to rival silicon solar cells, but they contain toxic lead."
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The samples tested in this study were all made from a varying blend of cesium, bismuth, tin, and iodine. They were also annealed at different temperatures and treated with different organic salt additives. To thoroughly characterise the material properties as well as automate the experimental process, the researchers also examined the data using machine learning.
"In recent years, machine learning has been very helpful in better understanding the properties of materials. These studies require vast amounts of experimental data, and combining automated experiments with machine-learning techniques is an ideal solution," senior author Akinori Saeki said in a statement.
The authors hope to automate even more of the process in the future, making it easier to examine completely new materials. "This method is ideal for exploring areas where there's no existing data," said Nishikawa.
The research team have had promising results with their robotic system so far. The measurement process is both fully automated and highly accurate, allowing work to be completed in one-sixth of the usual time needed.