Want Solar Power But on a Budget? Use Blu-Ray Discs Instead!
Blu-rays have a great color to them that makes them perfect for capturing light. Physicists call this structural color as is not created from actual pigments but from translucent shapes that bounce off the light and reflect in a way that makes them appear colored.
Blu-rays have a great color to them that makes them perfect for capturing light. Physicists call this structural color as is not created from actual pigments but from translucent shapes that bounce off the light and reflect in a way that makes them appear colored. As well as looking pretty, this makes them a great tool to use for solar panels (especially if you’re on a budget or need to do some recycling).
A group of engineers from Northwestern University went about proving this theory that Blu-ray discs would be suitable to use as solar panels by replicating the surface of a Blu-ray disc and transferring it onto a solar panel. But, in order to do that, first, the team had to trim the edges off a Blu-ray disc. Then they had to open the disc to reveal the patterned surface of the disc (which is where all the data is encoded). Next, they poured liquefied plastic onto the patterned surface and left it overnight to harden. They then fitted the solar panels with the newly made Blu-ray patterned plastic, stamped in a similar fashion to that of cookie dough. As a control, the team also made another batch but left this lot unstamped.
The results of the tests proved that the patterned panels were far more efficient than those that had a smooth surface. The patterns of a Blu-ray’s data seem to work perfectly in terms of capturing just the right light, and for that reason, make them great to use as solar panels. Jiaxing Huang is a chemist who was in charge of the research and he said, “It’s as if electrical engineers and computer scientists developing the Blu-ray technology have been subconsciously trying to do our jobs, too.”
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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