Scientists Turn Food Waste into Energy Storage Devices
Food waste is a big problem. Not only in the United Sates but all over the world, there are tons and tons of leftover food waste, and now scientists have found a way to make use of it.
Food waste is a big problem. Not only in the United Sates but all over the world, there are tons and tons of leftover food waste, and now scientists have found a way to make use of it. Sugar alcohols are a waste product of the food industry that is found in abundance. Up until now, this by-product has proved to be fairly useless, but scientists discovered that when mixed with carbon nanotubes it could be used as a storage device for both excess wind and energy power.
The use of battery and flywheel energy storage has increased over the past five years, largely to keep up with the increase in renewable energy generation. While some scientists have been looking at using sugar alcohols as a material for making thermal storage possible, there are limitations that have held them back. But Huaichen Zhang, Silvia V Nedea and others at the Eindhoven University of Technology in The Netherlands decided to do things a little differently as they explored how mixing nanotubes with sugar alcohols affected their energy storage potential.–
During the study, the researchers observed what happened when nanotubes were mixed with both erythritol and xylitol (sugar alcohols) that are both found naturally in various foods. The results showed that, apart from one exception, that as the nanotube diameter decreased so did the heat transfer within the mixture. They also found that the higher the density of the combinations, the better the heat transfer. Zhang and the rest of the team are hoping that these new discoveries will help the future design of sugar alcohol storage systems and could help bring down waste at the same time.
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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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