” The Bionic Leaf ” Mimics The Photosynthesis and Converts Solar Energy Into Chemical Energy or Liquid Fuel
Innovation in science hits us as researchers from Harvard University have created what they are calling the ‘bionic leaf.‘ This amazing invention is set to revolutionize new renewable energy techniques and even exceed mother nature in the efficiency of photosynthesis.
Innovation in science hits us as researchers from Harvard University have created what they are calling the ‘bionic leaf.‘ This amazing invention is set to revolutionize new renewable energy techniques and even exceed mother nature in the efficiency of photosynthesis.
The bionic leaf has two main goals which are to store the energy that is generated by the sun and regenerate the carbon dioxide in the atmosphere into liquid fuels. This is a new wave of renewable energy technology and has many benefits to it. As well as using the sun’s natural energy for fuel the bionic leaf will actually reduce the number of greenhouse gasses in the atmosphere.
After using electricity to split the water, the hydrogen gas portion is fed to a bacterium called Ralstonia eutrophic. This bacterium then uses both the hydrogen and carbon dioxide to grow and produce certain compounds that are useful in making things such as alcohol or as precursors to plastic.
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With more time and money spent on development and research of this fascinating area, scientists and researchers are sure to increase the efficiency of the bionic leaf even further and all while reducing our carbon footprint. Maybe we should all take a leaf out of their book, eh?
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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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