Researchers Used Graphene for Converting Electricity Into Light
Converting electricity into the light is not a new concept, and was first noted back in the 1930’s by the Soviet physicist, Pavel Cerenkov. However, during this time, Pavel concluded that this phenomenon was not relevant to Earthbound technology as only works when objects are nearing the speed of light and dismissed the idea.
Converting electricity into the light is not a new concept, and was first noted back in the 1930’s by the Soviet physicist, Pavel Cerenkov. However, during this time, Pavel concluded that this phenomenon was not relevant to Earthbound technology as only works when objects are nearing the speed of light and dismissed the idea. But, scientists have now reopened this form of research stating that with the use of graphene this process is not only possible, but much more energy efficient than other forms used today.
Researchers collaborated from various parts of the globe to find the best way of using graphene effectively to do the job. During their studies, the researchers learned that when light hits graphene it slows down considerably, and this reduced speed of photons is almost the same as the speed of electrons as the pass through the same area. With these two particles passing at almost the same speed, it is a great possibility the interactions will occur, and will produce light rather than trapping it.
The conversion of the two properties is only made possible because the electronic speed can approach the light speed and break the light barrier. Graphene has been favored to be used because as well as being very energy efficient, it can be easily integrated with electronics. Currently, all the research conducted so far has been theoretical, but there are plans to take it to the next level and build working prototypes to demonstrate the system and strengthen the bond between physics and nanotechnology.
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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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