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submitted 1 year ago by will_a113@lemmy.ml to c/science@lemmy.ml

Scientists have figured out how to harness Brownian motion -- literally the thermal energy of individual molecules -- to make electricity, by cleverly connecting diodes up to pieces of graphene, which are atom-thick sheets of Carbon. The team has successfully demonstrated their theory (which was previously thought to be impossible by prominent physicists like Richard Feynman), and are now trying to make a kind of micro-harvester that can basically produce inexhaustible power for things like smart sensors.

The most impressive thing about the system is that it doesn't require a thermal gradient to do work, like other kinds of heat-harvesting systems (Stirling engines, Peltier junctions, etc.). As long as it's a bit above absolute zero, there's enough thermal energy "in the system" to make the graphene vibrate continuously, which induces a current that the diodes can then pump out.

Original journal link: https://journals.aps.org/pre/abstract/10.1103/PhysRevE.108.024130

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[-] SmoothIsFast@citizensgaming.com 2 points 1 year ago

Well if this works as they say I'd guess this isn't working without a temperature gradient, just a very small one that is found throughout the molecules in the graphene sheet itself, hence why this needs to be above zero Kelvin and why I'd guess they are only targeting micro/nano sensors to power as they can't ever scale this beyond the inherent gradient present in graphene. I'm not a physicist so don't take my word as the gospel but at the same time I don't see why this is ruffling so many feathers when it clearly can't scale past these smaller voltages that they are targeting, which seems to hint at this just being a way to take advantage of the natural heat loss on graphene for small powered devices.

[-] rastilin@kbin.social 1 points 1 year ago

If it works then it doesn't matter how many feathers it ruffles. I had the perpetual motion thought as well, but if it works, it works.

this post was submitted on 22 Aug 2023
130 points (97.8% liked)

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