Big savings in energy for small machines

Inside each of us are billions of small molecular nanomachines that perform various tasks necessary for our survival.

In an innovative study, a team led by David Sivak, SFU physics professor, presented for the first time a strategy for handling these machines to maximize efficiency and conserve energy. This breakthrough could have consequences in several areas, including the creation of computer chips and more efficient solar cells for power generation.

The nanomachines are small, really small – a few billionths of a meter wide, in fact. They are also fast and capable of performing complex tasks: moving materials into a cell, building and decomposing molecules, processing and expressing genetic information.

The machines can perform these tasks by consuming remarkably little energy. A theory that predicts energy efficiency helps us understand how these microscopic machines work and what's wrong when they fail, says Sivak.

In the lab, Sivak's experimental collaborators manipulated a DNA hairpin, whose folding and unfolding mimic the mechanical movement of more complex molecular machines. As predicted by Sivak 's theory, they found that maximum efficiency and minimal energy loss were achieved if they were rapidly pulling on the hairpin when it was folded, but slowly when she was about to unfold.

Steven Large, a SFU physics graduate student and co-first author of the article, explains that DNA hairpins (and nanomachines) are so small and so flexible that they are constantly jostled by violent collisions with the surrounding molecules.

"Letting the hair pin unfold before your eyes saves you time and energy," says Mr. Large.

Sivak thinks the next step is to apply the theory to learn how to drive a molecular machine throughout its cycle of operation, while reducing the energy needed to do so.

So, what is the point of making nanomachines more efficient? Sivak says that potential applications could change the game in many areas.

"Among the possible uses include the design of computer chips and more efficient computer memories (reducing the energy consumption and heat they emit), making better renewable energy materials for processes such as artificial photosynthesis (increasing the energy recovered from the sun) and improving the autonomy of biomolecular machines for biotechnological applications such as drug delivery. "

The study was published in Proceedings of the National Academy of Sciences.