Sorry, it's been a while...

Yes, it's been almost a month since my last post. And I have to make one small correction- there was technically a meltdown at the Fukushima Daiichi plant. But I still stand by what I said.

I'm going to do a bit of recycling right now, so here's a little tidbit on oil droplets  I wrote about a year ago. I thought you might find it interesting. For some more of my thoughts over the last month, check out:

http://www.economist.com/blogs/babbage/2011/05/controlling_illegal_fishing

In the meantime, enjoy this bit about oil drops.

Like lipids through a maze

Oil droplets may be used to solve complex network problems (from 05.06.10)

The maze is a long-standing test of problem-solving and learning skills. From rats looking for cheese to children running through a labyrinth, finding the end usually requires a trial and error approach. The successful maze solver must correct a few wrong turns along the way, staying focused enough on the end goal to not get disoriented and distracted licking one’s own paws.

Now it seems that lipid droplets laced with acid have moved into the ranks of successful maze navigators. Bartosz Grzybowski and colleagues at Northwestern University found that lipid droplets can successfully navigate mazes, and can even turn back when they encounter dead ends. In this case the “cheese” is an acid which diffuses through the maze to create a pH gradient. Since the laced droplets themselves slowly release acid, the side of the droplet facing the exit becomes more acidic while the side facing the start of the maze becomes more basic. This difference in acidity creates surface tension on the droplet, which propels the droplet towards the finish line.

Two types of acid-laced droplets were used, based either on mineral oil or on dichloromethane, an organic solvent. Dichloromethane releases the acid faster than mineral oil, and the two lipids displayed different properties. The mineral oil always chose the shortest possible route. More interestingly, the faster-moving dichloromethane behaved like a cab driver encountering unexpected roadworks; it didn’t always choose the shortest route but was able to correct itself when it found a dead end. In some situations this required the droplet to backtrack for a period of time before resuming its path. When two droplets were simultaneously introduced into the maze, they rarely got in each other’s way.

This system could be useful in a number of ways. On a practical level, the movement of acid-laced droplets could be used as a micropump in equipment such as medical diagnostic tools or DNA microchips. If the system is scalable, the maze could also be used to solve more complex network problems. Tracing the paths of different droplets attracted to different targets may serve as a model for the flow of traffic through roads or websites. Robotics and plant and facility layouts could also be modeled using oil drops. The dichloromethane drop’s ability to correct errors could show what happens when slower-moving regions are introduced into the system. At what point will the drop change from a slower but more direct route to a longer but faster route?

There are two types of maze-solving experiments, testing spatial navigation or learning respectively. The oil drop experiment examines spatial navigation, where the maze-runner has no previous knowledge of the maze. To examine learning, the maze runner is placed in the same maze repeatedly; the time needed to complete the maze decreases as the runner learns. Lipid droplets can navigate, but living organisms still seem to have the edge on learning.