Firefly lanterns inspire efficient LEDs
A published study has revealed that light-emitting diodes inspired by the glow of fireflies can increase light extraction by more than 50 percent.
The more efficient light sources take their inspiration from the jagged scales found inside fireflies, which enhance the glow coming from the creatures. An overlayer mimics the action of the scales and reduces the amount of energy needed by the LED.
“The most important aspect of this work is that it shows how much we can learn by carefully observing nature,” said Annick Bay, a PhD student at the University of Namur in Belgium.
In the fireflies (members of the genus Photuris) a certain amount of light is reflected back into the lantern by the insect’s cuticle — a part of the exoskeleton. But in some fireflies the reflection can be reduced by an arrangement of jagged scales, meaning more light escapes and the firefly appears brighter.
In the LEDs, an overlayer composed of a light-sensitive material which has been lasered into shape mimics the action of these scales, improving efficiency and reducing the energy demand of the light.
“We refer to the edge structures as having a factory roof shape,” says Bay. “The tips of the scales protrude and have a tilted slope, like a factory roof.”
As an added bonus, the efficiency enhancing coating can be applied retrospectively to existing LEDs.
Two studies, published yesterday in Optics Express, describe the shape of scales on the abdomen of fireflies, and an experiment that placed similar structures on LEDs, brightening their output by 55 percent.
Bumblebee Flight Paths Could Inspire Faster Computers
Researchers at Queen Mary University of London have found that bumblebees are capable of complex problem solving that could ultimately lead to faster computer networks and microchips. The researchers discovered that bumblebees find the shortest route among landmarks, in this case flowers, through a simple but effective method.
The researchers set up five fake flowers in a field, each with a little bit of sucrose to entice the bees, and outfitted with motion-triggered web cams. They tracked the bees’ flight paths with tiny bumblebee-mounted radar transponders to see how long it took them to find the fastest route starting from the nest, visiting all five flowers and then back to the nest. The team then modeled the flight paths and found that, amazingly, the bees were able to find the quickest route after trying just 20 out of the 120 possible routes. And the researchers were more surprised that it seemed that the bees were using trial and error, which is a more complex behavior typically seen only in larger-brained animals.
The key, it seems, to their quickly find the shortest route was a simple system where after discovering all five flowers, the bees would start trying new routes. If a new route between flowers was the fastest yet, it would increase the probability that it would be tried again — essentially the bees were committing the fastest routes to memory and eliminating the slower ones until finally an optimal route was found.
Head of Computational and Systems Biology at Rothamsted Research, Professor Chris Rawlings said,”This is an exciting result because it shows that seemingly complex behaviours can be described by relatively simple rules which can be described mathematically.”
The mathematics is what could eventually be used to build faster computer networks, sequence DNA or help delivery companies find the most efficient routes among cities. And just as important, it could help to protect the bumblebees themselves. The researchers found that when a flower was moved or removed, the bees would keep visiting that location for an extended period of time, but then eventually find its new location or a new flower.
“This means we can now use mathematics to inform us when bee behaviour might be affected by their environment and to assess, for example, the impact of changes in the landscape,” Rawlings said.
Alan Turing’s Patterns in Nature, and Beyond
Near the end of his life, the great mathematician Alan Turing wrote his first and last paper on biology and chemistry, about how a certain type of chemical reaction ought to produce many patterns seen in nature.
Called “The Chemical Basis of Morphogenesis,” it was an entirely theoretical work. But in following decades, long after Turing tragically took his own life in 1954, scientists found his speculations to be reality.
First found in chemicals in dishes, then in the stripes and spirals and whorls of animals, so-called Turing patterns abounded. Some think that Turing patterns may actually extend to ecosystems, even to galaxies. That’s still speculation — but a proof published Feb. 11 in Science of Turing patterns in a controlled three-dimensional chemical system are even more suggestion of just how complex the patterns can be.
How Turing Patterns Work
At the heart of any Turing pattern is a so-called reaction-diffusion system. It consists of an “activator,” a chemical that can make more of itself; an “inhibitor,” that slows production of the activator; and a mechanism for diffusing the chemicals.
Many combinations of chemicals can fit this system: What matters isn’t their individual identity, but how they interact, with concentrations oscillating between high and low and spreading across an area. These simple units then suffice to produce very complex patterns.
Proving Their Existence
Even though what appeared to be Turing patterns were immediately evident in nature, it wasn’t easy to be sure they were produced by reaction-diffusion systems, rather than some other mechanism.
The breakthrough came during the 1980s, when chemists were able to produce Turing patterns in the laboratory, on thin slabs of gel. In these controlled systems, the reactions could be closely followed, simulated on computers and unambiguously demonstrated as true Turing patterns.
At left in each photograph is a real seashell. At right is a computer-generated image of a pattern produced by a Turing pattern simulation.
At left in each photograph is the eye of a popper fish. At right is a computer-generated image of a pattern generated by a Turing pattern simulation.