active matter

cheerios-effect-inspires-novel-robot-design

Cheerios effect inspires novel robot design

3D printing, active matter, Cheerios effect, fluid dynamics, Marangoni effect, Physics, robotics, Science / /

There’s a common popular science demonstration involving “soap boats,” in which liquid soap poured onto the surface of water creates a propulsive flow driven by gradients in surface tension. But it doesn’t last very long since the soapy surfactants rapidly saturate the water surface, eliminating that surface tension. Using ethanol to create similar “cocktail boats” can significantly extend the effect because the alcohol evaporates rather than saturating the water.

That simple classroom demonstration could also be used to propel tiny robotic devices across liquid surfaces to carry out various environmental or industrial tasks, according to a preprint posted to the physics arXiv. The authors also exploited the so-called “Cheerios effect” as a means of self-assembly to create clusters of tiny ethanol-powered robots.

As previously reported, those who love their Cheerios for breakfast are well acquainted with how those last few tasty little “O”s tend to clump together in the bowl: either drifting to the center or to the outer edges. The “Cheerios effect is found throughout nature, such as in grains of pollen (or, alternatively, mosquito eggs or beetles) floating on top of a pond; small coins floating in a bowl of water; or fire ants clumping together to form life-saving rafts during floods. A 2005 paper in the American Journal of Physics outlined the underlying physics, identifying the culprit as a combination of buoyancy, surface tension, and the so-called “meniscus effect.”

It all adds up to a type of capillary action. Basically, the mass of the Cheerios is insufficient to break the milk’s surface tension. But it’s enough to put a tiny dent in the surface of the milk in the bowl, such that if two Cheerios are sufficiently close, the curved surface in the liquid (meniscus) will cause them to naturally drift toward each other. The “dents” merge and the “O”s clump together. Add another Cheerio into the mix, and it, too, will follow the curvature in the milk to drift toward its fellow “O”s.

Physicists made the first direct measurements of the various forces at work in the phenomenon in 2019. And they found one extra factor underlying the Cheerios effect: The disks tilted toward each other as they drifted closer in the water. So the disks pushed harder against the water’s surface, resulting in a pushback from the liquid. That’s what leads to an increase in the attraction between the two disks.

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hydrogels-can-learn-to-play-pong

Hydrogels can learn to play Pong

active matter, chemistry, hydrogels, materials science, Physics, polymers, Pong, Science, smart materials / /

It’s all about the feedback loops —

Work could lead to new “smart” materials that can learn and adapt to their environment.

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This electroactive polymer hydrogel “learned” to play Pong. Credit: Cell Reports Physical Science/Strong et al.

Pong will always hold a special place in the history of gaming as one of the earliest arcade video games. Introduced in 1972, it was a table tennis game featuring very simple graphics and gameplay. In fact, it’s simple enough that even non-living materials known as hydrogels can “learn” to play the game by “remembering” previous patterns of electrical stimulation, according to a new paper published in the journal Cell Reports Physical Science.

“Our research shows that even very simple materials can exhibit complex, adaptive behaviors typically associated with living systems or sophisticated AI,” said co-author Yoshikatsu Hayashi, a biomedical engineer at the University of Reading in the UK. “This opens up exciting possibilities for developing new types of ‘smart’ materials that can learn and adapt to their environment.”

Hydrogels are soft, flexible biphasic materials that swell but do not dissolve in water. So a hydrogel may contain a large amount of water but still maintain its shape, making it useful for a wide range of applications. Perhaps the best-known use is soft contact lenses, but various kinds of hydrogels are also used in breast implants, disposable diapers, EEG and ECG medical electrodes, glucose biosensors, encapsulating quantum dots, solar-powered water purification, cell cultures, tissue engineering scaffolds, water gel explosives, actuators for soft robotics, supersonic shock-absorbing materials, and sustained-release drug delivery systems, among other uses.

In April, Hayashi co-authored a paper showing that hydrogels can “learn” to beat in rhythm with an external pacemaker, something previously only achieved with living cells. They exploited the intrinsic ability of the hydrogels to convert chemical energy into mechanical oscillations, using the pacemaker to apply cyclic compressions. They found that when the oscillation of a gel sample matched the harmonic resonance of the pacemaker’s beat, the system kept a “memory” of that resonant oscillation period and could retain that memory even when the pacemaker was turned off. Such hydrogels might one day be a useful substitute for heart research using animals, providing new ways to research conditions like cardiac arrhythmia.

For this latest work, Hayashi and co-authors were partly inspired by a 2022 study in which brain cells in a dish—dubbed DishBrain—were electrically stimulated in such a way as to create useful feedback loops, enabling them to “learn” to play Pong (albeit badly). As Ars Science Editor John Timmer reported at the time:

Pong proved to be an excellent choice for the experiments. The environment only involves a couple of variables: the location of the paddle and the location of the ball. The paddle can only move along a single line, so the motor portion of things only needs two inputs: move up or move down. And there’s a clear reward for doing things well: you avoid an end state where the ball goes past the paddles and the game stops. It is a great setup for testing a simple neural network.

Put in Pong terms, the sensory portion of the network will take the positional inputs, determine an action (move the paddle up or down), and then generate an expectation for what the next state will be. If it’s interpreting the world correctly, that state will be similar to its prediction, and thus the sensory input will be its own reward. If it gets things wrong, then there will be a large mismatch, and the network will revise its connections and try again.

There were a few caveats—even the best systems didn’t play Pong all that well—but the approach mostly worked. Those systems comprising either mouse or human neurons saw the average length of Pong rallies increase over time, indicating they might be learning the game’s rules. Systems based on non-neural cells, or those lacking a reward system, didn’t see this sort of improvement. The findings provided some evidence that neural networks formed from actual neurons spontaneously develop the ability to learn. And that could explain some of the learning capabilities of actual brains, where smaller groups of neurons are organized into functional units.

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