Aurelia Butler

Can you crumple up two sheets of paper the exact same way? Probably not — the very flexibility that lets flexible structures from paper to biopolymers and membranes undergo many types of large deformations makes them notoriously difficult to control. Researchers from the Georgia Institute of Technology, Universiteit van Amsterdam, and Universiteit Leiden have shed new light on this fundamental challenge, demonstrating that new physical theories provide precise predictions of the deformations of certain structures, as recently published in Nature Communications.
In the paper, Michael Czajkowski and D. Zeb Rocklin from Georgia Tech, Corentin Coulais from Universiteit van Amsterdam, and Martin van Hecke of AMOLF and Universiteit Leiden approach a highly studied exotic elastic material, uncover an intuitive geometrical description of the pronounced — or nonlinear — soft deformations, and show how to activate any of these deformations on-demand with minimal inputs. This new theory reveals that a flexible mechanical structure is governed by some of the same math as electromagnetic waves, phase transitions, and even black holes.
“So many other systems struggle with how to be strong and solid in some ways but flexible and compliant in others, from the human body and micro-organisms to clothing and industrial robots,” said Rocklin. “These structures solve that problem in an incredibly elegant way that permits a single folding mechanism to generate a wide family of deformations. We’ve shown that a single folding mode can transform a structure into an infinite family of shapes.”
A Brief History of Metamaterials
Metamaterials rely on the use of hinges, folds, cuts, and “flexible” ingredients to display the variety of counterintuitive physics that has been steadily revealed over the past decade of intense research. Many of these new behaviors have emerged from the development of auxetics, materials that tend to shrink in all directions when they are compressed from any direction rather than bulging outward. Although the researchers’ chosen structure, “Rotating Squares,” is already one of the most heavily researched metamaterials, they uncovered entirely new and powerful physics hiding within its deformations.
“Normally complex real-world structures defy analytical physics, which made it all the more thrilling when Michael found that his conformal predictions could account for 99.9% of the variance in Corentin’s structure,” said Rocklin. “This new approach could allow us to predict and control tough, flexible structures from the size of skyscrapers to the microscale.”
Conformal Findings
The results of this paper rely on the novel observation that these maximally auxetic metamaterials deform conformally, which the researchers confirmed with a high degree of accuracy. This means that any angle drawn on the material before and after deformation will still look like the same angle. This seemingly mundane observation activates powerful mathematical structures.
This conformal insight allows for a variety of pen-and-paper analytic advances: a nonlinear energy functional, deformation fitting methods, new prediction methods etc. This culminates with a recipe to choose any of these conformal deformations in an exact, reversible, and mathematically straightforward manner via the manipulation of the boundary. By choosing how much the boundary is stretched, the overall shape can be picked from infinite possibilities.
Such deformation control is still limited by the essential nature of conformal deformations. However, the underlying principles are quite general, and researchers are working to apply these new principles to more varied and complex structures.
“Our results are very promising for the soft microscopic robotics that are being developed for non-invasive surgical purposes,” said Czajkowski. “In this effort, scalability and precise external control are two of the primary goalposts, and our style of deformation control seems perfectly suited for the job.”
The jump to more provocative applications is likely not far off, as the realm of metamaterials has steadily become populated with manipulatable faces, a variety of new grabbers and hands, and even an elastic worm that can thread a series of needles. These advances will become essential in the effort to develop soft microscopic robots, which must be externally manipulated to move through a body and perform noninvasive surgeries. More

The field of animal ecology has entered the era of big data and the Internet of Things. Unprecedented amounts of data are now being collected on wildlife populations, thanks to sophisticated technology such as satellites, drones and terrestrial devices like automatic cameras and sensors placed on animals or in their surroundings. These data have become so easy to acquire and share that they have shortened distances and time requirements for researchers while minimizing the disrupting presence of humans in natural habitats. Today, a variety of AI programs are available to analyze large datasets, but they’re often general in nature and ill-suited to observing the exact behavior and appearance of wild animals. A team of scientists from EPFL and other universities has outlined a pioneering approach to resolve that problem and develop more accurate models by combining advances in computer vision with the expertise of ecologists. Their findings, which appear today in Nature Communications, open up new perspectives on the use of AI to help preserve wildlife species.
Building up cross-disciplinary know-how
Wildlife research has gone from local to global. Modern technology now offers revolutionary new ways to produce more accurate estimates of wildlife populations, better understand animal behavior, combat poaching and halt the decline in biodiversity. Ecologists can use AI, and more specifically computer vision, to extract key features from images, videos and other visual forms of data in order to quickly classify wildlife species, count individual animals, and glean certain information, using large datasets. The generic programs currently used to process such data often work like black boxes and don’t leverage the full scope of existing knowledge about the animal kingdom. What’s more, they’re hard to customize, sometimes suffer from poor quality control, and are potentially subject to ethical issues related to the use of sensitive data. They also contain several biases, especially regional ones; for example, if all the data used to train a given program were collected in Europe, the program might not be suitable for other world regions.
“We wanted to get more researchers interested in this topic and pool their efforts so as to move forward in this emerging field. AI can serve as a key catalyst in wildlife research and environmental protection more broadly,” says Prof. Devis Tuia, the head of EPFL’s Environmental Computational Science and Earth Observation Laboratory and the study’s lead author. If computer scientists want to reduce the margin of error of an AI program that’s been trained to recognize a given species, for example, they need to be able to draw on the knowledge of animal ecologists. These experts can specify which characteristics should be factored into the program, such as whether a species can survive at a given latitude, whether it’s crucial for the survival of another species (such as through a predator-prey relationship) or whether the species’ physiology changes over its lifetime. “We used this approach to improve a bear-recognition program a few years ago,” says Prof. Mackenzie Mathis, a neuroscientist at EPFL and co-author of the study. “A researcher studying bear DNA had installed automatic cameras in bear habitats in order to recognize individual animals. But bears shed half of their body fat when they hibernate, meaning the generic programs she used were no longer able to recognize the bears once the season changed. We therefore added criteria to the program that can not only look at whether an animal has a given characteristic, but also be tweaked manually to allow for possible deviations.”
Getting the word out about existing initiatives
The idea of forging stronger ties between computer vision and ecology came up as Tuia, Mathis and others discussed their research challenges at various conferences over the past two years. They saw that such collaboration could be extremely useful in preventing certain wildlife species from going extinct. A handful of initiatives have already been rolled out in this direction; some of them are listed in the Nature Communications article. For instance, Tuia and his team at EPFL have developed a program that can recognize animal species based on drone images. It was tested recently on a seal population. Meanwhile, Mathis and her colleagues have unveiled an open-source software package called DeepLabCut that allows scientists to estimate and track animal poses with remarkable accuracy. It’s already been downloaded 300,000 times. DeepLabCut was designed for lab animals but can be used for other species as well. Researchers at other universities have developed programs too, but it’s hard for them to share their discoveries since no real community has yet been formed in this area. Other scientists often don’t know these programs exist or which one would be best for their specific research.
That said, initial steps towards such a community have been taken through various online forums. The Nature Communications article aims for a broader audience, however, consisting of researchers from around the world. “A community is steadily taking shape,” says Tuia. “So far we’ve used word of mouth to build up an initial network. We first started two years ago with the people who are now the article’s other lead authors: Benjamin Kellenberger, also at EPFL; Sara Beery at Caltech in the US; and Blair Costelloe at the Max Planck Institute in Germany.”
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Materials provided by Ecole Polytechnique Fédérale de Lausanne. Original written by Cécilia Carron. Note: Content may be edited for style and length. More

The word “robot” would probably conjure up images of hard metallic bodies that are invulnerable to attacks. In modern day-to-day life, however, robots are hardly needed for defending against enemy attacks. Instead, they are required to perform more mundane tasks such as handling delicate objects and interacting with humans. Unfortunately, conventional robots perform poorly at such seemingly simple tasks. Moreover, they’re heavy and often noisy.
This is where “soft” robots have the upper hand. Made of materials called “elastomers” (materials with high viscosity and elasticity), soft robots absorb shocks better, can adapt better to their environments, and are safer compared to conventional robots. This has allowed for a broad range of applications, including medicine and surgery, manipulation, and wearable technology. However, many of these soft robots rely on fluidic systems, which still use pumps operated by mechanical parts (motors and bearings). As a result, they are still heavy and noisy.
One way around this problem is to use chemical reactions to drive pumps. But while such systems are definitely lightweight and quiet, they don’t perform as well as conventional pumps. Is there a way to beat this trade-off? Turns out, the answer is yes. A team of researchers from Shibaura Institute of Technology (SIT), Japan, led by Prof. Shingo Maeda, introduced an “electrohydrodynamic” (EHD) pump that uses electrochemical reactions to drive pumps. The EHD pumps have all the advantages of pumps driven by chemical reactions and none of their issues.
Now, in a recent study, the team, including Prof. Maeda, Yu Kawajima, Dr. Yuhei Yamada (all from the Department of Engineering Science and Mechanics, SIT), and Associate Professor Hiroki Shigemune (Department of Electrical Engineering, SIT) has gone one step further, designing a “self-sensing” EHD pump that uses an electrochemical dual transducer (ECDT) to sense the fluid flow, which, in turn, activates electrochemical reactions and increases current. “Self-sensing technology has attracted much attention recently for compactifying soft robots. Incorporating sensors in soft robots enhances their multifunctionality, but often make for complex wiring and bloating. Self-sensing actuation technology can help solve this issue and allow for miniaturization of soft robots,” explains Prof. Maeda. This paper was made available online on 7 January 2022 and was published in Volume 14 Issue 2 of the journal ACS Applied Materials & Interfaces on 19 January 2022.
The team based the ECDT design on the EHD pump they had previously designed. The pump consisted of a symmetrical arrangement of planar electrodes, which allowed an easy control of the flow direction by simply changing the voltage. Moreover, the arrangement enabled an obstruction-free flow and in the same amount in each direction owing to same strength of the electric field on either side.
The team evaluated sensing performance in terms of range of detectable flow, rate, sensitivity, response, and relaxation times, and also used mathematical modeling to understand the sensing mechanism. “The ECDT can easily be integrated into a fluidic system without bloating or complexity,” says Yu Kuwajima, doctoral student at the Smart Materials Laboratory (SIT) and the first author of the study. Additionally, the researchers tested its performance by using it to drive a suction cup to detect, grab, and release objects.
“The advantages of the ECDT are that it does not require any special equipment or complex processing for its fabrication. Moreover, it is small, lightweight, and demonstrates a wide range of sensitivity,” says Prof. Maeda.
However, the ECDT is more than just about soft robot miniaturization. It is a step towards a future in which humans and robots would not simply co-exist but their interaction would become fluid and natural. An exciting prospect to entertain, for sure! More

A large-scale study has found that simulation-based surgical training produced an increase of surgeons’ skills for more complex surgeries.
Practice makes perfect, but what if the practice can be life-threatening? Dangerous professions, such as aviation or the military, require extensive simulation-based training to limit the potential peril as a person gains experience and learns how to do the job. With advances in technology, simulation-based training is an option to improve skills in even more fields, including surgery.
An international research team leading a randomized controlled trial across 10 countries found that while simulation-based training did not statistically improve initial learning curves regarding surgeon’s general proficiency, it did produce an increase of skills in more complex surgeries, with fewer total complications and ureteric injuries in the simulation group. The results were published in the journal European Urology.
“To date, there have been limited data, mostly from small-scale studies conducted with medical students, assessing the transferability of surgical simulation,” said one of the paper’s authors, Takashige Abe, Associate Professor of Urology at Hokkaido University’s Graduate School of Medicine in Japan. “The aim of this multicenter international randomized controlled trial was to evaluate whether surgical residents who undergo additional simulation training are able to achieve proficiency sooner and with better patient outcomes when compared to standard operation room-based training.”
The trial followed 65 participants in 10 countries for 18 months, or to a completion of 25 procedures. Split relatively evenly by location, a total of 32 participants received simulation-based training and 33 received conventional apprenticeship-style training. Both remained supervised by more experienced surgeons. Altogether, the participants performed a total of 1,140 surgeries, either semi-rigid or flexible ureteroscopy to remove ureteral or renal stones, respectively, demonstrating “mixed results” in proficiency.
“For our primary outcome measure, while we showed what might be deemed a clinically meaningful difference, it was not statistically significant,” Abe said. “However, when stratified to each procedure type, there were higher rates of proficiency in the simulation-based training group when it came to the more technically challenging flexible ureteroscopy procedure.”
Abe also noted that those who underwent simulation-based training outperformed the other group, scoring higher on a standard assessment for each surgery.
“Simulation-based training led to higher overall proficiency scores than for conventional training, and fewer procedures were required to achieve proficiency in the complex form of the index procedure, with fewer serious complications overall,” Abe said. “It is expected that the results of the trial will have a positive impact for advanced procedural training beyond the fields of surgery and urology in order to promote patients’ safety as well as better surgical outcomes.”
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Materials provided by Hokkaido University. Note: Content may be edited for style and length. More

Whether for use in cybersecurity, gaming or scientific simulation, the world needs true random numbers, but generating them is harder than one might think. But a group of Brown University physicists has developed a technique that can potentially generate millions of random digits per second by harnessing the behavior of skyrmions — tiny magnetic anomalies that arise in certain two-dimensional materials.
Their research, published in Nature Communications, reveals previously unexplored dynamics of single skyrmions, the researchers say. Discovered around a half-decade ago, skyrmions have sparked interest in physics as a path toward next-generation computing devices that take advantage of the magnetic properties of particles — a field known as spintronics.
“There has been a lot of research into the global dynamics of skyrmions, using their movements as a basis for performing computations,” said Gang Xiao, chair of the Department of Physics at Brown and senior author of the research. “But in this work, we show that purely random fluctuations in the size of skyrmions can be useful as well. In this case, we show that we can use those fluctuations to generate random numbers, potentially as many as 10 million digits per second.”
Most random numbers produced by computers aren’t random in the strictest sense. Computers use an algorithm to generate random numbers based on an initial starting place, a seed number. But because the algorithm used to generate the number is deterministic, the numbers aren’t truly random. With enough information about the algorithm or its output, it could be possible for someone to find patterns in the numbers that the algorithm produces. While pseudorandom numbers are sufficient in many settings, applications like data security — which uses numbers that can’t be guessed by an outside party — require true random numbers.
Methods of producing true random numbers often draw on the natural world. Random fluctuations in electrical current flowing through a resistor, for example, can be used to generate random numbers. Other techniques harness the inherent randomness in quantum mechanics — the behavior of particles at the tiniest scale.
This new study adds skyrmions to the list of true random number generators. More

Smaller chips, faster computers, less energy consumption. Novel concepts based on semiconductor nanowires are expected to make transistors in microelectronic circuits better and more efficient. Electron mobility plays a key role in this: The faster electrons can accelerate in these tiny wires, the faster a transistor can switch and the less energy it requires. A team of researchers from the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), the TU Dresden and NaMLab has now succeeded in experimentally demonstrating that electron mobility in nanowires is remarkably enhanced when the shell places the wire core under tensile strain. This phenomenon offers novel opportunities for the development of ultrafast transistors.
Nanowires have a unique property: These ultra-thin wires can sustain very high elastic strains without damaging the crystal structure of the material. And yet the materials themselves are not unusual. Gallium arsenide, for example, is widely used in industrial manufacturing, and is known to have a high intrinsic electron mobility.
Tension creates speed
To further enhance this mobility, the Dresden researchers produced nanowires consisting of a gallium arsenide core and an indium aluminum arsenide shell. The different chemical ingredients result in the crystal structures in the shell and the core having slightly different lattice spacings. This causes the shell to exert a high mechanical strain on the much thinner core. The gallium arsenide in the core changes its electronic properties. “We influence the effective mass of electrons in the core. The electrons become lighter, so to speak, which makes them more mobile,” explained Dr. Emmanouil Dimakis, scientist at the HZDR’s Institute of Ion Beam Physics and Materials Research and initiator of the recently published study.
What started out as a theoretical prediction has now been proven experimentally by the researchers in the recently published study. “We knew that the electrons in the core ought to be even more mobile in the tensile-strained crystal structure. But what we did not know was the extent to which the wire shell would affect electron mobility in the core. The core is extremely thin, allowing electrons to interact with the shell and be scattered by it,” remarked Dimakis. A series of measurements and tests demonstrated this effect: Despite interaction with the shell, electrons in the core of the wires under investigation moved approximately thirty percent faster at room temperature than electrons in comparable nanowires that were strain-free or in bulk gallium arsenide.
Revealing the core
The researchers measured electron mobility by applying contactless optical spectroscopy: Using an optical laser pulse, they set electrons free inside the material. The scientists selected the light-pulse energy such that the shell seems practically transparent to the light, and free electrons are only produced in the wire core. Subsequent high-frequency terahertz pulses caused the free electrons to oscillate. “We practically give the electrons a kick and they start oscillating in the wire,” explained PD Dr. Alexej Pashkin, who optimized the measurements for testing the core-shell nanowires under investigation in collaboration with his team at the HZDR.
Comparing the results with models reveals how the electrons move: The higher their speed and the fewer obstacles they encounter, the longer the oscillation lasts. “This is actually a standard technique. But this time we did not measure the whole wire — comprising the core and the shell — but only the tiny core. This was a new challenge for us. The core accounts for around one percent of the material. In other words, we excite about a hundred times fewer electrons and get a signal that is a hundred times weaker,” stated Pashkin.
Consequently, the choice of sample was also a critical step. A typical sample contains an average of around 20,000 to 100,000 nanowires on a piece of substrate measuring roughly one square millimeter. If the wires are spaced even closer together on the sample, an undesirable effect can occur: Neighboring wires interact with each other, creating a signal similar to that of a single, thicker wire, and distorting the measurements. If this effect is not detected, the electron velocity obtained is too low. To rule out such interference, the Dresden research team carried out additional modelling as well as a series of measurements for nanowires with different densities.
Prototypes for fast transistors
Trends in microelectronics and the semiconductor industry increasingly demand smaller transistors that switch ever faster. Experts anticipate that novel nanowire concepts for transistors will also make inroads into industrial production over the next few years. The development achieved in Dresden is particularly promising for ultra-fast transistors. The researchers’ next step will be to develop the first prototypes based on the studied nanowires and to test their suitability for use. To do this, they intend to apply, test and enhance metallic contacts on the nanowires, as well as testing the doping of nanowires with silicon and optimizing manufacturing processes.
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Materials provided by Helmholtz-Zentrum Dresden-Rossendorf. Note: Content may be edited for style and length. More