Aurelia Butler

A new study challenges the conventional approach to designing soft robotics and a class of materials called metamaterials by utilizing the power of computer algorithms. Researchers from the University of Illinois Urbana-Champaign and Technical University of Denmark can now build multimaterial structures without dependence on human intuition or trial-and-error to produce highly efficient actuators and energy absorbers that mimic designs found in nature.
The study, led by Illinois civil and environmental engineering professor Shelly Zhang, uses optimization theory and an algorithm-based design process called topology optimization. Also known as digital synthesis, the design process builds composite structures that can precisely achieve complex prescribed mechanical responses.
The study results are published in the Proceedings of the National Academy of Sciences.
“The complex mechanical responses called for in soft robotics and metamaterials require the use of multiple materials — but building these types of structures can be a challenge,” Zhang said. “There are so many materials to choose from, and determining the optimal combination of materials to fit a specific function presents an overwhelming amount of data for a researcher to process.”
Zhang’s team set its sights on designing macroscale structures with the prescribed properties of swift stiffening, large-scale deformation buckling, multiphase stability and long-lasting force plateaus.
The new digital synthesis process generated structures with optimal geometric characteristics composed of the optimal materials for the prescribed functions. More

Physicists at UC Santa Barbara have become the first to experimentally observe a quirky behavior of the quantum world: a “quantum boomerang” effect that occurs when particles in a disordered system are kicked out of their locations. Instead of landing elsewhere as one might expect, they turn around and come back to where they started and stop there.
“It’s really a fundamentally quantum mechanical effect,” said atomic physicist David Weld, whose lab produced the effect and documented it in a paper published in Physical Review X. “There’s no classical explanation for this phenomenon.”
The boomerang effect has its roots in a phenomenon that physicist Philip Anderson predicted roughly 60 years ago, a disorder-induced behavior called Anderson localization which inhibits transport of electrons. The disorder, according to the paper’s lead author Roshan Sajjad, can be the result of imperfections in a material’s atomic lattice, whether they be impurities, defects, misalignments or other disturbances.
“This type of disorder will keep them from basically dispersing anywhere,” Sajjad said. As a result, the electrons localize instead of zipping along the lattice, turning what would otherwise be a conducting material into an insulator. From this rather sticky quantum condition, the quantum boomerang effect was predicted a few years ago to arise.
Launching disordered electrons away from their localized position and following them to observe their behavior is extremely difficult if not currently impossible, but the Weld Lab had a few tricks up its sleeve. Using a gas of 100,000 ultracold lithium atoms suspended in a standing wave of light and “kicking” them, emulating a so-called quantum kicked rotor (“similar to a periodically kicked pendulum,” both Weld and Sajjad said), the researchers were able to create the lattice and the disorder, and observe the launch and return of the boomerang. They worked in momentum space, a method that evades some experimental difficulties without changing the underlying physics of the boomerang effect.
“In normal, position space, if you’re looking for the boomerang effect, you’d give your electron some finite velocity and then look for whether it came back to the same spot,” Sajjad explained. “Because we’re in momentum space, we start with a system that is at zero average momentum, and we look for some departure followed by a return to zero average momentum.”
Using their quantum kicked rotor they pulsed the lattice a few dozen times, noting an initial shift in average momentum. Over time and despite repeated kicks, however, average momentum returned to zero. More

A team from the University of Illinois has revamped the popular crop growth simulation software BioCro, making it a more user-friendly and efficient way to predict crop yield. The updated version, BioCro II, allows modelers to use the technology much more easily and includes faster and more accurate algorithms.
“In the original BioCro, all the math that the modelers were using was mixed into the programming language, which many people weren’t familiar with, so it was easy to make mistakes,” said Justin McGrath, a Research Plant Physiologist for the U.S. Department of Agriculture, Agricultural Research Service (USDA-ARS) at Illinois. “BioCro II separates those so modelers can do less programming and can instead focus on the equations.”
Separating the equations from the programming language allows researchers to try new simulations more easily. For example, if a project is looking at how a gene can help plants to use light more efficiently, the equations for that specific gene can be added to existing models, rather than having to change the entire model to include the new information. This development also allows for the software to operate well with other models, a large improvement from the original BioCro.
In a recent study, published in in silico Plants, McGrath and his team discuss all the improvements they made to the original BioCro software, and why they were necessary to improve modeling capabilities for researchers.
“If you’ve got a gene and you’re wondering how much it can improve yield, you have a tiny piece in the context of the whole plant. Modeling lets you take that one change, put it in the plant and compare yield with and without that change,” said Edward Lochocki, lead author on the paper and postdoctoral researcher for RIPE. “With the updates we’ve made in BioCro II, if you have ten gene changes to make, you can look at all of them quickly and gauge relative importance before moving the work into the field.”
This work is part of Realizing Increased Photosynthetic Efficiency (RIPE), an international research project that aims to increase global food production by developing food crops that turn the sun’s energy into food more efficiently with support from the Bill & Melinda Gates Foundation, Foundation for Food & Agriculture Research, and U.K. Foreign, Commonwealth & Development Office.
“BioCro II represents a complete revamp of the original BioCro, eliminating significant duplication of code, improving the efficiency of code, and eliminating hard-wired parameters,” said RIPE Director Stephen Long, Ikenberry Endowed University Chair of Crop Sciences and Plant Biology at Illinois’ Carl R. Woese Institute for Genomic Biology. “All these changes make it much easier to use the model for new species and cultivars, as well as link to other models, as indeed recently demonstrated by adapting BioCro II for soybean.”
With the latest updates, crop modeling with BioCro II is going to give researchers the ability to quickly test ideas and get results to farmers faster.
RIPE is led by the University of Illinois in partnership with The Australian National University, Chinese Academy of Sciences, Commonwealth Scientific and Industrial Research Organisation, Lancaster University, Louisiana State University, University of California, Berkeley, University of Cambridge, University of Essex, and U.S. Department of Agriculture, Agricultural Research Service. More

Researchers have developed a new metasurface-based antenna that represents an important step toward making it practical to harvest energy from radio waves, such as the ones used in cell phone networks or Bluetooth connections. This technology could potentially provide wireless power to sensors, LEDs and other simple devices with low energy requirements.
“By eliminating wired connections and batteries, these antennas could help reduce costs, improve reliability and make some electrical systems more efficient,” said research team leader Jiangfeng Zhou from the University of South Florida. “This would be useful for powering smart home sensors such as those used for temperature, lighting and motion or sensors used to monitor the structure of buildings or bridges, where replacing a battery might be difficult or impossible.”
In the journal Optical Materials Express, the researchers report that lab tests of their new antenna showed that it can harvest 100 microwatts of power, enough to power simple devices, from low power radio waves. This was possible because the metamaterial used to make the antenna exhibits perfect absorption of radio waves and was designed to work with low intensities.
“Although more work is needed to miniaturize the antenna, our device crosses a key threshold of 100 microwatts of harvested power with high efficiency using ambient power levels found in the real world,” said Clayton Fowler, the team member who fabricated the sample and performed the measurements. “The technology could also be adapted so that a radio wave source could be provided to power or charge devices around a room.”
Harvesting energy from the air
Scientists have been trying to capture energy from radio waves for quite some time, but it has been difficult to obtain enough energy to be useful. This is changing thanks to the development of metamaterials and the ever-growing number of ambient sources of radio frequency energy available, such as cell phone networks, Wi-Fi, GPS, and Bluetooth signals.
“With the huge explosion in radio wave-based technologies, there will be a lot of waste electromagnetic emissions that could be collected,” said Zhou. “This, combined with advancements in metamaterials, has created a ripe environment for new devices and applications that could benefit from collecting this waste energy and putting it to use.”
Metamaterials use small, carefully designed structures to interact with light and radio waves in ways that naturally occurring materials do not. To make the energy-harvesting antenna, the researchers used a metamaterial designed for high absorption of radio waves and that allows a higher voltage to flow across the device’s diode. This improved its efficiency at turning radio waves into power, particularly at low intensity.
Testing with ambient power levels
For lab tests of the device, which measured 16 cm by 16 cm, the researchers measured the amount of power harvested while changing the power and frequency of a radio source between 0.7 and 2.0 GHz. They demonstrated the ability to harvest 100 microwatts of power from radio waves with an intensity of just 0.4 microwatts per centimeter squared, approximately the level of intensity of the radio waves 100 meters from a cell phone tower.
“We also placed a cell phone very close to the antenna during a phone call, and it captured enough energy to power an LED during the call,” said Zhou. “Although it would be more practical to harvest energy from cell phone towers, this demonstrated the power capturing abilities of the antenna.”
Because the current version of the antenna is much larger than most of the devices it would potentially power, the researchers are working to make it smaller. They would also like to make a version that could collect energy from multiple types of radio waves simultaneously so that more energy could be gathered.
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While modern computers are already very fast, they also consume vast amounts of electricity. For some years now a new technology has been much talked about, which although it is still in its infancy could one day revolutionise computer technology — spintronics. The word is a portmanteau meaning “spin” and “electronics,” because with these components electrons no longer flow through computer chips, but the spin of the electrons serves as the information carrier. A team of researchers with staff from Goethe University Frankfurt has now identified materials that have surprisingly fast properties for spintronics. The results have been published in the specialist magazine “Nature Materials.”
“You have to imagine the electron spins as if they were tiny magnetic needles which are attached to the atoms of a crystal lattice and which communicate with one another,” says Cornelius Krellner, Professor for Experimental Physics at Goethe University Frankfurt. How these magnetic needles react with one another fundamentally depends on the properties of the material. To date ferromagnetic materials have been examined in spintronics above all; with these materials — similarly to iron magnets — the magnetic needles prefer to point in one direction. In recent years, however, the focus has been placed on so-called antiferromagnets to a greater degree, because these materials are said to allow for even faster and more efficient switchability than other spintronic materials.
With antiferromagnets the neighbouring magnetic needles always point in opposite directions. If an atomic magnetic needle is pushed in one direction, the neighbouring needle turns to face in the opposite direction. This in turn causes the next but one neighbour to point in the same direction as the first needle again. “As this interplay takes place very quickly and with virtually no friction loss, it offers considerable potential for entirely new forms of electronic componentry,” explains Krellner.
Above all crystals with atoms from the group of rare earths are regarded as interesting candidates for spintronics as these comparatively heavy atoms have strong magnetic moments — chemists call the corresponding states of the electrons 4f orbitals. Among the rare-earth metals — some of which are neither rare nor expensive — are elements such as praseodymium and neodymium, which are also used in magnet technology. The research team has now studied seven materials with differing rare-earth atoms in total, from praseodymium to holmium.
The problem in the development of spintronic materials is that perfectly designed crystals are required for such components as the smallest discrepancies immediately have a negative impact on the overall magnetic order in the material. This is where the expertise in Frankfurt came into play. “The rare earths melt at about 1000 degrees Celsius, but the rhodium that is also needed for the crystal does not melt until about 2000 degrees Celsius,” says Krellner. “This is why customary crystallisation methods do not function here.”
Instead the scientists used hot indium as a solvent. The rare earths, as well as the rhodium and silicon that are required, dissolve in this at about 1500 degrees Celsius. The graphite crucible was kept at this temperature for about a week and then gently cooled. As a result the desired crystals grew in the form of thin disks with an edge length of two to three millimetres. These were then studied by the team with the aid of X-rays produced on the Berlin synchrotron BESSY II and on the Swiss Light Source of the Paul Scherrer Institute in Switzerland.
“The most important finding is that in the crystals which we have grown the rare-earth atoms react magnetically with one another very quickly and that the strength of these reactions can be specifically adjusted through the choice of atoms,” says Krellner. This opens up the path for further optimisation — ultimately spintronics is still purely fundamental research and years away from the production of commercial components.
There are still a great many problems to be solved on the path to market maturity, however. Thus, the crystals — which are produced in blazing heat — only deliver convincing magnetic properties at temperatures of less than minus 170 degrees Celsius. “We suspect that the operating temperatures can be raised significantly by adding iron atoms or similar elements,” says Krellner. “But it remains to be seen whether the magnetic properties are then just as positive.” Thanks to the new results the researchers now have a better idea of where it makes sense to change parameters, however.
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A new data analysis tool developed by Yale researchers has revealed the specific immune cell types associated with increased risk of death from COVID-19, they report Feb. 28 in the journal Nature Biotechnology.
Immune system cells such as T cells and antibody-producing B cells are known to provide broad protection against pathogens such as SARS-CoV-2, the virus that causes COVID-19. And large-scale data analyses of millions of cells have given scientists a broad overview of the immune system response to this particular virus. However, they have also found that some immune cell responses — including by cell types that are usually protective — can occasionally trigger deadly inflammation and death in patients.
Other data analysis tools that allow for examination down to the level of single cells have given scientists some clues about culprits in severe COVID cases. But such focused views often lack the context of particular cell groupings that might cause better or poorer outcomes.
The Multiscale PHATE tool, a machine learning tool developed at Yale, allows researchers to pass through all resolutions of data, from millions of cells to a single cell, within minutes. The technology builds on an algorithm called PHATE, created in the lab of Smita Krishnaswamy, associate professor of genetics and computer science, which overcomes many of the shortcomings of existing data visualization tools.
“Machine learning algorithms typically focus on a single resolution view of the data, ignoring information that can be found in other more focused views,” said Manik Kuchroo, a doctoral candidate at Yale School of Medicine who helped develop the technology and is co-lead author of the paper. “For this reason, we created Multiscale PHATE which allows users to zoom in and focus on specific subsets of their data to perform more detailed analysis.”
Kuchroo, who works in Krishnaswamy’s lab, used the new tool to analyze 55 million blood cells taken from 163 patients admitted to Yale New Haven Hospital with severe cases of COVID-19. Looking broadly, they found that high levels T cells seem to be protective against poor outcomes while high levels of two white blood cell types known as granulocytes and monocytes were associated with higher levels of mortality.
However, when the researchers drilled down to a more granular level they discovered that TH17, a helper T cell, was also associated with higher mortality when clustered with the immune system cells IL-17 and IFNG.
By measuring quantities of these cells in the blood, they could predict whether the patient lived or died with 83% accuracy, the researchers report.
“We were able to rank order risk factors of mortality to show which are the most dangerous,” Krishnaswamy said.
In theory, the new data analytical tool could be used to fine tune risk assessment in a host of diseases, she said.
Jessie Huang in the Yale Department of Computer Science and Patrick Wong in the Department of Immunobiology are co-lead authors of the paper. Akiko Iwasaki, the Waldemar Von Zedtwitz Professor of Immunobiology, is co-corresponding author.
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Using computer drug simulations, researchers have found that doctors need to be wary of prescribing a particular treatment for all types of cancer and patients.
The drug, called metformin, has traditionally been prescribed for diabetes but has been used in clinical settings as a cancer treatment in recent years.
The researchers say while metformin shows great promise, it also has negative consequences for some types of cancers.
“Metformin is a wonder drug, and we are just beginning to understand all its possible benefits,” said Mehrshad Sadria, a PhD candidate in applied mathematics at the University of Waterloo. “Doctors need to examine the value of the drug on a case-by-case basis, because for some cancers and some patient profiles, it may actually have the opposite of the intended effect by protecting tumour cells against stress.”
The computer-simulated treatments use models that replicate both the drug and the cancerous cells in a virtual environment. Such models can give clinical trials in humans a considerable head-start and can provide insights to medical practitioners that would take much longer to be discovered in the field.
“In clinical settings, drugs can sometimes be prescribed in a trial and error manner,” said Anita Layton, professor of applied mathematics and Canada 150 Research Chair in mathematical biology and medicine at Waterloo. “Our mathematical models help accelerate clinical trials and remove some of the guesswork. What we see with this drug is that it can do a lot of good but needs more study.”
The researchers say their work shows the importance of precision medicine when considering the use of metformin for cancer and other diseases. Precision medicine is an approach that assumes each patient requires individualized medical assessment and treatment.
“Diseases and treatments are complicated,” Sadria said. “Everything about the patient matters, and even small differences can have a big impact on the effect of a drug, such as age, gender, genetic and epigenetic profiles. All these things are important and can affect a patient’s drug outcome. In addition, no one drug works for everyone, so doctors need to take a close look at each patient when considering treatments like metformin.”
Sadria, Layton and co-author Deokhwa Seo’s paper was published in the journal BioMed Central Cancer.
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