Aurelia Butler

Scientists have made a path-breaking discovery in strontium ruthenate — with potential for new applications in quantum electronics.
Since the discovery of superconductivity in Sr2RuO4 in 1994, hundreds of studies have been published on this compound, which have suggested that Sr2RuO4 is a very special system with unique properties. These properties make Sr2RuO4 a material with great potential, for example, for the development of future technologies including superconducting spintronics and quantum electronics by virtue of its ability to carry lossless electrical currents and magnetic information simultaneously. An international research team led by scientists at the University of Konstanz has been now able to answer one of the most interesting open questions on Sr2RuO4: why does the superconducting state of this material exhibit some features that are typically found in materials known as ferromagnets, which are considered being antagonists to superconductors? The team has found that Sr2RuO4 hosts a new form of magnetism, which can coexist with superconductivity and exists independently of superconductivity as well. The results have been published in the current issue of Nature Communications.
After a research study that lasted several years and involved 26 researchers from nine different universities and research institutions, the missing piece of the puzzle seems to have been found. Alongside the University of Konstanz, the universities of Salerno, Cambridge, Seoul, Kyoto and Bar Ilan as well as the Japan Atomic Energy Agency, the Paul Scherrer Institute and the Centro Nazionale delle Ricerche participated in the study.
So far not the right tool to find evidence
“Despite decades of research on Sr2RuO4, there had been no evidence for the existence of this unusual type of magnetism in this material. A few years ago, however, we wondered if the reconstruction that happens in this material on the surface, where the crystal structure exhibits some small changes at the atomic scale level, could also lead to an electronic ordering with magnetic properties. Following this intuition, we realized that this question had probably not been addressed because nobody had used the “right tool” to find evidence for this magnetism, which we thought could be extremely weak and only limited to a few atomic layers from the surface of the material” states the leader of this international research study, Professor Angelo Di Bernardo from the University of Konstanz, whose research focuses on superconducting spintronic and quantum devices based on innovative materials.
To carry out the experiment, the team used high-quality single crystals of Sr2RuO4 prepared by the group of Dr Antonio Vecchione from the Centro Nazionale delle Ricerche (CNR) Spin in Salerno. “Making large crystals of Sr2RuO4 without any impurities was a big challenge albeit crucial for the success of the experiment, since defects would have given a signal similar to the magnetic signal which we were hunting,” says Dr Vecchione. More

A busy commuter is ready to walk out the door, only to realize they’ve misplaced their keys and must search through piles of stuff to find them. Rapidly sifting through clutter, they wish they could figure out which pile was hiding the keys.
Researchers at MIT have created a robotic system that can do just that. The system, RFusion, is a robotic arm with a camera and radio frequency (RF) antenna attached to its gripper. It fuses signals from the antenna with visual input from the camera to locate and retrieve an item, even if the item is buried under a pile and completely out of view.
The RFusion prototype the researchers developed relies on RFID tags, which are cheap, battery-less tags that can be stuck to an item and reflect signals sent by an antenna. Because RF signals can travel through most surfaces (like the mound of dirty laundry that may be obscuring the keys), RFusion is able to locate a tagged item within a pile.
Using machine learning, the robotic arm automatically zeroes-in on the object’s exact location, moves the items on top of it, grasps the object, and verifies that it picked up the right thing. The camera, antenna, robotic arm, and AI are fully integrated, so RFusion can work in any environment without requiring a special set up.
While finding lost keys is helpful, RFusion could have many broader applications in the future, like sorting through piles to fulfill orders in a warehouse, identifying and installing components in an auto manufacturing plant, or helping an elderly individual perform daily tasks in the home, though the current prototype isn’t quite fast enough yet for these uses.
“This idea of being able to find items in a chaotic world is an open problem that we’ve been working on for a few years. Having robots that are able to search for things under a pile is a growing need in industry today. Right now, you can think of this as a Roomba on steroids, but in the near term, this could have a lot of applications in manufacturing and warehouse environments,” said senior author Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science and director of the Signal Kinetics group in the MIT Media Lab. More

Ultrashort pulses of light are proven indistinguishable from continuous illumination, in terms of controlling the electronic states of atomically-thin material tungsten disulfide (WS2).
A new, Swinburne-led study proves that ultrashort pulses of light can be used to drive transitions to new phases of matter, aiding the search for future Floquet-based, low-energy electronics.
There is significant interest in transiently controlling the band-structure of a monolayer semiconductor by using ultra-short pulses of light to create and control exotic new phases of matter.
The resulting temporary states known as Floquet-Bloch states are interesting from a pure research standpoint as well as for a proposed new class of transistor based on Floquet topological insulators (FTIs).
In an important finding, the ultra-short pulses of light necessary for detecting the formation of Floquet states were shown to be as effective in triggering the state as continuous illumination, an important question that, until now, had been largely ignored.
A CONTINUOUS WAVE OR ULTRASHORT-PULSES: THE PROBLEM WITH TIME
Floquet physics, which has been used to predict how an insulator can be transformed into an FTI, is predicated on a purely sinusoidal field, ie continuous, monochromatic (single wavelength) illumination that has no beginning or end. More

Inequalities in income affect how well children do in maths — but not reading, the most comprehensive study of its kind has found.
Looking at data stretching from 1992 to 2019, the analysis, published in the journal Educational Review, revealed that 10-year-olds in US states with bigger gaps in income did less well in maths than those living in areas of America where earnings were more evenly distributed.
With income inequality in the US the highest in the developed world, researcher Professor Joseph Workman argues that addressing social inequality may do more to boost academic achievement than reforming schools or curricula — favoured methods of policymakers.
Income inequality — a measure of how unevenly income is distributed through a population — has long been associated with a host of health and social problems including mental health issues, lack of trust, higher rates of imprisonment and lower rates of social mobility.
It may also affect academic achievement, through various routes.
For instance, income inequality is linked to higher rates of divorce, substance abuse and child maltreatment, the stresses of which may affect a child’s development. It is also associated with higher odds of babies being of a low weight a birth — something which can raise their risk of developmental delays as they grow up. More

Automated vehicles could be made more pedestrian-friendly thanks to new research which could help them predict when people will cross the road.
University of Leeds-led scientists investigating how to better understand human behaviour in traffic say that neuroscientific theories of how the brain makes decisions can be used in automated vehicle technology to improve safety and make them more human-friendly.
The researchers set out to determine whether a decision-making model called drift diffusion could predict when pedestrians would cross a road in front of approaching cars, and whether it could be used in scenarios where the car gives way to the pedestrian, either with or without explicit signals. This prediction capability will allow the autonomous vehicle to communicate more effectively with pedestrians, in terms of its movements in traffic and any external signals such as flashing lights, to maximise traffic flow and decrease uncertainty.
Drift diffusion models assume that people reach decisions after accumulation of sensory evidence up to a threshold at which the decision is made.
Professor Gustav Markkula, from the University of Leeds’ Institute for Transport Studies and the senior author of the study, said: “When making the decision to cross, pedestrians seem to be adding up lots of different sources of evidence, not only relating to the vehicle’s distance and speed, but also using communicative cues from the vehicle in terms of deceleration and headlight flashes.
“When a vehicle is giving way, pedestrians will often feel quite uncertain about whether the car is actually yielding, and will often end up waiting until the car has almost come to a full stop before starting to cross. Our model clearly shows this state of uncertainty borne out, meaning it can be used to help design how automated vehicles behave around pedestrians in order to limit uncertainty, which in turn can improve both traffic safety and traffic flow. More

Biologists at Cold Spring Harbor Laboratory (CSHL) are using a mathematical approach developed in CSHL Assistant Professor David McCandlish’s lab to find solutions to a diverse set of biological problems. Originally created as a way to understand interactions between different mutations in proteins, the tool is now being used by McCandlish and his collaborators to learn about the complexities of gene expression and the chromosomal mutations associated with cancer. McCandlish says:
“This is one of the things that’s really fascinating about mathematical research, is sometimes you can see connections between topics, which on the surface they seem so different, but at a mathematical level, they might be using some of the same technical ideas.”
All of these questions involve mapping the likelihood of different variations on a biological theme: which combinations of mutations are most likely to arise in a particular protein, for example, or which chromosome mutations are most often found together in the same cancer cell. McCandlish explains that these are problems of density estimation — a statistical tool that predicts how often an event happens. Density estimation can be relatively straightforward, such as charting different heights within a group of people. But when dealing with complex biological sequences, such as the hundreds, or thousands of amino acids that are strung together to build a protein, predicting the probability of each potential sequence becomes astonishingly complex.
McCandlish explains the fundamental problem his team is using math to address: “Sometimes if you make, say one mutation to a protein sequence, it doesn’t do anything. The protein works fine. And if you make a second mutation, it still works fine, but then if you put the two of them together, now you’ve got a broken protein. We’ve been trying to come up with methods to model not just interactions between pairs of mutations, but between three or four or any number of mutations.”
The methods they have developed can be used to interpret data from experiments that measure how hundreds of thousands of different combinations of mutations impact the function of a protein.
This study, reported in the Proceedings of the National Academy of Sciences, began with conversations with two other CSHL colleagues: CSHL Fellow Jason Sheltzer and Associate Professor Justin Kinney. They worked with McCandlish to apply his methods to gene expression and the evolution of cancer mutations. Software released by McCandlish’s team will enable other researchers to use these same approaches in their own work. He says he hopes it will be applied to a variety of biological problems.
Story Source:
Materials provided by Cold Spring Harbor Laboratory. Original written by Jennifer Michalowski. Note: Content may be edited for style and length. More

In what should be a win-win-win for the environment, a process developed at Rice University to extract valuable metals from electronic waste would also use up to 500 times less energy than current lab methods and produce a byproduct clean enough for agricultural land.
The flash Joule heating method introduced last year to produce graphene from carbon sources like waste food and plastic has been adapted to recover rhodium, palladium, gold and silver for reuse.
A report in Nature Communications by the Rice lab of chemist James Tour also shows highly toxic heavy metals including chromium, arsenic, cadmium, mercury and lead are removed from the flashed materials, leaving a byproduct with minimal metal content.
Instantly heating the waste to 3,400 Kelvin (5,660 degrees Fahrenheit) with a jolt of electricity vaporizes the precious metals, and the gases are vented away for separation, storage or disposal. Tour said that with more than 40 million tons of e-waste produced globally every year, there is plenty of potential for “urban mining.”
“Here, the largest growing source of waste becomes a treasure,” Tour said. “This will curtail the need to go all over the world to mine from ores in remote and dangerous places, stripping the Earth’s surface and using gobs of water resources. The treasure is in our dumpsters.”
He noted an increasingly rapid turnover of personal devices like cell phones has driven the worldwide rise of electronic waste, with only about 20% of landfill waste currently being recycled. More