Aurelia Butler

A group of researchers from Pisa, Jyväskylä, San Sebastian and MIT have demonstrated how a heterostructure consisting of superconductors and magnets can be used to create uni-directional current like that found in semiconductor diodes.
These novel superconductor diodes, however, operate at much lower temperatures than their semiconductor counterparts and are therefore useful in quantum technologies.
Electronics for quantum technology
Most of our everyday electronic appliances, such as radios, logic components or solar panels, rely on diodes where current can flow primarily in one direction. Such diodes rely on the electronic properties of semiconductor systems which cease to work at the ultralow sub-Kelvin temperatures required in tomorrow’s quantum technology. Superconductors are metals whose electrical resistivity is usually zero but, when contacted with other metals, can exhibit high contact resistance.
This can be understood from the energy gap, which indicates a forbidden region for electronic excitations that form in superconductors. It resembles the energy gap in semiconductors but is typically much smaller. While the presence of such a gap has been known for decades, the diode-like feature has not been previously observed, because it requires breaking the usually robust symmetry of the contact’s current-voltage characteristics.
The new work demonstrates how this symmetry can be broken with the help of a ferromagnetic insulator suitably placed in the junction. Since a big part of today’s research on quantum technologies is based on superconducting materials operating at ultralow temperatures, this innovation is readily available for them.
Power of collaboration
The research finding was made as part of the SUPERTED project, which is being funded under the EU’s Future and Emerging Technologies (FET Open). This project aims at creating the world’s first superconducting thermoelectric detector of electromagnetic radiation, based on superconductor/magnet heterostructures.
“Actually, finding the diode functionality was a pleasant surprise, a consequence of the thorough characterization of SUPERTED samples,” explains Elia Strambini, from Istituto Nanoscienze — CNR and Scuola Normale Superiore (SNS) in Pisa, who made the initial discovery.
Francesco Giazotto, from Istituto Nanoscienze — CNR and SNS and who led the experimental efforts, says:
“I believe this finding is promising for several tasks in quantum technology, such as current rectification or current limiting.”
SUPERTED coordinator; Professor Tero Heikkilä, from the University of Jyväskylä, worked on the theory behind the effect: “This finding showed the power of collaboration between different types of researchers, from materials science to superconducting electronics and theory. Without European support such collaboration would not take place.” More

The best things in life are unlikely to occur. In many situations, taking at least moderate risks yields higher expected rewards. Yet many people struggle with taking such risks: they are overly cautious and forego high payoffs. “However, we are not alone in this struggle, but we can observe and learn from others,” says Wataru Toyokawa. “We therefore wanted to find out whether social learning can also rescue us from adverse risk aversion.” The answer is yes, as the authors from the Cluster of Excellence Centre for the Advanced Study of Collective Behaviour showed in a just published study in the journal eLife.
Collective rescue occurs even among a biased collective
It is a long-established finding that collectives achieve better decisions by aggregating information or judgments, known as the wisdom of crowds. Individual errors cancel each other out, so that collectives do the right thing even if many individuals err. However, the wisdom of crowds does not work directly here, because the crowd is not wise; rather, the collective is biased towards undue risk aversion. “I wondered how social learning could still be beneficial in such a situation,” states Toyokawa. “Simply copying the majority would not help us at all, it would even yield more extreme risk aversion. So, if social learning helps at all, it must be by a different mechanism.”
To uncover these mechanisms, Toyokawa developed a dynamical mathematical model, which predicted that social learning can indeed promote favourable risk taking. He then proceeded to review the predictions from his model in large-scale online experiments with human subjects. Each participant played a browser-based game where they could choose between a variety of options — which might turn out good or bad, and with different probabilities. Toyokawa observed: “When the subjects played individually without any information from other participants, they predominantly preferred safe options with lower rewards. However, when social learning was possible, that is, when participants could see what others chose — but not know how successful others’ choices were -, it became more and more likely that they choose riskier options with higher expected rewards.” In other words, social learners made riskier choices that were more rewarding in the long run.
Occasionally copying others increases exploration and persistence
“By observing others’ choices, we could make smarter decisions, even though every single individual’s own decisions might be unduly risk averse,” Toyokawa summarizes. “Herewith, we identified a key mechanism underlying this counter-intuitive result: risk-aversion was mitigated not because the majority chose the risky option, nor were individuals simply attracted towards the majority. Rather, participants’ choices became risker even though the majority chose the safer alternative at the outset, by striking a right balance between what they experienced themselves and what they observed from others.”
Wolfgang Gaissmaier stresses that this is a striking demonstration of the power of social learning: “Under social influence, individuals became more explorative and more persistent in trying out the risky, more profitable option, even if that option might sometimes disappoint them in the short run. And once individual risk aversion was reduced, this process perpetuated itself, as there were more and more risk takers around to be copied.”
“The finding that adverse risk aversion is mitigated under social influence will help us better understand the evolution of learning under social interaction,” concludes Wataru Toyokawa. “The study suggests that social learning is advantageous in wider environmental conditions than previously assumed.”
The study was funded by the Cluster of Excellence Centre for the Advanced Study of Collective Behaviour
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Ever smaller and more intricate — without miniaturization, we wouldn’t have the components today that are required for high-performance laptops, compact smartphones or high-resolution endoscopes. Research is now being carried out in the nanoscale on switches, rotors or motors that comprise of only a few atoms in order to build what are known as molecular machines. A research team at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) has successfully built the world’s smallest energy powered gear wheel with corresponding counterpart. The nano gear unit is the first that can also be actively controlled and driven. The researchers’ findings have recently been published in the journal Nature Chemistry.
Miniaturization plays a key role in the further development of modern technologies and makes it possible to manufacture smaller devices that have more power. It also plays a significant role in manufacturing, since it allows materials and functional materials or medication to be produced at previously unprecedented levels of precision. Now, research has entered the nanoscale — which is invisible to the naked eye — focusing on individual atoms and molecules. The significance of this new field of research is demonstrated by the Nobel Prize for Chemistry, which was awarded for research into molecular machines in 2016.
Some important components used in molecular machines such as switches, rotors, forceps, robot arms or even motors already exist in the nanoscale. A further essential component for any machine is the gear wheel, which allows changes in direction and speed and enables movements to be connected to each other. Molecular counterparts also exist for gear wheels, however, up to now, they have only moved passively back and forth, which is not extremely useful for a molecular machine.
The molecular gear wheel developed by the research team led by Prof. Dr. Henry Dube, Chair of Organic Chemistry I at FAU and previously head of a junior research group at LMU in Munich, measures only 1.6 nm, which corresponds to around 50,000ths of the thickness of a human hair — the smallest of its kind. But that’s not all. The research team has succeeded in actively powering a molecular gear wheel and its counterpart and has thus solved a fundamental problem in the construction of machines on the nanoscale.
The gear unit comprises two components that are interlocked with each other and are made up of only 71 atoms. One component is a triptycene molecule whose structure is similar to a propeller or bucket wheel (shown in light gray in the animation). The second component is a flat fragment of a thioindigo molecule, similar to a small plate (shown in gold in the animation). If the plate rotates 180 degrees, the propeller rotates by only 120 degrees. The result is a 2:3 transmission ratio.
The nano gear unit is controlled by light, making it a molecular photogear. As they are directly driven by the light energy, the plate and the triptycene propeller move in locked synchronous rotation. Heat alone was not sufficient in order to make the gear unit rotate, as the FAU team discovered. When the researchers heated the solution around the gear unit in the dark, the propeller turned, but the plate did not — the gear “slipped.” The researchers thus came to the conclusion that the nano gear unit can be activated and controlled using a light source.
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Chemists at the U.S. Department of Energy’s Brookhaven National Laboratory have developed a new machine-learning (ML) framework that can zero in on which steps of a multistep chemical conversion should be tweaked to improve productivity. The approach could help guide the design of catalysts — chemical “dealmakers” that speed up reactions.
The team developed the method to analyze the conversion of carbon monoxide (CO) to methanol using a copper-based catalyst. The reaction consists of seven fairly straightforward elementary steps.
“Our goal was to identify which elementary step in the reaction network or which subset of steps controls the catalytic activity,” said Wenjie Liao, the first author on a paper describing the method just published in the journal Catalysis Science & Technology. Liao is a graduate student at Stony Brook University who has been working with scientists in the Catalysis Reactivity and Structure (CRS) group in Brookhaven Lab’s Chemistry Division.
Ping Liu, the CRS chemist who led the work, said, “We used this reaction as an example of our ML framework method, but you can put any reaction into this framework in general.”
Targeting activation energies
Picture a multistep chemical reaction as a rollercoaster with hills of different heights. The height of each hill represents the energy needed to get from one step to the next. Catalysts lower these “activation barriers” by making it easier for reactants to come together or allowing them to do so at lower temperatures or pressures. To speed up the overall reaction, a catalyst must target the step or steps that have the biggest impact. More

Computational detective work by U.S. and German physicists has confirmed cerium zirconium pyrochlore is a 3D quantum spin liquid.
Despite the name, quantum spin liquids are solid materials in which quantum entanglement and the geometric arrangement of atoms frustrate the natural tendency of electrons to magnetically order themselves in relation to one another. The geometric frustration in a quantum spin liquid is so severe that electrons fluctuate between quantum magnetic states no matter how cold they become.
Theoretical physicists routinely work with quantum mechanical models that manifest quantum spin liquids, but finding convincing evidence that they exist in actual physical materials has been a decadeslong challenge. While a number of 2D or 3D materials have been proposed as possible quantum spin liquids, Rice University physicist Andriy Nevidomskyy said there’s no established consensus among physicists that any of them qualify.
Nevidomskyy is hoping that will change based on the computational sleuthing he and colleagues from Rice, Florida State University and the Max Planck Institute for Physics of Complex Systems in Dresden, Germany, published this month in the open-access journal npj Quantum Materials.
“Based on all the evidence we have today, this work confirms that the single crystals of the cerium pyrochlore identified as candidate 3D quantum spin liquids in 2019 are indeed quantum spin liquids with fractionalized spin excitations,” he said.
The inherent property of electrons that leads to magnetism is spin. Each electron behaves like a tiny bar magnet with a north and south pole, and when measured, individual electron spins always point up or down. In most everyday materials, spins point up or down at random. But electrons are antisocial by nature, and this can cause them to arrange their spins in relation to their neighbors in some circumstances. In magnets, for example, spins are collectively arranged in the same direction, and in antiferromagnets they are arranged in an up-down, up-down pattern. More

Settling a key dispute in the wireless communications field, researchers at the National Institute of Standards and Technology (NIST) found that transmission performance is consistent across different bands of the millimeter-wave (mmWave) spectrum targeted for high-speed, data-rich 5G systems.
Wireless systems are moving to the mmWave spectrum at 10-100 gigahertz (GHz), above crowded cellular frequencies as well as early 5G systems around 3 GHz. System operators tend to prefer lower bands of the new mmWave spectrum. One reason is that they are influenced by a formula that says more signals are lost at higher frequencies due to smaller wavelengths resulting in a smaller useful antenna area. But until now, measurements of this effect by many organizations have disagreed over whether this is true.
NIST researchers developed a new method to measure frequency effects, using the 26.5-40 GHz band as a target example. After extensive study in the laboratory and two real-world environments, NIST results confirmed that the main signal path — over a clear “line of sight” between transmitter and receiver — does not vary by frequency, a generally accepted thesis for traditional wireless systems but until now not proven for the mmWave spectrum. The results are described in a new paper.
The team also found that signal losses in secondary paths — where transmissions are reflected, bent or diffused into clusters of reflections — can vary somewhat by frequency, depending on the type of path. Reflective paths, which are the second strongest and critical for maintaining connectivity, lost only a little signal strength at higher frequencies. The weaker bent and diffuse paths lost a bit more. Until now, the effects of frequency on this so-called multipath were unknown.
“This work may serve to demyth many misconceptions about propagation about higher frequencies in 5G and 6G,” NIST electrical engineer Camillo Gentile said. “In short, while performance will be worse at higher frequencies, the drop in performance is incremental. So we do expect the deployment at 5G and eventually at 6G to be successful.”
The NIST method emphasizes innovative measurement procedures and enhanced equipment calibration to make sure only the transmission channel is measured. The researchers used NIST’s SAMURAI (Synthetic Aperture Measurement UnceRtainty for Angle of Incidence) channel sounder, which supports design and repeatable testing of 5G mmWave devices with unprecedented accuracy across a wide range of signal frequencies and scenarios. The NIST system is unique in that antenna beams can be steered in any direction for precise angle-of-arrival estimates.
NIST’s main innovations in the new study, as discussed in the paper, were calibration procedures to remove the effects of channel sounder equipment from the measurements, extension of an existing algorithm to determine from a single measurement how individual paths vary by frequency, and studies in an industrial control center and a conference room to classify the types of paths involved and determine any frequency effects.
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An intermediate layer consisting of a few atoms is helping to improve the transport of spin currents from one material to another. Until now, this process involves significant losses. A team from Martin Luther University Halle-Wittenberg (MLU), the Max Planck Institute (MPI) for Microstructure Physics, and the Freie Universität Berlin reports in the scientific journal ACS Nano Letters on how this can be avoided. The researchers thus demonstrate important new insights relevant for many spintronic applications, for example energy-efficient and ultra-fast storage technologies of the future.
In modern microelectronics, the charge of electrons is used to carry information in electronic components, mobile phones and storage media. The charge transport requires a relatively large amount of energy and generates heat. Spintronics could offer an energy-saving alternative. The basic idea is to utilise spin in information processing. Spin is the intrinsic angular momentum of the electrons that creates a magnetic moment. This generates the magnetism that will ultimately be used to process information.
In spintronics, spin currents also have to be transferred from one material to the next. “In many cases, the spin transport across interfaces is a very lossy process,” explains physicist Professor Georg Woltersdorf from MLU, who led the study. The team looked for a way to mitigate these losses by using an approach that, at first, sounds rather contradictory: they integrated an insulating barrier between the two materials. “We designed the insulator at the atomic level so that it turned metallic and could conduct the spin currents. This enabled us to significantly improve the spin transport and optimise the interfacial properties,” says Woltersdorf, summing up the process. The material samples were produced at the Max Planck Institute for Microstructure Physics. The unexpected effect was discovered through measurements of spin transport conducted at MLU and the Freie Universität Berlin. The team also provides the theoretical basis for the new discovery. According to Woltersdorf, this can be described using relatively simple models without spin-orbit coupling.
The results are highly relevant for many spintronic applications. For example, they can be used to improve spintronic terahertz emitters. Terahertz radiation is not not only applied in research, but also in high-frequency electronics, medicine, materials testing and communication technology.
The study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) and the European Union.
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It’s hard to tell the difference between a newborn boy and girl based solely on temperament characteristics such as the baby’s propensity to display fear, smile or laugh. But once babies reach around a year old that begins to change.
A new study in PLOS ONE used machine learning to analyze temperament data on 4,438 babies in an attempt to classify the infants by gender and age.
The results indicate it is far easier for computer algorithms to determine the age of a baby than it is for them to decipher a baby’s gender based off temperament data during the infant’s first 48 weeks of life.
However, once the babies passed 48 weeks of age, gender classification improved for the multiple algorithms considered, suggesting gender differences in infancy become more accentuated around this time.
“It is at least suggestive of a picture where temperament begins to differentiate by gender in a more powerful way around age one,” said Maria Gartstein, lead author of the study and a professor of psychology at Washington State University.
Previous research has investigated age and gender-based temperament differences in babies, but few if any studies have looked at the two variables together. More