Aurelia Butler

A novel technique for studying vortices in quantum fluids has been developed by Lancaster physicists.
Andrew Guthrie, Sergey Kafanov, Theo Noble, Yuri Pashkin, George Pickett and Viktor Tsepelin, in collaboration with scientists from Moscow State University, used tiny mechanical resonators to detect individual quantum vortices in superfluid helium.
Their work is published in the current volume of Nature Communications.
This research into quantum turbulence is simpler than turbulence in the real world, which is observed in everyday phenomena such as surf, fast flowing rivers, billowing storm clouds, or chimney smoke. Despite the fact it is so commonplace and is found at every level, from the galaxies to the subatomic, it is still not fully understood.
Physicists know the fundamental Navier-Stokes Equations which govern the flow of fluids such as air and water, but despite centuries of trying, the mathematical equations still cannot be solved.
Quantum turbulence may provide the clues to an answer.
Turbulence in quantum fluids is much simpler than its “messy” classical counterpart, and being made up of identical singly-quantised vortices, can be thought of as providing an “atomic theory” of the phenomenon.
Unhelpfully, turbulence in quantum systems, for example in superfluid helium 4, takes place on microscopic scales, and so far scientists have not had tools with sufficient precision to probe eddies this small.
But now the Lancaster team, working at temperature of a few thousandths of a degree above absolute zero, has harnessed nanoscience to allow the detection of single quantum vortices (with core sizes on a par with atomic diameters) by using a nanoscale “guitar string “in the superfluid.
How the team does it is to trap a single vortex along the length of the “string” (a bar of around 100 nanometres across). The resonant frequency of the bar changes when a vortex is trapped, and thus the capture and release rate of vortices can be followed, opening a window into the turbulent structure.
Dr Sergey Kafanov who initiated this research said: “The devices developed have many other uses, one of which is to ping the end of a partially trapped vortex to study the nanoscale oscillations of the vortex core. Hopefully the studies will add to our insight into turbulence and may provide clues on how to solve these stubborn equations.”
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As more private data is stored and shared digitally, researchers are exploring new ways to protect data against attacks from bad actors. Current silicon technology exploits microscopic differences between computing components to create secure keys, but artificial intelligence (AI) techniques can be used to predict these keys and gain access to data. Now, Penn State researchers have designed a way to make the encrypted keys harder to crack.
Led by Saptarshi Das, assistant professor of engineering science and mechanics, the researchers used graphene — a layer of carbon one atom thick — to develop a novel low-power, scalable, reconfigurable hardware security device with significant resilience to AI attacks. They published their findings in Nature Electronics today (May 10).
“There has been more and more breaching of private data recently,” Das said. “We developed a new hardware security device that could eventually be implemented to protect these data across industries and sectors.”
The device, called a physically unclonable function (PUF), is the first demonstration of a graphene-based PUF, according to the researchers. The physical and electrical properties of graphene, as well as the fabrication process, make the novel PUF more energy-efficient, scalable, and secure against AI attacks that pose a threat to silicon PUFs.
The team first fabricated nearly 2,000 identical graphene transistors, which switch current on and off in a circuit. Despite their structural similarity, the transistors’ electrical conductivity varied due to the inherent randomness arising from the production process. While such variation is typically a drawback for electronic devices, it’s a desirable quality for a PUF not shared by silicon-based devices.
After the graphene transistors were implemented into PUFs, the researchers modeled their characteristics to create a simulation of 64 million graphene-based PUFs. To test the PUFs’ security, Das and his team used machine learning, a method that allows AI to study a system and find new patterns. The researchers trained the AI with the graphene PUF simulation data, testing to see if the AI could use this training to make predictions about the encrypted data and reveal system insecurities.
“Neural networks are very good at developing a model from a huge amount of data, even if humans are unable to,” Das said. “We found that AI could not develop a model, and it was not possible for the encryption process to be learned.”
This resistance to machine learning attacks makes the PUF more secure because potential hackers could not use breached data to reverse engineer a device for future exploitation, Das said. Even if the key could be predicted, the graphene PUF could generate a new key through a reconfiguration process requiring no additional hardware or replacement of components.
“Normally, once a system’s security has been compromised, it is permanently compromised,” said Akhil Dodda, an engineering science and mechanics graduate student conducting research under Das’s mentorship. “We developed a scheme where such a compromised system could be reconfigured and used again, adding tamper resistance as another security feature.”
With these features, as well as the capacity to operate across a wide range of temperatures, the graphene-based PUF could be used in a variety of applications. Further research can open pathways for its use in flexible and printable electronics, household devices and more.
Paper co-authors include Dodda, Shiva Subbulakshmi Radhakrishnan, Thomas Schranghamer and Drew Buzzell from Penn State; and Parijat Sengupta from Purdue University. Das is also affiliated with the Penn State Department of Materials Science and Engineering and the Materials Research Institute.
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Log on to any app store, and parents will find hundreds of options for children that claim to be educational. But new research suggests these apps might not be as beneficial to children as they seem.
A new study analyzed some of the most downloaded educational apps for kids using a set of four criteria designed to evaluate whether an app provides a high-quality educational experience for children. The researchers found that most of the apps scored low, with free apps scoring even lower than their paid counterparts on some criteria.
Jennifer Zosh, associate professor of human development and family studies at Penn State Brandywine, said the study — recently published in the Journal of Children and Media — suggests apps shouldn’t replace human interaction nor do they guarantee learning.
“Parents shouldn’t automatically trust that something marked ‘educational’ in an app store is actually educational,” Zosh said. “By co-playing apps with their children, talking to them about what is happening as they play, pointing out what is happening in the real world that relates to something shown in an app, and selecting apps that minimize distraction, they are able to leverage the pillars of learning and can successfully navigate this new digital childhood.”
According to previous research, about 98 percent of kids ages eight and under live in a home with some type of mobile device, like a smartphone or tablet. While watching videos and playing games are popular ways children spend their time on these devices, the researchers said there are also many apps that are not only popular but claim to be educational.
Marisa Meyer, a research assistant at the University of Michigan, said the idea for the study came about when reviewing the top-downloaded apps on the Google Play marketplace for different research. More

The promise of a quantum internet depends on the complexities of harnessing light to transmit quantum information over fiber optic networks. A potential step forward was reported today by researchers in Sweden who developed integrated chips that can generate light particles on demand and without the need for extreme refrigeration.
Quantum computing today relies on states of matter, that is, electrons which carry qubits of information to perform multiple calculations simultaneously, in a fraction of the time it takes with classical computing.
The co-author of the research, Val Zwiller, Professor at KTH Royal Institute of Technology, says that in order to integrate quantum computing seamlessly with fiber-optic networks — which are used by the internet today — a more promising approach would be to harness optical photons.
“The photonic approach offers a natural link between communication and computation,” he says. “That’s important, since the end goal is to transmit the processed quantum information using light.”
But in order for photons to deliver qubits on-demand in quantum systems, they need to be emitted in a deterministic, rather than probabilistic, fashion. This can be accomplished at extremely low temperatures in artificial atoms, but today the research group at KTH reported a way to make it work in optical integrated circuits — at room temperature.
The new method enables photon emitters to be precisely positioned in integrated optical circuits that resemble copper wires for electricity, except that they carry light instead, says co-author of the research, Ali Elshaari, Associate Professor at KTH Royal Institute of Technology.
The researchers harnessed the single-photon-emitting properties of hexagonal boron nitride (hBN), a layered material. hBN is a compound commonly used is used ceramics, alloys, resins, plastics and rubbers to give them self-lubricating properties. They integrated the material with silicon nitride waveguides to direct the emitted photons.
Quantum circuits with light are either operated at cryogenic temperatures — plus 4 Kelvin above absolute zero — using atom-like single photon sources, or at room temperature using random single photon sources, Elshaari says. By contrast, the technique developed at KTH enables optical circuits with on-demand emission of light particles at room temperature.
“In existing optical circuits operating at room temperature, you never know when the single photon is generated unless you do a heralding measurement,” Elshaari says. “We realized a deterministic process that precisely positions light-particles emitters operating at room temperature in an integrated photonic circuit.”
The researchers reported coupling of hBN single photon emitter to silicon nitride waveguides, and they developed a method to image the quantum emitters. Then in a hybrid approach, the team built the photonic circuits with respect to the quantum sources locations using a series of steps involving electron beam lithography and etching, while still preserving the high quality nature of the quantum light.
The achievement opens a path to hybrid integration, that is, incorporating atom-like single-photon emitters into photonic platforms that cannot emit light efficiently on demand.
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Computer scientists at the University of California San Diego have developed a more accurate navigation system that will allow robots to better negotiate busy clinical environments in general and emergency departments more specifically. The researchers have also developed a dataset of open source videos to help train robotic navigation systems in the future.
The team, led by Professor Laurel Riek and Ph.D. student Angelique Taylor, detail their findings in a paper for the International Conference on Robotics and Automation taking place May 30 to June 5 in Xi’an, China.
The project stemmed from conversations with clinicians over several years. The consensus was that robots would best help physicians, nurses and staff in the emergency department by delivering supplies and materials. But this means robots have to know how to avoid situations where clinicians are busy tending to a patient in critical or serious condition.
“To perform these tasks, robots must understand the context of complex hospital environments and the people working around them,” said Riek, who holds appointments both in computer science and emergency medicine at UC San Diego.
Taylor and colleagues built the navigation system, the Safety Critical Deep Q-Network (SafeDQN), around an algorithm that takes into account how many people are clustered together in a space and how quickly and abruptly these people are moving. This is based on observations of clinicians’ behavior in the emergency department. When a patient’s condition worsens, a team immediately gathers around them to render aid. Clinicians’ movements are quick, alert and precise. The navigation system directs the robots to move around these clustered groups of people, staying out of the way.
“Our system was designed to deal with the worst case scenarios that can happen in the ED,” said Taylor, who is part of Riek’s Healthcare Robotics lab at the UC San Diego Department of Computer Science and Engineering.
The team trained the algorithm on videos from YouTube, mostly coming from documentaries and reality shows, such as “Trauma: Life in the ER” and “Boston EMS.” The set of more than 700 videos is available for other research teams to train other algorithms and robots.
Researchers tested their algorithm in a simulation environment, and compared its performance to other state-of-the-art robotic navigation systems. The SafeDQN system generated the most efficient and safest paths in all cases.
Next steps include testing the system on a physical robot in a realistic environment. Riek and colleagues plan to partner with UC San Diego Health researchers who operate the campus’ healthcare training and simulation center.
The algorithms could also be used outside of the emergency department, for example during search and rescue missions.
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A team of researchers at the Institute of Laser Engineering, Osaka University, in collaboration with Bielefeld University and Technical University Braunschweig in Germany, came closer to unraveling the complicated optical response of wide-bandgap semiconductor multiple quantum wells and how atomic-scale lattice vibration can generate free space terahertz emission. Their work provides a significant push towards the application of laser terahertz emission microscopes to nano-seismology of wide-bandgap quantum devices.
Terahertz (THz) waves can be generated by ultrafast processes occurring in a material. By looking at THz emission, researchers have been able to study different processes at the quantum level — from simple bulk semiconductors to advanced quantum materials such as multiple quantum wells .
The THz research group led by Prof. Masayoshi Tonouchi at the Institute of Laser Engineering, Osaka University and his PhD student Abdul Mannan, together with international collaborators Prof. Dmitry Turchinovich at Bielefeld University and Prof. Andreas Hangleiter at Technical University of Braunschweig, has measured multifunction response in buried GaInN/GaN multiple quantum wells (MQWs) which includes dynamic screening effect of the built-in field inside the GaInN quantum wells, capacitive charge oscillation between GaN and GaInN quantum wells, and acoustic wave beams launched by the stress release between GaN and GaInN. All these functions can be monitored by observing THz emission into free space. In addition, it was proven that the propagating acoustic waves provide a new technique to evaluate the thickness of buried structure in devices at the resolution of 10 nm on the wafer scale, making nano-seismology a unique LTEM application for wide-bandgap quantum devices.
Probing buried structures in opto-acoustic devices at ultra-high resolution is still an unexplored area of research. In the present work, acoustically driven electromagnetic THz emission into free space is utilized for probing GaInN/GaN MQWs sandwiched in GaN material. Laser-induced polarization dynamics of charge carriers results in a partial release of coherent acoustic phonons (CAPs) in GaInN/GaN MQW. This CAP pulse propagating within a material creates the associated electric polarization wave-packet. Once the propagating CAP pulse encounters the discontinuity of acoustic impedance or piezoelectric constant within the structure, this will lead to the transient change in the associated electric polarization, which serves as the source of the acoustically driven electromagnetic THz emission into free space. The temporal separation between ultrafast polarization dynamics in GaInN/GaN MQW and acoustically driven THz emission gives the thickness of the CAP-propagating medium (nano seismology).
The specialist team organized for THz emission spectroscopy, opto-THz science, and wide-bandgap/quantum-well semiconductor material science has made a significant step towards 3D dynamic characterization, including buried active layers in various materials and devices. “A 3D active tool to characterize ultrafast carrier dynamics, strain physics, phonon dynamics, and ultrafast dielectric responses locally in a non-contact and non-destructive manner has become an essential area of research for new materials and devices. We hope the present work contributes to such an evolution,” says Prof. Masayoshi Tonouchi.
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How is it possible to move in the desired direction without a brain or nervous system? Single-celled organisms apparently manage this feat without any problems: for example, they can swim towards food with the help of small flagellar tails.
How these extremely simply built creatures manage to do this was not entirely clear until now. However, a research team at TU Wien (Vienna) has now been able to simulate this process on the computer: They calculated the physical interaction between a very simple model organism and its environment. This environment is a liquid with a non-uniform chemical composition, it contains food sources that are unevenly distributed.
The simulated organism was equipped with the ability to process information about food in its environment in a very simple way. With the help of a machine learning algorithm, the information processing of the virtual being was then modified and optimised in many evolutionary steps. The result was a computer organism that moves in its search for food in a very similar way to its biological counterparts.
Chemotaxis: Always going where the chemistry is right
“At first glance, it is surprising that such a simple model can solve such a difficult task,” says Andras Zöttl, who led the research project, which was carried out in the “Theory of Soft Matter” group (led by Gerhard Kahl) at the Institute of Theoretical Physics at TU Wien. “Bacteria can use receptors to determine in which direction, for example, the oxygen or nutrient concentration is increasing, and this information then triggers a movement into the desired direction. This is called chemotaxis.”
The behaviour of other, multicellular organisms can be explained by the interconnection of nerve cells. But a single-celled organism has no nerve cells — in this case, only extremely simple processing steps are possible within the cell. Until now, it was not clear how such a low degree of complexity could be sufficient to connect simple sensory impressions — for example from chemical sensors — with targeted motor activity. More

Climate change, a pandemic or the coordinated activity of neurons in the brain: In all of these examples, a transition takes place at a certain point from the base state to a new state. Researchers at the Technical University of Munich (TUM) have discovered a universal mathematical structure at these so-called tipping points. It creates the basis for a better understanding of the behavior of networked systems.
It is an essential question for scientists in every field: How can we predict and influence changes in a networked system? “In biology, one example is the modelling of coordinated neuron activity,” says Christian Kühn, professor of multiscale and stochastic dynamics at TUM. Models of this kind are also used in other disciplines, for example when studying the spread of diseases or climate change.
All critical changes in networked systems have one thing in common: a tipping point where the system makes a transition from a base state to a new state. This may be a smooth shift, where the system can easily return to the base state. Or it can be a sharp, difficult-to-reverse transition where the system state can change abruptly or “explosively.” Transitions of this kind also occur in climate change, for example with the melting of the polar ice caps. In many cases, the transitions result from the variation of a single parameter, such as the rise in concentrations of greenhouse gases behind climate change.
Similar structures in many models
In some cases — such as climate change — a sharp tipping point would have extremely negative effects, while in others it would be desirable. Consequently, researchers have used mathematical models to investigate how the type of transition is influenced by the introduction of new parameters or conditions. “For example, you could vary another parameter, perhaps related to how people change their behavior in a pandemic. Or you might adjust an input in a neural system,” says Kühn. “In these examples and many other cases, we have seen that we can go from a continuous to a discontinuous transition or vice versa.”
Kühn and Dr. Christian Bick of Vrije Universiteit Amsterdam studied existing models from various disciplines that were created to understand certain systems. “We found it remarkable that so many mathematical structures related to the tipping point looked very similar in those models,” says Bick. “By reducing the problem to the most basic possible equation, we were able to identify a universal mechanism that decides on the type of tipping point and is valid for the greatest possible number of models.”
Universal mathematical tool
The scientists have thus described a new core mechanism that makes it possible to calculate whether a networked system will have a continuous or discontinuous transition. “We provide a mathematical tool that can be applied universally — in other words, in theoretical physics, the climate sciences and in neurobiology and other disciplines — and works independently of the specific case at hand,” says Kühn.
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