Computers Math

Researchers at the University of California, Riverside, have used a nanoscale synthetic antiferromagnet to control the interaction between magnons — research that could lead to faster and more energy-efficient computers.
In ferromagnets, electron spins point in the same direction. To make future computer technologies faster and more energy-efficient, spintronics research employs spin dynamics — fluctuations of the electron spins — to process information. Magnons, the quantum-mechanical units of spin fluctuations, interact with each other, leading to nonlinear features of the spin dynamics. Such nonlinearities play a central role in magnetic memory, spin torque oscillators, and many other spintronic applications.
For example, in the emergent field of magnetic neuromorphic networks — a technology that mimics the brain — nonlinearities are essential for tuning the response of magnetic neurons. Also, in another frontier area of research, nonlinear spin dynamics may become instrumental.
“We anticipate the concepts of quantum information and spintronics to consolidate in hybrid quantum systems,” said Igor Barsukov, an assistant professor at the Department of Physics & Astronomy who led the study that appears in Applied Materials & Interfaces. “We will have to control nonlinear spin dynamics at the quantum level to achieve their functionality.”
Barsukov explained that in nanomagnets, which serve as building blocks for many spintronic technologies, magnons show quantized energy levels. Interaction between the magnons follows certain symmetry rules. The research team learned to engineer the magnon interaction and identified two approaches to achieve nonlinearity: breaking the symmetry of the nanomagnet’s spin configuration; and modifying the symmetry of the magnons. They chose the second approach.
“Modifying magnon symmetry is the more challenging but also more application-friendly approach,” said Arezoo Etesamirad, the first author of the research paper and a graduate student in Barsukov’s lab.
In their approach, the researchers subjected a nanomagnet to a magnetic field that showed nonuniformity at characteristic nanometer length scales. This nanoscale nonuniform magnetic field itself had to originate from another nanoscale object.
For a source of such a magnetic field, the researchers used a nanoscale synthetic antiferromagnet, or SAF, consisting of two ferromagnetic layers with antiparallel spin orientation. In its normal state, SAF generates nearly no stray field — the magnetic field surrounding the SAF, which is very small. Once it undergoes the so-called spin-flop transition, the spins become canted and the SAF generates a stray field with nonuniformity at nanoscale, as needed. The researchers switched the SAF between the normal state and the spin-flop state in a controlled manner to toggle the symmetry-breaking field on and off.
“We were able to manipulate the magnon interaction coefficient by at least one order of magnitude,” Etesamirad said. “This is a very promising result, which could be used to engineer coherent magnon coupling in quantum information systems, create distinct dissipative states in magnetic neuromorphic networks, and control large excitation regimes in spin-torque devices.”
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Materials provided by University of California – Riverside. Original written by Iqbal Pittalwala. Note: Content may be edited for style and length. More

Isaac Newton may have met his match.
For centuries, engineers have relied on physical laws — developed by Newton and others — to understand the stresses and strains on the materials they work with. But solving those equations can be a computational slog, especially for complex materials.
MIT researchers have developed a technique to quickly determine certain properties of a material, like stress and strain, based on an image of the material showing its internal structure. The approach could one day eliminate the need for arduous physics-based calculations, instead relying on computer vision and machine learning to generate estimates in real time.
The researchers say the advance could enable faster design prototyping and material inspections. “It’s a brand new approach,” says Zhenze Yang, adding that the algorithm “completes the whole process without any domain knowledge of physics.”
The research appears today in the journal Science Advances. Yang is the paper’s lead author and a PhD student in the Department of Materials Science and Engineering. Co-authors include former MIT postdoc Chi-Hua Yu and Markus Buehler, the McAfee Professor of Engineering and the director of the Laboratory for Atomistic and Molecular Mechanics.
Engineers spend lots of time solving equations. They help reveal a material’s internal forces, like stress and strain, which can cause that material to deform or break. Such calculations might suggest how a proposed bridge would hold up amid heavy traffic loads or high winds. Unlike Sir Isaac, engineers today don’t need pen and paper for the task. “Many generations of mathematicians and engineers have written down these equations and then figured out how to solve them on computers,” says Buehler. “But it’s still a tough problem. It’s very expensive — it can take days, weeks, or even months to run some simulations. So, we thought: Let’s teach an AI to do this problem for you.”
The researchers turned to a machine learning technique called a Generative Adversarial Neural Network. They trained the network with thousands of paired images — one depicting a material’s internal microstructure subject to mechanical forces, and the other depicting that same material’s color-coded stress and strain values. With these examples, the network uses principles of game theory to iteratively figure out the relationships between the geometry of a material and its resulting stresses. More

New research from the University of Surrey has shown that silicon could be one of the most powerful materials for photonic informational manipulation — opening up new possibilities for the production of lasers and displays.
While computer chips’ extraordinary success has confirmed silicon as the prime material for electronic information control, silicon has a reputation as a poor choice for photonics; there are no commercially available silicon light-emitting diodes, lasers or displays.
Now, in a paper published by Light: Science and Applications journal, a Surrey-led international team of scientists has shown that silicon is an outstanding candidate for creating a device that can control multiple light beams.
The discovery means that it is now possible to produce silicon processors with built-in abilities for light beams to control other beams — boosting the speed and efficiency of electronic communications.
This is possible thanks to the wavelength band called the far-infrared or terahertz region of the electromagnetic spectrum. The effect works with a property called a nonlinearity, which is used to manipulate laser beams — for example, changing their colour. Green laser pointers work this way: they take the output from a very cheap and efficient but invisible infrared laser diode and change the colour to green with a nonlinear crystal that halves the wavelength.
Other kinds of nonlinearity can produce an output beam with a third of the wavelength or be used to redirect a laser beam to control the direction of the beam’s information. The stronger the nonlinearity, the easier it is to control with weaker input beams.
The researchers found that silicon possesses the strongest nonlinearity of this type ever discovered. Although the study was carried out with the crystal being cooled to very low cryogenic temperatures, such strong nonlinearities mean that extremely weak beams can be used.
Ben Murdin, co-author of the study and Professor of Physics at the University of Surrey, said: “Our finding was lucky because we weren’t looking for it. We were trying to understand how a very small number of phosphorus atoms in a silicon crystal could be used for making a quantum computer and how to use light beams to control quantum information stored in the phosphorus atoms.
“We were astonished to find that the phosphorus atoms were re-emitting light beams that were almost as bright as the very intense laser we were shining on them. We shelved the data for a couple of years while we thought about proving where the beams were coming from. It’s a great example of the way science proceeds by accident, and also how pan-European teams can still work together very effectively.”
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If a base station in a local area network tries to use a directional beam to transmit a signal to a user trying to connect to the network — instead of using a wide area network broadcast, as base stations commonly do — how does it know which direction to send the beam?
Researchers from Rice University and Brown University developed a link discovery method in 2020 using terahertz radiation, with high-frequency waves above 100 gigahertz. For this work, they deferred the question of what would happen if a wall or other reflector nearby creates a non-line-of-sight (NLOS) path from the base station to the receiver and focused on the simpler situation where the only existing path was along the line-of-sight (LOS).
In APL Photonics, from AIP Publishing, those same researchers address this question by considering two different generic types of transmitters and exploring how their characteristics can be used to determine whether an NLOS path contributes to the signal received by the receiver.
“One type of transmitter sends all frequencies more or less in the same direction,” said Daniel Mittleman, co-author and an engineering professor at Brown, “while the other type sends different frequencies in different directions, exhibiting strong angular dispersion. The situation is quite different in these two different cases.”
The researchers’ work shows that the transmitter sending different frequencies in different directions has distinct advantages in its ability to detect the NLOS path and distinguish them from the LOS path.
“A well-designed receiver would be able to detect both frequencies and use their properties to recognize the two paths and tell them apart,” Mittleman said.
Many recent reports within academic literature have focused on various challenges involved in using terahertz signals for wireless communications. Indeed, the term 6G has become a buzzword to encompass future generations of wireless systems that use these ultrahigh-frequency signals.
“For terahertz signals to be used for wireless communications, many challenges must be overcome, and one of the biggest is how to detect and exploit NLOS paths,” said Mittleman.
This work is among the first to provide a quantitative consideration of how to detect and exploit NLOS paths, as well as a comparison of the behavior of different transmitters within this context.
“For most realistic indoor scenarios we can envision for an above-100 gigahertz wireless network, the issue of NLOS path is definitely going to require careful consideration,” said Mittleman. “We need to know how to exploit these link opportunities to maintain connectivity.”
If, for example, the LOS path is blocked by something, an NLOS path can be used to maintain the link between the base station and receiver.
“Interestingly, with a transmitter creating strong angular dispersion, sometimes an NLOS link can provide even faster connectivity than the LOS link,” said Yasaman Ghasempour, co-author and assistant professor at Rice University. “But you can’t take advantage of such opportunities if you don’t know the NLOS path exists or how to find it.”
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An international team with researchers from the University of Bayreuth has succeeded for the first time in discovering a previously unknown two-dimensional material by using modern high-pressure technology. The new material, beryllonitrene, consists of regularly arranged nitrogen and beryllium atoms. It has an unusual electronic lattice structure that shows great potential for applications in quantum technology. Its synthesis required a compression pressure that is about one million times higher than the pressure of the Earth’s atmosphere. The scientists have presented their discovery in the journal Physical Review Letters.
Since the discovery of graphene, which is made of carbon atoms, interest in two-dimensional materials has grown steadily in research and industry. Under extremely high pressures of up to 100 gigapascals, researchers from the University of Bayreuth, together with international partners, have now produced novel compounds composed of nitrogen and beryllium atoms. These are beryllium polynitrides, some of which conform to the monoclinic, others to the triclinic crystal system. The triclinic beryllium polynitrides exhibit one unusual characteristic when the pressure drops. They take on a crystal structure made up of layers. Each layer contains zigzag nitrogen chains connected by beryllium atoms. It can therefore be described as a planar structure consisting of BeN? pentagons and Be?N? hexagons. Thus, each layer represents a two-dimensional material, beryllonitrene.
Qualitatively, beryllonitrene is a new 2D material. Unlike graphene, the two-dimensional crystal structure of beryllonitrene results in a slightly distorted electronic lattice. Because of its resulting electronic properties, beryllonitrene would be excellently suited for applications in quantum technology if it could one day be produced on an industrial scale. In this still young field of research and development, the aim is to use the quantum mechanical properties and structures of matter for technical innovations — for example, for the construction of high-performance computers or for novel encryption techniques with the goal of secure communication.
“For the first time, close international cooperation in high-pressure research has now succeeded in producing a chemical compound in that was previously completely unknown. This compound could serve as a precursor for a 2D material with unique electronic properties. The fascinating achievement was only possible with the help of a laboratory-generated compression pressure almost a million times greater than the pressure of the Earth’s atmosphere. Our study thus once again proves the extraordinary potential of high-pressure research in materials science,” says co-author Prof. Dr. Natalia Dubrovinskaia from the Laboratory for Crystallography at the University of Bayreuth. “However, there is no possibility of devising a process for the production of beryllonitrene on an industrial scale as long as extremely high pressures, such as can only be generated in the research laboratory, are required for this. Nevertheless, it is highly significant that the new compound was created during decompression and that it can exist under ambient conditions. In principle, we cannot rule out that one day it will be possible to reproduce beryllonitrene or a similar 2D material with technically less complex processes and use it industrially. With our study, we have opened up new prospects for high-pressure research in the development of technologically promising 2D materials that may surpass graphene,” says corresponding author Prof. Dr. Leonid Dubrovinsky from the Bavarian Research Institute of Experimental Geochemistry & Geophysics at the University of Bayreuth.
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University of South Australia researchers have drawn inspiration from a 300-million-year-old superior flying machine — the dragonfly — to show why future flapping wing drones will probably resemble the insect in shape, wings and gearing.
A team of PhD students led by UniSA Professor of Sensor Systems, Javaan Chahl, spent part of the 2020 COVID-19 lockdown designing and testing key parts of a dragonfly-inspired drone that might match the insect’s extraordinary skills in hovering, cruising and aerobatics.
The UniSA students worked remotely on the project, solving mathematical formulas at home on whiteboards, digitising stereo photographs of insect wings into 3D models, and using spare rooms as rapid prototyping workshops to test parts of the flapping wing drone.
Their findings have been published in the journal Drones.
Describing the dragonfly as the “apex insect flyer,” Prof Chahl says numerous engineering lessons can be learned from its mastery in the air.
“Dragonflies are supremely efficient in all areas of flying. They need to be. After emerging from under water until their death (up to six months), male dragonflies are involved in perpetual, dangerous combat against male rivals. Mating requires an aerial pursuit of females and they are constantly avoiding predators. Their flying abilities have evolved over millions of years to ensure they survive,” Prof Chahl says. More

Engineers at Duke University have developed the world’s first fully recyclable printed electronics. By demonstrating a crucial and relatively complex computer component — the transistor — created with three carbon-based inks, the researchers hope to inspire a new generation of recyclable electronics to help fight the growing global epidemic of electronic waste.
The work appears online April 26 in the journal Nature Electronics.
“Silicon-based computer components are probably never going away, and we don’t expect easily recyclable electronics like ours to replace the technology and devices that are already widely used,” said Aaron Franklin, the Addy Professor of Electrical and Computer Engineering at Duke. “But we hope that by creating new, fully recyclable, easily printed electronics and showing what they can do, that they might become widely used in future applications.”
As people worldwide adopt more electronics into their lives, there’s an ever-growing pile of discarded devices that either don’t work anymore or have been cast away in favor of a newer model. According to a United Nations estimate, less than a quarter of the millions of pounds of electronics thrown away each year is recycled. And the problem is only going to get worse as the world upgrades to 5G devices and the Internet of Things (IoT) continues to expand.
Part of the problem is that electronic devices are difficult to recycle. Large plants employ hundreds of workers who hack at bulky devices. But while scraps of copper, aluminum and steel can be recycled, the silicon chips at the heart of the devices cannot.
In the new study, Franklin and his laboratory demonstrate a completely recyclable, fully functional transistor made out of three carbon-based inks that can be easily printed onto paper or other flexible, environmentally friendly surfaces. Carbon nanotubes and graphene inks are used for the semiconductors and conductors, respectively. While these materials are not new to the world of printed electronics, Franklin says, the path to recyclability was opened with the development of a wood-derived insulating dielectric ink called nanocellulose.
“Nanocellulose is biodegradable and has been used in applications like packaging for years,” said Franklin. “And while people have long known about its potential applications as an insulator in electronics, nobody has figured out how to use it in a printable ink before. That’s one of the keys to making these fully recyclable devices functional.”
The researchers developed a method for suspending crystals of nanocellulose that were extracted from wood fibers that — with the sprinkling of a little table salt — yields an ink that performs admirably as an insulator in their printed transistors. Using the three inks in an aerosol jet printer at room temperature, the team shows that their all-carbon transistors perform well enough for use in a wide variety of applications, even six months after the initial printing.
The team then demonstrates just how recyclable their design is. By submerging their devices in a series of baths, gently vibrating them with sound waves and centrifuging the resulting solution, the carbon nanotubes and graphene are sequentially recovered with an average yield of nearly 100%. Both materials can then be reused in the same printing process while losing very little of their performance viability. And because the nanocellulose is made from wood, it can simply be recycled along with the paper it was printed on.
Compared to a resistor or capacitor, a transistor is a relatively complex computer component used in devices such as power control or logic circuits and various sensors. Franklin explains that, by demonstrating a fully recyclable, multifunctional printed transistor first, he hopes to make a first step toward the technology being commercially pursued for simple devices. For example, Franklin says he could imagine the technology being used in a large building needing thousands of simple environmental sensors to monitor its energy use or customized biosensing patches for tracking medical conditions.
“Recyclable electronics like this aren’t going to go out and replace an entire half-trillion-dollar industry by any means, and we’re certainly nowhere near printing recyclable computer processors,” said Franklin. “But demonstrating these types of new materials and their functionality is hopefully a stepping stone in the right direction for a new type of electronics lifecycle.”
This work was supported by the Department of Defense Congressionally Directed Medical Research Program (W81XWH-17-2-0045), the National Institutes of Health (1R01HL146849) and the Air Force Office of Scientific Research (FA9550-18-1-0222).
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Materials provided by Duke University. Original written by Ken Kingery. Note: Content may be edited for style and length. More

Anyone with children knows that while controlling one child can be hard, controlling many at once can be nearly impossible. Getting swarms of robots to work collectively can be equally challenging, unless researchers carefully choreograph their interactions — like planes in formation — using increasingly sophisticated components and algorithms. But what can be reliably accomplished when the robots on hand are simple, inconsistent, and lack sophisticated programming for coordinated behavior?
A team of researchers led by Dana Randall, ADVANCE Professor of Computing and Daniel Goldman, Dunn Family Professor of Physics, both at Georgia Institute of Technology, sought to show that even the simplest of robots can still accomplish tasks well beyond the capabilities of one, or even a few, of them. The goal of accomplishing these tasks with what the team dubbed “dumb robots” (essentially mobile granular particles) exceeded their expectations, and the researchers report being able to remove all sensors, communication, memory and computation — and instead accomplishing a set of tasks through leveraging the robots’ physical characteristics, a trait that the team terms “task embodiment.”
The team’s BOBbots, or “behaving, organizing, buzzing bots” that were named for granular physics pioneer Bob Behringer, are “about as dumb as they get,” explains Randall. “Their cylindrical chassis have vibrating brushes underneath and loose magnets on their periphery, causing them to spend more time at locations with more neighbors.” The experimental platform was supplemented by precise computer simulations led by Georgia Tech physics student Shengkai Li, as a way to study aspects of the system inconvenient to study in the lab.
Despite the simplicity of the BOBbots, the researchers discovered that, as the robots move and bump into each other, “compact aggregates form that are capable of collectively clearing debris that is too heavy for one alone to move,” according to Goldman. “While most people build increasingly complex and expensive robots to guarantee coordination, we wanted to see what complex tasks could be accomplished with very simple robots.”
Their work, as reported April 23, 2021 in the journal Science Advances, was inspired by a theoretical model of particles moving around on a chessboard. A theoretical abstraction known as a self-organizing particle system was developed to rigorously study a mathematical model of the BOBbots. Using ideas from probability theory, statistical physics and stochastic algorithms, the researchers were able to prove that the theoretical model undergoes a phase change as the magnetic interactions increase — abruptly changing from dispersed to aggregating in large, compact clusters, similar to phase changes we see in common everyday systems, like water and ice.
“The rigorous analysis not only showed us how to build the BOBbots, but also revealed an inherent robustness of our algorithm that allowed some of the robots to be faulty or unpredictable,” notes Randall, who also serves as a professor of computer science and adjunct professor of mathematics at Georgia Tech.
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