Computers Math

A new electrical method to conveniently change the direction of electron flow in some quantum materials could have implications for the development of next-generation electronic devices and quantum computers. A team of researchers from Penn State developed and demonstrated the method in materials that exhibit the quantum anomalous Hall (QAH) effect — a phenomenon in which the flow of electrons along the edge of a material does not lose energy. The team described the work in a paper appearing today (Oct. 19) in the journal Nature Materials.
“As electronic devices get smaller and computational demands get larger, it is increasingly important to find ways to improve the efficiency of information transfer, which includes the control of electron flow,” said Cui-Zu Chang, Henry W. Knerr Early Career Professor and associate professor of physics at Penn State and co-corresponding author of the paper. “The QAH effect is promising because there is no energy loss as electrons flow along the edges of materials.”
In 2013, Chang was the first to experimentally demonstrate this quantum phenomenon. Materials exhibiting this effect are referred to as QAH insulators, which are a type of topological insulator — a thin layer of film only a couple dozen atoms thick — that have been made magnetic so that they only conduct current on their edges. Because the electrons travel cleanly in one direction, the effect is referred to as dissipationless, meaning no energy is lost in the form of heat.
“In a QAH insulator, electrons on one side of the material travel in one direction, while those on the other side travel in the opposite direction, like a two-lane highway,” Chang said. “Our earlier work demonstrated how to scale up the QAH effect, essentially creating a multilane highway for faster electron transport. In this study, we develop a new electrical method to control the transport direction of the electron highway and provide a way for those electrons to make an immediate U-turn.”
The researchers fabricated a QAH insulator with specific, optimized properties. They found that applying a 5-millisecond current pulse to the QAH insulator impacts the internal magnetism of the material and causes the electrons to change directions. The ability to change direction is critical for optimizing information transfer, storage, and retrieval in quantum technologies. Unlike current electronics, where data is stored in a binary state as on or off — as one or zero — quantum data can be stored simultaneously in a range of possible states. Changing the flow of electrons is an important step in writing and reading these quantum states.
“The previous method to switch the direction of electron flow relied on an external magnet to alter the material’s magnetism, but using magnets in electronic devices is not ideal,” said Chao-Xing Liu, professor of physics at Penn State and co-corresponding author of the paper. “Bulky magnets are not practical for small devices like smartphones, and an electronic switch is typically much faster than a magnetic switch. In this work, we found a convenient electronic method to change the direction of electron flow.”
The researchers previously optimized the QAH insulator so that they could take advantage of a physical mechanism in the system to control its internal magnetism. More

In a paper published today in Nature Photonics, researchers from the University of Oxford, along with collaborators from the Universities of Muenster, Heidelberg, and Exeter, report on their development of integrated photonic-electronic hardware capable of processing three-dimensional (3D) data, substantially boosting data processing parallelism for AI tasks.
Conventional computer chip processing efficiency doubles every 18 months, but the processing power required by modern AI tasks is currently doubling around every 3.5 months. This means that new computing paradigms are urgently needed to cope with the rising demand.
One approach is to use light instead of electronics — this allows multiple calculations to be carried out in parallel using different wavelengths to represent different sets of data. Indeed, in ground breaking work published in the journal Nature in 2021, many of the same authors demonstrated a form of integrated photonic processing chip that could carry out matrix vector multiplication (a crucial task for AI and machine learning applications) at speeds far outpacing the fastest electronic approaches. This work resulted in the birth of the photonic AI company, Salience Labs, a spin-out from the University of Oxford.
Now the team has gone further by adding an extra parallel dimension to the processing capability of their photonic matrix-vector multiplier chips. This “higher-dimensional” processing is enabled by exploiting multiple different radio frequencies to encode the data, propelling parallelism to a level far beyond that previously achieved.
As a test case the team applied their novel hardware to the task of assessing the risk of sudden death from electrocardiograms of heart disease patients. They were able to successfully analyse 100 electrocardiogram signals simultaneously, identifying the risk of sudden death with a 93.5% accuracy.
The researchers further estimated that even with a moderate scaling of 6 inputs × 6 outputs, this approach can outperform state-of-the-art electronic processors, potentially providing a 100-times enhancement in energy efficiency and compute density. The team anticipates further enhancement in computing parallelism in the future, by exploiting more degrees of freedom of light, such as polarization and mode multiplexing.
First author Dr Bowei Dong at the Department of Materials, University of Oxford said: ‘We previously assumed that using light instead of electronics could increase parallelism only by the use of different wavelengths — but then we realised that using radio frequencies to represent data opens up yet another dimension, enabling superfast parallel processing for emerging AI hardware.’
Professor Harish Bhaskaran, Department of Materials, University of Oxford and CO-founder of Salience Labs, who led the work said: ‘This is an exciting time to be doing research in AI hardware at the fundamental scale, and this work is one example of how what we assumed was a limit can be further surpassed.’ More

Researchers have invented an experimental wearable device that generates power from a user’s bending finger and can create and store memories, in a promising step towards health monitoring and other technologies.
The innovation features a single nanomaterial incorporated into a stretchable casing fitted to a person’s finger. The nanomaterial enabled the device to generate power with the user bending their finger.
The super-thin material also allows the device to perform memory tasks, as outlined below.
Multifunctional devices normally require several materials in layers, which involves the time-consuming challenge of stacking nanomaterials with high precision.
The team, led by RMIT University and the University of Melbourne in collaboration with other Australian and international institutions, made the proof-of-concept device with the rust of a low-temperature liquid metal called bismuth, which is safe and well suited for wearable applications.
Senior lead researcher Dr Ali Zavabeti said the invention could be developed to create medical wearables that monitor vital signs — incorporating the researchers’ recent work with a similar material that enabled gas sensing — and memorise personalised data.
“The innovation was used in our experiments to write, erase and re-write images in nanoscale, so it could feasibly be developed to one day encode bank notes, original art or authentication services,” said Zavabeti, an engineer from RMIT and the University of Melbourne. More

Robotic prosthetic ankles that are controlled by nerve impulses allow amputees to move more “naturally,” improving their stability, according to a new study from North Carolina State University and the University of North Carolina at Chapel Hill.
“This work focused on ‘postural control,’ which is surprisingly complicated,” says Helen Huang, corresponding author of the study and the Jackson Family Distinguished Professor in the Joint Department of Biomedical Engineering at NC State and UNC.
“Basically, when we are standing still, our bodies are constantly making adjustments in order to keep us stable. For example, if someone bumps into us when we are standing in line, our legs make a wide range of movements that we are not even necessarily aware of in order to keep us upright. We work with people who have lower limb amputations, and they tell us that achieving this sort of stability with prosthetic devices is a significant challenge. And this study demonstrates that robotic prosthetic ankles which are controlled using electromyographic (EMG) signals are exceptionally good at allowing users to achieve this natural stability.” EMG signals are the electrical signals recorded from an individual’s muscles.
The new study builds on previous work, which demonstrated that neural control of a powered prosthetic ankle can restore a range of abilities, including standing on challenging surfaces and squatting.
For this study, the researchers worked with five people who had amputations below the knee on one leg. Study participants were fitted with a prototype robotic prosthetic ankle that responds to EMG signals that are picked up by sensors on the leg.
“Basically, the sensors are placed over the muscles at the site of the amputation,” says Aaron Fleming, co-author of the study and recent Ph.D. graduate from NC State. “When a study participant thinks about moving the amputated limb, this sends electrical signals through the residual muscle in the lower limb. The sensors pick these signals up through the skin and translate those signals into commands for the prosthetic device.”
The researchers conducted general training for study participants using the prototype device, so that they were somewhat familiar with the technology. More

Taking inspiration from music streaming services, a team of engineers at the University of Michigan, Google and Georgia Tech has designed the simplest way for users to program their own exoskeleton assistance settings.
Of course, what’s simple for the users is more complex underneath, as a machine learning algorithm repeatedly offers pairs of assistance profiles that are most likely to be comfortable for the wearer. The user then selects one of these two, and the predictor offers another assistance profile that it believes might be better. This approach enables users to set the exoskeleton assistance based on their preferences using a very simple interface, conducive to implementing on a smartwatch or phone.
“It’s essentially like Pandora music,” said Elliott Rouse, U-M associate professor of robotics and mechanical engineering and corresponding author of the study in Science Robotics. “You give it feedback, a thumbs up or thumbs down, and it curates a radio station based on your feedback. This is a similar idea, but it’s with exoskeleton assistance settings. In both cases, we are creating a model of the user’s preferences and using this model to optimize the user’s experience.”
The team tested the approach with 14 participants, each wearing a pair of ankle exoskeletons as they walked at a steady pace of about 2.3 miles per hour. The volunteers could take as much time as they wanted between choices, although they were limited to 50 choices. Most participants were choosing the same assistance profile repeatedly by the 45th decision.
After 50 rounds, the experimental team began testing the users to see whether the final assistance profile was truly the best — pairing it against 10 randomly generated (but plausible) profiles. On average, participants chose the settings suggested by the algorithm about nine out of 10 times, which highlights the accuracy of the proposed approach.
“By using clever algorithms and a touch of AI, our system figures out what users want with easy yes-or-no questions,” said Ung Hee Lee, a recent U-M doctoral graduate from mechanical engineering and first author of the study, now at the robotics company Nuro. “I’m excited that this approach will make wearable robots comfortable and easy to use, bringing them closer to becoming a normal part of our day-to-day life.”
The control algorithm manages four exoskeleton settings: how much assistance to give (peak torque), how long to go between peaks (timing), and how the exoskeleton both ramps up and reduces the assistance on either side of each peak. This assistance approach is based on how our calf muscle adds force to propel us forward in each step. More

Ever since Antonie van Leeuwenhoek discovered the world of bacteria through a microscope in the late seventeenth century, humans have tried to look deeper into the world of the infinitesimally small.
There are, however, physical limits to how closely we can examine an object using traditional optical methods. This is known as the ‘diffraction limit’ and is determined by the fact that light manifests as a wave. It means a focused image can never be smaller than half the wavelength of light used to observe an object.
Attempts to break this limit with “super lenses” have all hit the hurdle of extreme visual losses, making the lenses opaque. Now physicists at the University of Sydney have shown a new pathway to achieve superlensing with minimal losses, breaking through the diffraction limit by a factor of nearly four times. The key to their success was to remove the super lens altogether.
The research is published today in Nature Communications.
The work should allow scientists to further improve super-resolution microscopy, the researchers say. It could advance imaging in fields as varied as cancer diagnostics, medical imaging, or archaeology and forensics.
Lead author of the research, Dr Alessandro Tuniz from the School of Physics and University of Sydney Nano Institute, said: “We have now developed a practical way to implement superlensing, without a super lens.
“To do this, we placed our light probe far away from the object and collected both high- and low-resolution information. By measuring further away, the probe doesn’t interfere with the high-resolution data, a feature of previous methods.”
Previous attempts have tried to make super lenses using novel materials. However, most materials absorb too much light to make the super lens useful. More

As the COVID-19 pandemic scattered and isolated people, researchers across Virginia Tech connected for a data-driven collaboration seeking improved drugs to fight the disease and potentially many other illnesses.
A multidisciplinary collaboration spanning several colleges at Virginia Tech resulted in a newly published study, “Data Driven Computational Design and Experimental Validation of Drugs for Accelerated Mitigation of Pandemic-like Scenarios,” in the Journal of Physical Chemistry Letters.
The study focuses on using computer algorithms to generate adaptations to molecules in compounds for existing and potential medications that can improve those molecules’ ability to bind to the main protease, a protein-based enzyme that breaks down complex proteins, in SARS-CoV-2, the virus that causes COVID-19.
This process allows exponentially more molecular adaptations to be considered than traditional trial-and-error methods of testing drugs one by one could allow. Candidate molecule adaptations can be identified among myriad possibilities, then narrowed to a few or one that can be created in a laboratory and tested for effectiveness.
“We present a novel transferable data-driven framework that can be used to accelerate the design of new small molecules and materials, with desired properties, by changing the combination of building blocks as well as decorating them with functional groups,” said Sanket A. Deshmukh, associate professor of chemical engineering in the College of Engineering. A “functional group” is a cluster of atoms that generally retains its characteristic properties, regardless of the other atoms in the molecule.
“Interestingly, the newly designed functionalized drug not only had a better half maximal effective concentration value than its parent drug, but also several of the proposed and used antivirals including Remdesivir,” Deshmukh said, referring to a measure of compound potency.
Moving through all the phases of the study would not have been possible without extensive cross-departmental collaboration. More

Artificial intelligence has exploded in popularity and is being harnessed by some scientists to predict which molecules could treat illnesses, or to quickly screen existing medicines for new applications. Researchers reporting in ACS Central Science have used one such deep learning algorithm, and found that dihydroartemisinin (DHA), an antimalarial drug and derivative of a traditional Chinese medicine, could treat osteoporosis as well. The team showed that in mice, DHA effectively reversed osteoporosis-related bone loss.
In healthy people, there is a balance between the osteoblasts that build new bone and osteoclasts that break it down. But when the “demolition crew” becomes overactive, it can result in bone loss and a disease called osteoporosis, which typically affects older adults. Current treatments for osteoporosis primarily focus on slowing the activity of osteoclasts. But osteoblasts — or more specifically, their precursors known as bone marrow mesenchymal stem cells (BMMSCs) — could be the basis for a different approach. During osteoporosis, these multipotent cells tend to turn into fat-creating cells instead, but they could be reprogrammed to help treat the disease. Previously, Zhengwei Xie and colleagues developed a deep learning algorithm that could predict how effectively certain small-molecule drugs reversed changes to gene expression associated with the disease. This time, joined by Yan Liu and Weiran Li, they wanted to use the algorithm to find a new treatment strategy for osteoporosis that focused on BMMSCs.
The team ran the program on a profile of differently expressed genes in newborn and adult mice. One of the top-ranked compounds identified was DHA, a derivative of artemisinin and a key component of malaria treatments. Administering DHA extract for six weeks to mice with induced osteoporosis significantly reduced bone loss in their femurs and nearly completely preserved bone structure. To improve delivery, the team designed a more robust system using injected, DHA-loaded nanoparticles. Bones of mice with osteoporosis that received the treatment were similar to those of the control group, and the treatment showed no evidence of toxicity. In further tests, the team determined that DHA interacted with BMMSCs to maintain their stemness and ultimately produce more osteoblasts. The researchers say that this work demonstrates that DHA is a promising therapeutic agent for osteoporosis.
The authors acknowledge funding from the National Natural Science Foundations of China, the Beijing International Science and Technology Cooperation, the Beijing Natural Science Foundation, Peking University Clinical Medicine Plus X — Young Scholars Project, the Ten-Thousand Talents Program, the Key R & D Plan of Ningxia Hui Autonomous Region, the Innovative Research Team of High-Level Local Universities in Shanghai, the Beijing Nova Program, the China National Postdoctoral Program for Innovative Talents, the China Postdoctoral Science Foundation, and the Peking University Medicine Sailing Program for Young Scholars’ Scientific & Technological Innovation. More