Computers Math

In a ground-breaking experiment, scientists from the University of Groningen, together with colleagues from the Dutch universities of Nijmegen and Twente and the Harbin Institute of Technology (China), have discovered the existence of a superconductive state that was first predicted in 2017. They present evidence for a special variant of the FFLO superconductive state on 24 May in the journal Nature. This discovery could have significant applications, particularly in the field of superconducting electronics.
The lead author of the paper is Professor Justin Ye, who heads the Device Physics of Complex Materials group at the University of Groningen. Ye and his team have been working on the Ising superconducting state. This is a special state that can resist magnetic fields that generally destroy superconductivity, and that was described by the team in 2015. In 2019, they created a device comprising a double layer of molybdenum disulfide that could couple the Ising superconductivity states residing in the two layers. Interestingly, the device created by Ye and his team makes it possible to switch this protection on or off using an electric field, resulting in a superconducting transistor.
Elusive
The coupled Ising superconductor device sheds light on a long-standing challenge in the field of superconductivity. In 1964, four scientists (Fulde, Ferrell, Larkin, and Ovchinnikov) predicted a special superconducting state that could exist under conditions of low temperature and strong magnetic field, referred to as the FFLO state. In standard superconductivity, electrons travel in opposite directions as Cooper pairs. Since they travel at the same speed, these electrons have a total kinetic momentum of zero. However, in the FFLO state, there is a small speed difference between the electrons in the Cooper pairs, which means that there is a net kinetic momentum.
‘This state is very elusive and there are only a handful of articles claiming its existence in normal superconductors,’ says Ye. ‘However, none of these are conclusive.’ To create the FFLO state in a conventional superconductor, a strong magnetic field is needed. But the role played by the magnetic field needs careful tweaking. Simply put, for two roles to be played by the magnetic field, we need to use the Zeeman effect. This separates electrons in Cooper pairs based on the direction of their spins (a magnetic moment), but not on the orbital effect — the other role that normally destroys superconductivity. ‘It is a delicate negotiation between superconductivity and the external magnetic field,’ explains Ye.
Fingerprint
Ising superconductivity, which Ye and his collaborators introduced and published in the journal Science in 2015, suppresses the Zeeman effect. ‘By filtering out the key ingredient that makes conventional FFLO possible, we provided ample space for the magnetic field to play its other role, namely the orbital effect,’ says Ye.
‘What we have demonstrated in our paper is a clear fingerprint of the orbital effect-driven FFLO state in our Ising superconductor,’ explains Ye. ‘This is an unconventional FFLO state, first described in theory in 2017.’ The FFLO state in conventional superconductors requires extremely low temperatures and a very strong magnetic field, which makes it difficult to create. However, in Ye’s Ising superconductor, the state is reached with a weaker magnetic field and at higher temperatures.
Transistors
In fact, Ye first observed signs of an FFLO state in his molybdenum disulfide superconducting device in 2019. ‘At that time, we could not prove this, because the samples were not good enough,’ says Ye. However, his PhD student Puhua Wan has since succeeded in producing samples of the material that fulfilled all the requirements to show that there is indeed a finite momentum in the Cooper pairs. ‘The actual experiments took half a year, but the analysis of the results added another year,’ says Ye. Wan is the first author of the Nature paper.
This new superconducting state needs further investigation. Ye: ‘There is a lot to learn about it. For example, how does the kinetic momentum influence the physical parameters? Studying this state will provide new insights into superconductivity. And this may enable us to control this state in devices such as transistors. That is our next challenge.’ More

Researchers at Oregon State University and Baylor University have made a breakthrough toward reducing the energy consumption of the photonic chips used in data centers and supercomputers.
The findings are important because a data center can consume up to 50 times more energy per square foot of floor space than a typical office building, according to the U.S. Department of Energy.
A data center houses an organization’s information technology operations and equipment; it stores, processes and disseminates data and applications. Data centers account for roughly 2% of all electricity use in the United States, the DOE says.
According to the U.S. International Trade Commission, the number of data centers has risen rapidly as data demand has soared. In the United States, home to many firms that produce and consume vast amounts of data including Facebook, Amazon, Microsoft and Google, there are more than 2,600 data centers.
The advance by John Conley of the OSU College of Engineering, former Oregon State colleague Alan Wang, now of Baylor, and OSU graduate students Wei-Che Hsu, Ben Kupp and Nabila Nujhat involves a new, ultra-energy-efficient method to compensate for temperature variations that degrade photonic chips. Such chips “will form the high-speed communication backbone of future data centers and supercomputers,” Conley said.
The circuitry in photonic chips uses photons — particles of light — rather than the electrons that course through conventional computer chips. Moving at the speed of light, photons enable the extremely rapid, energy-efficient transmission of data.
The issue with photonic chips is that up until now, significant energy has been required to keep their temperature stable and performance high. The team led by Wang, however, has shown that it’s possible to reduce the energy needed for temperature control by a factor of more than 1 million.
“Alan is an expert in photonic materials and devices and my area of expertise is atomic layer deposition and electronic devices,” Conley said. “We were able to make working prototypes that show temperature can be controlled via gate voltage, which means using virtually no electric current.”
Presently, Wang said, the photonics industry exclusively relies on components known as “thermal heaters” to fine tune the working wavelengths of high-speed, electro-optic devices and optimize their performance. These thermal heaters consume several milliwatts of electricity per device.
“That might not sound like much considering that a typical LED lightbulb uses 6 to 10 watts,” Wang said. “However, multiply those several milliwatts by millions of devices and they add up quickly, so that approach faces challenges as systems scale up and become bigger and more powerful.”
“Our method is much more acceptable for the planet,” Conley added. “It will one day allow data centers to keep getting faster and more powerful while using less energy so that we can access ever more powerful applications driven by machine learning, such as ChatGPT, without feeling guilty.” More

A research group has developed a prototype calcium (Ca) metal rechargeable battery capable of 500 cycles of repeated charge-discharge — the benchmark for practical use.
The breakthrough was reported in the journal Advanced Science on May 19, 2023.
With the use of electric vehicles and grid-scale energy storage systems on the rise, the need to explore alternatives to lithium-ion batteries (LIBs) has never been greater. One such replacement is Ca metal batteries. As the fifth most abundant element in earth’s crust, calcium is widely available and inexpensive, and has higher energy density potential than LIBs. Its properties are also thought to help accelerate ion transport and diffusion in electrolytes and cathode materials, giving it an edge over other LIB-alternatives such as magnesium and zinc.
But many hurdles remain in the way of Ca metal batteries’ commercial viability. The lack of an efficient electrolyte and the absence of cathode materials with sufficient Ca2+ storage capabilities have proved to be the main stumbling blocks.
Back in 2021, some members of the current research group provided a solution to the former problem when they realized a new fluorine-free calcium (Ca) electrolyte based on a hydrogen (monocarborane) cluster. The electrolyte demonstrated markedly improved electrochemical performances such as high conductivity and high electrochemical stabilities.
“For our current research, we tested the long-term operation of a Ca metal battery with a copper sulfide (CuS) nanoparticle/carbon composite cathode and a hydride-based electrolyte,” says Kazuaki Kisu, assistant professor at Tohoku University’s Institute for Materials Research (IMR).
Also a natural mineral, CuS has favorable electrochemical properties. Its layered structure enables it to store a variety of cations, including lithium, sodium and magnesium. It has a large theoretical capacity of 560 mAh g-1 — two to three times higher than present cathode materials for lithium-ion batteries.
Through nanoparticulation and compositing with carbon materials, Kisu and his collegues were able to create a cathode capable of storing large amounts of calcium ions. When employed with the hydride-type electrolyte, they produce a battery with a highly stable cycling performance. The prototype battery maintained 92% capacity retention over 500 cycles based on the capacity of the 10th cycle.
The group is confident that their breakthrough will help advance research into cathode materials for Ca-based batteries. “Our study confirms the feasibility of Ca metal anodes for long-term operations, and we are hopeful the results will expedite the development of Ca metal batteries,” says Kisu. More

A robotic bee that can fly fully in all directions has been developed by Washington State University researchers.
With four wings made out of carbon fiber and mylar as well as four light-weight actuators to control each wing, the Bee++ prototype is the first to fly stably in all directions. That includes the tricky twisting motion known as yaw, with the Bee++ fully achieving the six degrees of free movement that a typical flying insect displays.
Led by Néstor O. Pérez-Arancibia, Flaherty associate professor in WSU’s School of Mechanical and Materials Engineering, the researchers report on their work in the journal, IEEE Transactions on Robotics. Pérez-Arancibia will present the results at the IEEE International Conference on Robotics and Automation at the end of this month.
Researchers have been trying to develop artificial flying insects for more than 30 years, said Pérez-Arancibia. They could someday be used for many applications, including for artificial pollination, search and rescue efforts in tight spaces, biological research, or environmental monitoring, including in hostile environments.
But just getting the tiny robots to take off and land required development of controllers that act the way an insect brain does.
“It’s a mixture of robotic design and control,” he said. “Control is highly mathematical, and you design a sort of artificial brain. Some people call it the hidden technology, but without those simple brains, nothing would work.”
Researchers initially developed a two-winged robotic bee, but it was limited in its movement. In 2019, Pérez-Arancibia and two of his PhD students for the first time built a four-winged robot light enough to take off. To do two maneuvers known as pitching or rolling, the researchers make the front wings flap in a different way than the back wings for pitching and the right wings flap in a different way than the left wings for rolling, creating torque that rotates the robot about its two main horizontal axes.

But being able to control the complex yaw motion is tremendously important, he said. Without it, robots spin out of control, unable to focus on a point. Then they crash.
“If you can’t control yaw, you’re super limited,” he said. “If you’re a bee, here is the flower, but if you can’t control the yaw, you are spinning all the time as you try to get there.”
Having all degrees of movement is also critically important for evasive maneuvers or tracking objects.
“The system is highly unstable, and the problem is super hard,” he said. “For many years, people had theoretical ideas about how to control yaw, but nobody could achieve it due to actuation limitations.”
To allow their robot to twist in a controlled manner, the researchers took a cue from insects and moved the wings so that they flap in an angled plane. They also increased the amount of times per second their robot can flap its wings — from 100 to 160 times per second.
“Part of the solution was the physical design of the robot, and we also invented a new design for the controller — the brain that tells the robot what to do,” he said.
Weighing in at 95 mg with a 33-millimeter wingspan, the Bee++ is still bigger than real bees, which weigh around 10 milligrams. Unlike real insects, it can only fly autonomously for about five minutes at a time, so it is mostly tethered to a power source through a cable. The researchers are also working to develop other types of insect robots, including crawlers and water striders.
Pérez-Arancibia’s former PhD students at the University of Southern California, Ryan M. Bena, Xiufeng Yang, and Ariel A. Calderón, co-authored the article. The work was funded by the National Science Foundation and DARPA. The WSU Foundation and the Palouse Club through WSU’s Cougar Cage program has also provided support. More

A quarter of a century ago, theoretical physicists at the University of Innsbruck made the first proposal on how to transmit quantum information via quantum repeaters over long distances which would open the door to the construction of a worldwide quantum information network. Now, a new generation of Innsbruck researchers has built a quantum repeater node for the standard wavelength of telecommunication networks and transmitted quantum information over tens of kilometers.
Quantum networks connect quantum processors or quantum sensors with each other. This allows tap-proof communication and high-performance distributed sensor networks. Between network nodes, quantum information is exchanged by photons that travel through optical waveguides. Over long distances, however, the likelihood of photons being lost increases dramatically. As quantum information cannot simply be copied and amplified, 25 years ago Hans Briegel, Wolfgang Dür, Ignacio Cirac and Peter Zoller, then all at the University of Innsbruck, provided the blueprints for a quantum repeater. These feature light-matter entanglement sources and memories to create entanglement in independent network links that are connected between them by a so-called entanglement swap to finally distribute entanglement over long distances.
Even transmission over 800 kilometers possible
Quantum physicists led by Ben Lanyon from the Department of Experimental Physics at the University of Innsbruck have now succeeded in building the core parts of a quantum repeater — a fully functioning network node made with two single matter systems enabling entanglement creation with a photon at the standard frequency of the telecommunications network and entanglement swapping operations. The repeater node consists of two calcium ions captured in an ion trap within an optical resonator as well as single photon conversion to the telecom wavelength. The scientists thus demonstrated the transfer of quantum information over a 50-kilometer-long optical fiber, with the quantum repeater placed exactly halfway between starting and end point. The researchers were also able to calculate which improvements of this design would be necessary to make transmission over 800 kilometers possible which would allow to connect Innsbruck to Vienna.
The current results were published in Physical Review Letters. Funding for the research was provided by a START award from the Austrian Science Fund FWF, the Austrian Academy of Sciences and the European Union, among others. Lanyon’s team is part of the Quantum Internet Alliance, an international project under the EU Quantum Flagship. More

Cryptocurrency transactions may be costing more than just transaction fees. The electricity used for these transactions is more than what some countries, like Argentina and Australia, use in an entire year.
Published estimates of the total global electricity usage for cryptocurrency assets such as Bitcoin are between 120 and 240 billion kilowatt-hours per year, according to the White House Office of Science and Technology. The United States leads these numbers.
Finance and business experts have debated the ramifications of cryptocurrency and mining, but little focus has been placed on the impact of these activities on the power grid and energy consumption until now.
Dr. Le Xie, professor in the Department of Electrical and Computer Engineering at Texas A&M University and associate director of the Texas A&M Energy Institute, is at the center of this effort to understand how cryptocurrency mining impacts the power grid and how to use this information for further research, education and policymaking.
Even as technology improves, allowing users to do more while using less energy, cryptocurrency mining is computationally intensive, and the measure of power on the blockchain network, or hash rate, is still rising.
During the summer heatwave of 2022 in Texas, Xie and his collaborators found an 18% reduction in worldwide cryptocurrency mining. The decrease was linked to the stress on the Texas power grid, which led the Electric Reliability Council of Texas to issue a request for energy consumers to conserve energy.

“There seems to be a very strong negative correlation between the mining demand and the systemwide total net demand,” Xie said. “When the grid is stressed, crypto miners are shutting down, which demonstrates a potential for demand flexibility.”
For example, when the grid is under stress due to a heat wave, homeowners consume more air conditioning and, in turn, more power. Compared to these types of firm demand, the cryptocurrency mining demand shows good potential for providing flexibilities during times when peak energy usage in other areas is vital.
Their findings are published in the March issue of the Institute of Electrical and Electronics Engineers Transactions on Energy Markets, Policy and Regulation and the June issue of Advances in Applied Energy.
In these papers, Xie and his students provide data to allow a first step into studying these mining facilities’ carbon footprint and the impact on grid reliability and wholesale electricity prices. Ultimately, location matters, and many factors play a part in this complex discussion.
“Increasing firm demand will invariably result in a decrease in grid reliability,” Xie said. “However, with crypto mining modeled as a flexible load that can be turned off during the stressed moments, it can be a positive contributor to the grid reliability.”
Xie is the lead for the Blockchain and Energy Research Consortium at Texas A&M, which is a collaboration between a team of Texas A&M researchers and industry partners. Their mission is to provide an unbiased multidisciplinary resource to communicate recent developments in the intersection of blockchain and energy.
Although cryptocurrency is still in its infancy, one thing is certain — increasing energy usage will be critical as this emerging industry for transactions continues to advance. With that in mind, Xie is continuing his research to find a solution that helps take advantage of blockchain-enabled technologies while ensuring a sustainable grid operation. More

A University of Minnesota Twin Cities-led team has developed a first-of-its-kind, breakthrough method that makes it easier to create high-quality metal oxide thin films out of “stubborn” metals that have historically been difficult to synthesize in an atomically precise manner. This research paves the way for scientists to develop better materials for various next-generation applications including quantum computing, microelectronics, sensors, and energy catalysis.
The researchers’ paper is published in Nature Nanotechnology, a peer-reviewed, scientific journal run by Nature Publishing Group.
“This is truly remarkable discovery, as it unveils an unparalleled and simple way for navigating material synthesis at the atomic scale by harnessing the power of epitaxial strain,” said Bharat Jalan, senior author on the paper and a professor and Shell Chair in the University of Minnesota Department of Chemical Engineering and Materials Science. “This breakthrough represents a significant advancement with far-reaching implications in a broad range of fields. Not only does it provide a means to achieve atomically-precise synthesis of quantum materials, but it also holds immense potential for controlling oxidation-reduction pathways in various applications, including catalysis and chemical reactions occurring in batteries or fuel cells.”
“Stubborn” metals oxides, such as those based on ruthenium or iridium, play a crucial role in numerous applications in quantum information sciences and electronics. However, converting them into thin films has been a challenge for researchers due to the inherent difficulties in oxidizing metals using high-vacuum processes.
The fabrication of these materials has perplexed materials scientists for decades. While some researchers have successfully achieved oxidation, the methods used thus far have been costly, unsafe, or have resulted in poor material quality.
The University of Minnesota researchers’ solution? Give it a stretch.

While attempting to synthesize metal oxides using conventional molecular beam epitaxy, a low-energy technique that generates single layers of material in an ultra-high vacuum chamber, the researchers stumbled upon a groundbreaking revelation. They found that incorporating a concept called “epitaxial strain” — effectively stretching the metals at the atomic level — significantly simplifies the oxidation process of these stubborn metals.
“This enables the creation of technologically important metal oxides out of stubborn metals in ultra-high vacuum atmospheres, which has been a longstanding problem,” said Sreejith Nair, first author of the paper and a University of Minnesota chemical engineering Ph.D. student. “The current synthesis approaches have limits, and we need to find new ways to push those limits further so that we can make better quality materials. Our new method of stretching the material at the atomic scale is one way to improve the performance of the current technology.”
Although the University of Minnesota team used iridium and ruthenium as examples in this paper, their method has the potential to generate atomically-precise oxides of any hard-to-oxidize metal. With this groundbreaking discovery, the researchers aim to empower scientists worldwide to synthesize these novel materials.
The researchers worked closely with collaborators at Auburn University, the University of Delaware, Brookhaven National Laboratory, Argonne National Laboratory, and fellow University of Minnesota Department of Chemical Engineering and Materials Science Professor Andre Mkhoyan’s lab to verify their method.
“When we looked at these metal oxide films very closely using very powerful electron microscopes, we captured the arrangements of the atoms and determined their types,” Mkhoyan explained. “Sure enough, they were nicely and periodically arranged as they should be in these crystalline films.”
This research was funded primarily by the United States Department of Energy (DOE), the Air Force Office of Scientific Research (AFOSR), and the University of Minnesota’s Materials Research Science and Engineering Center (MRSEC).
In addition to Jalan, Nair, and Mkhoyan, the research team included University of Minnesota Twin Cities researchers Zhifei Yang, Dooyong Lee, and Silu Guo; Brookhaven National Laboratory researcher Jerzy Sadowski; Auburn University researchers Spencer Johnson, Ryan Comes, and Wencan Jin; University of Delaware researchers Abdul Saboor and Anderson Janotti; and Argonne National Laboratory researchers Yan Li and Hua Zhou. Parts of the work were carried out at the University of Minnesota’s Characterization Facility. More

Artificial intelligence has exploded across our news feeds, with ChatGPT and related AI technologies becoming the focus of broad public scrutiny. Beyond popular chatbots, biologists are finding ways to leverage AI to probe the core functions of our genes.
Previously, University of California San Diego researchers who investigate DNA sequences that switch genes on used artificial intelligence to identify an enigmatic puzzle piece tied to gene activation, a fundamental process involved in growth, development and disease. Using machine learning, a type of artificial intelligence, School of Biological Sciences Professor James T. Kadonaga and his colleagues discovered the downstream core promoter region (DPR), a “gateway” DNA activation code that’s involved in the operation of up to a third of our genes.
Building from this discovery, Kadonaga and researchers Long Vo ngoc and Torrey E. Rhyne have now used machine learning to identify “synthetic extreme” DNA sequences with specifically designed functions in gene activation. Publishing in the journal Genes & Development, the researchers tested millions of different DNA sequences through machine learning (AI) by comparing the DPR gene activation element in humans versus fruit flies (Drosophila). By using AI, they were able to find rare, custom-tailored DPR sequences that are active in humans but not fruit flies and vice versa. More generally, this approach could now be used to identify synthetic DNA sequences with activities that could be useful in biotechnology and medicine.
“In the future, this strategy could be used to identify synthetic extreme DNA sequences with practical and useful applications. Instead of comparing humans (condition X) versus fruit flies (condition Y) we could test the ability of drug A (condition X) but not drug B (condition Y) to activate a gene,” said Kadonaga, a distinguished professor in the Department of Molecular Biology. “This method could also be used to find custom-tailored DNA sequences that activate a gene in tissue 1 (condition X) but not in tissue 2 (condition Y). There are countless practical applications of this AI-based approach. The synthetic extreme DNA sequences might be very rare, perhaps one-in-a-million — if they exist they could be found by using AI.”
Machine learning is a branch of AI in which computer systems continually improve and learn based on data and experience. In the new research, Kadonaga, Vo ngoc (a former UC San Diego postdoctoral researcher now at Velia Therapeutics) and Rhyne (a staff research associate) used a method known as support vector regression to “train” machine learning models with 200,000 established DNA sequences based on data from real-world laboratory experiments. These were the targets presented as examples for the machine learning system. They then “fed” 50 million test DNA sequences into the machine learning systems for humans and fruit flies and asked them to compare the sequences and identify unique sequences within the two enormous data sets.
While the machine learning systems showed that human and fruit fly sequences largely overlapped, the researchers focused on the core question of whether the AI models could identify rare instances where gene activation is highly active in humans but not in fruit flies. The answer was a resounding “yes.” The machine learning models succeeded in identifying human-specific (and fruit fly-specific) DNA sequences. Importantly, the AI-predicted functions of the extreme sequences were verified in Kadonaga’s laboratory by using conventional (wet lab) testing methods.
“Before embarking on this work, we didn’t know if the AI models were ‘intelligent’ enough to predict the activities of 50 million sequences, particularly outlier ‘extreme’ sequences with unusual activities. So, it’s very impressive and quite remarkable that the AI models could predict the activities of the rare one-in-a-million extreme sequences,” said Kadonaga, who added that it would be essentially impossible to conduct the comparable 100 million wet lab experiments that the machine learning technology analyzed since each wet lab experiment would take nearly three weeks to complete.
The rare sequences identified by the machine learning system serve as a successful demonstration and set the stage for other uses of machine learning and other AI technologies in biology.
“In everyday life, people are finding new applications for AI tools such as ChatGPT. Here, we’ve demonstrated the use of AI for the design of customized DNA elements in gene activation. This method should have practical applications in biotechnology and biomedical research,” said Kadonaga. “More broadly, biologists are probably at the very beginning of tapping into the power of AI technology.” More