Aurelia Butler

Water that simply will not freeze, no matter how cold it gets — a research group involving the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) has discovered a quantum state that could be described in this way. Experts from the Institute of Solid State Physics at the University of Tokyo in Japan, Johns Hopkins University in the United States, and the Max Planck Institute for the Physics of Complex Systems (MPI-PKS) in Dresden, Germany, managed to cool a special material to near absolute zero temperature. They found that a central property of atoms — their alignment — did not “freeze,” as usual, but remained in a “liquid” state. The new quantum material could serve as a model system to develop novel, highly sensitive quantum sensors. The team has presented its findings in the journal Nature Physics.
On first sight, quantum materials do not look different from normal substances — but they sure do their own thing: Inside, the electrons interact with unusual intensity, both with each other and with the atoms of the crystal lattice. This intimate interaction results in powerful quantum effects that not only act on the microscopic, but also on the macroscopic scale. Thanks to these effects, quantum materials exhibit remarkable properties. For example, they can conduct electricity completely loss-free at low temperatures. Often, even slight changes in temperature, pressure, or electrical voltage are enough to drastically change the behavior of the material.
In principle, magnets can also be regarded as quantum materials; after all, magnetism is based on the intrinsic spin of the electrons in the material. “In some ways, these spins can behave like a liquid,” explains Prof. Jochen Wosnitza from the Dresden High Field Magnetic Laboratory (HLD) at HZDR. “As temperatures drop, these disordered spins can then freeze, much like water freezes into ice.” For example, certain kind of magnets, so-called ferromagnets, are non-magnetic above their “freezing,” or more precisely ordering point. Only when they drop below it can they become permanent magnets.
High-purity material
The international team intended to create a quantum state in which the atomic alignment that is associated with the spins did not order, even at ultracold temperatures — similar to a liquid that will not solidify, even in extreme cold. To achieve this state, the research group used a special material — a compound of the elements, praseodymium, zirconium, and oxygen. They assumed that in this material, the properties of the crystal lattice would enable the electron spins to interact with their orbitals around the atoms in a special way.
“The prerequisite, however, was to have crystals of extreme purity and quality,” Prof. Satoru Nakatsuji of the University of Tokyo explains. It took several attempts, but eventually the team was able to produce crystals pure enough for their experiment: In a cryostat, a kind of super thermos flask, the experts gradually cooled their sample down to 20 millikelvin — just one fiftieth of a degree above absolute zero. To see how the sample responded to this cooling process and inside the magnetic field, they measured how much it changed in length. In another experiment, the group recorded how the crystal reacted to ultrasound waves being directly sent through it.
An intimate interplay
The result: “Had the spins ordered, it should have caused an abrupt change in the behavior of the crystal, such as a sudden change in length,” Dr. Sergei Zherlitsyn, HLD’s expert in ultrasound investigations, describes. “Yet, as we observed, nothing happened! There were no sudden changes in either length or in its response to ultrasound waves.” The conclusion: The pronounced interplay of spins and orbitals had prevented ordering, which is why the atoms remained in their liquid quantum state — the first time such a quantum state had been observed. Further investigations in magnetic fields confirmed this assumption.
This basic research result could also have practical implications one day: “At some point we might be able to use the new quantum state to develop highly sensitive quantum sensors,” Jochen Wosnitza speculates. “To do this, however, we still have to figure out how to generate excitations in this state systematically.” Quantum sensing is considered a promising technology of the future. Because their quantum nature makes them extremely sensitive to external stimuli, quantum sensors can register magnetic fields or temperatures with far greater precision than conventional sensors. More

Using smart technology to monitor the health, reproductivity, location, and environmental conditions of cattle can help with food safety and supply chain efficiency, but this monitoring adds energy cost to an already highly emissive industry. To combat this, researchers publishing in the journal iScience on December 1 have designed a wearable smart device for cows that captures the kinetic energy created by even their smallest movements and uses it to power smart ranch technology.
“On a ranch, monitoring environmental and health information of cattle can help prevent diseases and improve the efficiency of pasture breeding and management,” says co-author Zutao Zhang, an energy researcher at Southwest Jiaotong University in China. “This information can include oxygen concentration, air temperature and humidity, amount of exercise, reproductive cycles, disease, and milk production.”
The team’s smart ranch design involves cows wearing small sensory devices around their ankles and necks that are powered by everything cows do as they go about their regular ranch activities. “There is a tremendous amount of kinetic energy that can be harvested in cattle’s daily movements, such as walking, running, and even neck movement,” says co-author Yajia Pan, also an energy researcher at Southwest Jiaotong University.Once captured, the energy is stored in a lithium battery and used to power the device.
“Our kinetic energy harvester specially harvests the kinetic energy of weak motion,” says Zhang. The team’s design is unique because it contains a motion enhancement mechanism that uses magnets and a pendulum to amplify small movements the cows make.
Zhang hopes that implementing smart technology in ranches will be part of a larger effort to improve the world’s food systems. “With the development of 5G technology and the Internet of Things, the operation of the entire industrial chain of the food system is more intelligent and transparent,” he says.
Zhang and his colleagues also tested the devices on humans and found that a light jog was enough to power temperature measurement in the device. The researchers see future applications in sports monitoring, healthcare, smart home, and the construction of human wireless sensor networks.
“Kinetic energy is everywhere in the environment — leaves swaying in the wind, the movement of people and animals, the undulation of waves, the rotation of the earth — these phenomena all contain a lot of kinetic energy,” says Zhang, “We shouldn’t let this energy go to waste.”
This work was supported by the National Natural Foundation of China, Science and Technology Projects of Sichuan, and Science and Technology Projects of Chengdu.
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The gap between human creativity and artificial intelligence seems to be narrowing. Previous studies have compared AI-generated versus human-written poems and whether people can distinguish between them.
Now, a study led by Yoshiyuki Ueda at Kyoto University Institute for the Future of Human and Society, has shown AI’s potential in creating literary art such as haiku — the shortest poetic form in the world — rivaling that of humans without human help.
Ueda’s team compared AI-generated haiku without human intervention, also known as human out of the loop, or HOTL, with a contrasting method known as human in the loop, or HITL.
The project involved 385 participants, each of whom evaluated 40 haiku poems — 20 each of HITL and HOTL — plus 40 composed entirely by professional haiku writers.
“It was interesting that the evaluators found it challenging to distinguish between the haiku penned by humans and those generated by AI,” remarks Ueda.
From the results, HITL haiku received the most praise for their poetic qualities, whereas HOTL and human-only verses had similar scores.
“In addition, a phenomenon called algorithm aversion was observed among our evaluators. They were supposed to be unbiased but instead became influenced by a kind of reverse psychology,” explains the author.
“In other words, they tended to unconsciously give lower scores to those they felt were AI-generated.”
Ueda points out that his research has put a spotlight on algorithm aversion as a new approach to AI art.
“Our results suggest that the ability of AI in the field of haiku creation has taken a leap forward, entering the realm of collaborating with humans to produce more creative works. Realizing the existence of algorithmic aversion will lead people to re-evaluate their appreciation of AI art.”
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Cambridge researchers have developed a method for measuring overall fitness accurately on wearable devices — and more robustly than current consumer smartwatches and fitness monitors — without the wearer needing to exercise.
Normally, tests to accurately measure VO2max — a key measurement of overall fitness and an important predictor of heart disease and mortality risk — require expensive laboratory equipment and are mostly limited to elite athletes. The new method uses machine learning to predict VO2max — the capacity of the body to carry out aerobic work — during everyday activity, without the need for contextual information such as GPS measurements.
In what is by far the largest study of its kind, the researchers gathered activity data from more than 11,000 participants in the Fenland Study using wearable sensors, with a subset of participants tested again seven years later. The researchers used the data to develop a model to predict VO2max, which was then validated against a third group who carried out a standard lab-based exercise test. The model showed a high degree of accuracy compared to lab-based tests, and outperforms other approaches.
Some smartwatches and fitness monitors currently on the market claim to provide an estimate of VO2max, but since the algorithms powering these predictions aren’t published and are subject to change at any time, it’s unclear whether the predictions are accurate, or whether an exercise regime is having any effect on an individual’s VO2max over time.
The Cambridge-developed model is robust, transparent and provides accurate predictions based on heart rate and accelerometer data only. Since the model can also detect fitness changes over time, it could also be useful in estimating fitness levels for entire populations and identifying the effects of lifestyle trends. The results are reported in the journal npj Digital Medicine.
A measurement of VO2max is considered the ‘gold standard’ of fitness tests. Professional athletes, for example, test their VO2max by measuring their oxygen consumption while they exercise to the point of exhaustion. There are other ways of measuring fitness in the laboratory, like heart rate response to exercise tests, but these require equipment like a treadmill or exercise bike. Additionally, strenuous exercise can be a risk to some individuals. More

Scientists have, for the first time, developed a quantum experiment that allows them to study the dynamics, or behavior, of a special kind of theoretical wormhole. The experiment has not created an actual wormhole (a rupture in space and time), rather it allows researchers to probe connections between theoretical wormholes and quantum physics, a prediction of so-called quantum gravity. Quantum gravity refers to a set of theories that seek to connect gravity with quantum physics, two fundamental and well-studied descriptions of nature that appear inherently incompatible with each other.
“We found a quantum system that exhibits key properties of a gravitational wormhole yet is sufficiently small to implement on today’s quantum hardware,” says Maria Spiropulu, the principal investigator of the U.S. Department of Energy Office of Science research program Quantum Communication Channels for Fundamental Physics (QCCFP) and the Shang-Yi Ch’en Professor of Physics at Caltech. “This work constitutes a step toward a larger program of testing quantum gravity physics using a quantum computer. It does not substitute for direct probes of quantum gravity in the same way as other planned experiments that might probe quantum gravity effects in the future using quantum sensing, but it does offer a powerful testbed to exercise ideas of quantum gravity.”
The research will be published December 1 in the journal Nature. The study’s first authors are Daniel Jafferis of Harvard University and Alexander Zlokapa (BS ’21), a former undergraduate student at Caltech who started on this project for his bachelor’s thesis with Spiropulu and has since moved on to graduate school at MIT.
Wormholes are bridges between two remote regions in spacetime. They have not been observed experimentally, but scientists have theorized about their existence and properties for close to 100 years. In 1935, Albert Einstein and Nathan Rosen described wormholes as tunnels through the fabric of spacetime in accordance with Einstein’s general theory of relativity, which describes gravity as a curvature of spacetime. Researchers call wormholes Einstein-Rosen bridges after the two physicists who invoked them, while the term “wormhole” itself was coined by physicist John Wheeler in the 1950s.
The notion that wormholes and quantum physics, specifically entanglement (a phenomenon in which two particles can remain connected across vast distances), may have a connection was first proposed in theoretical research by Juan Maldacena and Leonard Susskind in 2013. The physicists speculated that wormholes (or “ER”) were equivalent to entanglement (also known as “EPR” after Albert Einstein, Boris Podolsky [PhD ’28], and Nathan Rosen, who first proposed the concept). In essence, this work established a new kind of theoretical link between the worlds of gravity and quantum physics. “It was a very daring and poetic idea,” says Spiropulu of the ER = EPR work.
Later, in 2017, Jafferis, along with his colleagues Ping Gao and Aron Wall, extended the ER = EPR idea to not just wormholes but traversable wormholes. The scientists concocted a scenario in which negative repulsive energy holds a wormhole open long enough for something to pass through from one end to the other. The researchers showed that this gravitational description of a traversable wormhole is equivalent to a process known as quantum teleportation. In quantum teleportation, a protocol that has been experimentally demonstrated over long distances via optical fiber and over the air, information is transported across space using the principles of quantum entanglement. More

It’s easy to control the trajectory of a basketball: all we have to do is apply mechanical force coupled with human skill. But controlling the movement of quantum systems such as atoms and electrons is much more challenging, as these minuscule scraps of matter often fall prey to perturbations that knock them off their path in unpredictable ways. Movement within the system degrades — a process called damping — and noise from environmental effects such as temperature also disturbs its trajectory.
One way to counteract the damping and the noise is to apply stabilizing pulses of light or voltage of fluctuating intensity to the quantum system. Now researchers from Okinawa Institute of Science and Technology (OIST) in Japan have shown that they can use artificial intelligence to discover these pulses in an optimized way to appropriately cool a micro-mechanical object to its quantum state and control its motion. Their research was published in November, 2022, in Physical Review Research as a Letter.
Micro-mechanical objects, which are large compared to an atom or electron, behave classically when kept at a high temperature, or even at room temperature. However, if such mechanical modes can be cooled down to their lowest energy state, which physicists call the ground state, quantum behaviour could be realised in such systems. These kinds of mechanical modes then can be used as ultra-sensitive sensors for force, displacement, gravitational acceleration etc. as well as for quantum information processing and computing.
“Technologies built from quantum systems offer immense possibilities,” said Dr. Bijita Sarma, the article’s lead author and a Postdoctoral Scholar at OIST Quantum Machines Unit in the lab of Professor Jason Twamley. “But to benefit from their promise for ultraprecise sensor design, high-speed quantum information processing, and quantum computing, we must learn to design ways to achieve fast cooling and control of these systems.”
The machine learning-based method that she and her colleagues designed demonstrates how artificial controllers can be used to discover non-intuitive, intelligent pulse sequences that can cool a mechanical object from high to ultracold temperatures faster than other standard methods. These control pulses are self-discovered by the machine learning agent. The work showcases the utility of artificial machine intelligence in the development of quantum technologies.
Quantum computing has the potential to revolutionise the world by enabling high computing speeds and reformatting cryptographic techniques. That is why, many research institutes and big-tech companies such as Google and IBM are investing a lot of resources in developing such technologies. But to enable this, researchers must achieve complete control over the operation of such quantum systems at very high speed, so that the effects of noise and damping can be eliminated.
“In order to stabilize a quantum system, control pulses must be fast — and our artificial intelligence controllers have shown the promise to achieve such feat,” Dr Sarma said. “Thus, our proposed method of quantum control using an AI controller could provide a breakthrough in the field of high-speed quantum computing, and it might be a first step to achieve quantum machines that are self-driving, similar to self-driving cars. We are hopeful that such methods will attract many quantum researchers for future technological developments.” More

Until now, it has been extremely tedious to perform measurements with high sensitivity and high spatial resolution using X-ray light in the tender energy range of 1.5 — 5.0 keV. Yet this X-ray light is ideal for investigating energy materials such as batteries or catalysts, but also biological systems. A team from HZB has now solved this problem: The newly developed monochromator optics increase the photon flux in the tender energy range by a factor of 100 and thus enable highly precise measurements of nanostructured systems. The method was successfully tested for the first time on catalytically active nanoparticles and microchips.
A climate-neutral energy supply requires a wide variety of materials for energy conversion processes, for example catalytically active materials and new electrodes for batteries. Many of these materials have nanostructures that increase their functionality. When investigating these samples, spectroscopic measurements to detect the chemical properties are ideally combined with X-ray imaging with high spatial resolution at the nanoscale. However, since key elements in these materials, such as molybdenum, silicon or sulphur, react predominantly to X-rays in the so-called tender photon energy range, there has been a major problem until now.
This is because in this “tender” energy range between soft and hard X-rays, conventional X-ray optics from plane grating or crystal monochromators deliver only very low efficiencies. A team from HZB has now solved this problem: “We have developed novel monochromator optics. These optics are based on an adapted, multilayer-coated sawtooth grating with a plane mirror,” says Frank Siewert from the HZB Optics and Beamlines Department. The new monochromator concept increases the photon flux in the tender X-ray range by a factor of 100 and thus enables highly sensitive spectromicroscopic measurements with high resolutions for the first time. “Within a short time we were able to collect data from NEXAFS spectromicroscopy on the nanoscale. We have demonstrated this on catalytically active nanoparticles and modern microchip structures,” says Stephan Werner, first author of the publication. “The new development now enables experiments that would otherwise have required months of data collection,” Werner emphasises.
“This monochromator will become the method of choice for imaging in this X-ray energy range, not only at synchrotrons worldwide, but also at free-electron lasers and laboratory sources,” says Gerd Schneider, who heads the X-ray Microscopy Department at HZB. He expects enormous effects on many areas of materials research: Studies in the tender X-ray range could significantly advance the development of energy materials and thus contribute to climate-neutral solutions for electricity and energy supply.
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Even the best water filters let some things through, but designing improved materials and then testing them is time consuming and difficult. Now, researchers in ACS Central Science report that artificial intelligence (AI) could speed up the development of promising materials. In a proof-of-concept study, they simulated different patterns of water-attracting and water-repelling groups lining a filter’s porous membrane and found optimal arrangements that should let water through easily and slow down some contaminants.
Filter systems, ranging from faucet attachments to room-sized industrial systems, clean up water for drinking and other uses. However, current filtration membranes have a hard time if the water is extremely dirty or has small, neutral molecules, such as boric acid — a common insecticide used on crop plants. This is because synthetic porous materials are generally limited to sorting compounds by either size or charge. But biological membranes have pores made of proteins, such as aquaporin, that can separate water from other molecules by both size and charge because of the different types of functional groups, or collections of atoms, lining the channels. Inspired to do the same with a synthetic porous material, M. Scott Shell and colleagues wanted to use computers to design the inside of a carbon nanotube pore to filter boric acid-containing water.
The researchers simulated a carbon nanotube channel with hydroxyl (water-attracting) and/or methyl (water-repelling) groups tethered to each atom on the inner wall. Then they designed and tested thousands of functional group patterns with optimization algorithms and machine learning, a type of AI, to assess how quickly water and boric acid would move through the pore. Here’s what they found: The optimal patterns had one or two rows of hydroxyl groups sandwiched between methyl groups, forming rings around the midsection of the pore. In these simulations, water went through the pore nearly twice as fast as boric acid. Another series of simulations showed that other neutral solutes, including phenol, benzene and isopropanol, could also become separated from water with the optimized carbon nanotube designs.This study demonstrates AI’s usefulness toward developing water purification membranes with novel properties, the researchers say, and could form the basis of a new type of filter system. They add that the approach could be adapted to design surfaces that could have unique interactions with water or other molecules, such as coatings that resist fouling.
The authors acknowledge funding from the U.S. Department of Energy (via the Center for Materials for Water and Energy Systems (M-WET), an Energy Frontier Research Center) with additional support from the U.S. National Science Foundation, the California NanoSystems Institute, the Materials Research Science and Engineering Center and a National Science Foundation Graduate Research Fellowship.
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