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      Muon magnetism could hint at a breakdown of physics’ standard model

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      A mysterious magnetic property of subatomic particles called muons hints that new fundamental particles may be lurking undiscovered.

      In a painstakingly precise experiment, muons’ gyrations within a magnetic field seem to defy predictions of the standard model of particle physics, which describes known fundamental particles and forces. The result strengthens earlier evidence that muons, the heavy kin of electrons, behave unexpectedly.

      “It’s a very big deal,” says theoretical physicist Bhupal Dev of Washington University in St. Louis. “This could be the long-awaited sign of new physics that we’ve all hoped for.”

      Muons’ misbehavior could point to the existence of new types of particles that alter muons’ magnetic properties. Muons behave like tiny magnets, each with a north and south pole. The strength of that magnet is tweaked by transient quantum particles that constantly flit into and out of existence, adjusting the muon’s magnetism by an amount known as the muon magnetic anomaly. Physicists can predict the value of the magnetic anomaly by considering the contributions of all known particles. If any fundamental particles are in hiding, their additional effects on the magnetic anomaly could give them away.

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      Muons and electrons share a family resemblance, but muons are about 200 times as massive. That makes muons more sensitive to the effects of hypothetical heavy particles. “The muon kind of hits the sweet spot,” says Aida El-Khadra of the University of Illinois at Urbana-Champaign.

      To measure the magnetic subtleties of the muon, physicists flung billions of the particles around the huge, doughnut-shaped magnet of the Muon g−2 experiment at Fermilab in Batavia, Ill. (SN: 9/19/18). Inside that magnet, the orientation of the muons’ magnetic poles wobbled, or precessed. Notably, the rate of that precession diverged slightly from the standard model expectation, physicists report April 7 in a virtual seminar, and in a paper published in Physical Review Letters.

      “This is a really complex experiment,” says Tsutomu Mibe of the KEK High Energy Accelerator Research Organization in Japan. “This is excellent work.”

      To avoid bias, the team worked under self-imposed secrecy, keeping the final number hidden from themselves as they analyzed the data. At the moment the answer was finally revealed, says physicist Meghna Bhattacharya of the University of Mississippi in Oxford, “I was having goose bumps.” The researchers found a muon magnetic anomaly of 0.00116592040, accurate to within 46 millionths of a percent. The theoretical prediction pegs the number at 0.00116591810. That discrepancy “hints toward new physics,” Bhattacharya says.

      A previous measurement of this type, from an experiment completed in 2001 at Brookhaven National Laboratory in Upton, N.Y., also seemed to disagree with theoretical predictions  (SN: 2/15/01). When the new result is combined with the earlier discrepancy, the measurement diverges from the prediction by a statistical measure of 4.2 sigma — tantalizingly close to the typical five-sigma benchmark for claiming a discovery. “We have to wait for more data from the Fermilab experiment to really be convinced that this is a real discovery, but it is becoming more and more interesting,” says theoretical physicist Carlos Wagner of the University of Chicago.

      According to quantum physics, muons are constantly emitting and absorbing particles in a frenzy that makes theoretical calculations of the magnetic anomaly extremely complex. An international team of more than 170 physicists, co-led by El-Khadra, finalized the theoretical prediction in December 2020 in Physics Reports.

      Many physicists believe that this theoretical prediction is solid, and unlikely to budge with further investigation. But some debate lingers. Using a computational technique called lattice QCD for a particularly thorny part of the calculation gives an estimate that falls closer to the experimentally measured value, physicist Zoltan Fodor and colleagues report April 7 in Nature. If Fodor and colleagues’ calculation is correct, “it could change how we see the experiment,” says Fodor, of Pennsylvania State University, perhaps making it easier to explain the experimental results with the standard model. But he notes that his team’s prediction would need to be confirmed by other calculations before being taken as seriously as the “gold standard” prediction.

      As theoretical physicists continue to refine their predictions, experimental estimates will improve too: Muon g−2 (pronounced gee-minus-two) physicists have analyzed only a fraction of their data so far. And Mibe and colleagues are planning an experiment using a different technique at J-PARC, the Japan Proton Accelerator Research Complex in Tokai, to begin in 2025.

      If the discrepancy between experiment and prediction holds up, scientists will need to find an explanation that goes beyond the standard model. Physicists already believe that the standard model can’t explain everything that’s out there: The universe seems to be pervaded by invisible dark matter, for example, that standard model particles can’t account for.

      Some physicists speculate that the explanation for the muon magnetic anomaly may be connected to known puzzles of particle physics. For example, a new particle might simultaneously explain dark matter and the Muon g−2 result. Or there may be a connection to unexpected features of certain particle decays observed in the LHCb experiment at the CERN particle physics lab near Geneva (SN: 4/20/17), recently strengthened by new results posted at arXiv.org on March 22.

      The Muon g−2 measurement will intensify such investigations, says Muon g−2 physicist Jason Crnkovic of the University of Mississippi. “This is an exciting result because it’s going to generate a lot of conversations.” More

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      Microscopic images reveal the science and beauty of face masks

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      Studying fabrics at very high magnification helps determine how some face masks filter out particles better than others. And the close-ups reveal an unseen beauty of the mundane objects that have now become an essential part of life around the world.

      As scientists continue to show how effective masks can be at slowing the spread of the new coronavirus, particularly when they have a good fit and are worn correctly, some have taken microscopic approaches (SN: 2/12/21).

      “Embedded in microscale textures are clues as to why materials have various properties,” says Edward Vicenzi, a microanalysis expert at the Smithsonian’s Museum Conservation Institute in Suitland, Md. “Unraveling that evidence turns out to be a fun job.”

      Before the pandemic, Vicenzi spent his days observing meteorites, stones and other museum specimens under the microscope. But in March 2020, as the COVID-19 pandemic progressed, he and colleagues from the National Institute for Standards and Technology in Gaithersburg, Md., felt a strong desire to contribute to beating back the virus. So they started studying face-covering materials instead.

      Cotton flannel: A network of cotton fibers “hovers” above a woven surface in this view of the fabric. This chaotic arrangement gives cotton flannel fibers additional opportunities to grab particles as they flow through the fabric. E.P. Vicenzi/Smithsonian’s Museum Conservation Institute and NIST

      Polyester-cotton blend: Disheveled natural cotton fibers (pale) contrast with nearly identical polyester fibers (blue) in this false-color image. Polyester fibers are highly organized, mostly straight and smooth, making them less effective than cotton fibers alone at trapping nanoscale particles. E.P. Vicenzi/Smithsonian’s Museum Conservation Institute and NIST

      Rayon: Like patterns observed on rigatoni pasta, grooves run along the length of rayon fibers. Unlike cotton flannels, rayon has no apparent weblike structures formed from raised fibers, making it easier for particles to move from one side of the synthetic fabric to the other. E.P. Vicenzi/Smithsonian’s Museum Conservation Institute and NIST

      Wool flannel: Seen in cross-section, these fibers resemble a hurricane swirl. Wool flannel can also form fiber webs that block particles, but those webs are not as effective as ones in 100-percent cotton, researchers found. E.P. Vicenzi/Smithsonian’s Museum Conservation Institute and NIST

      N95 mask: In an N95 mask (seen in false color cross-section), a thin outer layer (top) and thick inner layer (bottom) sandwich a filtration layer (purple), which traps the smallest particles. The multilayered assemblage made of plastic is melted and blown into a weblike fabric, which makes N95s filter particles better than cloth masks, even cotton ones. E.P. Vicenzi/Smithsonian’s Museum Conservation Institute and NIST

      Using a scanning electron microscope, Vicenzi and colleagues have examined dozens of materials, including coffee filters, pillowcases, surgical masks and N95 masks. In 2020, the team found that N95 respirator masks are the most effective at providing protection from aerosols like the ones in which SARS-CoV-2, the virus that causes COVID-19, travels. And the researchers reported that synthetic fabrics, like chiffon or rayon, don’t trap as many particles as tightly woven cotton flannels.

      Microscopic textures can explain each fabric’s ability to filter out aerosols. The random nature of cotton fibers — with its wrinkled texture and complex shapes such as kinks, bends and folds — probably allows cotton to trap more nanoscale particles than other fabrics, Vicenzi says. In contrast, polyester fabrics have highly organized, mostly straight and smooth fibers, which makes them less efficient as face masks.

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      Cotton flannels also provide additional protection by absorbing moisture from breath, Vicenzi and colleagues report March 8 in ACS Applied Nano Materials.

      “Since cotton loves water, it swells up in humid environments, and that makes it harder for particles to make their way through a mask,” says Vicenzi. Polyester and nylon masks, on the other hand, “repel water from your breath, so there’s no added benefit.”

      Through his work, Vicenzi has explored the unseen world of face-covering materials. Some textiles remind him of food, such as rayon’s fibers that resemble the texture of rigatoni pasta. Others, like wool, remind him of atmospheric patterns such as the swirl of a hurricane.

      Vicenzi plans to keep observing face masks up close. And he hopes his research helps people decide how to best protect themselves and others during the COVID-19 pandemic. “It’s nice to use an effective material for a mask if you can,” he says. “However, wearing any mask compared to none at all makes the biggest difference in slowing the spread of pathogens.”

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      This soft robot withstands crushing pressures at the ocean’s greatest depths

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      Inspired by a strange fish that can withstand the punishing pressures of the deepest reaches of the ocean, scientists have devised a soft autonomous robot capable of keeping its fins flapping — even in the deepest part of the Mariana Trench.
      The team, led by roboticist Guorui Li of Zhejiang University in Hangzhou, China, successfully field-tested the robot’s ability to swim at depths ranging from 70 meters to nearly 11,000 meters, it reports March 4 in Nature.
      Challenger Deep is the lowest of the low, the deepest part of the Mariana Trench. It bottoms out at about 10,900 meters below sea level (SN: 12/11/12). The pressure from all that overlying water is about a thousand times the atmospheric pressure at sea level, translating to about 103 million pascals (or 15,000 pounds per square inch). “It’s about the equivalent of an elephant standing on top of your thumb,” says deep-sea physiologist and ecologist Mackenzie Gerringer of State University of New York at Geneseo, who was not involved in the new study.

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      The tremendous pressures at these hadal depths — the deepest ocean zone, between 6,000 and 11,000 meters — present a tough engineering challenge, Gerringer says. Traditional deep-sea robots or manned submersibles are heavily reinforced with rigid metal frames so as not to crumple — but these vessels are bulky and cumbersome, and the risk of structural failure remains high.
      To design robots that can maneuver gracefully through shallower waters, scientists have previously looked to soft-bodied ocean creatures, such as the octopus, for inspiration (SN: 9/17/14). As it happens, such a deep-sea muse also exists: Pseudoliparis swirei, or the Mariana hadal snailfish, a mostly squishy, translucent fish that lives as much as 8,000 meters deep in the Mariana Trench.
      In 2018, researchers described three newly discovered species of deep-sea snailfish (one shown) found in the Pacific Ocean’s Atacama Trench, living at depths down to about 7,500 meters. Also found in the Mariana Trench, such fish are well adapted for living in high-pressure, deep-sea environments, with only partially hardened skulls and soft, streamlined, energy-efficient bodies.Newcastle University
      Gerringer, one of the researchers who first described the deep-sea snailfish in 2014, constructed a 3-D printed soft robot version of it several years later to better understand how it swims. Her robot contained a synthesized version of the watery goo inside the fish’s body that most likely adds buoyancy and helps it swim more efficiently (SN: 1/3/18).
      But devising a robot that can swim under extreme pressure to investigate the deep-sea environment is another matter. Autonomous exploration robots require electronics not only to power their movement, but also to perform various tasks, whether testing water chemistry, lighting up and filming the denizens of deep ocean trenches, or collecting samples to bring back to the surface. Under the squeeze of water pressure, these electronics can grind against one another.
      So Li and his colleagues decided to borrow one of the snailfish’s adaptations to high-pressure life: Its skull is not completely fused together with hardened bone. That extra bit of malleability allows the pressure on the skull to equalize. In a similar vein, the scientists decided to distribute the electronics — the “brain” — of their robot fish farther apart than they normally would, and then encase them in soft silicone to keep them from touching.
      The design of the new soft robot (left) was inspired by the deep-sea snailfish (illustrated, right), which is adapted to live in the very high-pressure environments of the deepest parts of the ocean. The snailfish’s skull is incompletely ossified, or hardened, which allows external and internal pressures to equalize. Spreading apart the robot’s sensitive electronics and encasing them in silicone keeps the parts from squeezing together. The robots flapping fins are inspired by the thin pectoral fins of the fish (although the real fish doesn’t use its fins to swim).Li et al/ Nature 2021
      The team also designed a soft body that slightly resembles the snailfish, with two fins that the robot can use to propel itself through the water. (Gerringer notes that the actual snailfish doesn’t flap its fins, but wriggles its body like a tadpole.) To flap the fins, the robot is equipped with batteries that power artificial muscles: electrodes sandwiched between two membranes that deform in response to the electrical charge.
      The team tested the robot in several environments: 70 meters deep in a lake; about 3,200 meters deep in the South China Sea; and finally, at the very bottom of the ocean. The robot was allowed to swim freely in the first two trials. For the Challenger Deep trial, however, the researchers kept a tight grip, using the extendable arm of a deep-sea lander to hold the robot while it flapped its fins.
      This machine “pushes the boundaries of what can be achieved” with biologically inspired soft robots, write robotocists Cecilia Laschi of the National University of Singapore and Marcello Calisti of the University of Lincoln in England. The pair have a commentary on the research in the same issue of Nature. That said, the machine is still a long way from deployment, they note. It swims more slowly than other underwater robots, and doesn’t yet have the power to withstand powerful underwater currents. But it “lays the foundations” for future such robots to help answer lingering questions about these mysterious reaches of the ocean, they write.
      [embedded content]
      Researchers successfully ran a soft autonomous robot through several field tests at different depths in the ocean. At 3,224 meters deep in the South China Sea, the tests demonstrated that the robot could swim autonomously (free swim test). The team also tested the robot’s ability to move under even the most extreme pressures in the ocean. A deep-sea lander’s extendable arm held the robot as it flapped its wings at a depth of 10,900 meters in the Challenger Deep, the lowest part of the Mariana Trench (extreme pressure test). These tests suggest that such robots may, in future, be able to aid in autonomous exploration of the deepest parts of the ocean, the researchers say.
      Deep-sea trenches are known to be teeming with microbial life, which happily feed on the bonanza of organic material — from algae to animal carcasses — that finds its way to the bottom of the sea. That microbial activity hints that the trenches may play a significant role in Earth’s carbon cycle, which is in turned linked to the planet’s regulation of its climate.
      The discovery of microplastics in Challenger Deep is also incontrovertible evidence that even the bottom of the ocean isn’t really that far away, Gerringer says (SN: 11/20/20). “We’re impacting these deep-water systems before we’ve even found out what’s down there. We have a responsibility to help connect these seemingly otherworldly systems, which are really part of our planet.” More

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      ‘Designer molecules’ could create tailor-made quantum devices

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      Quantum bits made from “designer molecules” are coming into fashion. By carefully tailoring the composition of molecules, researchers are creating chemical systems suited to a variety of quantum tasks.
      “The ability to control molecules … makes them just a beautiful and wonderful system to work with,” said Danna Freedman, a chemist at Northwestern University in Evanston, Ill. “Molecules are the best.” Freedman described her research February 8 at the annual meeting of the American Association for the Advancement of Science, held online.
      Quantum bits, or qubits, are analogous to the bits found in conventional computers. But rather than existing in a state of either 0 or 1, as standard bits do, qubits can possess both values simultaneously, enabling new types of calculations impossible for conventional computers.
      Besides their potential use in quantum computers, molecules can also serve as quantum sensors, devices that can make extremely sensitive measurements, such as sussing out minuscule electromagnetic forces (SN: 3/23/18).

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      In Freedman and colleagues’ qubits, a single chromium ion, an electrically charged atom, sits at the center of the molecule. The qubit’s value is represented by that chromium ion’s electronic spin, a measure of the angular momentum of its electrons. Additional groups of atoms are attached to the chromium; by swapping out some of the atoms in those groups, the researchers can change the qubit’s properties to alter how it functions.
      Recently, Freedman and colleagues crafted molecules to fit one particular need: molecular qubits that respond to light. Lasers can set the values of the qubits and help read out the results of calculations, the researchers reported in the Dec. 11 Science. Another possibility might be to create molecules that are biocompatible, Freedman says, so they can be used for sensing conditions inside living tissue.
      Molecules have another special appeal: All of a given type are exactly the same. Many types of qubits are made from bits of metal or other material deposited on a surface, resulting in slight differences between qubits on an atomic level. But using chemical techniques to build up molecules atom by atom means the qubits are identical, making for better-performing devices. “That’s something really powerful about the bottom-up approach that chemistry affords,” said Freedman.
      Scientists are already using individual atoms and ions in quantum devices (SN: 6/29/17), but molecules are more complicated to work with, thanks to their multiple constituents. As a result, molecules are a relatively new quantum resource, Caltech physicist Nick Hutzler said at the meeting. “People don’t even really know what you can do with [molecules] yet.… But people are discovering new things every day.” More

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      How one physicist is unraveling the mathematics of knitting

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      Physicist Elisabetta Matsumoto is an avid knitter and has been since taking up the hobby as a child. During graduate school at the University of Pennsylvania in 2009, Matsumoto came across an unusually knotty stitch while knitting a pattern for a Japanese red dragon. “I have books with thousands of different stitch patterns, but the one in the red dragon wall hanging was one I had never seen,” she says. That got her thinking about the geometry of stitches and, eventually, led her to study the mathematics of knitting.
      There are a hundred or so basic stitches, Matsumoto says. By varying stitch combinations, a knitter can alter the elasticity, mechanical strength and 3-D structure of the resulting fabric. Yarn on its own isn’t very elastic. But when knitted, the yarn gives rise to fabric that can stretch by more than twice its length while the yarn itself barely stretches.
      Matsumoto, now at the Georgia Institute of Technology in Atlanta, is teasing out the mathematical rules that dictate how stitches impart such unique properties to fabrics. She hopes to develop a catalog of stitch types, their combinations and the resulting fabric properties. Knitters, scientists and manufacturers could all benefit from a dictionary of knits, she says.
      Elisabetta Matsumoto, a physicist at the Georgia Institute of Technology in Atlanta, hopes to create a dictionary of knits that could be used to manipulate physical properties of materials.Courtesy of Elisabetta Matsumoto
      Matsumoto’s research builds on knot theory (SN: 10/31/08), a set of mathematical principles that define how knots form. These principles have helped explain how DNA folds and unfolds and how a molecule’s makeup and distribution in space impart it with physical and chemical characteristics (SN: 5/23/08; SN: 8/27/18). Matsumoto is using knot theory to understand how each stitch entangles with its neighbors. “The types of stitches, the differences in their geometries as well as the order in which you put those stitches together into a textile may determine [the fabric’s] properties,” she says.
      Making tiny changes, such as altering a couple of crossings in a knot, could have a huge impact on the mechanics of the textile. For instance, a fabric made of just one stitch type, such as a knit or purl, tends to curl at the edges. But combine the two stitch types together in alternating rows or columns, and the fabric lays flat. And despite looking nearly identical, the fabrics have varying degrees of stretchiness, Matsumoto and grad student Shashank Markande reported in July in the Bridges 2020 Conference Proceedings.
      Matsumoto’s team is now training a computer to think like a knitter. Using yarn properties, mathematical stitch details and final knitted structures as inputs, a program can predict mechanical properties of fabrics. These predictions could someday help tailor materials for specific applications — from scaffolds for growing human tissue to wearable smart clothing (SN: 6/1/18) — and perhaps solve knotty problems of everyday life. More

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      A robot arm toting a Venus flytrap can grab delicate objects

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      A new robotic grabber is ripped straight from the plant world. The device, made with a severed piece of a Venus flytrap, can grasp tiny, delicate objects, researchers report January 25 in Nature Electronics.
      Normally, the carnivorous Dionaea muscipula scores a meal when unsuspecting prey touches delicate hairs on one of the plant’s jawlike leaves, triggering the trap to snap shut (SN: 10/14/20). But by sticking electrodes to the leaves and applying a small electric voltage, researchers designed a method to force Venus flytraps to close. Even when cut from the plant, the leaves retained the ability to shut upon command for up to a day, say materials scientist Wenlong Li and colleagues at Nanyang Technological University in Singapore.
      Integrating soft, flexible plant material into robotics could aid in picking up fragile objects that would otherwise be damaged by clunky, rigid graspers, the researchers say. So, Li’s team attached a piece of a flytrap to a robotic arm and used a smartphone app to control the trap. In experiments, the robotic grabber clutched a piece of wire one-half of a millimeter in diameter. And when not strapped to the robotic arm, the dismembered plant also caught a slowly moving 1-gram weight.
      One drawback: The traps take hours to reopen, meaning this bot had better make the catch on the first try.
      [embedded content]
      Scientists controlled a Venus flytrap outfitted with electrodes, using a smartphone to direct it to grasp small objects like a wire and a moving weight. More

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      Drones could help create a quantum internet

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      The quantum internet may be coming to you via drone.
      Scientists have now used drones to transmit particles of light, or photons, that share the quantum linkage called entanglement. The photons were sent to two locations a kilometer apart, researchers from Nanjing University in China report in a study to appear in Physical Review Letters.
      Entangled quantum particles can retain their interconnected properties even when separated by long distances. Such counterintuitive behavior can be harnessed to allow new types of communication. Eventually, scientists aim to build a global quantum internet that relies on transmitting quantum particles to enable ultrasecure communications by using the particles to create secret codes to encrypt messages. A quantum internet could also allow distant quantum computers to work together, or perform experiments that test the limits of quantum physics.
      Quantum networks made with fiber-optic cables are already beginning to be used (SN: 9/28/20). And a quantum satellite can transmit photons across China (SN: 6/15/17). Drones could serve as another technology for such networks, with the advantages of being easily movable as well as relatively quick and cheap to deploy.
      The researchers used two drones to transmit the photons. One drone created pairs of entangled particles, sending one particle to a station on the ground while relaying the other to the second drone. That machine then transmitted the particle it received to a second ground station a kilometer away from the first. In the future, fleets of drones could work together to send entangled particles to recipients in a variety of locations. More