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    Quantum tool opens door to uncharted phenomena

    Entanglement is a quantum phenomenon where the properties of two or more particles become interconnected in such a way that one cannot assign a definite state to each individual particle anymore. Rather, we have to consider all particles at once that share a certain state. The entanglement of the particles ultimately determines the properties of a material.
    “Entanglement of many particles is the feature that makes the difference,” emphasizes Christian Kokail, one of the first authors of the paper now published in Nature. “At the same time, however, it is very difficult to determine.” The researchers led by Peter Zoller at the University of Innsbruck and the Institute of Quantum Optics and Quantum Information (IQOQI) of the Austrian Academy of Sciences (ÖAW) now provide a new approach that can significantly improve the study and understanding of entanglement in quantum materials. In order to describe large quantum systems and extract information from them about the existing entanglement, one would naively need to perform an impossibly large number of measurements. “We have developed a more efficient description, that allows us to extract entanglement information from the system with drastically fewer measurements,” explains theoretical physicist Rick van Bijnen.
    In an ion trap quantum simulator with 51 particles, the scientists have imitated a real material by recreating it particle by particle and studying it in a controlled laboratory environment. Very few research groups worldwide have the necessary control of so many particles as the Innsbruck experimental physicists led by Christian Roos and Rainer Blatt. “The main technical challenge we face here is how to maintain low error rates while controlling 51 ions trapped in our trap and ensuring the feasibility of individual qubit control and readout,” explains experimentalist Manoj Joshi. In the process, the scientists witnessed for the first time effects in the experiment that had previously only been described theoretically. “Here we have combined knowledge and methods that we have painstakingly worked out together over the past years. It’s impressive to see that you can do these things with the resources available today,” says an excited Christian Kokail, who recently joined the Institute for Theoretical Atomic Molecular and Optical Physics at Harvard.
    Shortcut via temperature profiles
    In a quantum material, particles can be more or less strongly entangled. Measurements on a strongly entangled particle yield only random results. If the results of the measurements fluctuate very much — i.e., if they are purely random — then scientists refer to this as “hot.” If the probability of a certain result increases, it is a “cold” quantum object. Only the measurement of all entangled objects reveals the exact state. In systems consisting of very many particles, the effort for the measurement increases enormously. Quantum field theory has predicted that subregions of a system of many entangled particles can be assigned a temperature profile. These profiles can be used to derive the degree of entanglement of the particles.
    In the Innsbruck quantum simulator, these temperature profiles are determined via a feedback loop between a computer and the quantum system, with the computer constantly generating new profiles and comparing them with the actual measurements in the experiment. The temperature profiles obtained by the researchers show that particles that interact strongly with the environment are “hot” and those that interact little are “cold.” “This is exactly in line with expectations that entanglement is particularly large where the interaction between particles is strong,” says Christian Kokail.
    Opening doors to new areas of physics
    “The methods we have developed provide a powerful tool for studying large-scale entanglement in correlated quantum matter. This opens the door to the study of a new class of physical phenomena with quantum simulators that already are available today,” says quantum mastermind Peter Zoller. “With classical computers, such simulations can no longer be computed with reasonable effort.” The methods developed in Innsbruck will also be used to test new theory on such platforms.
    The results have been published in Nature. Financial support for the research was provided by the Austrian Science Fund FWF, the Austrian Research Promotion Agency FFG, the European Union, the Federation of Austrian Industries Tyrol and others. More

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    Nearly 400,000 new compounds added to open-access materials database

    New technology often calls for new materials — and with supercomputers and simulations, researchers don’t have to wade through inefficient guesswork to invent them from scratch.
    The Materials Project, an open-access database founded at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) in 2011, computes the properties of both known and predicted materials. Researchers can focus on promising materials for future technologies — think lighter alloys that improve fuel economy in cars, more efficient solar cells to boost renewable energy, or faster transistors for the next generation of computers.
    Now, Google DeepMind — Google’s artificial intelligence lab — is contributing nearly 400,000 new compounds to the Materials Project, expanding the amount of information researchers can draw upon. The dataset includes how the atoms of a material are arranged (the crystal structure) and how stable it is (formation energy).
    “We have to create new materials if we are going to address the global environmental and climate challenges,” said Kristin Persson, the founder and director of the Materials Project at Berkeley Lab and a professor at UC Berkeley. “With innovation in materials, we can potentially develop recyclable plastics, harness waste energy, make better batteries, and build cheaper solar panels that last longer, among many other things.”
    To generate the new data, Google DeepMind developed a deep learning tool called Graph Networks for Materials Exploration, or GNoME. Researchers trained GNoME using workflows and data that were developed over a decade by the Materials Project, and improved the GNoME algorithm through active learning. GNoME researchers ultimately produced 2.2 million crystal structures, including 380,000 that they are adding to the Materials Project and predict are stable, making them potentially useful in future technologies. The new results from Google DeepMind are published today in the journal Nature.
    Some of the computations from GNoME were used alongside data from the Materials Project to test A-Lab, a facility at Berkeley Lab where artificial intelligence guides robots in making new materials. A-Lab’s first paper, also published today in Nature, showed that the autonomous lab can quickly discover novel materials with minimal human input.
    Over 17 days of independent operation, A-Lab successfully produced 41 new compounds out of an attempted 58 — a rate of more than two new materials per day. For comparison, it can take a human researcher months of guesswork and experimentation to create one new material, if they ever reach the desired material at all.

    To make the novel compounds predicted by the Materials Project, A-Lab’s AI created new recipes by combing through scientific papers and using active learning to make adjustments. Data from the Materials Project and GNoME were used to evaluate the materials’ predicted stability.
    “We had this staggering 71% success rate, and we already have a few ways to improve it,” said Gerd Ceder, the principal investigator for A-Lab and a scientist at Berkeley Lab and UC Berkeley. “We’ve shown that combining the theory and data side with automation has incredible results. We can make and test materials faster than ever before, and adding more data points to the Materials Project means we can make even smarter choices.”
    The Materials Project is the most widely used open-access repository of information on inorganic materials in the world. The database holds millions of properties on hundreds of thousands of structures and molecules, information primarily processed at Berkeley Lab’s National Energy Research Science Computing Center. More than 400,000 people are registered as users of the site and, on average, more than four papers citing the Materials Project are published every day. The contribution from Google DeepMind is the biggest addition of structure-stability data from a group since the Materials Project began.
    “We hope that the GNoME project will drive forward research into inorganic crystals,” said Ekin Dogus Cubuk, lead of Google DeepMind’s Materials Discovery team. “External researchers have already verified more than 736 of GNoME’s new materials through concurrent, independent physical experiments, demonstrating that our model’s discoveries can be realized in laboratories.”
    The Materials Project is now processing the compounds from Google DeepMind and adding them into the online database. The new data will be freely available to researchers, and also feed into projects such as A-Lab that partner with the Materials Project.
    “I’m really excited that people are using the work we’ve done to produce an unprecedented amount of materials information,” said Persson, who is also the director of Berkeley Lab’s Molecular Foundry. “This is what I set out to do with the Materials Project: To not only make the data that I produced free and available to accelerate materials design for the world, but also to teach the world what computations can do for you. They can scan large spaces for new compounds and properties more efficiently and rapidly than experiments alone can.”
    By following promising leads from data in the Materials Project over the past decade, researchers have experimentally confirmed useful properties in new materials across several areas. Some show potential for use: in carbon capture (pulling carbon dioxide from the atmosphere) as photocatalysts (materials that speed up chemical reactions in response to light and could be used to break down pollutants or generate hydrogen) as thermoelectrics (materials that could help harness waste heat and turn it into electrical power) as transparent conductors (which might be useful in solar cells, touch screens, or LEDs)Of course, finding these prospective materials is only one of many steps to solving some of humanity’s big technology challenges.
    “Making a material is not for the faint of heart,” Persson said. “It takes a long time to take a material from computation to commercialization. It has to have the right properties, work within devices, be able to scale, and have the right cost efficiency and performance. The goal with the Materials Project and facilities like A-Lab is to harness data, enable data-driven exploration, and ultimately give companies more viable shots on goal.” More

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    This bird hasn’t been seen in 38 years. Its song may help track it down

    How do you look for an animal you don’t even know exists anymore?

    The last sighting of the purple-winged ground dove (Paraclaravis geoffroyi) — a small, bamboo-loving dove native to the South American Atlantic Forest in Brazil, Argentina and Paraguay — was in 1985. But, researchers wondered, was it possible to capture the elusive bird’s sound in the wild to find out if any individuals are left?

    It’s not an unheard-of idea. Scientists have used bioacoustics — a subfield of ecology that relies on sound to make environmental analyses — for everything from recording dolphins’ communication patterns to studying bats from afar to avoid virus spillover from humans (SN: 12/7/17; SN: 10/23/22). With artificial intelligence, it is now possible to use large audio datasets to train algorithms to spot different animal sounds within the cacophony of a natural background.

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    But the problem is that recordings of the purple-winged ground dove singing are as rare as the bird itself.

    “I came across [the bird’s song] watching a 1985 interview with Carlos Keller, a former bird breeder in São Paulo state, who had a few individuals of the dove,” says Carlos Araújo, an ecologist at the Instituto de Biología Subtropical at the Universidad Nacional de Misiones in Argentina. “And they sang while he spoke.”

    With Keller’s help, Araujo and colleagues accessed the decades-old recording and isolated the bird’s song.

    The next challenge was to see if it was even possible to identify individual bird songs amidst the sounds of other birds chirping, leaves rustling, rain falling, insects whirring and gnawing and larger animals moving through the forest.

    “We took a step back and did some analyses with other birds that are critically endangered but there are known individuals,” Araújo says. The team focused on three species found in Foz do Iguaçu, a national park that straddles the border of Brazil and Argentina: the cherry-throated tanager (Nemosia rourei), the Alagoas antwren (Myrmotherula snowi) and the blue-eyed ground-dove (Columbina cyanopis). These birds live in the same environments as the purple-winged ground dove. And the blue-eyed ground dove’s story inspires hope: The species went missing in 1941 and was rediscovered in 2016.

    To test their setup, the researchers looked for the cherry-throated tanager (shown) and two other rare birds.Ben Phalan/Parque das Aves

    The researchers installed 30 recorders in strategic spots along green areas in the Brazilian part of Foz do Iguaçu and recorded from July 2021 to April 2022. They also used data from another 100 recorders on the Argentinian side of Foz.

    “We went looking for the Guadua trinii bamboo to place the recorders,” says Benjamin Phalan, Head of Conservation at Parque das Aves, a private institution in Foz do Iguaçu focused on the conservation of Atlantic Forest birds. Like the purple-winged ground dove, the three bird species follow the flowering season of the G. trinii bamboo, which happens about once every 30 years.

    The team pushed through thickets of bamboo, braved ticks and biting flies, and watched out for venomous snakes such as jacaracas pit vipers. Bumping into these snakes is “rare but can happen. So we use galoshes or gaiters to protect us in case anyone steps on a snake or near it,” Phalan says.

    Carlos de Araujo installs a recording device in a South American forest. He and colleagues hope to pluck the song of rare birds out of the forest sounds the device picks up. Ben Phalan/Parque das Aves

    The recorders captured one minute of landscape sound every 10 minutes and generated about 3,000 days’ worth of recordings. “A lot of data to sift through,” says Araújo.

    Readily available analysis software wouldn’t work. These software, Araújo says, “need a lot of data input. With such rare species, we just don’t have that much data to train the identification algorithm.”

    So the team started from scratch, working with the little data they had for the three endangered birds. First, Araújo created a signal template — exactly like the birds’ singing — based on just a few recordings. The algorithm then compares that template with the soundscape recordings, separating signal from noise. If it spots a sound that is similar to the template, chances are that it is the bird that the researchers are looking for.

    The method relies on a statistical model “that is not new, but was used in a very clever and unusual way,” says David Donoso, an ecosystem ecology researcher at the Technische Universität Darmstadt in Germany. Donoso and colleagues recently used bioacoustics to investigate the recovery of Choco, a biodiversity hot spot in Ecuador that had been transformed in an agricultural area.

    There are different approaches to bioacoustics depending on what you’re looking for, Donoso says. “You can either use fewer recordings to map a whole animal soundscape to tell what species are there, like we did, or you can use lots of recordings to look for a single sound pattern,” he says. The study at Foz do Iguaçu “shows that you can use a relatively simple model to answer a complex question — and it works.”

    The tool worked reasonably well to identify the cherry-throated tanager and blue-eyed ground-dove singing, but not so much for the Alagoas antwren, Araújo’s team reports October 23 in Bioacoustics. “We’re trying to understand what happened, but we know that the algorithm works,” he says.

    The next step, Araújo says, is to refine the algorithm’s precision to find the Alagoas antwren and train it to look for the purple-winged ground dove. And they will do so at the same time. “We’re aiming at both goals at once because we’re running against the clock to find these birds,” Araújo says. “In the end, we are looking for a ghost.” But not a silent one, he hopes.  More

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    Network of robots can successfully monitor pipes using acoustic wave sensors

    An inspection design method and procedure by which mobile robots can inspect large pipe structures has been demonstrated with the successful inspection of multiple defects on a three-meter long steel pipe using guided acoustic wave sensors.
    The University of Bristol team, led by Professor Bruce Drinkwater and Professor Anthony Croxford, developed approach was used to review a long steel pipe with multiple defects, including circular holes with different sizes, a crack-like defect and pits, through a designed inspection path to achieve 100% detection coverage for a defined reference defect.
    In the study, published today in NDT and E International, they show how they were able to effectively examine large plate-like structures using a network of independent robots, each carrying sensors capable of both sending and receiving guided acoustic waves, working in pulse-echo mode.
    This approach has the major advantage of minimizing communication between robots, requires no synchronization and raises the possibility of on-board processing to lower data transfer costs and hence reducing overall inspection expenses. The inspection was divided into a defect detection and a defect localization stage.
    Lead author Dr Jie Zhang explained: “There are many robotic systems with integrated ultrasound sensors used for automated inspection of pipelines from their inside to allow the pipeline operator to perform required inspections without stopping the flow of product in the pipeline. However, available systems struggle to cope with varying pipe cross-sections or network complexity, inevitably leading to pipeline disruption during inspection. This makes them suitable for specific inspections of high value assets, such as oil and gas pipelines, but not generally applicable.
    “As the cost of mobile robots has reduced over recent years, it is increasingly possible to deploy multiple robots for a large area inspection. We take the existence of small inspection robots as its starting point, and explore how they can be used for generic monitoring of a structure. This requires inspection strategies, methodologies and assessment procedures that can be integrated with the mobile robots for accurate defect detection and localization that is low cost and efficient.
    “We investigate this problem by considering a network of robots, each with a single omnidirectional guided acoustic wave transducer. This configuration is considered as it is arguably the simplest, with good potential for integration in a low cost platform.”
    The methods employed are generally applicable to other related scenarios and allow the impact of any detection or localization method decisions to be quickly quantified. The methods could be used across other materials, pipe geometries, noise levels or guided wave modes, allowing the full range of sensor performance parameters, defects sizes and types and operating modalities to be explored. Also the techniques can be used to assess the detection and localization performance for specified inspection parameters, for example, predict the minimum detectable defect under a specified probability of detection and probability of false alarm.
    The team will now investigate collaboration opportunities with industries to advance current prototypes for actual pipe inspections. This work is funded by the UK’s Engineering and Physical Sciences Research Council (EPSRC) as a part of the Pipebots project. More

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    How do you make a robot smarter? Program it to know what it doesn’t know

    Modern robots know how to sense their environment and respond to language, but what they don’t know is often more important than what they do know. Teaching robots to ask for help is key to making them safer and more efficient.
    Engineers at Princeton University and Google have come up with a new way to teach robots to know when they don’t know. The technique involves quantifying the fuzziness of human language and using that measurement to tell robots when to ask for further directions. Telling a robot to pick up a bowl from a table with only one bowl is fairly clear. But telling a robot to pick up a bowl when there are five bowls on the table generates a much higher degree of uncertainty — and triggers the robot to ask for clarification.
    Because tasks are typically more complex than a simple “pick up a bowl” command, the engineers use large language models (LLMs) — the technology behind tools such as ChatGPT — to gauge uncertainty in complex environments. LLMs are bringing robots powerful capabilities to follow human language, but LLM outputs are still frequently unreliable, said Anirudha Majumdar, an assistant professor of mechanical and aerospace engineering at Princeton and the senior author of a study outlining the new method.
    “Blindly following plans generated by an LLM could cause robots to act in an unsafe or untrustworthy manner, and so we need our LLM-based robots to know when they don’t know,” said Majumdar.
    The system also allows a robot’s user to set a target degree of success, which is tied to a particular uncertainty threshold that will lead a robot to ask for help. For example, a user would set a surgical robot to have a much lower error tolerance than a robot that’s cleaning up a living room.
    “We want the robot to ask for enough help such that we reach the level of success that the user wants. But meanwhile, we want to minimize the overall amount of help that the robot needs,” said Allen Ren, a graduate student in mechanical and aerospace engineering at Princeton and the study’s lead author. Ren received a best student paper award for his Nov. 8 presentation at the Conference on Robot Learning in Atlanta. The new method produces high accuracy while reducing the amount of help required by a robot compared to other methods of tackling this issue.
    The researchers tested their method on a simulated robotic arm and on two types of robots at Google facilities in New York City and Mountain View, California, where Ren was working as a student research intern. One set of hardware experiments used a tabletop robotic arm tasked with sorting a set of toy food items into two different categories; a setup with a left and right arm added an additional layer of ambiguity.

    The most complex experiments involved a robotic arm mounted on a wheeled platform and placed in an office kitchen with a microwave and a set of recycling, compost and trash bins. In one example, a human asks the robot to “place the bowl in the microwave,” but there are two bowls on the counter — a metal one and a plastic one.
    The robot’s LLM-based planner generates four possible actions to carry out based on this instruction, like multiple-choice answers, and each option is assigned a probability. Using a statistical approach called conformal prediction and a user-specified guaranteed success rate, the researchers designed their algorithm to trigger a request for human help when the options meet a certain probability threshold. In this case, the top two options — place the plastic bowl in the microwave or place the metal bowl in the microwave — meet this threshold, and the robot asks the human which bowl to place in the microwave.
    In another example, a person tells the robot, “There is an apple and a dirty sponge … It is rotten. Can you dispose of it?” This does not trigger a question from the robot, since the action “put the apple in the compost” has a sufficiently higher probability of being correct than any other option.
    “Using the technique of conformal prediction, which quantifies the language model’s uncertainty in a more rigorous way than prior methods, allows us to get to a higher level of success” while minimizing the frequency of triggering help, said the study’s senior author Anirudha Majumdar, an assistant professor of mechanical and aerospace engineering at Princeton.
    Robots’ physical limitations often give designers insights not readily available from abstract systems. Large language models “might talk their way out of a conversation, but they can’t skip gravity,” said coauthor Andy Zeng, a research scientist at Google DeepMind. “I’m always keen on seeing what we can do on robots first, because it often sheds light on the core challenges behind building generally intelligent machines.”
    Ren and Majumdar began collaborating with Zeng after he gave a talk as part of the Princeton Robotics Seminar series, said Majumdar. Zeng, who earned a computer science Ph.D. from Princeton in 2019, outlined Google’s efforts in using LLMs for robotics, and brought up some open challenges. Ren’s enthusiasm for the problem of calibrating the level of help a robot should ask for led to his internship and the creation of the new method.
    “We enjoyed being able to leverage the scale that Google has” in terms of access to large language models and different hardware platforms, said Majumdar.
    Ren is now extending this work to problems of active perception for robots: For instance, a robot may need to use predictions to determine the location of a television, table or chair within a house, when the robot itself is in a different part of the house. This requires a planner based on a model that combines vision and language information, bringing up a new set of challenges in estimating uncertainty and determining when to trigger help, said Ren. More

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    Researchers engineer a material that can perform different tasks depending on temperature

    Researchers report that they have developed a new composite material designed to change behaviors depending on temperature in order to perform specific tasks. These materials are poised to be part of the next generation of autonomous robotics that will interact with the environment.
    The new study conducted by University of Illinois Urbana-Champaign civil and environmental engineering professor Shelly Zhang and graduate student Weichen Li, in collaboration with professor Tian Chen and graduate student Yue Wang from the University of Houston, uses computer algorithms, two distinct polymers and 3D printing to reverse engineer a material that expands and contracts in response to temperature change with or without human intervention.
    The study findings are reported in the journal Science Advances.
    “Creating a material or device that will respond in specific ways depending on its environment is very challenging to conceptualize using human intuition alone — there are just so many design possibilities out there,” Zhang said. “So, instead, we decided to work with a computer algorithm to help us determine the best combination of materials and geometry.”
    The team first used computer modeling to conceptualize a two-polymer composite that can behave differently under various temperatures based on user input or autonomous sensing.
    “For this study, we developed a material that can behave like soft rubber in low temperatures and as a stiff plastic in high temperatures,” Zhang said.
    Once fabricated into a tangible device, the team tested the new composite material’s ability to respond to temperature changes to perform a simple task — switch on LED lights.

    “Our study demonstrates that it is possible to engineer a material with intelligent temperature sensing capabilities, and we envision this being very useful in robotics,” Zhang said. “For example, if a robot’s carrying capacity needs to change when the temperature changes, the material will ‘know’ to adapt its physical behavior to stop or perform a different task.”
    Zhang said that one of the hallmarks of the study is the optimization process that helps the researchers interpolate the distribution and geometries of the two different polymer materials needed.
    “Our next goal is to use this technique to add another level of complexity to a material’s programmed or autonomous behavior, such as the ability to sense the velocity of some sort of impact from another object,” she said. “This will be critical for robotics materials to know how to respond to various hazards in the field.”
    The National Science Foundation supported this research. More

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    Capturing methane from the air would slow global warming. Can it be done?

    This summer was the hottest ever recorded on Earth, and 2023 is on track to be the hottest year. Heat waves threatened people’s health across North America, Europe and Asia. Canada had its worst wildfire season ever, and flames devastated the city of Lahaina in Maui. Los Angeles was pounded by an unheard-of summer tropical storm while rains in Libya caused devastating floods that left thousands dead and missing. This extreme weather is a warning sign that we are living in a climate crisis, and a call to action.

    Carbon dioxide emissions from burning fossil fuels are the main culprit behind climate change, and scientists say they must be reined in. But there’s another greenhouse gas to deal with: methane. Tackling methane may be the best bet for putting the brakes on rising temperatures in the short term, says Rob Jackson, an Earth systems scientist at Stanford University and chair of the Global Carbon Project, which tracks greenhouse gas emissions. “Methane is the strongest lever we have to slow global warming over the next few decades.”

    That’s because it’s relatively short-lived in the atmosphere — methane lasts about 12 years, while CO2 can stick around for hundreds of years. And on a molecule-per-molecule basis, methane is more potent. Over the 20-year period after it’s emitted, methane can warm the atmosphere more than 80 times as much as an equivalent amount of CO2.

    We already have strategies for cutting methane emissions — fixing natural gas leaks (methane is the main component of natural gas), phasing out coal (mining operations release methane), eating less meat and dairy (cows burp up lots of methane) and electrifying transportation and appliances. Implementing all existing methane-mitigation strategies could slow global warming by 30 percent over the next decade, research has shown.

    But some climate scientists, including Jackson, say we need to go further. Several methane sources will be difficult, if not impossible, to eliminate. That includes some human-caused emissions, such as those produced by rice paddies and cattle farming — though practices do exist to reduce these emissions (SN: 11/28/15, p. 22). Some natural sources are poised to release more methane as the world warms. There are signs that tropical wetlands are already releasing more of the gas into the atmosphere, and rapid warming in the Arctic could turn permafrost into a hot spot for methane-making microbes and release a bomb of methane stored in the currently frozen soil.

    So scientists want to develop ways to remove methane directly from the air.

    Three billion metric tons more methane exist in the atmosphere today than in preindustrial times. Removing that excess methane would cool the planet by 0.5 degrees Celsius, Jackson says.

    Similar “negative emissions” strategies are already in limited use for CO2. That gas is captured where it’s emitted, or directly from the air, and then stored somewhere. Methane, however, is a tricky molecule to capture, meaning scientists need different approaches.

    Most ideas are still in early research stages. The National Academies of Sciences, Engineering and Medicine is currently studying these potential technologies, their state of readiness and possible risks, and what further research and funding are needed. Some of the approaches include re-engineering bacteria that are already pros at eating methane and developing catalytic reactors to place in coal-mine vents and other methane-rich places to chemically transform the gas.

    “Methane is a sprint and CO2 is a marathon,” says Desirée Plata, a civil and environmental engineer at MIT. For scientists focused on removing greenhouse gases, it’s off to the races.

    Microbes already remove methane from the air

    Methane, CH4, is readily broken down in the atmosphere, where sunshine and highly reactive hydroxyl radicals are abundant. But it’s a different story when chemists try to work with the molecule. Methane’s four carbon-hydrogen bonds are strong and stable. Currently, chemists must expose the gas to extremely high temperatures and pressures to break it down.

    Even getting hold of the gas is difficult. Despite its potent warming power, it’s present in low concentrations in the atmosphere. Only 2 out of every 1 million air molecules are methane (by comparison, about 400 of every 1 million air molecules are CO2). So it’s challenging to grab enough methane to store it or efficiently convert it into something else.

    Nature’s chemists, however, can take up and transform methane even in these challenging conditions. These microbes, called methanotrophs, use enzymes to eat methane. The natural global uptake of methane by methanotrophs living in soil is about 30 million metric tons per year. Compare that with the roughly 350 million tons of methane that human activities pumped into the atmosphere in 2022, according to the International Energy Agency.

    Microbiologists want to know whether it’s possible to get these bacteria to take up more methane more quickly.

    Lisa Stein, a microbiologist at the University of Alberta in Edmonton, Canada, studies the genetics and physiology of these microbes. “We do basic research to understand how they thrive in different environments,” she says.

    Methanotrophs work especially slowly in low-oxygen environments, Stein says, like wetland muck and landfills, the kinds of places where methane is plentiful. In these environments, microbes that make methane, called methanogens, generate the gas faster than methanotrophs can gobble it up.

    But it might be possible to develop soil amendments and other ecosystem modifications to speed microbial methane uptake, Stein says. She’s also talking with materials scientists about engineering a surface to encourage methanotrophs to grow faster and thus speed up their methane consumption.

    Scientists hope to get around this speed bump with a more detailed understanding of the enzyme that helps many methanotrophs feast on methane. Methane monooxygenase, or MMO, grabs the molecule and, with the help of copper embedded in the enzyme, uses oxygen to break methane’s carbon-hydrogen bonds. The enzyme ultimately produces methanol that the microbes then metabolize.

    Boosting MMO’s speed could not only help with methane removal but also allow engineers to put methanotrophs to work in industrial systems. Turning methane into methanol would be the first step, followed by several faster reactions, to make an end product like plastic or fuel.

    Some bacteria, including Methylococcus capsulatus (shown), naturally break down methane with the enzyme methane monooxygenase. By studying the enzyme’s structure, scientists hope to speed up bacteria’s uptake of the greenhouse gas.Anne Fjellbirkeland/Wikimedia Commons (CC BY 2.5)

    “Methane monooxygenases are not superfast enzymes,” says Amy Rosenzweig, a chemist at Northwestern University in Evanston, Ill. Any reaction involving MMO will impose a speed limit on the proceedings. “That is the key step, and unless you understand it, it’s going to be very difficult to make an engineered organism do what you want,” Rosenzweig says.

    Enzymes are often shaped to fit their reactants — in this case, methane — like a glove. So having a clear view of MMO’s physical structure could help researchers tweak the enzyme’s actions. MMO is embedded in a lipid membrane in the cell. To image it, structural biologists have typically started by using detergents to remove the lipids, which inactivates the enzyme and results in an incomplete picture of it and its activity. But Rosenzweig and colleagues recently managed to image the enzyme in this lipid context. This unprecedented view of MMO in its native state, published in 2022 in Science, revealed a previously unseen site where copper binds.

    But that’s still not the entire picture. Rosenzweig says she hopes her structural studies, along with other work, will lead to a breakthrough soon enough to help forestall further consequences of global warming. “Maybe people get lucky and engineer a strain quickly,” Rosenzweig says. “You don’t know until you try.”

    Chemists make progress on catalysts

    Other scientists seek to put methane-destroying chemical reactors close to methane sources. These reactors typically use a catalyst to speed up the chemical reactions that convert methane into a less planet-warming molecule. These catalysts often require high temperatures or other stringent conditions to operate, contain expensive metals like platinum, and don’t work well at the concentrations of methane found in ambient air.

    One promising place to start, though, is coal mines. Coal mining is associated with tens of millions of tons of methane emissions worldwide every year. Although coal-fired power plants are being phased out in many countries, coal will be difficult to eliminate entirely due to its key role in steel production, says Plata, of MIT.

    To develop a catalyst that might work in a coal mine, Plata found inspiration in MMO. Her team developed a catalyst material based on a silicate material embedded with copper — the same metal found in MMO and much less expensive than those usually required to oxidize methane. The material is also porous, which improves the catalyst’s efficiency because it has a larger surface area, and thus more places for reactions to occur, than a nonporous material would. The catalyst turns methane into CO2, a reaction that releases heat, which is needed to further fuel the reaction. If methane concentrations are high enough, the reaction will be self-sustaining, Plata says.

    Turning methane into CO2 may sound counterproductive, but it reduces warming overall because methane traps much more heat than CO2 and is far less abundant in the atmosphere. If all the excess methane in the atmosphere were turned into CO2, according to a 2019 study led by Jackson, it would result in only 8.2 billion additional tons of CO2 — equivalent to just a few months of CO2 emissions at today’s rates. And the net effect would be to lessen the heating of the atmosphere by a sixth.

    Cattle feedlots are another place where Plata’s catalytic reactor might work. Barns outfitted with fans to keep cattle comfortable move air around, so reactors could be fitted to these ventilation systems. The next step is determining whether methane concentrations at industrial dairy farms are high enough for the catalyst to work.

    At Drumgoon Dairy in South Dakota, Elijah Martin (left) and Will Sawyer (right) test a small-scale thermal catalytic unit developed in Desirée Plata’s lab at MIT. The reactor transforms methane into carbon dioxide, which could lower the planet’s net warming rate because methane is a stronger greenhouse gas.D. Plata

    Another researcher making progress is energy scientist and engineer Arun Majumdar, one of Jackson’s collaborators at Stanford. In January, Majumdar published initial results describing a catalyst that converts methane into methanol, with an added boost from high-energy ultraviolet light. This UV blast adds the energy needed to overcome CH4’s stubborn bonds — and the carefully designed catalyst stays on target. Previous catalyst designs tended to produce a mix of CO2 and methanol, but this catalyst mostly sticks to making methanol.

    Is geoengineering a path to methane removal?

    A more extreme approach to speed up methane’s natural breakdown is to change the chemistry of the atmosphere itself. A few companies, such as the U.S.-based Blue Dot Change, have proposed releasing chemicals into the sky to enhance methane oxidation.

    Natalie Mahowald, an atmospheric chemist at Cornell University, decided to evaluate this type of geoengineering.

    “I’m not super excited about throwing more things into the atmosphere,” Mahowald says. To meet the goals of the Paris Agreement, limiting global warming to 1.5 to 2 degrees Celsius above the preindustrial average, though, it’s worth exploring all possibilities, she says. “If we’re going to meet these targets,” she says “we’re going to need some of these crazy ideas to work. So I’m willing to look at it. But I’m looking with a scientist’s critical eye.”

    The main strategy proposed by advocates would inject iron aerosols into the air over the ocean on a sunny day. These aerosols would react with salty sea spray aerosols to form chlorine, which would then attack methane in the atmosphere and initiate further chemical reactions that turn it into CO2. Mahowald wondered how much chlorine would be needed — and if there might be any unintended consequences.

    Detailed modeling revealed something alarming. The iron injections could have the opposite of the intended effect, Mahowald and colleagues reported in July in Nature Communications. Chlorine won’t attack methane if ozone is around. Instead, chlorine will first break down all the ozone it can find. But ozone plays a key role in generating the hydroxyl radicals that naturally break down atmospheric methane. So when ozone levels fall, Mahowald says, the concentration and lifetime of methane molecules in the atmosphere actually increases. To use this strategy to break down methane, geo­engineers would need to add a tremendous amount of chlorine to the atmosphere — enough to first break down the ozone, then attack methane.

    Removing 20 percent of the atmosphere’s methane, thus reducing the planet’s surface temperature by 0.2 degrees Celsius by 2050, for example, would require creating about 630 million tons of atmospheric chlorine every year. That would in turn require injecting perhaps tens of millions of tons of iron. A form of particulate matter, these iron aerosols could worsen air quality; inhaling particulate matter is associated with a range of health problems, particularly cardiovascular and lung disease. This atmospheric tinkering could also create hydrochloric acid that could reach the ocean and acidify it.

    And there’s no guarantee that some of the chlorine wouldn’t make it all the way up to the ozone layer, depleting the planetary shield that protects us from the sun’s harmful UV rays. Mahowald is still studying this possibility.

    Methane is a sprint and CO2 is a marathon. Desirée Plata

    Mahowald is ambivalent about doing research on geoengineering. “We’re just throwing out ideas here because we’re in a terrible, terrible position,” she says. She’s worried about what could happen if all the methane locked up in the world’s permafrost escapes. If scientists can figure out how to use iron aerosols effectively, without adverse effects — and if such geoengineering is accepted by society — we might need it.

    “We’re just trying to see, is there any hope this could work and would we ever want to do it? Would it have enough benefits to outweigh the disadvantages?”

    The committee organized by the National Academies to investigate methane removal is taking these kinds of ethical questions into account, as well as considering the potential cost and scale of technologies. Stein, a committee member, says a framework proposed by Spark Climate Solutions provides some guidance. The organization, a nonprofit based in San Francisco that evaluates methane-removal technologies, proposes investing in tech that can remove tens of millions of tons of methane per year in the coming decades, at a cost of less than $2,000 per ton. Spark cofounder David Mann says the numbers are designed to focus attention and investment on technologies that can make a real difference in curbing climate change in the near term.

    The National Academies group aims to make recommendations about research priorities on methane-removal technologies by next summer. It’s likely that a portfolio of different technologies will be necessary. What works in a cattle feedlot may not work at a wastewater treatment plant, for instance.

    Scientists focused on methane removal are eager for more researchers, research funding and companies to enter the fray — and quickly. “It’s been a crazy year,” Jackson says of 2023’s extreme weather. We’re already feeling the effects of global warming, but we can seize the moment, he says. “This problem is not something for our grandchildren. It’s here.” More

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    Nextgen computing: Hard-to-move quasiparticles glide up pyramid edges

    A new kind of “wire” for moving excitons, developed at the University of Michigan, could help enable a new class of devices, perhaps including room temperature quantum computers.
    What’s more, the team observed a dramatic violation of Einstein’s relation, used to describe how particles spread out in space, and leveraged it to move excitons in much smaller packages than previously possible.
    “Nature uses excitons in photosynthesis. We use excitons in OLED displays and some LEDs and solar cells,” said Parag Deotare, co-corresponding author of the study in ACS Nano supervising the experimental work, and an associate professor of electrical and computer engineering. “The ability to move excitons where we want will help us improve the efficiency of devices that already use excitons and expand excitonics into computing.”
    An exciton can be thought of as a particle (hence quasiparticle), but it’s really an electron linked with a positively-charged empty space in the lattice of the material (a “hole”). Because an exciton has no net electrical charge, moving excitons are not affected by parasitic capacitances, an electrical interaction between neighboring components in a device that causes energy losses. Excitons are also easy to convert to and from light, so they open the way for extremely fast and efficient computers that use a combination of optics and excitonics, rather than electronics.
    This combination could help enable room temperature quantum computing, said Mackillo Kira, co-corresponding author of the study supervising the theory, and a professor of electrical and computer engineering. Excitons can encode quantum information, and they can hang onto it longer than electrons can inside a semiconductor. But that time is still measured in picoseconds (10-12 seconds) at best, so Kira and others are figuring out how to use femtosecond laser pulses (10-15 seconds) to process information.
    “Full quantum-information applications remain challenging because degradation of quantum information is too fast for ordinary electronics,” he said. “We are currently exploring lightwave electronics as a means to supercharge excitonics with extremely fast processing capabilities.”
    However, the lack of net charge also makes excitons very difficult to move. Previously, Deotare had led a study that pushed excitons through semiconductors with acoustic waves. Now, a pyramid structure enables more precise transport for smaller numbers of excitons, confined to one dimension like a wire.

    It works like this:
    The team used a laser to create a cloud of excitons at a corner of the pyramid’s base, bouncing electrons out of the valence band of a semiconductor into the conduction band — but the negatively charged electrons are still attracted to the positively charged holes left behind in the valence band. The semiconductor is a single layer of tungsten diselenide semiconductor, just three atoms thick, draped over the pyramid like a stretchy cloth. And the stretch in the semiconductor changes the energy landscape that the excitons experience.
    It seems counterintuitive that the excitons should ride up the pyramid’s edge and settle at the peak when we imagine an energy landscape chiefly governed by gravity. But instead, the landscape is governed by how far apart the valence and conduction bands of the semiconductor are. The energy gap between the two, also known as the semiconductor’s band gap, shrinks where the semiconductor is stretched. The excitons migrate to the lowest energy state, funneled onto the pyramid’s edge where they then rise to its peak.
    Usually, an equation penned by Einstein is good at describing how a bunch of particles diffuses outward and drifts. However, the semiconductor was imperfect, and those defects acted as traps that would nab some of the excitons as they tried to drift by. Because the defects at the trailing side of the exciton cloud were filled in, that side of the distribution diffused outward as predicted. The leading edge, however, did not extend so far. Einstein’s relation was off by more than a factor of 10.
    “We’re not saying Einstein was wrong, but we have shown that in complicated cases like this, we shouldn’t be using his relation to predict the mobility of excitons from the diffusion,” said Matthias Florian, co-first-author of the study and a research investigator in electrical and computer engineering, working under Kira.
    To directly measure both, the team needed to detect single photons, emitted when the bound electrons and holes spontaneously recombined. Using time-of-flight measurements, they also figured out where the photons came from precisely enough to measure the distribution of excitons within the cloud.
    The study was supported by the Army Research Office (award no. W911NF2110207) and the Air Force Office of Scientific Research (award no. FA995-22-1-0530).
    The pyramid structure was built in the Lurie Nanofabrication Facility.
    The team has applied for patent protection with the assistance of U-M Innovation Partnerships and is seeking partners to bring the technology to market. More