Aurelia Butler

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    in Computers Math

    Finding a metal-oxide needle in a periodic table haystack

    I went to Caltech, and all I got was this T-shirt … and a new way to discover complex and interesting materials.
    Coupling computer automation with an ink-jet printer originally used to print T-shirt designs, researchers at Caltech and Google have developed a high-throughput method of identifying novel materials with interesting properties. In a trial run of the process, they screened hundreds of thousands of possible new materials and discovered one made from cobalt, tantalum, and tin that has tunable transparency and acts as a good catalyst for chemical reactions while remaining stable in strong acid electrolytes.
    The effort, described in a scientific article published in Proceedings of the National Academy of Sciences(PNAS), was led by John Gregoire and Joel Haber of Caltech, and Lusann Yang of Google. It builds on research conducted at the Joint Center for Artificial Photosynthesis (JCAP), a Department of Energy (DOE) Energy Innovation Hub at Caltech, and continues with JCAP’s successor, the Liquid Sunlight Alliance (LiSA), a DOE-funded effort that aims to streamline the complicated steps needed to convert sunlight into fuels, to make that process more efficient.
    Creating new materials is not as simple as dropping a few different elements into a test tube and shaking it up to see what happens. You need the elements that you combine to bond with each other at the atomic level to create something new and different rather than just a heterogeneous mixture of ingredients. With a nearly infinite number of possible combinations of the various squares on the periodic table, the challenge is knowing whichcombinations will yield such a material.
    “Materials discovery can be a bleak process. If you can’t predict where to find the desired properties, you could spend your entire career mixing random elements and never find anything interesting,” says Gregoire, research professor of applied physics and materials science, researcher at JCAP, and LiSA team lead.
    When combining a small number of individual elements, materials scientists can often make predictions about what properties a new material might have based on its constituent parts. However, that process quickly becomes untenable when more complicated mixtures are made. More

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    in Heart

    Potty-trained cattle could help reduce pollution

    You can lead a cow to a water closet, but can you make it pee there? It turns out that yes, you can.

    Researchers in Germany successfully trained cows to use a small, fenced-in area with artificial turf flooring as a bathroom stall. This could allow farms to easily capture and treat cow urine, which often pollutes air, soil and water, researchers report online September 13 in Current Biology. Components of that urine, such as nitrogen and phosphorus, could also be used to make fertilizer (SN: 4/6/21).

    The average cow can pee tens of liters per day, and there are some 1 billion cattle worldwide. In barns, cow pee typically mixes with poop on the floor to create a slurry that emits the air pollutant ammonia (SN: 1/4/19). Out in pastures, cow pee can leach into nearby waterways and release the potent greenhouse gas nitrous oxide (SN: 6/9/14).

    “I’m always of the mind, how can we get animals to help us in their management?” says Lindsay Matthews, a self-described cow psychologist who studies animal behavior at the University of Auckland in New Zealand. Matthews and colleagues set out to potty train 16 calves, which had the free time to learn a new skill. “They’re not so involved with milking and other systems,” he says. “They’re basically just hanging out, eating a bit of food, socializing and resting.”

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    Matthews was optimistic about the cows’ potty-training prospects. “I was convinced that we could do it,” he says. Cows “are much, much smarter than people give them credit for.” Each calf got 45 minutes of what the team calls “MooLoo training” per day. At first, the researchers enclosed the calves inside the makeshift bathroom stall and fed the animals a treat every time they peed.

    Once the calves made the connection between using the bathroom stall and receiving a treat, the team positioned the calves in a hallway leading to the stall. Whenever animals visited the little cows’ room, they got a treat; whenever calves peed in the hallway, the team spritzed them with water. “We had 11 of the 16 calves [potty trained] within about 10 days,” Matthews says. The remaining cows “are probably trainable too,” he adds. “It’s just that we didn’t have enough time.”

    [embedded content]
    Researchers successfully trained 11 calves, such as this one, to urinate in a bathroom stall. Once the cow relieved itself, a window in the stall opened, dispensing a molasses mixture as a treat. Toilet training cows on a large scale and collecting their urine to make fertilizer could cut down on agricultural pollution, the team says.

    Lindsay Whistance, a livestock researcher at the Organic Research Centre in Cirencester, England, is “not surprised by the results.” With proper training and motivation, “I fully expected cattle to be able to learn this task,” says Whistance, who was not involved in the study. The practicality of potty training cows on a large scale, she says, is another matter.

    For MooLoo training to become a widespread practice, “it has to be automated,” Matthews says. “We want to develop automated training systems, automated reward systems.” Those systems are still far from reality, but Matthews and colleagues hope they could have big impacts. If 80 percent of cow pee were collected in latrines, for instance, that could cut associated ammonia emissions in half, previous research suggests.

    “It’s those ammonia emissions that are key to the real environmental benefit, as well as potential for reducing water contamination,” says Jason Hill, a biosystems engineer at the University of Minnesota in St. Paul not involved in the work. “Ammonia from cattle is a major contributor to reduced human health,” he says (SN: 1/16/09). So potty training cattle could help create cleaner air — as well as a cleaner, more comfortable living space for cows themselves. More

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    in Computers Math

    Engineers create 3D-printed objects that sense how a user is interacting with them

    MIT researchers have developed a new method to 3D print mechanisms that detect how force is being applied to an object. The structures are made from a single piece of material, so they can be rapidly prototyped. A designer could use this method to 3D print “interactive input devices,” like a joystick, switch, or handheld controller, in one go.
    To accomplish this, the researchers integrated electrodes into structures made from metamaterials, which are materials divided into a grid of repeating cells. They also created editing software that helps users build these interactive devices.
    “Metamaterials can support different mechanical functionalities. But if we create a metamaterial door handle, can we also know that the door handle is being rotated, and if so, by how many degrees? If you have special sensing requirements, our work enables you to customize a mechanism to meet your needs,” says co-lead author Jun Gong, a former visiting PhD student at MIT who is now a research scientist at Apple.
    Gong wrote the paper alongside fellow lead authors Olivia Seow, a graduate student in the MIT Department of Electrical Engineering and Computer Science (EECS), and Cedric Honnet, a research assistant in the MIT Media Lab. Other co-authors are MIT graduate student Jack Forman and senior author Stefanie Mueller, who is an associate professor in EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented at the Association for Computing Machinery Symposium on User Interface Software and Technology next month.
    “What I find most exciting about the project is the capability to integrate sensing directly into the material structure of objects. This will enable new intelligent environments in which our objects can sense each interaction with them,” Mueller says. “For instance, a chair or couch made from our smart material could detect the user’s body when the user sits on it and either use it to query particular functions (such as turning on the light or TV) or to collect data for later analysis (such as detecting and correcting body posture).”
    Embedded electrodes
    Because metamaterials are made from a grid of cells, when the user applies force to a metamaterial object, some of the flexible, interior cells stretch or compress. More

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    in Computers Math

    Scientists can now assemble entire genomes on their personal computers in minutes

    Scientists at the Massachusetts Institute of Technology (MIT) and the Institut Pasteur in France have developed a technique for reconstructing whole genomes, including the human genome, on a personal computer. This technique is about a hundred times faster than current state-of-the-art approaches and uses one-fifth the resources. The study, published September 14 in the journal Cell Systems, allows for a more compact representation of genome data inspired by the way in which words, rather than letters, offer condensed building blocks for language models.
    “We can quickly assemble entire genomes and metagenomes, including microbial genomes, on a modest laptop computer,” says Bonnie Berger (@lab_berger), the Simons Professor of Mathematics at the Computer Science and AI Lab at MIT and an author of the study. “This ability is essential in assessing changes in the gut microbiome linked to disease and bacterial infections, such as sepsis, so that we can more rapidly treat them and save lives.”
    Genome assembly projects have come a long way since the Human Genome Project, which finished assembling the first complete human genome in 2003 for the cost of about $2.7 billion and more than a decade of international collaboration. But while human genome assembly projects no longer take years, they still require several days and massive computer power. Third-generation sequencing technologies offer terabytes of high-quality genomic sequences with tens of thousands of base pairs, yet genome assembly using such an immense quantity of data has proved challenging.
    To approach genome assembly more efficiently than current techniques, which involve making pairwise comparisons between all possible pairs of reads, Berger and colleagues turned to language models. Building from the concept of a de Bruijn graph, a simple, efficient data structure used for genome assembly, the researchers developed a minimizer-space de Bruin graph (mdBG), which uses short sequences of nucleotides called minimizers instead of single nucleotides.
    “Our minimizer-space de Bruijn graphs store only a small fraction of the total nucleotides, while preserving the overall genome structure, enabling them to be orders of magnitude more efficient than classical de Bruijn graphs,” says Berger.
    The researchers applied their method to assemble real HiFi data (which has almost perfect single-molecule read accuracy) for Drosophila melanogaster fruit flies, as well as human genome data provided by Pacific Biosciences (PacBio). When they evaluated the resulting genomes, Berger and colleagues found that their mdBG-based software required about 33 times less time and 8 times less random-access memory (RAM) computing hardware than other genome assemblers. Their software performed genome assembly for the HiFi human data 81 times faster with 18 times less memory usage than the Peregrine assembler and 338 times faster with 19 times less memory usage than the hifiasm assembler. More

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    in Computers Math

    New ocean temperature data help scientists make their hot predictions

    We’ve heard that rising temperatures will lead to rising sea levels, but what many may not realise is that most of the increase in energy in the climate system is occurring in the ocean.
    Now a study from UNSW Sydney and CSIRO researchers has shown that a relatively new ocean temperature measuring program — the Argo system of profiling floats — can help tell us which climate modelling for the 21st century we should be paying attention to the most.
    Professor John Church from UNSW’s Climate Change Research Centre in the School of Biological, Earth and Environmental Sciences says the study published today in Nature Climate Change is an attempt to narrow the projected range of future ocean temperature rises to the end of the 21st century using model simulations that are most consistent with the Argo’s findings in the years 2005 to 2019.
    “The models that projected very high absorption of heat by the ocean by 2100 also have unrealistically high ocean absorption over the Argo period of measurement,” Prof. Church says.
    “Likewise, there are models with lower heat absorption in the future that also don’t correspond to the Argo data. So we have effectively used the Argo observations to say, ‘which of these models best agree with the observations and therefore constrain projections for the future?'”
    Named after the boat which Greek mythological hero Jason travelled on in search of the golden fleece, the Argo floats are loaded with high-tech equipment that measures ocean temperatures to depths of up to 2000 metres. More

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    in Computers Math

    Taking lessons from a sea slug, study points to better hardware for artificial intelligence

    For artificial intelligence to get any smarter, it needs first to be as intelligent as one of the simplest creatures in the animal kingdom: the sea slug.
    A new study has found that a material can mimic the sea slug’s most essential intelligence features. The discovery is a step toward building hardware that could help make AI more efficient and reliable for technology ranging from self-driving cars and surgical robots to social media algorithms.
    The study, publishing this week in the Proceedings of the National Academy of Sciences, was conducted by a team of researchers from Purdue University, Rutgers University, the University of Georgia and Argonne National Laboratory.
    “Through studying sea slugs, neuroscientists discovered the hallmarks of intelligence that are fundamental to any organism’s survival,” said Shriram Ramanathan, a Purdue professor of materials engineering. “We want to take advantage of that mature intelligence in animals to accelerate the development of AI.”
    Two main signs of intelligence that neuroscientists have learned from sea slugs are habituation and sensitization. Habituation is getting used to a stimulus over time, such as tuning out noises when driving the same route to work every day. Sensitization is the opposite — it’s reacting strongly to a new stimulus, like avoiding bad food from a restaurant.
    AI has a really hard time learning and storing new information without overwriting information it has already learned and stored, a problem that researchers studying brain-inspired computing call the “stability-plasticity dilemma.” Habituation would allow AI to “forget” unneeded information (achieving more stability) while sensitization could help with retaining new and important information (enabling plasticity). More

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    How AI can help forecast how much Arctic sea ice will shrink

    In the next week or so, the sea ice floating atop the Arctic Ocean will shrink to its smallest size this year, as summer-warmed waters eat away at the ice’s submerged edges.

    Record lows for sea ice levels will probably not be broken this year, scientists say. In 2020, the ice covered 3.74 million square kilometers of the Arctic at its lowest point, coming nail-bitingly close to an all-time record low. Currently, sea ice is present in just under 5 million square kilometers of Arctic waters, putting it on track to become the 10th-lowest extent of sea ice in the area since satellite record keeping began in 1979. It’s an unexpected finish considering that in early summer, sea ice hit a record low for that time of year.

    The surprise comes in part because the best current statistical- and physics-based forecasting tools can closely predict sea ice extent only a few weeks in advance, but the accuracy of long-range forecasts falters. Now, a new tool that uses artificial intelligence to create sea ice forecasts promises to boost their accuracy — and can do the analysis relatively quickly, researchers report August 26 in Nature Communications.

    IceNet, a sea ice forecasting system developed by the British Antarctic Survey, or BAS, is “95 percent accurate in forecasting sea ice two months ahead — higher than the leading physics-based model SEAS5 — while running 2,000 times faster,” says Tom Andersson, a data scientist with BAS’s Artificial Intelligence lab. Whereas SEAS5 takes about six hours on a supercomputer to produce a forecast, IceNet can do the same in less than 10 seconds on a laptop. The system also shows a surprising ability to predict anomalous ice events — unusual highs or lows — up to four months in advance, Andersson and his colleagues found.

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    Tracking sea ice is crucial to keeping tabs on the impacts of climate change. While that’s more of a long game, the advanced notice provided by IceNet could have more immediate benefits, too. For instance, it could give scientists the lead time needed to assess, and plan for, the risks of Arctic fires or wildlife-human conflicts, and it could provide data that Indigenous communities need to make economic and environmental decisions.

    Arctic sea ice extent has steadily declined in all seasons since satellite records began in 1979 (SN: 9/25/19). Scientists have been trying to improve sea ice forecasts for decades, but success has proved elusive. “Forecasting sea ice is really hard because sea ice interacts in complex ways with the atmosphere above and ocean below,” Andersson says.

    [embedded content]
    In 2020, the sea ice in the Arctic shrank to its second lowest extent since satellite monitoring began in 1979. This animation uses those observations to show the change in sea ice coverage from March 5, when the ice was at its maximum, through September 15, when the ice reached its lowest point. The yellow line represents the average minimum extent from 1981 to 2010. Current forecasting tools can accurately predict these changes weeks in advance. A new AI-based tool can predict these changes with nearly 95 percent accuracy several months in advance.

    Existing forecast tools put the laws of physics into computer code to predict how sea ice will change in the future. But partly due to uncertainties in the physical systems governing sea ice, these models struggle to produce accurate long-range forecasts.

    Using a process called deep learning, Andersson and his colleagues loaded observational sea ice data from 1979 to 2011 and climate simulations covering 1850 to 2100 to train IceNet how to predict the state of future sea ice by processing the data from the past.

    To determine the accuracy of its forecasts, the team compared IceNet’s outputs to the observed sea ice extent from 2012 to 2020, and to the forecasts made by SEAS5, the widely cited tool used by the European Centre for Medium-Range Weather Forecasts. IceNet was as much as 2.9 percent more accurate than SEAS5, corresponding to a further 360,000 square kilometers of ocean being correctly labeled as “ice” or “no ice.”

    What’s more, in 2012, a sudden crash in summer sea ice extent heralded a new record low extent in September of that year. In running through past data, IceNet saw the dip coming months in advance. SEAS5 had inklings too but its projections that far out were off by a few hundred thousand square kilometers.

    “This is a significant step forward in sea ice forecasting, boosting our ability to produce accurate forecasts that were typically not thought possible and run them thousands of times faster,” says Andersson. He believes it’s possible that IceNet has better learned the physical processes that determine the evolution of sea ice from the training data while physics-based models still struggle to understand this information.

    “These machine learning techniques have only begun contributing to [forecasting] in the last couple years, and they’ve been doing amazingly well,” says Uma Bhatt, an atmospheric scientist at the University of Alaska Fairbanks Geophysical Institute who was not involved in the new study. She also leads the Sea Ice Prediction Network, a group of multidisciplinary scientists working to improve forecasting.

    Bhatt says that good seasonal ice forecasts are important for assessing the risk of Arctic wildfires, which are tied strongly to the presence of sea ice (SN: 6/23/20). “Knowing where the sea ice is going to be in the spring could potentially help you figure out where you’re likely to have fires — in Siberia, for example, as soon as the sea ice moves away from the shore, the land can warm up very quickly and help set the stage for a bad fire season.”

    Any improvement in sea ice forecasting can also help economic, safety and environmental planning in northern and Indigenous communities. For example, tens of thousands of walruses haul out on land to rest when the sea ice disappears (SN: 10/2/14). Human disturbances can trigger deadly stampedes and lead to high walrus mortality. With seasonal ice forecasts, biologists can anticipate rapid ice loss and manage haul-out sites in advance by limiting human access to those locations.

    Still, limitations remain. At four months of lead time, the system was about 91 percent accurate in predicting the location of September’s ice edge.IceNet, like other forecasting systems, struggles to produce accurate long-range forecasts for late summer due, in part, to what scientists call the “spring predictability barrier.” It’s crucial to know the condition of the sea ice at the start of the spring melting season to be able to forecast end-of-summer conditions.

    Another limit is “the fact that the weather is so variable,” says Mark Serreze, director of the National Snow and Ice Data Center in Boulder, Colo. Though sea ice seemed primed to set a new annual record low at the start of July, the speed of ice loss ultimately slowed due to cool atmospheric temperatures. “We know that sea ice responds very strongly to summer weather patterns, but we can’t get good weather predictions. Weather predictability is about 10 days in advance.” More

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    Just by changing its shape, scientists show they can alter material properties

    By confining the transport of electrons and ions in a patterned thin film, scientists find a way to potentially enhance material properties for design of next-generation electronics
    Like ripples in a pond, electrons travel like waves through materials, and when they collide and interact, they can give rise to new and interesting patterns.
    Scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have seen a new kind of wave pattern emerge in a thin film of metal oxide known as titania when its shape is confined. Confinement, the act of restricting materials within a boundary, can alter the properties of a material and the movement of molecules through it.
    In the case of titania, it caused electrons to interfere with each other in a unique pattern, which increased the oxide’s conductivity, or the degree to which it conducts electricity. This all happened at the mesoscale, a scale where scientists can see both quantum effects and the movement of electrons and molecules.
    In all, this work offers scientists more insight about how atoms, electrons and other particles behave at the quantum level. Such information could aid in designing new materials that can process information and be useful in other electronic applications.
    “What really set this work apart was the size of the scale we investigated,” said lead author Frank Barrows, a Northwestern University graduate student in Argonne’s Materials Science Division (MSD). “Investigating at this unique length scale enabled us to see really interesting phenomena that indicate there is interference happening at the quantum level, and at the same time gain new information about how electrons and ions interact.”
    Altering geometry to change material properties More