Aurelia Butler

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    in Computers Math

    AI’s memory-forming mechanism found to be strikingly similar to that of the brain

    An interdisciplinary team consisting of researchers from the Center for Cognition and Sociality and the Data Science Group within the Institute for Basic Science (IBS) revealed a striking similarity between the memory processing of artificial intelligence (AI) models and the hippocampus of the human brain. This new finding provides a novel perspective on memory consolidation, which is a process that transforms short-term memories into long-term ones, in AI systems.
    In the race towards developing Artificial General Intelligence (AGI), with influential entities like OpenAI and Google DeepMind leading the way, understanding and replicating human-like intelligence has become an important research interest. Central to these technological advancements is the Transformer model, whose fundamental principles are now being explored in new depth.
    The key to powerful AI systems is grasping how they learn and remember information. The team applied principles of human brain learning, specifically concentrating on memory consolidation through the NMDA receptor in the hippocampus, to AI models.
    The NMDA receptor is like a smart door in your brain that facilitates learning and memory formation. When a brain chemical called glutamate is present, the nerve cell undergoes excitation. On the other hand, a magnesium ion acts as a small gatekeeper blocking the door. Only when this ionic gatekeeper steps aside, substances are allowed to flow into the cell. This is the process that allows the brain to create and keep memories, and the gatekeeper’s (the magnesium ion) role in the whole process is quite specific.
    The team made a fascinating discovery: the Transformer model seems to use a gatekeeping process similar to the brain’s NMDA receptor. This revelation led the researchers to investigate if the Transformer’s memory consolidation can be controlled by a mechanism similar to the NMDA receptor’s gating process.
    In the animal brain, a low magnesium level is known to weaken memory function. The researchers found that long-term memory in Transformer can be improved by mimicking the NMDA receptor. Just like in the brain, where changing magnesium levels affect memory strength, tweaking the Transformer’s parameters to reflect the gating action of the NMDA receptor led to enhanced memory in the AI model. This breakthrough finding suggests that how AI models learn can be explained with established knowledge in neuroscience.
    C. Justin LEE, who is a neuroscientist director at the institute, said, “This research makes a crucial step in advancing AI and neuroscience. It allows us to delve deeper into the brain’s operating principles and develop more advanced AI systems based on these insights.”
    CHA Meeyoung, who is a data scientist in the team and at KAIST, notes, “The human brain is remarkable in how it operates with minimal energy, unlike the large AI models that need immense resources. Our work opens up new possibilities for low-cost, high-performance AI systems that learn and remember information like humans.”
    What sets this study apart is its initiative to incorporate brain-inspired nonlinearity into an AI construct, signifying a significant advancement in simulating human-like memory consolidation. The convergence of human cognitive mechanisms and AI design not only holds promise for creating low-cost, high-performance AI systems but also provides valuable insights into the workings of the brain through AI models. More

  • 138 Shares169 Views

    in Heart

    Invisible comet tails of mucus slow sinking flakes of ‘marine snow’

    WASHINGTON — Tiny, sinking flakes of detritus in the ocean fall more slowly thanks to the goop that surrounds each flake, new observations reveal.

    The invisible mucus makes “comet tails” that surround each flake, physicist Rahul Chajwa of Stanford University reported November 19 at the American Physical Society’s Division of Fluid Dynamics meeting. Those mucus tails slow the speed at which the flakes fall. That could affect the rate at which carbon gets sequestered deep in the oceans, making the physics of this sticky goo important for understanding Earth’s climate.

    Although scientists knew the goo was a component of the “marine snow” that falls in the ocean, they hadn’t previously measured its impact on sinking speed.

    Marine snow is made of dead and living phytoplankton, decaying organic matter, feces, bacteria and other aquatic sundries, all wrapped up in mucus that’s produced by the organisms. Like the gunk known for clogging airways during respiratory virus season, the mucus is what’s called a viscoelastic fluid (SN: 3/17/16). That’s something that flows like a liquid but exhibits elastic behavior as well, springing back after being stretched.

    This underwater blizzard is not easy to study. When observed in the ocean, the particles sink swiftly out of view. In the laboratory, the particles can be viewed for longer periods, but the trek ashore degrades the delicate marine snow and kills the living organisms within it.

    [embedded content]
    Tiny particles (white dots) within a seawater-filled chamber were used to measure the rate at which fluid flows around this flake of marine snow as it falls. The chamber is designed to keep the sinking snowflake in view of the camera.

    So Chajwa and colleagues built a physics lab at sea. Aboard a research vessel in the Gulf of Maine, the team collected marine snow particles in traps 80 meters below the water’s surface. Then they loaded their catch into a device onboard, designed to observe the particles falling.

    Nicknamed “the gravity machine,” it’s a fluid-filled wheel that rotates in order to keep an individual flake in view of a camera. It’s a bit like a hamster wheel for falling debris. As the flake sinks, the wheel turns so as to move the snow in the opposite direction, allowing the snowfall to be observed indefinitely. The gravity machine was itself mounted on a gimbal designed to stave off sloshing from the rocking of the ship.

    “It’s a very nice compromise between the real marine snow that you get in the ocean versus what you can do practically in the lab,” says biophysicist Anupam Sengupta of the University of Luxembourg, who was not involved with the research.

    To observe how the fluid flowed around the particles, the researchers added tiny beads within the fluid in the gravity machine. That revealed the rate of fluid flow around the particles. The speed of fluid flow was slowed in a comet tail–shaped region around the particle, revealing the invisible mucus that sinks along with the particle.

    Marine snow particles (one shown) are surrounded with invisible mucus. Drag the slider to see how fluid flows around the flake as it falls. Slower speeds (yellow) reveal mucus that trails the flake in a comet tail–shape (red dotted line). Left: Rahul Chajwa and Manu Prakash/PrakashLab/Stanford UniversityRight: Rahul Chajwa and Manu Prakash/PrakashLab/Stanford University

    The particles sank at speeds up to 200 meters per day. The mucus played a big role in sinking speed. “The more the mucus, the slower the particles sink,” Chajwa says. On average, the mucus causes the marine snow particles to linger twice as long in the upper 100 meters of the ocean as they otherwise would, Chajwa and colleagues determined.

    If it falls deep enough, marine snow can sequester carbon away from the atmosphere. That’s because living phytoplankton, like plants, take in carbon dioxide and release oxygen. When phytoplankton form marine snow, they take that carbon along with them as they sink. If a flake reaches the ocean floor, it can settle into a scum at the bottom that caches that carbon over long time periods. The faster the particles sink, the more likely they are to make it to the abyss before being eaten by critters (SN: 6/23/22).

    Knowing how fast the particles sink is important for calculating the ocean’s impact on Earth’s climate, and how that might change as the climate warms, the researchers say. The oceans are major players in the planet’s carbon cycle (SN: 12/2/21), and scientists estimate that oceans have taken up roughly 30 percent of the carbon dioxide released by humans since industrialization. Chajwa and colleagues hope that their results can be used to refine climate models, which currently do not take the mucus into account.

    So this mucus is nothing to sneeze at. “We’re talking about microscopic physics,” says Stanford physicist Manu Prakash, a coauthor of the work, which is also reported in a paper submitted October 3 at arXiv.org. “But multiply that by the volume of the ocean … that’s what gives you the scale of the problem.” More

  • 175 Shares169 Views

    in Computers Math

    Air conditioning has reduced mortality due to high temperatures in Spain by one third

    Air conditioning and heating systems have contributed considerably to reducing mortality linked to extreme temperatures in Spain, according to a study led by the Barcelona Institute for Global Health (ISGlobal), a centre supported by the “la Caixa” Foundation. The findings, published in Environment International, provide valuable insights for designing policies to adapt to climate change.
    Rising temperatures but lower mortality
    Spain, like many parts of the world, has experienced rising temperatures in recent decades, with the average annual mean temperature increasing at an average rate of 0.36°C per decade. The warming trend is even more pronounced in the summer months (0.40°C per decade). Surprisingly, this increase in temperature has coincided with a progressive reduction in mortality associated with heat. In addition, cold-related mortality has also decreased.
    “Understanding the factors that reduce susceptibility to extreme temperatures is crucial to inform health adaptation policies and to combat the negative effects of climate change,” says first author of the study, Hicham Achebak, researcher at ISGlobal and Inserm (France) and holder of a Marie Sklodowska-Curie Postdoctoral Fellowship from the European Commission.
    Effective societal adaptations
    In this study, Achebak and colleagues analysed the demographic and socioeconomic factors behind the observed reduction in heat and cold-related mortality, despite rising temperatures. They found that the increase in air conditioning (AC) prevalence in Spain was associated with a reduction in heat-related mortality, while the rise in heating prevalence was associated with a decrease in cold-related mortality. Specifically, AC was found to be responsible for about 28.6% of the decline in deaths due to heat and 31.5% of the decrease in deaths due to extreme heat between the late 1980s and the early 2010s. Heating systems contributed significantly, accounting for about 38.3% of the reduction in cold-related deaths and a substantial 50.8% decrease in extreme cold-related fatalities during the same period. The decrease in mortality due to cold would have been larger had there not been a demographic shift towards a higher proportion of people aged over 65, who are more susceptible to cold temperatures.
    The authors conclude that the reduction in heat-related mortality is largely the result of the country’s socioeconomic development over the study period, rather than specific interventions such as heat-wave warning systems.

    Four decades of data
    For the statistical analysis, the research team collected data on daily mortality (all causes) and weather (temperature and relative humidity) for 48 provinces in mainland Spain and the Balearic Islands, between January 1980 and December 2018. These data were then linked to 14 indicators of context (demographic and socioeconomic variables such as housing, income and education) for these populations over the same period.
    Implications for climate adaptation
    The results of the study extend previous findings on heat-related mortality in Spain and underscore the importance of air conditioning and heating as effective adaptation measures to mitigate the effects of heat and cold. “However, we observed large disparities in the presence of AC across provinces. AC is still unaffordable for many Spanish households,” says Achebak.
    The authors also point out that the widespread use of AC could further contribute to global warming depending on the source of electricity generation, which is why other cooling strategies, such as expanding green and blue spaces in cities, are also needed.
    “Our findings have important implications for the development of adaptation strategies to climate change. They also inform future projections of the impact of climate change on human health,” concludes Joan Ballester, ISGlobal researcher and study coordinator. More

  • 100 Shares179 Views

    in Computers Math

    Artificial intelligence can predict events in people’s lives

    Artificial intelligence developed to model written language can be utilized to predict events in people’s lives. A research project from DTU, University of Copenhagen, ITU, and Northeastern University in the US shows that if you use large amounts of data about people’s lives and train so-called ‘transformer models’, which (like ChatGPT) are used to process language, they can systematically organize the data and predict what will happen in a person’s life and even estimate the time of death.
    In a new scientific article, ‘Using Sequences of Life-events to Predict Human Lives’, published in Nature Computational Science, researchers have analyzed health data and attachment to the labour market for 6 million Danes in a model dubbed life2vec. After the model has been trained in an initial phase, i.e., learned the patterns in the data, it has been shown to outperform other advanced neural networks (see fact box) and predict outcomes such as personality and time of death with high accuracy.
    “We used the model to address the fundamental question: to what extent can we predict events in your future based on conditions and events in your past? Scientifically, what is exciting for us is not so much the prediction itself, but the aspects of data that enable the model to provide such precise answers,” says Sune Lehmann, professor at DTU and first author of the article.
    Predictions of time of death
    The predictions from Life2vec are answers to general questions such as: ‘death within four years’? When the researchers analyze the model’s responses, the results are consistent with existing findings within the social sciences; for example, all things being equal, individuals in a leadership position or with a high income are more likely to survive, while being male, skilled or having a mental diagnosis is associated with a higher risk of dying. Life2vec encodes the data in a large system of vectors, a mathematical structure that organizes the different data. The model decides where to place data on the time of birth, schooling, education, salary, housing and health.
    “What’s exciting is to consider human life as a long sequence of events, similar to how a sentence in a language consists of a series of words. This is usually the type of task for which transformer models in AI are used, but in our experiments we use them to analyze what we call life sequences, i.e., events that have happened in human life,” says Sune Lehmann.
    Raising ethical questions
    The researchers behind the article point out that ethical questions surround the life2vec model, such as protecting sensitive data, privacy, and the role of bias in data. These challenges must be understood more deeply before the model can be used, for example, to assess an individual’s risk of contracting a disease or other preventable life events.

    “The model opens up important positive and negative perspectives to discuss and address politically. Similar technologies for predicting life events and human behaviour are already used today inside tech companies that, for example, track our behaviour on social networks, profile us extremely accurately, and use these profiles to predict our behaviour and influence us. This discussion needs to be part of the democratic conversation so that we consider where technology is taking us and whether this is a development we want,” says Sune Lehmann.
    According to the researchers, the next step would be to incorporate other types of information, such as text and images or information about our social connections. This use of data opens up a whole new interaction between social and health sciences.
    The research project
    The research project ‘Using Sequences of Life-events to Predict Human Lives’ is based on labour market data and data from the National Patient Registry (LPR) and Statistics Denmark. The dataset includes all 6 million Danes and contains information on income, salary, stipend, job type, industry, social benefits, etc. The health dataset includes records of visits to healthcare professionals or hospitals, diagnosis, patient type and degree of urgency. The dataset spans from 2008 to 2020, but in several analyses, researchers focus on the 2008-2016 period and an age-restricted subset of individuals.
    Transformer model
    A transformer model is an AI, deep learning data architecture used to learn about language and other tasks. The models can be trained to understand and generate language. The transformer model is designed to be faster and more efficient than previous models and is often used to train large language models on large datasets.
    Neural networks
    A neural network is a computer model inspired by the brain and nervous system of humans and animals. There are many different types of neural networks (e.g. transformer models). Like the brain, a neural network is made up of artificial neurons. These neurons are connected and can send signals to each other. Each neuron receives input from other neurons and then calculates an output passed on to other neurons. A neural network can learn to solve tasks by training on large amounts of data. Neural networks rely on training data to learn and improve their accuracy over time. But once these learning algorithms are fine-tuned for accuracy, they are potent tools in computer science and artificial intelligence that allow us to classify and group data at high speed. One of the most well-known neural networks is Google’s search algorithm.  More

  • 138 Shares129 Views

    in Heart

    3 Antarctic glaciers show rapidly accelerated ice loss from ocean warming

    SAN FRANCISCO — Several Antarctic glaciers are undergoing dramatic acceleration and ice loss. Hektoria Glacier, the worst affected, has quadrupled its sliding speed and lost 25 kilometers of ice off its front in just 16 months, scientists say.

    The rapid retreat “is really unheard of,” says Mathieu Morlighem, a glaciologist at Dartmouth College who was not part of the team reporting these findings.

    The collapse was triggered by unusually warm ocean temperatures, which caused sea ice to retreat. This allowed a series of large waves to hit a section of coastline that is normally shielded from them. “What we’re seeing here is an indication of what could happen elsewhere” in Antarctica, says Naomi Ochwat, a glaciologist at the University of Colorado Boulder who presented the findings December 11 at the American Geophysical Union meeting.

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    Hektoria Glacier, Green Glacier, and Crane Glacier sit near the tip of the Antarctic Peninsula, which reaches up toward South America. The crescent moon–shaped bay, called the Larsen B Embayment, once seemed stable. As these glaciers oozed off the coastline, their ice used to merge into a floating slab around 200 meters thick. This slab, called the Larsen B Ice Shelf, was about the size of Rhode Island and filled the entire bay.

    Having existed for over 10,000 years, this ice shelf buttressed and stabilized the glaciers flowing into it. But during a warm summer in 2002, it suddenly fragmented into thousands of skinny icebergs (SN: 3/27/02).

    Hektoria, Green, and Crane glaciers — no longer contained by the ice shelf —  began to flow into the ocean several times faster than they had before, shedding billions of tons of ice over the next decade.

    Then starting in 2011, the hemorrhaging slowed down. The thin veneer of sea ice that forms over the bay each winter began to persist year round, preserved by a series of cold summers. This “landfast ice,” attached firmly to the coastline, grew five to 10 meters thick, stabilizing the glaciers. Their floating tongues gradually advanced back into the bay. But things changed abruptly in early 2022. On January 19 and 20, the landfast ice disintegrated into fragments, which drifted away.

    Satellite images taken just 10 days apart reveal the dramatic breakup of sea ice in Antarctica’s Larsen B Embayment. On January 16, 2022, sea ice filled the bay (left). By January 26 (right), the ice had fractured and was drifting away following a series of powerful waves that struck the bay several days earlier. Left: Joshua Stevens, MODIS/LANCE/EOSDIS/NASA, WORLDVIEW/GIBS/NASARight: Joshua Stevens, MODIS/LANCE/EOSDIS/NASA, WORLDVIEW/GIBS/NASA

    Using data from ocean buoys farther north, Ochwat and colleagues determined that a series of powerful waves, higher than 1.5 meters, had swept in from the northeast — cracking apart the landfast ice. Those waves were highly unusual for this area.

    The Southern Ocean, which encircles Antarctica, holds some of the world’s roughest waters. The Antarctic Peninsula extends up into this turbulent region, but its east side, where the Larsen B Embayment sits, rarely feels the waves. It is normally protected by several hundred kilometers of pack ice — floes of sea ice, pressed together by ocean currents — that dampen the waves, leaving the waters near Larsen as flat as a mirror.

    In 2022, water temperatures near the surface of the Southern Ocean rose several tenths of a degree Celsius higher than normal, causing pack ice to shrink and peel away from the peninsula. This exposed the area to waves, which then broke up the landfast sea ice.

    The glaciers accelerated as their floating tongues, no longer held in place, fragmented into bergs. Crane Glacier lost 11 kilometers of ice, nearly erasing its floating tongue; Green Glacier lost 18 kilometers, encompassing all of its floating ice.

    Hektoria lost all 15 kilometers of its floating ice — followed by another 10 kilometers of ice that is normally more stable, because it rests on the seafloor. That “is faster than any tidewater glacier retreat that we know of,” Ochwat says.

    The previous standout, Alaska’s Columbia Glacier, had lost 20 kilometers of ice in 30 years, records show. But Hektoria lost its 10 kilometers of nonfloating ice in just five months — including 2.5 kilometers that crumbled in a 3-day period.

    All of this suggests that people trying to predict sea level rise need to consider sea ice, Morlighem says. Up until now, “its role in [glacier] dynamics has been completely ignored.”

    Ochwat is waiting to see what will happen as the current Antarctic summer heats up between December and March. Hektoria and the other glaciers have been retreating only during summer months, when sea ice is absent; they pause during winter, when the surface of the bay freezes for a few months.

    If Antarctic sea ice continues to shrink, as it has since 2022, it could spell trouble, says study coauthor Ted Scambos, a glaciologist also at UC Boulder. “You’re going to have a longer section of coastline where wave action can act on the front of ice shelves and glaciers,” potentially accelerating glacial retreat. More

  • 125 Shares159 Views

    in Computers Math

    Computational model captures the elusive transition states of chemical reactions

    During a chemical reaction, molecules gain energy until they reach what’s known as the transition state — a point of no return from which the reaction must proceed. This state is so fleeting that it’s nearly impossible to observe it experimentally.
    The structures of these transition states can be calculated using techniques based on quantum chemistry, but that process is extremely time-consuming. A team of MIT researchers has now developed an alternative approach, based on machine learning, that can calculate these structures much more quickly — within a few seconds.
    Their new model could be used to help chemists design new reactions and catalysts to generate useful products like fuels or drugs, or to model naturally occurring chemical reactions such as those that might have helped to drive the evolution of life on Earth.
    “Knowing that transition state structure is really important as a starting point for thinking about designing catalysts or understanding how natural systems enact certain transformations,” says Heather Kulik, an associate professor of chemistry and chemical engineering at MIT, and the senior author of the study.
    Chenru Duan PhD ’22 is the lead author of a paper describing the work, which appears today in Nature Computational Science. Cornell University graduate student Yuanqi Du and MIT graduate student Haojun Jia are also authors of the paper.
    Fleeting transitions
    For any given chemical reaction to occur, it must go through a transition state, which takes place when it reaches the energy threshold needed for the reaction to proceed. The probability of any chemical reaction occurring is partly determined by how likely it is that the transition state will form.

    “The transition state helps to determine the likelihood of a chemical transformation happening. If we have a lot of something that we don’t want, like carbon dioxide, and we’d like to convert it to a useful fuel like methanol, the transition state and how favorable that is determines how likely we are to get from the reactant to the product,” Kulik says.
    Chemists can calculate transition states using a quantum chemistry method known as density functional theory. However, this method requires a huge amount of computing power and can take many hours or even days to calculate just one transition state.
    Recently, some researchers have tried to use machine-learning models to discover transition state structures. However, models developed so far require considering two reactants as a single entity in which the reactants maintain the same orientation with respect to each other. Any other possible orientations must be modeled as separate reactions, which adds to the computation time.
    “If the reactant molecules are rotated, then in principle, before and after this rotation they can still undergo the same chemical reaction. But in the traditional machine-learning approach, the model will see these as two different reactions. That makes the machine-learning training much harder, as well as less accurate,” Duan says.
    The MIT team developed a new computational approach that allowed them to represent two reactants in any arbitrary orientation with respect to each other, using a type of model known as a diffusion model, which can learn which types of processes are most likely to generate a particular outcome. As training data for their model, the researchers used structures of reactants, products, and transition states that had been calculated using quantum computation methods, for 9,000 different chemical reactions.
    “Once the model learns the underlying distribution of how these three structures coexist, we can give it new reactants and products, and it will try to generate a transition state structure that pairs with those reactants and products,” Duan says.

    The researchers tested their model on about 1,000 reactions that it hadn’t seen before, asking it to generate 40 possible solutions for each transition state. They then used a “confidence model” to predict which states were the most likely to occur. These solutions were accurate to within 0.08 angstroms (one hundred-millionth of a centimeter) when compared to transition state structures generated using quantum techniques. The entire computational process takes just a few seconds for each reaction.
    “You can imagine that really scales to thinking about generating thousands of transition states in the time that it would normally take you to generate just a handful with the conventional method,” Kulik says.
    Modeling reactions
    Although the researchers trained their model primarily on reactions involving compounds with a relatively small number of atoms — up to 23 atoms for the entire system — they found that it could also make accurate predictions for reactions involving larger molecules.
    “Even if you look at bigger systems or systems catalyzed by enzymes, you’re getting pretty good coverage of the different types of ways that atoms are most likely to rearrange,” Kulik says.
    The researchers now plan to expand their model to incorporate other components such as catalysts, which could help them investigate how much a particular catalyst would speed up a reaction. This could be useful for developing new processes for generating pharmaceuticals, fuels, or other useful compounds, especially when the synthesis involves many chemical steps.
    “Traditionally all of these calculations are performed with quantum chemistry, and now we’re able to replace the quantum chemistry part with this fast generative model,” Duan says.
    Another potential application for this kind of model is exploring the interactions that might occur between gases found on other planets, or to model the simple reactions that may have occurred during the early evolution of life on Earth, the researchers say.
    The research was funded by the U.S. Office of Naval Research and the National Science Foundation. More

  • 175 Shares109 Views

    in Computers Math

    Ultrafast lasers map electrons ‘going ballistic’ in graphene, with implications for next-gen electronic devices

    Research appearing in ACS Nano, a premier journal on nanoscience and nanotechnology, reveals the ballistic movement of electrons in graphene in real time.
    The observations, made at the University of Kansas’ Ultrafast Laser Lab, could lead to breakthroughs in governing electrons in semiconductors, fundamental components in most information and energy technology.
    “Generally, electron movement is interrupted by collisions with other particles in solids,” said lead author Ryan Scott, a doctoral student in KU’s Department of Physics & Astronomy. “This is similar to someone running in a ballroom full of dancers. These collisions are rather frequent — about 10 to 100 billion times per second. They slow down the electrons, cause energy loss and generate unwanted heat. Without collisions, an electron would move uninterrupted within a solid, similar to cars on a freeway or ballistic missiles through air. We refer to this as ‘ballistic transport.'”
    Scott performed the lab experiments under the mentorship of Hui Zhao, professor of physics & astronomy at KU. They were joined in the work by former KU doctoral student Pavel Valencia-Acuna, now a postdoctoral researcher at the Northwest Pacific National Laboratory.
    Zhao said electronic devices utilizing ballistic transport could potentially be faster, more powerful and more energy efficient.
    “Current electronic devices, such as computers and phones, utilize silicon-based field-effect transistors,” Zhao said. “In such devices, electrons can only drift with a speed on the order of centimeters per second due to the frequent collisions they encounter. The ballistic transport of electrons in graphene can be utilized in devices with fast speed and low energy consumption.”
    The KU researchers observed the ballistic movement in graphene, a promising material for next-generation electronic devices. First discovered in 2004 and awarded the Nobel Prize in Physics in 2010, graphene is made of a single layer of carbon atoms forming a hexagonal lattice structure — somewhat like a soccer net.

    “Electrons in graphene move as if their ‘effective’ mass is zero, making them more likely to avoid collisions and move ballistically,” Scott said. “Previous electrical experiments, by studying electrical currents produced by voltages under various conditions, have revealed signs of ballistic transport. However, these techniques aren’t fast enough to trace the electrons as they move.”
    According to the researchers, electrons in graphene (or any other semiconductor) are like students sitting in a full classroom, where students can’t freely move around because the desks are full. The laser light can free electrons to momentarily vacate a desk, or ‘hole’ as physicists call them.
    “Light can provide energy to an electron to liberate it so that it can move freely,” Zhao said. “This is similar to allowing a student to stand up and walk away from their seat. However, unlike a charge-neutral student, an electron is negatively charged. Once the electron has left its ‘seat,’ the seat becomes positively charged and quickly drags the electron back, resulting in no more mobile electrons — like the student sitting back down.”
    Because of this effect, the super-light electrons in graphene can only stay mobile for about one-trillionth of a second before falling back to its seat. This short time presents a severe challenge to observing the movement of the electrons. To address this problem, the KU researchers designed and fabricated a four-layer artificial structure with two graphene layers separated by two other single-layer materials, molybdenum disulphide and molybdenum diselenide.
    “With this strategy, we were able to guide the electrons to one graphene layer while keeping their ‘seats’ in the other graphene layer,” Scott said. “Separating them with two layers of molecules, with a total thickness of just 1.5 nanometers, forces the electrons to stay mobile for about 50-trillionths of a second, long enough for the researchers, equipped with lasers as fast as 0.1 trillionth of a second, to study how they move.”
    The researchers use a tightly focused laser spot to liberate some electrons in their sample. They trace these electrons by mapping out the “reflectance” of the sample, or the percentage of light they reflect.

    “We see most objects because they reflect light to our eyes,” Scott said. “Brighter objects have larger reflectance. On the other hand, dark objects absorb light, which is why dark clothes become hot in the summer. When a mobile electron moves to a certain location of the sample, it makes that location slightly brighter by changing how electrons in that location interact with light. The effect is very small — even with everything optimized, one electron only changes the reflectance by 0.1 part per million.”
    To detect such a small change, the researchers liberated 20,000 electrons at once, using a probe laser to reflect off the sample and measure this reflectance, repeating the process 80 million times for each data point. They found the electrons on average move ballistically for about 20-trillionths of a second with a speed of 22 kilometers per second before running into something that terminates their ballistic motion.
    The research was funded by a grant from the Department of Energy under the program of Physical Behavior of Materials.
    Zhao said currently his lab is working to refine their material design to guide electrons more efficiently to the desired graphene layer, and trying to find ways to make them move longer distances ballistically. More

  • 138 Shares169 Views

    in Heart

    COP28 nations agreed to ‘transition’ from fossil fuels. That’s too slow, experts say

    Days of contentious wrangling in Dubai at the United Nations’ 28th annual climate summit ended December 13 with a historic agreement to “transition away” from fossil fuels and accelerate climate action over the next decade. The organization touted the agreement as a moment of global solidarity, marking “the beginning of the end” of the fossil fuel era.

    But the final agreement reached at COP28, signed by nearly 200 nations, did not include language that explicitly mandated phasing out fossil fuel energy, deeply frustrating many nations as well as climate scientists and activists.

    The agreement is considered the world’s first “global stocktake,” an inventory of climate actions and progress made since the 2015 Paris Agreement to limit global warming to “well below” 2 degrees Celsius above the preindustrial average (SN: 12/12/15).

    It acknowledges the conclusions of scientific research that greenhouse gas emissions will need to be cut by 43 percent by 2030 compared with 2019 levels, in order to limit global warming to 1.5 degrees Celsius by the end of the century. It then calls on nations to speed up climate actions before 2030 so as to reach global net zero by 2050 — in which greenhouse gases entering the atmosphere are balanced by their removal from the atmosphere. Among the actions called for are increasing global renewable energy generation, phasing down coal power and phasing out fossil fuel subsidies.

    But among many scientists gathered in San Francisco at the American Geophysical Union’s annual meeting to discuss climate change’s impacts to Earth’s atmosphere, polar regions, oceans and biosphere, the reaction to the language in the agreement was more frustrated than celebratory.

    “The beginning of the end? I wish it was the middle of the end,” says climate scientist Luke Parsons of the Nature Conservancy, who is based in Durham, N.C. “But you have to start somewhere, I guess.”

    It is a step forward, says Ted Scambos, a glaciologist at the University of Colorado Boulder. “Saying it out loud, that we are aiming to phase out fossil fuels, is huge.”

    It’s not a moment too soon: The globe is already experiencing many climate change–linked extreme weather events, including the hottest 12 months ever recorded (SN: 11/9/23). Still, Scambos says, “it’s a tribute to the science and the negotiators that we can take this step now, before the disastrous global impacts truly get underway.” But, he added, “I fear that the pace [of future climate action] will … still be driven by impacts arriving at our collective doors.”

    Other researchers had a grimmer take.

    “It was weak sauce,” says climate scientist Michael Mann of the University of Pennsylvania. “What we really need is a commitment to phase out fossil fuels, on a very specific timeline: We’re going to reduce carbon emissions by 50 percent this decade, bring them down to zero mid-century. Instead, they agreed to transition away from fossil fuels — the analogy that I use is, you’re diagnosed with diabetes, and you tell your doctor you’re going to transition away from doughnuts. That’s not going to cut it. It didn’t meet the moment.”

    Eric Rignot, a glaciologist at the University of California, Irvine, called the agreement “deeply disappointing and misleading,” noting that it didn’t include any language specifically calling for phasing out fossil fuels. Furthermore, he says, “COP28 keeps entertaining the idea that 1.5 degrees Celsius may be achievable, but everyone is offtrack to meet that goal. [And] for ice sheets and glaciers, even 1.5 degrees is not sustainable.”  There already are fears, for instance, that the melting of Greenland’s ice sheet can’t be stopped (SN: 8/9/21).

    Even if the world stays close to that average temperature, “the ice sheets are going to be retreating,” says Rob DeConto, a glaciologist at the University of Massachusetts at Amherst. “But you start getting out toward the end of the century, and all hell is going to break loose if we go much above 1.5. You’re talking about actually exceeding the limits of adaptation around so much of our coastlines.”  

    On December 12, the eighth anniversary of the signing of the Paris Agreement, the European Union’s Copernicus Climate Change Service noted that the world has, in effect, “lost” 19 years by delaying action to reduce fossil fuel emissions. Back in 2015, climate projections suggested that Earth’s average temperature would reach the 1.5 degrees C threshold by the year 2045 — then 30 years away. Now, projections show that the planet may reach that benchmark by 2034, just 11 years in the future.

    “We’ve got a shrinking window of opportunity,” Mann says. “And that window of opportunity will close if we don’t make dramatic and immediate reductions to our carbon emissions.” More