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    Proton-conducting materials could enable new green energy technologies

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    As the name suggests, most electronic devices today work through the movement of electrons. But materials that can efficiently conduct protons — the nucleus of the hydrogen atom — could be key to a number of important technologies for combating global climate change.Most proton-conducting inorganic materials available now require undesirably high temperatures to achieve sufficiently high conductivity. However, lower-temperature alternatives could enable a variety of technologies, such as more efficient and durable fuel cells to produce clean electricity from hydrogen, electrolyzers to make clean fuels such as hydrogen for transportation, solid-state proton batteries, and even new kinds of computing devices based on iono-electronic effects.In order to advance the development of proton conductors, MIT engineers have identified certain traits of materials that give rise to fast proton conduction. Using those traits quantitatively, the team identified a half-dozen new candidates that show promise as fast proton conductors. Simulations suggest these candidates will perform far better than existing materials, although they still need to be conformed experimentally. In addition to uncovering potential new materials, the research also provides a deeper understanding at the atomic level of how such materials work.The new findings are described in the journal Energy and Environmental Sciences, in a paper by MIT professors Bilge Yildiz and Ju Li, postdocs Pjotrs Zguns and Konstantin Klyukin, and their collaborator Sossina Haile and her students from Northwestern University. Yildiz is the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering, and Materials Science and Engineering.“Proton conductors are needed in clean energy conversion applications such as fuel cells, where we use hydrogen to produce carbon dioxide-free electricity,” Yildiz explains. “We want to do this process efficiently, and therefore we need materials that can transport protons very fast through such devices.”Present methods of producing hydrogen, for example steam methane reforming, emit a great deal of carbon dioxide. “One way to eliminate that is to electrochemically produce hydrogen from water vapor, and that needs very good proton conductors,” Yildiz says. Production of other important industrial chemicals and potential fuels, such as ammonia, can also be carried out through efficient electrochemical systems that require good proton conductors.But most inorganic materials that conduct protons can only operate at temperatures of 200 to 600 degrees Celsius (roughly 450 to 1,100 Fahrenheit), or even higher. Such temperatures require energy to maintain and can cause degradation of materials. “Going to higher temperatures is not desirable because that makes the whole system more challenging, and the material durability becomes an issue,” Yildiz says. “There is no good inorganic proton conductor at room temperature.” Today, the only known room-temperature proton conductor is a polymeric material that is not practical for applications in computing devices because it can’t easily be scaled down to the nanometer regime, she says.To tackle the problem, the team first needed to develop a basic and quantitative understanding of exactly how proton conduction works, taking a class of inorganic proton conductors, called solid acids. “One has to first understand what governs proton conduction in these inorganic compounds,” she says. While looking at the materials’ atomic configurations, the researchers identified a pair of characteristics that directly relates to the materials’ proton-carrying potential.As Yildiz explains, proton conduction first involves a proton “hopping from a donor oxygen atom to an acceptor oxygen. And then the environment has to reorganize and take the accepted proton away, so that it can hop to another neighboring acceptor, enabling long-range proton diffusion.” This process happens in many inorganic solids, she says. Figuring out how that last part works — how the atomic lattice gets reorganized to take the accepted proton away from the original donor atom — was a key part of this research, she says.The researchers used computer simulations to study a class of materials called solid acids that become good proton conductors above 200 degrees Celsius. This class of materials has a substructure called the polyanion group sublattice, and these groups have to rotate and take the proton away from its original site so it can then transfer to other sites. The researchers were able to identify the phonons that contribute to the flexibility of this sublattice, which is essential for proton conduction. Then they used this information to comb through vast databases of theoretically and experimentally possible compounds, in search of better proton conducting materials.As a result, they found solid acid compounds that are promising proton conductors and that have been developed and produced for a variety of different applications but never before studied as proton conductors; these compounds turned out to have just the right characteristics of lattice flexibility. The team then carried out computer simulations of how the specific materials they identified in their initial screening would perform under relevant temperatures, to confirm their suitability as proton conductors for fuel cells or other uses. Sure enough, they found six promising materials, with predicted proton conduction speeds faster than the best existing solid acid proton conductors.“There are uncertainties in these simulations,” Yildiz cautions. “I don’t want to say exactly how much higher the conductivity will be, but these look very promising. Hopefully this motivates the experimental field to try to synthesize them in different forms and make use of these compounds as proton conductors.”Translating these theoretical findings into practical devices could take some years, she says. The likely first applications would be for electrochemical cells to produce fuels and chemical feedstocks such as hydrogen and ammonia, she says.The work was supported by the U.S. Department of Energy, the Wallenberg Foundation, and the U.S. National Science Foundation. More

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      in Environment

      China-based emissions of three potent climate-warming greenhouse gases spiked in past decade

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      When it comes to heating up the planet, not all greenhouse gases are created equal. They vary widely in their global warming potential (GWP), a measure of how much infrared thermal radiation a greenhouse gas would absorb over a given time frame once it enters the atmosphere. For example, measured over a 100-year period, the GWP of methane is about 28 times that of carbon dioxide (CO2), and the GWPs of a class of greenhouse gases known as perfluorocarbons (PFCs) are thousands of times that of CO2. The lifespans in the atmosphere of different greenhouse gases also vary widely. Methane persists in the atmosphere for around 10 years; CO2 for over 100 years, and PFCs for up to tens of thousands of years.Given the high GWPs and lifespans of PFCs, their emissions could pose a major roadblock to achieving the aspirational goal of the Paris Agreement on climate change — to limit the increase in global average surface temperature to 1.5 degrees Celsius above preindustrial levels. Now, two new studies based on atmospheric observations inside China and high-resolution atmospheric models show a rapid rise in Chinese emissions over the last decade (2011 to 2020 or 2021) of three PFCs: tetrafluoromethane (PFC-14) and hexafluoroethane (PFC-116) (results in PNAS), and perfluorocyclobutane (PFC-318) (results in Environmental Science & Technology).Both studies find that Chinese emissions have played a dominant role in driving up global emission levels for all three PFCs.The PNAS study identifies substantial PFC-14 and PFC-116 emission sources in the less-populated western regions of China from 2011 to 2021, likely due to the large amount of aluminum industry in these regions. The semiconductor industry also contributes to some of the emissions detected in the more economically developed eastern regions. These emissions are byproducts from aluminum smelting, or occur during the use of the two PFCs in the production of semiconductors and flat panel displays. During the observation period, emissions of both gases in China rose by 78 percent, accounting for most of the increase in global emissions of these gases.The ES&T study finds that during 2011-20, a 70 percent increase in Chinese PFC-318 emissions (contributing more than half of the global emissions increase of this gas) — originated primarily in eastern China. The regions with high emissions of PFC-318 in China overlap with geographical areas densely populated with factories that produce polytetrafluoroethylene (PTFE, commonly used for nonstick cookware coatings), implying that PTFE factories are major sources of PFC-318 emissions in China. In these factories, PFC-318 is formed as a byproduct.“Using atmospheric observations from multiple monitoring sites, we not only determined the magnitudes of PFC emissions, but also pinpointed the possible locations of their sources,” says Minde An, a postdoc at the MIT Center for Global Change Science (CGCS), and corresponding author of both studies. “Identifying the actual source industries contributing to these PFC emissions, and understanding the reasons for these largely byproduct emissions, can provide guidance for developing region- or industry-specific mitigation strategies.”“These three PFCs are largely produced as unwanted byproducts during the manufacture of otherwise widely used industrial products,” says MIT professor of atmospheric sciences Ronald Prinn, director of both the MIT Joint Program on the Science and Policy of Global Change and CGCS, and a co-author of both studies. “Phasing out emissions of PFCs as early as possible is highly beneficial for achieving global climate mitigation targets and is likely achievable by recycling programs and targeted technological improvements in these industries.”Findings in both studies were obtained, in part, from atmospheric observations collected from nine stations within a Chinese network, including one station from the Advanced Global Atmospheric Gases Experiment (AGAGE) network. For comparison, global total emissions were determined from five globally distributed, relatively unpolluted “background” AGAGE stations, as reported in the latest United Nations Environment Program and World Meteorological Organization Ozone Assessment report. More

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      in Energy

      AI method radically speeds predictions of materials’ thermal properties

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      It is estimated that about 70 percent of the energy generated worldwide ends up as waste heat.If scientists could better predict how heat moves through semiconductors and insulators, they could design more efficient power generation systems. However, the thermal properties of materials can be exceedingly difficult to model.The trouble comes from phonons, which are subatomic particles that carry heat. Some of a material’s thermal properties depend on a measurement called the phonon dispersion relation, which can be incredibly hard to obtain, let alone utilize in the design of a system.A team of researchers from MIT and elsewhere tackled this challenge by rethinking the problem from the ground up. The result of their work is a new machine-learning framework that can predict phonon dispersion relations up to 1,000 times faster than other AI-based techniques, with comparable or even better accuracy. Compared to more traditional, non-AI-based approaches, it could be 1 million times faster.This method could help engineers design energy generation systems that produce more power, more efficiently. It could also be used to develop more efficient microelectronics, since managing heat remains a major bottleneck to speeding up electronics.“Phonons are the culprit for the thermal loss, yet obtaining their properties is notoriously challenging, either computationally or experimentally,” says Mingda Li, associate professor of nuclear science and engineering and senior author of a paper on this technique.Li is joined on the paper by co-lead authors Ryotaro Okabe, a chemistry graduate student; and Abhijatmedhi Chotrattanapituk, an electrical engineering and computer science graduate student; Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT; as well as others at MIT, Argonne National Laboratory, Harvard University, the University of South Carolina, Emory University, the University of California at Santa Barbara, and Oak Ridge National Laboratory. The research appears in Nature Computational Science.Predicting phononsHeat-carrying phonons are tricky to predict because they have an extremely wide frequency range, and the particles interact and travel at different speeds.A material’s phonon dispersion relation is the relationship between energy and momentum of phonons in its crystal structure. For years, researchers have tried to predict phonon dispersion relations using machine learning, but there are so many high-precision calculations involved that models get bogged down.“If you have 100 CPUs and a few weeks, you could probably calculate the phonon dispersion relation for one material. The whole community really wants a more efficient way to do this,” says Okabe.The machine-learning models scientists often use for these calculations are known as graph neural networks (GNN). A GNN converts a material’s atomic structure into a crystal graph comprising multiple nodes, which represent atoms, connected by edges, which represent the interatomic bonding between atoms.While GNNs work well for calculating many quantities, like magnetization or electrical polarization, they are not flexible enough to efficiently predict an extremely high-dimensional quantity like the phonon dispersion relation. Because phonons can travel around atoms on X, Y, and Z axes, their momentum space is hard to model with a fixed graph structure.To gain the flexibility they needed, Li and his collaborators devised virtual nodes.They create what they call a virtual node graph neural network (VGNN) by adding a series of flexible virtual nodes to the fixed crystal structure to represent phonons. The virtual nodes enable the output of the neural network to vary in size, so it is not restricted by the fixed crystal structure.Virtual nodes are connected to the graph in such a way that they can only receive messages from real nodes. While virtual nodes will be updated as the model updates real nodes during computation, they do not affect the accuracy of the model.“The way we do this is very efficient in coding. You just generate a few more nodes in your GNN. The physical location doesn’t matter, and the real nodes don’t even know the virtual nodes are there,” says Chotrattanapituk.Cutting out complexitySince it has virtual nodes to represent phonons, the VGNN can skip many complex calculations when estimating phonon dispersion relations, which makes the method more efficient than a standard GNN. The researchers proposed three different versions of VGNNs with increasing complexity. Each can be used to predict phonons directly from a material’s atomic coordinates.Because their approach has the flexibility to rapidly model high-dimensional properties, they can use it to estimate phonon dispersion relations in alloy systems. These complex combinations of metals and nonmetals are especially challenging for traditional approaches to model.The researchers also found that VGNNs offered slightly greater accuracy when predicting a material’s heat capacity. In some instances, prediction errors were two orders of magnitude lower with their technique.A VGNN could be used to calculate phonon dispersion relations for a few thousand materials in just a few seconds with a personal computer, Li says.This efficiency could enable scientists to search a larger space when seeking materials with certain thermal properties, such as superior thermal storage, energy conversion, or superconductivity.Moreover, the virtual node technique is not exclusive to phonons, and could also be used to predict challenging optical and magnetic properties.In the future, the researchers want to refine the technique so virtual nodes have greater sensitivity to capture small changes that can affect phonon structure.“Researchers got too comfortable using graph nodes to represent atoms, but we can rethink that. Graph nodes can be anything. And virtual nodes are a very generic approach you could use to predict a lot of high-dimensional quantities,” Li says.“The authors’ innovative approach significantly augments the graph neural network description of solids by incorporating key physics-informed elements through virtual nodes, for instance, informing wave-vector dependent band-structures and dynamical matrices,” says Olivier Delaire, associate professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University, who was not involved with this work. “I find that the level of acceleration in predicting complex phonon properties is amazing, several orders of magnitude faster than a state-of-the-art universal machine-learning interatomic potential. Impressively, the advanced neural net captures fine features and obeys physical rules. There is great potential to expand the model to describe other important material properties: Electronic, optical, and magnetic spectra and band structures come to mind.”This work is supported by the U.S. Department of Energy, National Science Foundation, a Mathworks Fellowship, a Sow-Hsin Chen Fellowship, the Harvard Quantum Initiative, and the Oak Ridge National Laboratory. More

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      in Environment

      Study finds health risks in switching ships from diesel to ammonia fuel

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      As container ships the size of city blocks cross the oceans to deliver cargo, their huge diesel engines emit large quantities of air pollutants that drive climate change and have human health impacts. It has been estimated that maritime shipping accounts for almost 3 percent of global carbon dioxide emissions and the industry’s negative impacts on air quality cause about 100,000 premature deaths each year.Decarbonizing shipping to reduce these detrimental effects is a goal of the International Maritime Organization, a U.N. agency that regulates maritime transport. One potential solution is switching the global fleet from fossil fuels to sustainable fuels such as ammonia, which could be nearly carbon-free when considering its production and use.But in a new study, an interdisciplinary team of researchers from MIT and elsewhere caution that burning ammonia for maritime fuel could worsen air quality further and lead to devastating public health impacts, unless it is adopted alongside strengthened emissions regulations.Ammonia combustion generates nitrous oxide (N2O), a greenhouse gas that is about 300 times more potent than carbon dioxide. It also emits nitrogen in the form of nitrogen oxides (NO and NO2, referred to as NOx), and unburnt ammonia may slip out, which eventually forms fine particulate matter in the atmosphere. These tiny particles can be inhaled deep into the lungs, causing health problems like heart attacks, strokes, and asthma.The new study indicates that, under current legislation, switching the global fleet to ammonia fuel could cause up to about 600,000 additional premature deaths each year. However, with stronger regulations and cleaner engine technology, the switch could lead to about 66,000 fewer premature deaths than currently caused by maritime shipping emissions, with far less impact on global warming.“Not all climate solutions are created equal. There is almost always some price to pay. We have to take a more holistic approach and consider all the costs and benefits of different climate solutions, rather than just their potential to decarbonize,” says Anthony Wong, a postdoc in the MIT Center for Global Change Science and lead author of the study.His co-authors include Noelle Selin, an MIT professor in the Institute for Data, Systems, and Society and the Department of Earth, Atmospheric and Planetary Sciences (EAPS); Sebastian Eastham, a former principal research scientist who is now a senior lecturer at Imperial College London; Christine Mounaïm-Rouselle, a professor at the University of Orléans in France; Yiqi Zhang, a researcher at the Hong Kong University of Science and Technology; and Florian Allroggen, a research scientist in the MIT Department of Aeronautics and Astronautics. The research appears this week in Environmental Research Letters.Greener, cleaner ammoniaTraditionally, ammonia is made by stripping hydrogen from natural gas and then combining it with nitrogen at extremely high temperatures. This process is often associated with a large carbon footprint. The maritime shipping industry is betting on the development of “green ammonia,” which is produced by using renewable energy to make hydrogen via electrolysis and to generate heat.“In theory, if you are burning green ammonia in a ship engine, the carbon emissions are almost zero,” Wong says.But even the greenest ammonia generates nitrous oxide (N2O), nitrogen oxides (NOx) when combusted, and some of the ammonia may slip out, unburnt. This nitrous oxide would escape into the atmosphere, where the greenhouse gas would remain for more than 100 years. At the same time, the nitrogen emitted as NOx and ammonia would fall to Earth, damaging fragile ecosystems. As these emissions are digested by bacteria, additional N2O  is produced.NOx and ammonia also mix with gases in the air to form fine particulate matter. A primary contributor to air pollution, fine particulate matter kills an estimated 4 million people each year.“Saying that ammonia is a ‘clean’ fuel is a bit of an overstretch. Just because it is carbon-free doesn’t necessarily mean it is clean and good for public health,” Wong says.A multifaceted modelThe researchers wanted to paint the whole picture, capturing the environmental and public health impacts of switching the global fleet to ammonia fuel. To do so, they designed scenarios to measure how pollutant impacts change under certain technology and policy assumptions.From a technological point of view, they considered two ship engines. The first burns pure ammonia, which generates higher levels of unburnt ammonia but emits fewer nitrogen oxides. The second engine technology involves mixing ammonia with hydrogen to improve combustion and optimize the performance of a catalytic converter, which controls both nitrogen oxides and unburnt ammonia pollution.They also considered three policy scenarios: current regulations, which only limit NOx emissions in some parts of the world; a scenario that adds ammonia emission limits over North America and Western Europe; and a scenario that adds global limits on ammonia and NOx emissions.The researchers used a ship track model to calculate how pollutant emissions change under each scenario and then fed the results into an air quality model. The air quality model calculates the impact of ship emissions on particulate matter and ozone pollution. Finally, they estimated the effects on global public health.One of the biggest challenges came from a lack of real-world data, since no ammonia-powered ships are yet sailing the seas. Instead, the researchers relied on experimental ammonia combustion data from collaborators to build their model.“We had to come up with some clever ways to make that data useful and informative to both the technology and regulatory situations,” he says.A range of outcomesIn the end, they found that with no new regulations and ship engines that burn pure ammonia, switching the entire fleet would cause 681,000 additional premature deaths each year.“While a scenario with no new regulations is not very realistic, it serves as a good warning of how dangerous ammonia emissions could be. And unlike NOx, ammonia emissions from shipping are currently unregulated,” Wong says.However, even without new regulations, using cleaner engine technology would cut the number of premature deaths down to about 80,000, which is about 20,000 fewer than are currently attributed to maritime shipping emissions. With stronger global regulations and cleaner engine technology, the number of people killed by air pollution from shipping could be reduced by about 66,000.“The results of this study show the importance of developing policies alongside new technologies,” Selin says. “There is a potential for ammonia in shipping to be beneficial for both climate and air quality, but that requires that regulations be designed to address the entire range of potential impacts, including both climate and air quality.”Ammonia’s air quality impacts would not be felt uniformly across the globe, and addressing them fully would require coordinated strategies across very different contexts. Most premature deaths would occur in East Asia, since air quality regulations are less stringent in this region. Higher levels of existing air pollution cause the formation of more particulate matter from ammonia emissions. In addition, shipping volume over East Asia is far greater than elsewhere on Earth, compounding these negative effects.In the future, the researchers want to continue refining their analysis. They hope to use these findings as a starting point to urge the marine industry to share engine data they can use to better evaluate air quality and climate impacts. They also hope to inform policymakers about the importance and urgency of updating shipping emission regulations.This research was funded by the MIT Climate and Sustainability Consortium. More

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      in Environment

      Study: Weaker ocean circulation could enhance CO2 buildup in the atmosphere

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      As climate change advances, the ocean’s overturning circulation is predicted to weaken substantially. With such a slowdown, scientists estimate the ocean will pull down less carbon dioxide from the atmosphere. However, a slower circulation should also dredge up less carbon from the deep ocean that would otherwise be released back into the atmosphere. On balance, the ocean should maintain its role in reducing carbon emissions from the atmosphere, if at a slower pace.However, a new study by an MIT researcher finds that scientists may have to rethink the relationship between the ocean’s circulation and its long-term capacity to store carbon. As the ocean gets weaker, it could release more carbon from the deep ocean into the atmosphere instead.The reason has to do with a previously uncharacterized feedback between the ocean’s available iron, upwelling carbon and nutrients, surface microorganisms, and a little-known class of molecules known generally as “ligands.” When the ocean circulates more slowly, all these players interact in a self-perpetuating cycle that ultimately increases the amount of carbon that the ocean outgases back to the atmosphere.“By isolating the impact of this feedback, we see a fundamentally different relationship between ocean circulation and atmospheric carbon levels, with implications for the climate,” says study author Jonathan Lauderdale, a research scientist in MIT’s Department of Earth, Atmospheric, and Planetary Sciences. “What we thought is going on in the ocean is completely overturned.”Lauderdale says the findings show that “we can’t count on the ocean to store carbon in the deep ocean in response to future changes in circulation. We must be proactive in cutting emissions now, rather than relying on these natural processes to buy us time to mitigate climate change.”His study appears today in the journal Nature Communications.Box flowIn 2020, Lauderdale led a study that explored ocean nutrients, marine organisms, and iron, and how their interactions influence the growth of phytoplankton around the world. Phytoplankton are microscopic, plant-like organisms that live on the ocean surface and consume a diet of carbon and nutrients that upwell from the deep ocean and iron that drifts in from desert dust.The more phytoplankton that can grow, the more carbon dioxide they can absorb from the atmosphere via photosynthesis, and this plays a large role in the ocean’s ability to sequester carbon.For the 2020 study, the team developed a simple “box” model, representing conditions in different parts of the ocean as general boxes, each with a different balance of nutrients, iron, and ligands — organic molecules that are thought to be byproducts of phytoplankton. The team modeled a general flow between the boxes to represent the ocean’s larger circulation — the way seawater sinks, then is buoyed back up to the surface in different parts of the world.This modeling revealed that, even if scientists were to “seed” the oceans with extra iron, that iron wouldn’t have much of an effect on global phytoplankton growth. The reason was due to a limit set by ligands. It turns out that, if left on its own, iron is insoluble in the ocean and therefore unavailable to phytoplankton. Iron only becomes soluble at “useful” levels when linked with ligands, which keep iron in a form that plankton can consume. Lauderdale found that adding iron to one ocean region to consume additional nutrients robs other regions of nutrients that phytoplankton there need to grow. This lowers the production of ligands and the supply of iron back to the original ocean region, limiting the amount of extra carbon that would be taken up from the atmosphere.Unexpected switchOnce the team published their study, Lauderdale worked the box model into a form that he could make publicly accessible, including ocean and atmosphere carbon exchange and extending the boxes to represent more diverse environments, such as conditions similar to the Pacific, the North Atlantic, and the Southern Ocean. In the process, he tested other interactions within the model, including the effect of varying ocean circulation.He ran the model with different circulation strengths, expecting to see less atmospheric carbon dioxide with weaker ocean overturning — a relationship that previous studies have supported, dating back to the 1980s. But what he found instead was a clear and opposite trend: The weaker the ocean’s circulation, the more CO2 built up in the atmosphere.“I thought there was some mistake,” Lauderdale recalls. “Why were atmospheric carbon levels trending the wrong way?”When he checked the model, he found that the parameter describing ocean ligands had been left “on” as a variable. In other words, the model was calculating ligand concentrations as changing from one ocean region to another.On a hunch, Lauderdale turned this parameter “off,” which set ligand concentrations as constant in every modeled ocean environment, an assumption that many ocean models typically make. That one change reversed the trend, back to the assumed relationship: A weaker circulation led to reduced atmospheric carbon dioxide. But which trend was closer to the truth?Lauderdale looked to the scant available data on ocean ligands to see whether their concentrations were more constant or variable in the actual ocean. He found confirmation in GEOTRACES, an international study that coordinates measurements of trace elements and isotopes across the world’s oceans, that scientists can use to compare concentrations from region to region. Indeed, the molecules’ concentrations varied. If ligand concentrations do change from one region to another, then his surprise new result was likely representative of the real ocean: A weaker circulation leads to more carbon dioxide in the atmosphere.“It’s this one weird trick that changed everything,” Lauderdale says. “The ligand switch has revealed this completely different relationship between ocean circulation and atmospheric CO2 that we thought we understood pretty well.”Slow cycleTo see what might explain the overturned trend, Lauderdale analyzed biological activity and carbon, nutrient, iron, and ligand concentrations from the ocean model under different circulation strengths, comparing scenarios where ligands were variable or constant across the various boxes.This revealed a new feedback: The weaker the ocean’s circulation, the less carbon and nutrients the ocean pulls up from the deep. Any phytoplankton at the surface would then have fewer resources to grow and would produce fewer byproducts (including ligands) as a result. With fewer ligands available, less iron at the surface would be usable, further reducing the phytoplankton population. There would then be fewer phytoplankton available to absorb carbon dioxide from the atmosphere and consume upwelled carbon from the deep ocean.“My work shows that we need to look more carefully at how ocean biology can affect the climate,” Lauderdale points out. “Some climate models predict a 30 percent slowdown in the ocean circulation due to melting ice sheets, particularly around Antarctica. This huge slowdown in overturning circulation could actually be a big problem: In addition to a host of other climate issues, not only would the ocean take up less anthropogenic CO2 from the atmosphere, but that could be amplified by a net outgassing of deep ocean carbon, leading to an unanticipated increase in atmospheric CO2 and unexpected further climate warming.”  More

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      in Environment

      How to increase the rate of plastics recycling

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      While recycling systems and bottle deposits have become increasingly widespread in the U.S., actual rates of recycling are “abysmal,” according to a team of MIT researchers who studied the rates for recycling of PET, the plastic commonly used in beverage bottles. However, their findings suggest some ways to change this.The present rate of recycling for PET, or polyethylene terephthalate, bottles nationwide is about 24 percent and has remained stagnant for a decade, the researchers say. But their study indicates that with a nationwide bottle deposit program, the rates could increase to 82 percent, with nearly two-thirds of all PET bottles being recycled into new bottles, at a net cost of just a penny a bottle when demand is robust. At the same time, they say, policies would be needed to ensure a sufficient demand for the recycled material.The findings are being published today in the Journal of Industrial Ecology, in a paper by MIT professor of materials science and engineering Elsa Olivetti, graduate students Basuhi Ravi and Karan Bhuwalka, and research scientist Richard Roth.The team looked at PET bottle collection and recycling rates in different states as well as other nations with and without bottle deposit policies, and with or without curbside recycling programs, as well as the inputs and outputs of various recycling companies and methods. The researchers say this study is the first to look in detail at the interplay between public policies and the end-to-end realities of the packaging production and recycling market.They found that bottle deposit programs are highly effective in the areas where they are in place, but at present there is not nearly enough collection of used bottles to meet the targets set by the packaging industry. Their analysis suggests that a uniform nationwide bottle deposit policy could achieve the levels of recycling that have been mandated by proposed legislation and corporate commitments.The recycling of PET is highly successful in terms of quality, with new products made from all-recycled material virtually matching the qualities of virgin material. And brands have shown that new bottles can be safely made with 100 percent postconsumer waste. But the team found that collection of the material is a crucial bottleneck that leaves processing plants unable to meet their needs. However, with the right policies in place, “one can be optimistic,” says Olivetti, who is the Jerry McAfee Professor in Engineering and the associate dean of the School of Engineering.“A message that we have found in a number of cases in the recycling space is that if you do the right work to support policies that think about both the demand but also the supply,” then significant improvements are possible, she says. “You have to think about the response and the behavior of multiple actors in the system holistically to be viable,” she says. “We are optimistic, but there are many ways to be pessimistic if we’re not thinking about that in a holistic way.”For example, the study found that it is important to consider the needs of existing municipal waste-recovery facilities. While expanded bottle deposit programs are essential to increase recycling rates and provide the feedstock to companies recycling PET into new products, the current facilities that process material from curbside recycling programs will lose revenue from PET bottles, which are a relatively high-value product compared to the other materials in the recycled waste stream. These companies would lose a source of their income if the bottles are collected through deposit programs, leaving them with only the lower-value mixed plastics.The researchers developed economic models based on rates of collection found in the states with deposit programs, recycled-content requirements, and other policies, and used these models to extrapolate to the nation as a whole. Overall, they found that the supply needs of packaging producers could be met through a nationwide bottle deposit system with a 10-cent deposit per bottle — at a net cost of about 1 cent per bottle produced when demand is strong. This need not be a federal program, but rather one where the implementation would be left up to the individual states, Olivetti says.Other countries have been much more successful in implementing deposit systems that result in very high participation rates. Several European countries manage to collect more than 90 percent of PET bottles for recycling, for example. But in the U.S., less than 29 percent are collected, and after losses in the recycling chain about 24 percent actually get recycled, the researchers found. Whereas 73 percent of Americans have access to curbside recycling, presently only 10 states have bottle deposit systems in place.Yet the demand is there so far. “There is a market for this material,” says Olivetti. While bottles collected through mixed-waste collection can still be recycled to some extent, those collected through deposit systems tend to be much cleaner and require less processing, and so are more economical to recycle into new bottles, or into textiles.To be effective, policies need to not just focus on increasing rates of recycling, but on the whole cycle of supply and demand and the different players involved, Olivetti says. Safeguards would need to be in place to protect existing recycling facilities from the lost revenues they would suffer as a result of bottle deposits, perhaps in the form of subsidies funded by fees on the bottle producers, to avoid putting these essential parts of the processing chain out of business. And other policies may be needed to ensure the continued market for the material that gets collected, including recycled content requirements and extended producer responsibility regulations, the team found.At this stage, it’s important to focus on the specific waste streams that can most effectively be recycled, and PET, along with many metals, clearly fit that category. “When we start to think about mixed plastic streams, that’s much more challenging from an environmental perspective,” she says. “Recycling systems need to be pursuing extended producers’ responsibility, or specifically thinking about materials designed more effectively toward recycled content,” she says.It’s also important to address “what the right metrics are to design for sustainably managed materials streams,” she says. “It could be energy use, could be circularity [for example, making old bottles into new bottles], could be around waste reduction, and making sure those are all aligned. That’s another kind of policy coordination that’s needed.” More

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      in Environment

      Making climate models relevant for local decision-makers

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      Climate models are a key technology in predicting the impacts of climate change. By running simulations of the Earth’s climate, scientists and policymakers can estimate conditions like sea level rise, flooding, and rising temperatures, and make decisions about how to appropriately respond. But current climate models struggle to provide this information quickly or affordably enough to be useful on smaller scales, such as the size of a city. Now, authors of a new open-access paper published in the Journal of Advances in Modeling Earth Systems have found a method to leverage machine learning to utilize the benefits of current climate models, while reducing the computational costs needed to run them. “It turns the traditional wisdom on its head,” says Sai Ravela, a principal research scientist in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS) who wrote the paper with EAPS postdoc Anamitra Saha. Traditional wisdomIn climate modeling, downscaling is the process of using a global climate model with coarse resolution to generate finer details over smaller regions. Imagine a digital picture: A global model is a large picture of the world with a low number of pixels. To downscale, you zoom in on just the section of the photo you want to look at — for example, Boston. But because the original picture was low resolution, the new version is blurry; it doesn’t give enough detail to be particularly useful. “If you go from coarse resolution to fine resolution, you have to add information somehow,” explains Saha. Downscaling attempts to add that information back in by filling in the missing pixels. “That addition of information can happen two ways: Either it can come from theory, or it can come from data.” Conventional downscaling often involves using models built on physics (such as the process of air rising, cooling, and condensing, or the landscape of the area), and supplementing it with statistical data taken from historical observations. But this method is computationally taxing: It takes a lot of time and computing power to run, while also being expensive. A little bit of both In their new paper, Saha and Ravela have figured out a way to add the data another way. They’ve employed a technique in machine learning called adversarial learning. It uses two machines: One generates data to go into our photo. But the other machine judges the sample by comparing it to actual data. If it thinks the image is fake, then the first machine has to try again until it convinces the second machine. The end-goal of the process is to create super-resolution data. Using machine learning techniques like adversarial learning is not a new idea in climate modeling; where it currently struggles is its inability to handle large amounts of basic physics, like conservation laws. The researchers discovered that simplifying the physics going in and supplementing it with statistics from the historical data was enough to generate the results they needed. “If you augment machine learning with some information from the statistics and simplified physics both, then suddenly, it’s magical,” says Ravela. He and Saha started with estimating extreme rainfall amounts by removing more complex physics equations and focusing on water vapor and land topography. They then generated general rainfall patterns for mountainous Denver and flat Chicago alike, applying historical accounts to correct the output. “It’s giving us extremes, like the physics does, at a much lower cost. And it’s giving us similar speeds to statistics, but at much higher resolution.” Another unexpected benefit of the results was how little training data was needed. “The fact that that only a little bit of physics and little bit of statistics was enough to improve the performance of the ML [machine learning] model … was actually not obvious from the beginning,” says Saha. It only takes a few hours to train, and can produce results in minutes, an improvement over the months other models take to run. Quantifying risk quicklyBeing able to run the models quickly and often is a key requirement for stakeholders such as insurance companies and local policymakers. Ravela gives the example of Bangladesh: By seeing how extreme weather events will impact the country, decisions about what crops should be grown or where populations should migrate to can be made considering a very broad range of conditions and uncertainties as soon as possible.“We can’t wait months or years to be able to quantify this risk,” he says. “You need to look out way into the future and at a large number of uncertainties to be able to say what might be a good decision.”While the current model only looks at extreme precipitation, training it to examine other critical events, such as tropical storms, winds, and temperature, is the next step of the project. With a more robust model, Ravela is hoping to apply it to other places like Boston and Puerto Rico as part of a Climate Grand Challenges project.“We’re very excited both by the methodology that we put together, as well as the potential applications that it could lead to,” he says.  More

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      in Energy

      Reducing carbon emissions from long-haul trucks

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      People around the world rely on trucks to deliver the goods they need, and so-called long-haul trucks play a critical role in those supply chains. In the United States, long-haul trucks moved 71 percent of all freight in 2022. But those long-haul trucks are heavy polluters, especially of the carbon emissions that threaten the global climate. According to U.S. Environmental Protection Agency estimates, in 2022 more than 3 percent of all carbon dioxide (CO2) emissions came from long-haul trucks.The problem is that long-haul trucks run almost exclusively on diesel fuel, and burning diesel releases high levels of CO2 and other carbon emissions. Global demand for freight transport is projected to as much as double by 2050, so it’s critical to find another source of energy that will meet the needs of long-haul trucks while also reducing their carbon emissions. And conversion to the new fuel must not be costly. “Trucks are an indispensable part of the modern supply chain, and any increase in the cost of trucking will be felt universally,” notes William H. Green, the Hoyt Hottel Professor in Chemical Engineering and director of the MIT Energy Initiative.For the past year, Green and his research team have been seeking a low-cost, cleaner alternative to diesel. Finding a replacement is difficult because diesel meets the needs of the trucking industry so well. For one thing, diesel has a high energy density — that is, energy content per pound of fuel. There’s a legal limit on the total weight of a truck and its contents, so using an energy source with a lower weight allows the truck to carry more payload — an important consideration, given the low profit margin of the freight industry. In addition, diesel fuel is readily available at retail refueling stations across the country — a critical resource for drivers, who may travel 600 miles in a day and sleep in their truck rather than returning to their home depot. Finally, diesel fuel is a liquid, so it’s easy to distribute to refueling stations and then pump into trucks.Past studies have examined numerous alternative technology options for powering long-haul trucks, but no clear winner has emerged. Now, Green and his team have evaluated the available options based on consistent and realistic assumptions about the technologies involved and the typical operation of a long-haul truck, and assuming no subsidies to tip the cost balance. Their in-depth analysis of converting long-haul trucks to battery electric — summarized below — found a high cost and negligible emissions gains in the near term. Studies of methanol and other liquid fuels from biomass are ongoing, but already a major concern is whether the world can plant and harvest enough biomass for biofuels without destroying the ecosystem. An analysis of hydrogen — also summarized below — highlights specific challenges with using that clean-burning fuel, which is a gas at normal temperatures.Finally, the team identified an approach that could make hydrogen a promising, low-cost option for long-haul trucks. And, says Green, “it’s an option that most people are probably unaware of.” It involves a novel way of using materials that can pick up hydrogen, store it, and then release it when and where it’s needed to serve as a clean-burning fuel.Defining the challenge: A realistic drive cycle, plus diesel values to beatThe MIT researchers believe that the lack of consensus on the best way to clean up long-haul trucking may have a simple explanation: Different analyses are based on different assumptions about the driving behavior of long-haul trucks. Indeed, some of them don’t accurately represent actual long-haul operations. So the first task for the MIT team was to define a representative — and realistic — “drive cycle” for actual long-haul truck operations in the United States. Then the MIT researchers — and researchers elsewhere — can assess potential replacement fuels and engines based on a consistent set of assumptions in modeling and simulation analyses.To define the drive cycle for long-haul operations, the MIT team used a systematic approach to analyze many hours of real-world driving data covering 58,000 miles. They examined 10 features and identified three — daily range, vehicle speed, and road grade — that have the greatest impact on energy demand and thus on fuel consumption and carbon emissions. The representative drive cycle that emerged covers a distance of 600 miles, an average vehicle speed of 55 miles per hour, and a road grade ranging from negative 6 percent to positive 6 percent.The next step was to generate key values for the performance of the conventional diesel “powertrain,” that is, all the components involved in creating power in the engine and delivering it to the wheels on the ground. Based on their defined drive cycle, the researchers simulated the performance of a conventional diesel truck, generating “benchmarks” for fuel consumption, CO2 emissions, cost, and other performance parameters.Now they could perform parallel simulations — based on the same drive-cycle assumptions — of possible replacement fuels and powertrains to see how the cost, carbon emissions, and other performance parameters would compare to the diesel benchmarks.The battery electric optionWhen considering how to decarbonize long-haul trucks, a natural first thought is battery power. After all, battery electric cars and pickup trucks are proving highly successful. Why not switch to battery electric long-haul trucks? “Again, the literature is very divided, with some studies saying that this is the best idea ever, and other studies saying that this makes no sense,” says Sayandeep Biswas, a graduate student in chemical engineering.To assess the battery electric option, the MIT researchers used a physics-based vehicle model plus well-documented estimates for the efficiencies of key components such as the battery pack, generators, motor, and so on. Assuming the previously described drive cycle, they determined operating parameters, including how much power the battery-electric system needs. From there they could calculate the size and weight of the battery required to satisfy the power needs of the battery electric truck.The outcome was disheartening. Providing enough energy to travel 600 miles without recharging would require a 2 megawatt-hour battery. “That’s a lot,” notes Kariana Moreno Sader, a graduate student in chemical engineering. “It’s the same as what two U.S. households consume per month on average.” And the weight of such a battery would significantly reduce the amount of payload that could be carried. An empty diesel truck typically weighs 20,000 pounds. With a legal limit of 80,000 pounds, there’s room for 60,000 pounds of payload. The 2 MWh battery would weigh roughly 27,000 pounds — significantly reducing the allowable capacity for carrying payload.Accounting for that “payload penalty,” the researchers calculated that roughly four electric trucks would be required to replace every three of today’s diesel-powered trucks. Furthermore, each added truck would require an additional driver. The impact on operating expenses would be significant.Analyzing the emissions reductions that might result from shifting to battery electric long-haul trucks also brought disappointing results. One might assume that using electricity would eliminate CO2 emissions. But when the researchers included emissions associated with making that electricity, that wasn’t true.“Battery electric trucks are only as clean as the electricity used to charge them,” notes Moreno Sader. Most of the time, drivers of long-haul trucks will be charging from national grids rather than dedicated renewable energy plants. According to Energy Information Agency statistics, fossil fuels make up more than 60 percent of the current U.S. power grid, so electric trucks would still be responsible for significant levels of carbon emissions. Manufacturing batteries for the trucks would generate additional CO2 emissions.Building the charging infrastructure would require massive upfront capital investment, as would upgrading the existing grid to reliably meet additional energy demand from the long-haul sector. Accomplishing those changes would be costly and time-consuming, which raises further concern about electrification as a means of decarbonizing long-haul freight.In short, switching today’s long-haul diesel trucks to battery electric power would bring major increases in costs for the freight industry and negligible carbon emissions benefits in the near term. Analyses assuming various types of batteries as well as other drive cycles produced comparable results.However, the researchers are optimistic about where the grid is going in the future. “In the long term, say by around 2050, emissions from the grid are projected to be less than half what they are now,” says Moreno Sader. “When we do our calculations based on that prediction, we find that emissions from battery electric trucks would be around 40 percent lower than our calculated emissions based on today’s grid.”For Moreno Sader, the goal of the MIT research is to help “guide the sector on what would be the best option.” With that goal in mind, she and her colleagues are now examining the battery electric option under different scenarios — for example, assuming battery swapping (a depleted battery isn’t recharged but replaced by a fully charged one), short-haul trucking, and other applications that might produce a more cost-competitive outcome, even for the near term.A promising option: hydrogenAs the world looks to get off reliance on fossil fuels for all uses, much attention is focusing on hydrogen. Could hydrogen be a good alternative for today’s diesel-burning long-haul trucks?To find out, the MIT team performed a detailed analysis of the hydrogen option. “We thought that hydrogen would solve a lot of the problems we had with battery electric,” says Biswas. It doesn’t have associated CO2 emissions. Its energy density is far higher, so it doesn’t create the weight problem posed by heavy batteries. In addition, existing compression technology can get enough hydrogen fuel into a regular-sized tank to cover the needed distance and range. “You can actually give drivers the range they want,” he says. “There’s no issue with ‘range anxiety.’”But while using hydrogen for long-haul trucking would reduce carbon emissions, it would cost far more than diesel. Based on their detailed analysis of hydrogen, the researchers concluded that the main source of incurred cost is in transporting it. Hydrogen can be made in a chemical facility, but then it needs to be distributed to refueling stations across the country. Conventionally, there have been two main ways of transporting hydrogen: as a compressed gas and as a cryogenic liquid. As Biswas notes, the former is “super high pressure,” and the latter is “super cold.” The researchers’ calculations show that as much as 80 percent of the cost of delivered hydrogen is due to transportation and refueling, plus there’s the need to build dedicated refueling stations that can meet new environmental and safety standards for handling hydrogen as a compressed gas or a cryogenic liquid.Having dismissed the conventional options for shipping hydrogen, they turned to a less-common approach: transporting hydrogen using “liquid organic hydrogen carriers” (LOHCs), special organic (carbon-containing) chemical compounds that can under certain conditions absorb hydrogen atoms and under other conditions release them.LOHCs are in use today to deliver small amounts of hydrogen for commercial use. Here’s how the process works: In a chemical plant, the carrier compound is brought into contact with hydrogen in the presence of a catalyst under elevated temperature and pressure, and the compound picks up the hydrogen. The “hydrogen-loaded” compound — still a liquid — is then transported under atmospheric conditions. When the hydrogen is needed, the compound is again exposed to a temperature increase and a different catalyst, and the hydrogen is released.LOHCs thus appear to be ideal hydrogen carriers for long-haul trucking. They’re liquid, so they can easily be delivered to existing refueling stations, where the hydrogen would be released; and they contain at least as much energy per gallon as hydrogen in a cryogenic liquid or compressed gas form. However, a detailed analysis of using hydrogen carriers showed that the approach would decrease emissions but at a considerable cost.The problem begins with the “dehydrogenation” step at the retail station. Releasing the hydrogen from the chemical carrier requires heat, which is generated by burning some of the hydrogen being carried by the LOHC. The researchers calculate that getting the needed heat takes 36 percent of that hydrogen. (In theory, the process would take only 27 percent — but in reality, that efficiency won’t be achieved.) So out of every 100 units of starting hydrogen, 36 units are now gone.But that’s not all. The hydrogen that comes out is at near-ambient pressure. So the facility dispensing the hydrogen will need to compress it — a process that the team calculates will use up 20-30 percent of the starting hydrogen.Because of the needed heat and compression, there’s now less than half of the starting hydrogen left to be delivered to the truck — and as a result, the hydrogen fuel becomes twice as expensive. The bottom line is that the technology works, but “when it comes to really beating diesel, the economics don’t work. It’s quite a bit more expensive,” says Biswas. In addition, the refueling stations would require expensive compressors and auxiliary units such as cooling systems. The capital investment and the operating and maintenance costs together imply that the market penetration of hydrogen refueling stations will be slow.A better strategy: onboard release of hydrogen from LOHCsGiven the potential benefits of using of LOHCs, the researchers focused on how to deal with both the heat needed to release the hydrogen and the energy needed to compress it. “That’s when we had the idea,” says Biswas. “Instead of doing the dehydrogenation [hydrogen release] at the refueling station and then loading the truck with hydrogen, why don’t we just take the LOHC and load that onto the truck?” Like diesel, LOHC is a liquid, so it’s easily transported and pumped into trucks at existing refueling stations. “We’ll then make hydrogen as it’s needed based on the power demands of the truck — and we can capture waste heat from the engine exhaust and use it to power the dehydrogenation process,” says Biswas.In their proposed plan, hydrogen-loaded LOHC is created at a chemical “hydrogenation” plant and then delivered to a retail refueling station, where it’s pumped into a long-haul truck. Onboard the truck, the loaded LOHC pours into the fuel-storage tank. From there it moves to the “dehydrogenation unit” — the reactor where heat and a catalyst together promote chemical reactions that separate the hydrogen from the LOHC. The hydrogen is sent to the powertrain, where it burns, producing energy that propels the truck forward.Hot exhaust from the powertrain goes to a “heat-integration unit,” where its waste heat energy is captured and returned to the reactor to help encourage the reaction that releases hydrogen from the loaded LOHC. The unloaded LOHC is pumped back into the fuel-storage tank, where it’s kept in a separate compartment to keep it from mixing with the loaded LOHC. From there, it’s pumped back into the retail refueling station and then transported back to the hydrogenation plant to be loaded with more hydrogen.Switching to onboard dehydrogenation brings down costs by eliminating the need for extra hydrogen compression and by using waste heat in the engine exhaust to drive the hydrogen-release process. So how does their proposed strategy look compared to diesel? Based on a detailed analysis, the researchers determined that using their strategy would be 18 percent more expensive than using diesel, and emissions would drop by 71 percent.But those results need some clarification. The 18 percent cost premium of using LOHC with onboard hydrogen release is based on the price of diesel fuel in 2020. In spring of 2023 the price was about 30 percent higher. Assuming the 2023 diesel price, the LOHC option is actually cheaper than using diesel.Both the cost and emissions outcomes are affected by another assumption: the use of “blue hydrogen,” which is hydrogen produced from natural gas with carbon capture and storage. Another option is to assume the use of “green hydrogen,” which is hydrogen produced using electricity generated from renewable sources, such as wind and solar. Green hydrogen is much more expensive than blue hydrogen, so then the costs would increase dramatically.If in the future the price of green hydrogen drops, the researchers’ proposed plan would shift to green hydrogen — and then the decline in emissions would no longer be 71 percent but rather close to 100 percent. There would be almost no emissions associated with the researchers’ proposed plan for using LHOCs with onboard hydrogen release.Comparing the options on cost and emissionsTo compare the options, Moreno Sader prepared bar charts showing the per-mile cost of shipping by truck in the United States and the CO2 emissions that result using each of the fuels and approaches discussed above: diesel fuel, battery electric, hydrogen as a cryogenic liquid or compressed gas, and LOHC with onboard hydrogen release. The LOHC strategy with onboard dehydrogenation looked promising on both the cost and the emissions charts. In addition to such quantitative measures, the researchers believe that their strategy addresses two other, less-obvious challenges in finding a less-polluting fuel for long-haul trucks.First, the introduction of the new fuel and trucks to use it must not disrupt the current freight-delivery setup. “You have to keep the old trucks running while you’re introducing the new ones,” notes Green. “You cannot have even a day when the trucks aren’t running because it’d be like the end of the economy. Your supermarket shelves would all be empty; your factories wouldn’t be able to run.” The researchers’ plan would be completely compatible with the existing diesel supply infrastructure and would require relatively minor retrofits to today’s long-haul trucks, so the current supply chains would continue to operate while the new fuel and retrofitted trucks are introduced.Second, the strategy has the potential to be adopted globally. Long-haul trucking is important in other parts of the world, and Moreno Sader thinks that “making this approach a reality is going to have a lot of impact, not only in the United States but also in other countries,” including her own country of origin, Colombia. “This is something I think about all the time.” The approach is compatible with the current diesel infrastructure, so the only requirement for adoption is to build the chemical hydrogenation plant. “And I think the capital expenditure related to that will be less than the cost of building a new fuel-supply infrastructure throughout the country,” says Moreno Sader.Testing in the lab“We’ve done a lot of simulations and calculations to show that this is a great idea,” notes Biswas. “But there’s only so far that math can go to convince people.” The next step is to demonstrate their concept in the lab.To that end, the researchers are now assembling all the core components of the onboard hydrogen-release reactor as well as the heat-integration unit that’s key to transferring heat from the engine exhaust to the hydrogen-release reactor. They estimate that this spring they’ll be ready to demonstrate their ability to release hydrogen and confirm the rate at which it’s formed. And — guided by their modeling work — they’ll be able to fine-tune critical components for maximum efficiency and best performance.The next step will be to add an appropriate engine, specially equipped with sensors to provide the critical readings they need to optimize the performance of all their core components together. By the end of 2024, the researchers hope to achieve their goal: the first experimental demonstration of a power-dense, robust onboard hydrogen-release system with highly efficient heat integration.In the meantime, they believe that results from their work to date should help spread the word, bringing their novel approach to the attention of other researchers and experts in the trucking industry who are now searching for ways to decarbonize long-haul trucking.Financial support for development of the representative drive cycle and the diesel benchmarks as well as the analysis of the battery electric option was provided by the MIT Mobility Systems Center of the MIT Energy Initiative. Analysis of LOHC-powered trucks with onboard dehydrogenation was supported by the MIT Climate and Sustainability Consortium. Sayandeep Biswas is supported by a fellowship from the Martin Family Society of Fellows for Sustainability, and Kariana Moreno Sader received fellowship funding from MathWorks through the MIT School of Science. More

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      in Environment

      Microscopic defects in ice influence how massive glaciers flow, study shows

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      As they seep and calve into the sea, melting glaciers and ice sheets are raising global water levels at unprecedented rates. To predict and prepare for future sea-level rise, scientists need a better understanding of how fast glaciers melt and what influences their flow.Now, a study by MIT scientists offers a new picture of glacier flow, based on microscopic deformation in the ice. The results show that a glacier’s flow depends strongly on how microscopic defects move through the ice.The researchers found they could estimate a glacier’s flow based on whether the ice is prone to microscopic defects of one kind versus another. They used this relationship between micro- and macro-scale deformation to develop a new model for how glaciers flow. With the new model, they mapped the flow of ice in locations across the Antarctic Ice Sheet.Contrary to conventional wisdom, they found, the ice sheet is not a monolith but instead is more varied in where and how it flows in response to warming-driven stresses. The study “dramatically alters the climate conditions under which marine ice sheets may become unstable and drive rapid rates of sea-level rise,” the researchers write in their paper.“This study really shows the effect of microscale processes on macroscale behavior,” says Meghana Ranganathan PhD ’22, who led the study as a graduate student in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS) and is now a postdoc at Georgia Tech. “These mechanisms happen at the scale of water molecules and ultimately can affect the stability of the West Antarctic Ice Sheet.”“Broadly speaking, glaciers are accelerating, and there are a lot of variants around that,” adds co-author and EAPS Associate Professor Brent Minchew. “This is the first study that takes a step from the laboratory to the ice sheets and starts evaluating what the stability of ice is in the natural environment. That will ultimately feed into our understanding of the probability of catastrophic sea-level rise.”Ranganathan and Minchew’s study appears this week in the Proceedings of the National Academy of Sciences.Micro flowGlacier flow describes the movement of ice from the peak of a glacier, or the center of an ice sheet, down to the edges, where the ice then breaks off and melts into the ocean — a normally slow process that contributes over time to raising the world’s average sea level.In recent years, the oceans have risen at unprecedented rates, driven by global warming and the accelerated melting of glaciers and ice sheets. While the loss of polar ice is known to be a major contributor to sea-level rise, it is also the biggest uncertainty when it comes to making predictions.“Part of it’s a scaling problem,” Ranganathan explains. “A lot of the fundamental mechanisms that cause ice to flow happen at a really small scale that we can’t see. We wanted to pin down exactly what these microphysical processes are that govern ice flow, which hasn’t been represented in models of sea-level change.”The team’s new study builds on previous experiments from the early 2000s by geologists at the University of Minnesota, who studied how small chips of ice deform when physically stressed and compressed. Their work revealed two microscopic mechanisms by which ice can flow: “dislocation creep,” where molecule-sized cracks migrate through the ice, and “grain boundary sliding,” where individual ice crystals slide against each other, causing the boundary between them to move through the ice.The geologists found that ice’s sensitivity to stress, or how likely it is to flow, depends on which of the two mechanisms is dominant. Specifically, ice is more sensitive to stress when microscopic defects occur via dislocation creep rather than grain boundary sliding.Ranganathan and Minchew realized that those findings at the microscopic level could redefine how ice flows at much larger, glacial scales.“Current models for sea-level rise assume a single value for the sensitivity of ice to stress and hold this value constant across an entire ice sheet,” Ranganathan explains. “What these experiments showed was that actually, there’s quite a bit of variability in ice sensitivity, due to which of these mechanisms is at play.”A mapping matchFor their new study, the MIT team took insights from the previous experiments and developed a model to estimate an icy region’s sensitivity to stress, which directly relates to how likely that ice is to flow. The model takes in information such as the ambient temperature, the average size of ice crystals, and the estimated mass of ice in the region, and calculates how much the ice is deforming by dislocation creep versus grain boundary sliding. Depending on which of the two mechanisms is dominant, the model then estimates the region’s sensitivity to stress.The scientists fed into the model actual observations from various locations across the Antarctic Ice Sheet, where others had previously recorded data such as the local height of ice, the size of ice crystals, and the ambient temperature. Based on the model’s estimates, the team generated a map of ice sensitivity to stress across the Antarctic Ice Sheet. When they compared this map to satellite and field measurements taken of the ice sheet over time, they observed a close match, suggesting that the model could be used to accurately predict how glaciers and ice sheets will flow in the future.“As climate change starts to thin glaciers, that could affect the sensitivity of ice to stress,” Ranganathan says. “The instabilities that we expect in Antarctica could be very different, and we can now capture those differences, using this model.”  More