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      in Security

      MIT engineers introduce the Oreometer

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      When you twist open an Oreo cookie to get to the creamy center, you’re mimicking a standard test in rheology — the study of how a non-Newtonian material flows when twisted, pressed, or otherwise stressed. MIT engineers have now subjected the sandwich cookie to rigorous materials tests to get to the center of a tantalizing question: Why does the cookie’s cream stick to just one wafer when twisted apart?

      “There’s the fascinating problem of trying to get the cream to distribute evenly between the two wafers, which turns out to be really hard,” says Max Fan, an undergraduate in MIT’s Department of Mechanical Engineering.

      In pursuit of an answer, the team subjected cookies to standard rheology tests in the lab and found that no matter the flavor or amount of stuffing, the cream at the center of an Oreo almost always sticks to one wafer when twisted open. Only for older boxes of cookies does the cream sometimes separate more evenly between both wafers.

      The researchers also measured the torque required to twist open an Oreo, and found it to be similar to the torque required to turn a doorknob and about 1/10th what’s needed to twist open a bottlecap. The cream’s failure stress — i.e. the force per area required to get the cream to flow, or deform — is twice that of cream cheese and peanut butter, and about the same magnitude as mozzarella cheese. Judging from the cream’s response to stress, the team classifies its texture as “mushy,” rather than brittle, tough, or rubbery.

      So, why does the cookie’s cream glom to one side rather than splitting evenly between both? The manufacturing process may be to blame.

      “Videos of the manufacturing process show that they put the first wafer down, then dispense a ball of cream onto that wafer before putting the second wafer on top,” says Crystal Owens, an MIT mechanical engineering PhD candidate who studies the properties of complex fluids. “Apparently that little time delay may make the cream stick better to the first wafer.”

      The team’s study isn’t simply a sweet diversion from bread-and-butter research; it’s also an opportunity to make the science of rheology accessible to others. To that end, the researchers have designed a 3D-printable “Oreometer” — a simple device that firmly grasps an Oreo cookie and uses pennies and rubber bands to control the twisting force that progressively twists the cookie open. Instructions for the tabletop device can be found here.

      The new study, “On Oreology, the fracture and flow of ‘milk’s favorite cookie,’” appears today in Kitchen Flows, a special issue of the journal Physics of Fluids. It was conceived of early in the Covid-19 pandemic, when many scientists’ labs were closed or difficult to access. In addition to Owens and Fan, co-authors are mechanical engineering professors Gareth McKinley and A. John Hart.

      Confection connection

      A standard test in rheology places a fluid, slurry, or other flowable material onto the base of an instrument known as a rheometer. A parallel plate above the base can be lowered onto the test material. The plate is then twisted as sensors track the applied rotation and torque.

      Owens, who regularly uses a laboratory rheometer to test fluid materials such as 3D-printable inks, couldn’t help noting a similarity with sandwich cookies. As she writes in the new study:

      “Scientifically, sandwich cookies present a paradigmatic model of parallel plate rheometry in which a fluid sample, the cream, is held between two parallel plates, the wafers. When the wafers are counter-rotated, the cream deforms, flows, and ultimately fractures, leading to separation of the cookie into two pieces.”

      While Oreo cream may not appear to possess fluid-like properties, it is considered a “yield stress fluid” — a soft solid when unperturbed that can start to flow under enough stress, the way toothpaste, frosting, certain cosmetics, and concrete do.

      Curious as to whether others had explored the connection between Oreos and rheology, Owens found mention of a 2016 Princeton University study in which physicists first reported that indeed, when twisting Oreos by hand, the cream almost always came off on one wafer.

      “We wanted to build on this to see what actually causes this effect and if we could control it if we mounted the Oreos carefully onto our rheometer,” she says.

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      Cookie twist

      In an experiment that they would repeat for multiple cookies of various fillings and flavors, the researchers glued an Oreo to both the top and bottom plates of a rheometer and applied varying degrees of torque and angular rotation, noting the values  that successfully twisted each cookie apart. They plugged the measurements into equations to calculate the cream’s viscoelasticity, or flowability. For each experiment, they also noted the cream’s “post-mortem distribution,” or where the cream ended up after twisting open.

      In all, the team went through about 20 boxes of Oreos, including regular, Double Stuf, and Mega Stuf levels of filling, and regular, dark chocolate, and “golden” wafer flavors. Surprisingly, they found that no matter the amount of cream filling or flavor, the cream almost always separated onto one wafer.

      “We had expected an effect based on size,” Owens says. “If there was more cream between layers, it should be easier to deform. But that’s not actually the case.”

      Curiously, when they mapped each cookie’s result to its original position in the box, they noticed the cream tended to stick to the inward-facing wafer: Cookies on the left side of the box twisted such that the cream ended up on the right wafer, whereas cookies on the right side separated with cream mostly on the left wafer. They suspect this box distribution may be a result of post-manufacturing environmental effects, such as heating or jostling that may cause cream to peel slightly away from the outer wafers, even before twisting.

      The understanding gained from the properties of Oreo cream could potentially be applied to the design of other complex fluid materials.

      “My 3D printing fluids are in the same class of materials as Oreo cream,” she says. “So, this new understanding can help me better design ink when I’m trying to print flexible electronics from a slurry of carbon nanotubes, because they deform in almost exactly the same way.”

      As for the cookie itself, she suggests that if the inside of Oreo wafers were more textured, the cream might grip better onto both sides and split more evenly when twisted.

      “As they are now, we found there’s no trick to twisting that would split the cream evenly,” Owens concludes.

      This research was supported, in part, by the MIT UROP program and by the National Defense Science and Engineering Graduate Fellowship Program. More

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      in Energy

      A new heat engine with no moving parts is as efficient as a steam turbine

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      Engineers at MIT and the National Renewable Energy Laboratory (NREL) have designed a heat engine with no moving parts. Their new demonstrations show that it converts heat to electricity with over 40 percent efficiency — a performance better than that of traditional steam turbines.

      The heat engine is a thermophotovoltaic (TPV) cell, similar to a solar panel’s photovoltaic cells, that passively captures high-energy photons from a white-hot heat source and converts them into electricity. The team’s design can generate electricity from a heat source of between 1,900 to 2,400 degrees Celsius, or up to about 4,300 degrees Fahrenheit.

      The researchers plan to incorporate the TPV cell into a grid-scale thermal battery. The system would absorb excess energy from renewable sources such as the sun and store that energy in heavily insulated banks of hot graphite. When the energy is needed, such as on overcast days, TPV cells would convert the heat into electricity, and dispatch the energy to a power grid.

      With the new TPV cell, the team has now successfully demonstrated the main parts of the system in separate, small-scale experiments. They are working to integrate the parts to demonstrate a fully operational system. From there, they hope to scale up the system to replace fossil-fuel-driven power plants and enable a fully decarbonized power grid, supplied entirely by renewable energy.

      “Thermophotovoltaic cells were the last key step toward demonstrating that thermal batteries are a viable concept,” says Asegun Henry, the Robert N. Noyce Career Development Professor in MIT’s Department of Mechanical Engineering. “This is an absolutely critical step on the path to proliferate renewable energy and get to a fully decarbonized grid.”

      Henry and his collaborators have published their results today in the journal Nature. Co-authors at MIT include Alina LaPotin, Kevin Schulte, Kyle Buznitsky, Colin Kelsall, Andrew Rohskopf, and Evelyn Wang, the Ford Professor of Engineering and head of the Department of Mechanical Engineering, along with collaborators at NREL in Golden, Colorado.

      Jumping the gap

      More than 90 percent of the world’s electricity comes from sources of heat such as coal, natural gas, nuclear energy, and concentrated solar energy. For a century, steam turbines have been the industrial standard for converting such heat sources into electricity.

      On average, steam turbines reliably convert about 35 percent of a heat source into electricity, with about 60 percent representing the highest efficiency of any heat engine to date. But the machinery depends on moving parts that are temperature- limited. Heat sources higher than 2,000 degrees Celsius, such as Henry’s proposed thermal battery system, would be too hot for turbines.

      In recent years, scientists have looked into solid-state alternatives — heat engines with no moving parts, that could potentially work efficiently at higher temperatures.

      “One of the advantages of solid-state energy converters are that they can operate at higher temperatures with lower maintenance costs because they have no moving parts,” Henry says. “They just sit there and reliably generate electricity.”

      Thermophotovoltaic cells offered one exploratory route toward solid-state heat engines. Much like solar cells, TPV cells could be made from semiconducting materials with a particular bandgap — the gap between a material’s valence band and its conduction band. If a photon with a high enough energy is absorbed by the material, it can kick an electron across the bandgap, where the electron can then conduct, and thereby generate electricity — doing so without moving rotors or blades.

      To date, most TPV cells have only reached efficiencies of around 20 percent, with the record at 32 percent, as they have been made of relatively low-bandgap materials that convert lower-temperature, low-energy photons, and therefore convert energy less efficiently.

      Catching light

      In their new TPV design, Henry and his colleagues looked to capture higher-energy photons from a higher-temperature heat source, thereby converting energy more efficiently. The team’s new cell does so with higher-bandgap materials and multiple junctions, or material layers, compared with existing TPV designs.

      The cell is fabricated from three main regions: a high-bandgap alloy, which sits over a slightly lower-bandgap alloy, underneath which is a mirror-like layer of gold. The first layer captures a heat source’s highest-energy photons and converts them into electricity, while lower-energy photons that pass through the first layer are captured by the second and converted to add to the generated voltage. Any photons that pass through this second layer are then reflected by the mirror, back to the heat source, rather than being absorbed as wasted heat.

      The team tested the cell’s efficiency by placing it over a heat flux sensor — a device that directly measures the heat absorbed from the cell. They exposed the cell to a high-temperature lamp and concentrated the light onto the cell. They then varied the bulb’s intensity, or temperature, and observed how the cell’s power efficiency — the amount of power it produced, compared with the heat it absorbed — changed with temperature. Over a range of 1,900 to 2,400 degrees Celsius, the new TPV cell maintained an efficiency of around 40 percent.

      “We can get a high efficiency over a broad range of temperatures relevant for thermal batteries,” Henry says.

      The cell in the experiments is about a square centimeter. For a grid-scale thermal battery system, Henry envisions the TPV cells would have to scale up to about 10,000 square feet (about a quarter of a football field), and would operate in climate-controlled warehouses to draw power from huge banks of stored solar energy. He points out that an infrastructure exists for making large-scale photovoltaic cells, which could also be adapted to manufacture TPVs.

      “There’s definitely a huge net positive here in terms of sustainability,” Henry says. “The technology is safe, environmentally benign in its life cycle, and can have a tremendous impact on abating carbon dioxide emissions from electricity production.”

      This research was supported, in part, by the U.S. Department of Energy. More

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      in Energy

      Engineers enlist AI to help scale up advanced solar cell manufacturing

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      Perovskites are a family of materials that are currently the leading contender to potentially replace today’s silicon-based solar photovoltaics. They hold the promise of panels that are far thinner and lighter, that could be made with ultra-high throughput at room temperature instead of at hundreds of degrees, and that are cheaper and easier to transport and install. But bringing these materials from controlled laboratory experiments into a product that can be manufactured competitively has been a long struggle.

      Manufacturing perovskite-based solar cells involves optimizing at least a dozen or so variables at once, even within one particular manufacturing approach among many possibilities. But a new system based on a novel approach to machine learning could speed up the development of optimized production methods and help make the next generation of solar power a reality.

      The system, developed by researchers at MIT and Stanford University over the last few years, makes it possible to integrate data from prior experiments, and information based on personal observations by experienced workers, into the machine learning process. This makes the outcomes more accurate and has already led to the manufacturing of perovskite cells with an energy conversion efficiency of 18.5 percent, a competitive level for today’s market.

      The research is reported today in the journal Joule, in a paper by MIT professor of mechanical engineering Tonio Buonassisi, Stanford professor of materials science and engineering Reinhold Dauskardt, recent MIT research assistant Zhe Liu, Stanford doctoral graduate Nicholas Rolston, and three others.

      Perovskites are a group of layered crystalline compounds defined by the configuration of the atoms in their crystal lattice. There are thousands of such possible compounds and many different ways of making them. While most lab-scale development of perovskite materials uses a spin-coating technique, that’s not practical for larger-scale manufacturing, so companies and labs around the world have been searching for ways of translating these lab materials into a practical, manufacturable product.

      “There’s always a big challenge when you’re trying to take a lab-scale process and then transfer it to something like a startup or a manufacturing line,” says Rolston, who is now an assistant professor at Arizona State University. The team looked at a process that they felt had the greatest potential, a method called rapid spray plasma processing, or RSPP.

      The manufacturing process would involve a moving roll-to-roll surface, or series of sheets, on which the precursor solutions for the perovskite compound would be sprayed or ink-jetted as the sheet rolled by. The material would then move on to a curing stage, providing a rapid and continuous output “with throughputs that are higher than for any other photovoltaic technology,” Rolston says.

      “The real breakthrough with this platform is that it would allow us to scale in a way that no other material has allowed us to do,” he adds. “Even materials like silicon require a much longer timeframe because of the processing that’s done. Whereas you can think of [this approach as more] like spray painting.”

      Within that process, at least a dozen variables may affect the outcome, some of them more controllable than others. These include the composition of the starting materials, the temperature, the humidity, the speed of the processing path, the distance of the nozzle used to spray the material onto a substrate, and the methods of curing the material. Many of these factors can interact with each other, and if the process is in open air, then humidity, for example, may be uncontrolled. Evaluating all possible combinations of these variables through experimentation is impossible, so machine learning was needed to help guide the experimental process.

      But while most machine-learning systems use raw data such as measurements of the electrical and other properties of test samples, they don’t typically incorporate human experience such as qualitative observations made by the experimenters of the visual and other properties of the test samples, or information from other experiments reported by other researchers. So, the team found a way to incorporate such outside information into the machine learning model, using a probability factor based on a mathematical technique called Bayesian Optimization.

      Using the system, he says, “having a model that comes from experimental data, we can find out trends that we weren’t able to see before.” For example, they initially had trouble adjusting for uncontrolled variations in humidity in their ambient setting. But the model showed them “that we could overcome our humidity challenges by changing the temperature, for instance, and by changing some of the other knobs.”

      The system now allows experimenters to much more rapidly guide their process in order to optimize it for a given set of conditions or required outcomes. In their experiments, the team focused on optimizing the power output, but the system could also be used to simultaneously incorporate other criteria, such as cost and durability — something members of the team are continuing to work on, Buonassisi says.

      The researchers were encouraged by the Department of Energy, which sponsored the work, to commercialize the technology, and they’re currently focusing on tech transfer to existing perovskite manufacturers. “We are reaching out to companies now,” Buonassisi says, and the code they developed has been made freely available through an open-source server. “It’s now on GitHub, anyone can download it, anyone can run it,” he says. “We’re happy to help companies get started in using our code.”

      Already, several companies are gearing up to produce perovskite-based solar panels, even though they are still working out the details of how to produce them, says Liu, who is now at the Northwestern Polytechnical University in Xi’an, China. He says companies there are not yet doing large-scale manufacturing, but instead starting with smaller, high-value applications such as building-integrated solar tiles where appearance is important. Three of these companies “are on track or are being pushed by investors to manufacture 1 meter by 2-meter rectangular modules [comparable to today’s most common solar panels], within two years,” he says.

      ‘The problem is, they don’t have a consensus on what manufacturing technology to use,” Liu says. The RSPP method, developed at Stanford, “still has a good chance” to be competitive, he says. And the machine learning system the team developed could prove to be important in guiding the optimization of whatever process ends up being used.

      “The primary goal was to accelerate the process, so it required less time, less experiments, and less human hours to develop something that is usable right away, for free, for industry,” he says.

      “Existing work on machine-learning-driven perovskite PV fabrication largely focuses on spin-coating, a lab-scale technique,” says Ted Sargent, University Professor at the University of Toronto, who was not associated with this work, which he says demonstrates “a workflow that is readily adapted to the deposition techniques that dominate the thin-film industry. Only a handful of groups have the simultaneous expertise in engineering and computation to drive such advances.” Sargent adds that this approach “could be an exciting advance for the manufacture of a broader family of materials” including LEDs, other PV technologies, and graphene, “in short, any industry that uses some form of vapor or vacuum deposition.” 

      The team also included Austin Flick and Thomas Colburn at Stanford and Zekun Ren at the Singapore-MIT Alliance for Science and Technology (SMART). In addition to the Department of Energy, the work was supported by a fellowship from the MIT Energy Initiative, the Graduate Research Fellowship Program from the National Science Foundation, and the SMART program. More

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      in Energy

      Chemical reactions for the energy transition

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      One challenge in decarbonizing the energy system is knowing how to deal with new types of fuels. Traditional fuels such as natural gas and oil can be combined with other materials and then heated to high temperatures so they chemically react to produce other useful fuels or substances, or even energy to do work. But new materials such as biofuels can’t take as much heat without breaking down.

      A key ingredient in such chemical reactions is a specially designed solid catalyst that is added to encourage the reaction to happen but isn’t itself consumed in the process. With traditional materials, the solid catalyst typically interacts with a gas; but with fuels derived from biomass, for example, the catalyst must work with a liquid — a special challenge for those who design catalysts.

      For nearly a decade, Yogesh Surendranath, an associate professor of chemistry at MIT, has been focusing on chemical reactions between solid catalysts and liquids, but in a different situation: rather than using heat to drive reactions, he and his team input electricity from a battery or a renewable source such as wind or solar to give chemically inactive molecules more energy so they react. And key to their research is designing and fabricating solid catalysts that work well for reactions involving liquids.

      Recognizing the need to use biomass to develop sustainable liquid fuels, Surendranath wondered whether he and his team could take the principles they have learned about designing catalysts to drive liquid-solid reactions with electricity and apply them to reactions that occur at liquid-solid interfaces without any input of electricity.

      To their surprise, they found that their knowledge is directly relevant. Why? “What we found — amazingly — is that even when you don’t hook up wires to your catalyst, there are tiny internal ‘wires’ that do the reaction,” says Surendranath. “So, reactions that people generally think operate without any flow of current actually do involve electrons shuttling from one place to another.” And that means that Surendranath and his team can bring the powerful techniques of electrochemistry to bear on the problem of designing catalysts for sustainable fuels.

      A novel hypothesis

      Their work has focused on a class of chemical reactions important in the energy transition that involve adding oxygen to small organic (carbon-containing) molecules such as ethanol, methanol, and formic acid. The conventional assumption is that the reactant and oxygen chemically react to form the product plus water. And a solid catalyst — often a combination of metals — is present to provide sites on which the reactant and oxygen can interact.

      But Surendranath proposed a different view of what’s going on. In the usual setup, two catalysts, each one composed of many nanoparticles, are mounted on a conductive carbon substrate and submerged in water. In that arrangement, negatively charged electrons can flow easily through the carbon, while positively charged protons can flow easily through water.

      Surendranath’s hypothesis was that the conversion of reactant to product progresses by means of two separate “half-reactions” on the two catalysts. On one catalyst, the reactant turns into a product, in the process sending electrons into the carbon substrate and protons into the water. Those electrons and protons are picked up by the other catalyst, where they drive the oxygen-to-water conversion. So, instead of a single reaction, two separate but coordinated half-reactions together achieve the net conversion of reactant to product.

      As a result, the overall reaction doesn’t actually involve any net electron production or consumption. It is a standard “thermal” reaction resulting from the energy in the molecules and maybe some added heat. The conventional approach to designing a catalyst for such a reaction would focus on increasing the rate of that reactant-to-product conversion. And the best catalyst for that kind of reaction could turn out to be, say, gold or palladium or some other expensive precious metal.

      However, if that reaction actually involves two half-reactions, as Surendranath proposed, there is a flow of electrical charge (the electrons and protons) between them. So Surendranath and others in the field could instead use techniques of electrochemistry to design not a single catalyst for the overall reaction but rather two separate catalysts — one to speed up one half-reaction and one to speed up the other half-reaction. “That means we don’t have to design one catalyst to do all the heavy lifting of speeding up the entire reaction,” says Surendranath. “We might be able to pair up two low-cost, earth-abundant catalysts, each of which does half of the reaction well, and together they carry out the overall transformation quickly and efficiently.”

      But there’s one more consideration: Electrons can flow through the entire catalyst composite, which encompasses the catalyst particle(s) and the carbon substrate. For the chemical conversion to happen as quickly as possible, the rate at which electrons are put into the catalyst composite must exactly match the rate at which they are taken out. Focusing on just the electrons, if the reaction-to-product conversion on the first catalyst sends the same number of electrons per second into the “bath of electrons” in the catalyst composite as the oxygen-to-water conversion on the second catalyst takes out, the two half-reactions will be balanced, and the electron flow — and the rate of the combined reaction — will be fast. The trick is to find good catalysts for each of the half-reactions that are perfectly matched in terms of electrons in and electrons out.

      “A good catalyst or pair of catalysts can maintain an electrical potential — essentially a voltage — at which both half-reactions are fast and are balanced,” says Jaeyune Ryu PhD ’21, a former member of the Surendranath lab and lead author of the study; Ryu is now a postdoc at Harvard University. “The rates of the reactions are equal, and the voltage in the catalyst composite won’t change during the overall thermal reaction.”

      Drawing on electrochemistry

      Based on their new understanding, Surendranath, Ryu, and their colleagues turned to electrochemistry techniques to identify a good catalyst for each half-reaction that would also pair up to work well together. Their analytical framework for guiding catalyst development for systems that combine two half-reactions is based on a theory that has been used to understand corrosion for almost 100 years, but has rarely been applied to understand or design catalysts for reactions involving small molecules important for the energy transition.

      Key to their work is a potentiostat, a type of voltmeter that can either passively measure the voltage of a system or actively change the voltage to cause a reaction to occur. In their experiments, Surendranath and his team use the potentiostat to measure the voltage of the catalyst in real time, monitoring how it changes millisecond to millisecond. They then correlate those voltage measurements with simultaneous but separate measurements of the overall rate of catalysis to understand the reaction pathway.

      For their study of the conversion of small, energy-related molecules, they first tested a series of catalysts to find good ones for each half-reaction — one to convert the reactant to product, producing electrons and protons, and another to convert the oxygen to water, consuming electrons and protons. In each case, a promising candidate would yield a rapid reaction — that is, a fast flow of electrons and protons out or in.

      To help identify an effective catalyst for performing the first half-reaction, the researchers used their potentiostat to input carefully controlled voltages and measured the resulting current that flowed through the catalyst. A good catalyst will generate lots of current for little applied voltage; a poor catalyst will require high applied voltage to get the same amount of current. The team then followed the same procedure to identify a good catalyst for the second half-reaction.

      To expedite the overall reaction, the researchers needed to find two catalysts that matched well — where the amount of current at a given applied voltage was high for each of them, ensuring that as one produced a rapid flow of electrons and protons, the other one consumed them at the same rate.

      To test promising pairs, the researchers used the potentiostat to measure the voltage of the catalyst composite during net catalysis — not changing the voltage as before, but now just measuring it from tiny samples. In each test, the voltage will naturally settle at a certain level, and the goal is for that to happen when the rate of both reactions is high.

      Validating their hypothesis and looking ahead

      By testing the two half-reactions, the researchers could measure how the reaction rate for each one varied with changes in the applied voltage. From those measurements, they could predict the voltage at which the full reaction would proceed fastest. Measurements of the full reaction matched their predictions, supporting their hypothesis.

      The team’s novel approach of using electrochemistry techniques to examine reactions thought to be strictly thermal in nature provides new insights into the detailed steps by which those reactions occur and therefore into how to design catalysts to speed them up. “We can now use a divide-and-conquer strategy,” says Ryu. “We know that the net thermal reaction in our study happens through two ‘hidden’ but coupled half-reactions, so we can aim to optimize one half-reaction at a time” — possibly using low-cost catalyst materials for one or both.

      Adds Surendranath, “One of the things that we’re excited about in this study is that the result is not final in and of itself. It has really seeded a brand-new thrust area in our research program, including new ways to design catalysts for the production and transformation of renewable fuels and chemicals.”

      This research was supported primarily by the Air Force Office of Scientific Research. Jaeyune Ryu PhD ’21 was supported by a Samsung Scholarship. Additional support was provided by a National Science Foundation Graduate Research Fellowship.

      This article appears in the Autumn 2021 issue of Energy Futures, the magazine of the MIT Energy Initiative. More

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      in Energy

      New program bolsters innovation in next-generation artificial intelligence hardware

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      The MIT AI Hardware Program is a new academia and industry collaboration aimed at defining and developing translational technologies in hardware and software for the AI and quantum age. A collaboration between the MIT School of Engineering and MIT Schwarzman College of Computing, involving the Microsystems Technologies Laboratories and programs and units in the college, the cross-disciplinary effort aims to innovate technologies that will deliver enhanced energy efficiency systems for cloud and edge computing.

      “A sharp focus on AI hardware manufacturing, research, and design is critical to meet the demands of the world’s evolving devices, architectures, and systems,” says Anantha Chandrakasan, dean of the MIT School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “Knowledge-sharing between industry and academia is imperative to the future of high-performance computing.”

      Based on use-inspired research involving materials, devices, circuits, algorithms, and software, the MIT AI Hardware Program convenes researchers from MIT and industry to facilitate the transition of fundamental knowledge to real-world technological solutions. The program spans materials and devices, as well as architecture and algorithms enabling energy-efficient and sustainable high-performance computing.

      “As AI systems become more sophisticated, new solutions are sorely needed to enable more advanced applications and deliver greater performance,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “Our aim is to devise real-world technological solutions and lead the development of technologies for AI in hardware and software.”

      The inaugural members of the program are companies from a wide range of industries including chip-making, semiconductor manufacturing equipment, AI and computing services, and information systems R&D organizations. The companies represent a diverse ecosystem, both nationally and internationally, and will work with MIT faculty and students to help shape a vibrant future for our planet through cutting-edge AI hardware research.

      The five inaugural members of the MIT AI Hardware Program are:  

      Amazon, a global technology company whose hardware inventions include the Kindle, Amazon Echo, Fire TV, and Astro; 
      Analog Devices, a global leader in the design and manufacturing of analog, mixed signal, and DSP integrated circuits; 
      ASML, an innovation leader in the semiconductor industry, providing chipmakers with hardware, software, and services to mass produce patterns on silicon through lithography; 
      NTT Research, a subsidiary of NTT that conducts fundamental research to upgrade reality in game-changing ways that improve lives and brighten our global future; and 
      TSMC, the world’s leading dedicated semiconductor foundry.

      The MIT AI Hardware Program will create a roadmap of transformative AI hardware technologies. Leveraging MIT.nano, the most advanced university nanofabrication facility anywhere, the program will foster a unique environment for AI hardware research.  

      “We are all in awe at the seemingly superhuman capabilities of today’s AI systems. But this comes at a rapidly increasing and unsustainable energy cost,” says Jesús del Alamo, the Donner Professor in MIT’s Department of Electrical Engineering and Computer Science. “Continued progress in AI will require new and vastly more energy-efficient systems. This, in turn, will demand innovations across the entire abstraction stack, from materials and devices to systems and software. The program is in a unique position to contribute to this quest.”

      The program will prioritize the following topics:

      analog neural networks;
      new roadmap CMOS designs;
      heterogeneous integration for AI systems;
      onolithic-3D AI systems;
      analog nonvolatile memory devices;
      software-hardware co-design;
      intelligence at the edge;
      intelligent sensors;
      energy-efficient AI;
      intelligent internet of things (IIoT);
      neuromorphic computing;
      AI edge security;
      quantum AI;
      wireless technologies;
      hybrid-cloud computing; and
      high-performance computation.

      “We live in an era where paradigm-shifting discoveries in hardware, systems communications, and computing have become mandatory to find sustainable solutions — solutions that we are proud to give to the world and generations to come,” says Aude Oliva, senior research scientist in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and director of strategic industry engagement in the MIT Schwarzman College of Computing.

      The new program is co-led by Jesús del Alamo and Aude Oliva, and Anantha Chandrakasan serves as chair. More

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      in Energy

      How to clean solar panels without water

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      Solar power is expected to reach 10 percent of global power generation by the year 2030, and much of that is likely to be located in desert areas, where sunlight is abundant. But the accumulation of dust on solar panels or mirrors is already a significant issue — it can reduce the output of photovoltaic panels by as much as 30 percent in just one month — so regular cleaning is essential for such installations.

      But cleaning solar panels currently is estimated to use about 10 billion gallons of water per year — enough to supply drinking water for up to 2 million people. Attempts at waterless cleaning are labor intensive and tend to cause irreversible scratching of the surfaces, which also reduces efficiency. Now, a team of researchers at MIT has devised a way of automatically cleaning solar panels, or the mirrors of solar thermal plants, in a waterless, no-contact system that could significantly reduce the dust problem, they say.

      The new system uses electrostatic repulsion to cause dust particles to detach and virtually leap off the panel’s surface, without the need for water or brushes. To activate the system, a simple electrode passes just above the solar panel’s surface, imparting an electrical charge to the dust particles, which are then repelled by a charge applied to the panel itself. The system can be operated automatically using a simple electric motor and guide rails along the side of the panel. The research is described today in the journal Science Advances, in a paper by MIT graduate student Sreedath Panat and professor of mechanical engineering Kripa Varanasi.

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      Despite concerted efforts worldwide to develop ever more efficient solar panels, Varanasi says, “a mundane problem like dust can actually put a serious dent in the whole thing.” Lab tests conducted by Panat and Varanasi showed that the dropoff of energy output from the panels happens steeply at the very beginning of the process of dust accumulation and can easily reach 30 percent reduction after just one month without cleaning. Even a 1 percent reduction in power, for a 150-megawatt solar installation, they calculated, could result in a $200,000 loss in annual revenue. The researchers say that globally, a 3 to 4 percent reduction in power output from solar plants would amount to a loss of between $3.3 billion and $5.5 billion.

      “There is so much work going on in solar materials,” Varanasi says. “They’re pushing the boundaries, trying to gain a few percent here and there in improving the efficiency, and here you have something that can obliterate all of that right away.”

      Many of the largest solar power installations in the world, including ones in China, India, the U.A.E., and the U.S., are located in desert regions. The water used for cleaning these solar panels using pressurized water jets has to be trucked in from a distance, and it has to be very pure to avoid leaving behind deposits on the surfaces. Dry scrubbing is sometimes used but is less effective at cleaning the surfaces and can cause permanent scratching that also reduces light transmission.

      Water cleaning makes up about 10 percent of the operating costs of solar installations. The new system could potentially reduce these costs while improving the overall power output by allowing for more frequent automated cleanings, the researchers say.

      “The water footprint of the solar industry is mind boggling,” Varanasi says, and it will be increasing as these installations continue to expand worldwide. “So, the industry has to be very careful and thoughtful about how to make this a sustainable solution.”

      Other groups have tried to develop electrostatic based solutions, but these have relied on a layer called an electrodynamic screen, using interdigitated electrodes. These screens can have defects that allow moisture in and cause them to fail, Varanasi says. While they might be useful on a place like Mars, he says, where moisture is not an issue, even in desert environments on Earth this can be a serious problem.

      The new system they developed only requires an electrode, which can be a simple metal bar, to pass over the panel, producing an electric field that imparts a charge to the dust particles as it goes. An opposite charge applied to a transparent conductive layer just a few nanometers thick deposited on the glass covering of the the solar panel then repels the particles, and by calculating the right voltage to apply, the researchers were able to find a voltage range sufficient to overcome the pull of gravity and adhesion forces, and cause the dust to lift away.

      Using specially prepared laboratory samples of dust with a range of particle sizes, experiments proved that the process works effectively on a laboratory-scale test installation, Panat says. The tests showed that humidity in the air provided a thin coating of water on the particles, which turned out to be crucial to making the effect work. “We performed experiments at varying humidities from 5 percent to 95 percent,” Panat says. “As long as the ambient humidity is greater than 30 percent, you can remove almost all of the particles from the surface, but as humidity decreases, it becomes harder.”

      Varanasi says that “the good news is that when you get to 30 percent humidity, most deserts actually fall in this regime.” And even those that are typically drier than that tend to have higher humidity in the early morning hours, leading to dew formation, so the cleaning could be timed accordingly.

      “Moreover, unlike some of the prior work on electrodynamic screens, which actually do not work at high or even moderate humidity, our system can work at humidity even as high as 95 percent, indefinitely,” Panat says.

      In practice, at scale, each solar panel could be fitted with railings on each side, with an electrode spanning across the panel. A small electric motor, perhaps using a tiny portion of the output from the panel itself, would drive a belt system to move the electrode from one end of the panel to the other, causing all the dust to fall away. The whole process could be automated or controlled remotely. Alternatively, thin strips of conductive transparent material could be permanently arranged above the panel, eliminating the need for moving parts.

      By eliminating the dependency on trucked-in water, by eliminating the buildup of dust that can contain corrosive compounds, and by lowering the overall operational costs, such systems have the potential to significantly improve the overall efficiency and reliability of solar installations, Varanasi says.

      The research was supported by Italian energy firm Eni. S.p.A. through the MIT Energy Initiative. More

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      in Energy

      Toward batteries that pack twice as much energy per pound

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      In the endless quest to pack more energy into batteries without increasing their weight or volume, one especially promising technology is the solid-state battery. In these batteries, the usual liquid electrolyte that carries charges back and forth between the electrodes is replaced with a solid electrolyte layer. Such batteries could potentially not only deliver twice as much energy for their size, they also could virtually eliminate the fire hazard associated with today’s lithium-ion batteries.

      But one thing has held back solid-state batteries: Instabilities at the boundary between the solid electrolyte layer and the two electrodes on either side can dramatically shorten the lifetime of such batteries. Some studies have used special coatings to improve the bonding between the layers, but this adds the expense of extra coating steps in the fabrication process. Now, a team of researchers at MIT and Brookhaven National Laboratory have come up with a way of achieving results that equal or surpass the durability of the coated surfaces, but with no need for any coatings.

      The new method simply requires eliminating any carbon dioxide present during a critical manufacturing step, called sintering, where the battery materials are heated to create bonding between the cathode and electrolyte layers, which are made of ceramic compounds. Even though the amount of carbon dioxide present is vanishingly small in air, measured in parts per million, its effects turn out to be dramatic and detrimental. Carrying out the sintering step in pure oxygen creates bonds that match the performance of the best coated surfaces, without that extra cost of the coating, the researchers say.

      The findings are reported in the journal Advanced Energy Materials, in a paper by MIT doctoral student Younggyu Kim, professor of nuclear science and engineering and of materials science and engineering Bilge Yildiz, and Iradikanari Waluyo and Adrian Hunt at Brookhaven National Laboratory.

      “Solid-state batteries have been desirable for different reasons for a long time,” Yildiz says. “The key motivating points for solid batteries are they are safer and have higher energy density,” but they have been held back from large scale commercialization by two factors, she says: the lower conductivity of the solid electrolyte, and the interface instability issues.

      The conductivity issue has been effectively tackled, and reasonably high-conductivity materials have already been demonstrated, according to Yildiz. But overcoming the instabilities that arise at the interface has been far more challenging. These instabilities can occur during both the manufacturing and the electrochemical operation of such batteries, but for now the researchers have focused on the manufacturing, and specifically the sintering process.

      Sintering is needed because if the ceramic layers are simply pressed onto each other, the contact between them is far from ideal, there are far too many gaps, and the electrical resistance across the interface is high. Sintering, which is usually done at temperatures of 1,000 degrees Celsius or above for ceramic materials, causes atoms from each material to migrate into the other to form bonds. The team’s experiments showed that at temperatures anywhere above a few hundred degrees, detrimental reactions take place that increase the resistance at the interface — but only if carbon dioxide is present, even in tiny amounts. They demonstrated that avoiding carbon dioxide, and in particular maintaining a pure oxygen atmosphere during sintering, could create very good bonding at temperatures up to 700 degrees, with none of the detrimental compounds formed.

      The performance of the cathode-electrolyte interface made using this method, Yildiz says, was “comparable to the best interface resistances we have seen in the literature,” but those were all achieved using the extra step of applying coatings. “We are finding that you can avoid that additional fabrication step, which is typically expensive.”

      The potential gains in energy density that solid-state batteries provide comes from the fact that they enable the use of pure lithium metal as one of the electrodes, which is much lighter than the currently used electrodes made of lithium-infused graphite.

      The team is now studying the next part of the performance of such batteries, which is how these bonds hold up over the long run during battery cycling. Meanwhile, the new findings could potentially be applied rapidly to battery production, she says. “What we are proposing is a relatively simple process in the fabrication of the cells. It doesn’t add much energy penalty to the fabrication. So, we believe that it can be adopted relatively easily into the fabrication process,” and the added costs, they have calculated, should be negligible.

      Large companies such as Toyota are already at work commercializing early versions of solid-state lithium-ion batteries, and these new findings could quickly help such companies improve the economics and durability of the technology.

      The research was supported by the U.S. Army Research Office through MIT’s Institute for Soldier Nanotechnologies. The team used facilities supported by the National Science Foundation and facilities at Brookhaven National Laboratory supported by the Department of Energy. More

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      in Energy

      More sensitive X-ray imaging

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      Scintillators are materials that emit light when bombarded with high-energy particles or X-rays. In medical or dental X-ray systems, they convert incoming X-ray radiation into visible light that can then be captured using film or photosensors. They’re also used for night-vision systems and for research, such as in particle detectors or electron microscopes.

      Researchers at MIT have now shown how one could improve the efficiency of scintillators by at least tenfold, and perhaps even a hundredfold, by changing the material’s surface to create certain nanoscale configurations, such as arrays of wave-like ridges. While past attempts to develop more efficient scintillators have focused on finding new materials, the new approach could in principle work with any of the existing materials.

      Though it will require more time and effort to integrate their scintillators into existing X-ray machines, the team believes that this method might lead to improvements in medical diagnostic X-rays or CT scans, to reduce dose exposure and improve image quality. In other applications, such as X-ray inspection of manufactured parts for quality control, the new scintillators could enable inspections with higher accuracy or at faster speeds.

      The findings are described today in the journal Science, in a paper by MIT doctoral students Charles Roques-Carmes and Nicholas Rivera; MIT professors Marin Soljacic, Steven Johnson, and John Joannopoulos; and 10 others.

      While scintillators have been in use for some 70 years, much of the research in the field has focused on developing new materials that produce brighter or faster light emissions. The new approach instead applies advances in nanotechnology to existing materials. By creating patterns in scintillator materials at a length scale comparable to the wavelengths of the light being emitted, the team found that it was possible to dramatically change the material’s optical properties.

      To make what they coined “nanophotonic scintillators,” Roques-Carmes says, “you can directly make patterns inside the scintillators, or you can glue on another material that would have holes on the nanoscale. The specifics depend on the exact structure and material.” For this research, the team took a scintillator and made holes spaced apart by roughly one optical wavelength, or about 500 nanometers (billionths of a meter).

      “The key to what we’re doing is a general theory and framework we have developed,” Rivera says. This allows the researchers to calculate the scintillation levels that would be produced by any arbitrary configuration of nanophotonic structures. The scintillation process itself involves a series of steps, making it complicated to unravel. The framework the team developed involves integrating three different types of physics, Roques-Carmes says. Using this system they have found a good match between their predictions and the results of their subsequent experiments.

      The experiments showed a tenfold improvement in emission from the treated scintillator. “So, this is something that might translate into applications for medical imaging, which are optical photon-starved, meaning the conversion of X-rays to optical light limits the image quality. [In medical imaging,] you do not want to irradiate your patients with too much of the X-rays, especially for routine screening, and especially for young patients as well,” Roques-Carmes says.

      “We believe that this will open a new field of research in nanophotonics,” he adds. “You can use a lot of the existing work and research that has been done in the field of nanophotonics to improve significantly on existing materials that scintillate.”

      “The research presented in this paper is hugely significant,” says Rajiv Gupta, chief of neuroradiology at Massachusetts General Hospital and an associate professor at Harvard Medical School, who was not associated with this work. “Nearly all detectors used in the $100 billion [medical X-ray] industry are indirect detectors,” which is the type of detector the new findings apply to, he says. “Everything that I use in my clinical practice today is based on this principle. This paper improves the efficiency of this process by 10 times. If this claim is even partially true, say the improvement is two times instead of 10 times, it would be transformative for the field!”

      Soljacic says that while their experiments proved a tenfold improvement in emission could be achieved in particular systems, by further fine-tuning the design of the nanoscale patterning, “we also show that you can get up to 100 times [improvement] in certain scintillator systems, and we believe we also have a path toward making it even better,” he says.

      Soljacic points out that in other areas of nanophotonics, a field that deals with how light interacts with materials that are structured at the nanometer scale, the development of computational simulations has enabled rapid, substantial improvements, for example in the development of solar cells and LEDs. The new models this team developed for scintillating materials could facilitate similar leaps in this technology, he says.

      Nanophotonics techniques “give you the ultimate power of tailoring and enhancing the behavior of light,” Soljacic says. “But until now, this promise, this ability to do this with scintillation was unreachable because modeling the scintillation was very challenging. Now, this work for the first time opens up this field of scintillation, fully opens it, for the application of nanophotonics techniques.” More generally, the team believes that the combination of nanophotonic and scintillators might ultimately enable higher resolution, reduced X-ray dose, and energy-resolved X-ray imaging.

      This work is “very original and excellent,” says Eli Yablonovitch, a professor of Electrical Engineering and Computer Sciences at the University of California at Berkeley, who was not associated with this research. “New scintillator concepts are very important in medical imaging and in basic research.”

      Yablonovitch adds that while the concept still needs to be proven in a practical device, he says that, “After years of research on photonic crystals in optical communication and other fields, it’s long overdue that photonic crystals should be applied to scintillators, which are of great practical importance yet have been overlooked” until this work.

      The research team included Ali Ghorashi, Steven Kooi, Yi Yang, Zin Lin, Justin Beroz, Aviram Massuda, Jamison Sloan, and Nicolas Romeo at MIT; Yang Yu at Raith America, Inc.; and Ido Kaminer at Technion in Israel. The work was supported, in part, by the U.S. Army Research Office and the U.S. Army Research Laboratory through the Institute for Soldier Nanotechnologies, by the Air Force Office of Scientific Research, and by a Mathworks Engineering Fellowship. More