More stories

  • in

    Pixel-by-pixel analysis yields insights into lithium-ion batteries

    By mining data from X-ray images, researchers at MIT, Stanford University, SLAC National Accelerator, and the Toyota Research Institute have made significant new discoveries about the reactivity of lithium iron phosphate, a material used in batteries for electric cars and in other rechargeable batteries.

    The new technique has revealed several phenomena that were previously impossible to see, including variations in the rate of lithium intercalation reactions in different regions of a lithium iron phosphate nanoparticle.

    The paper’s most significant practical finding — that these variations in reaction rate are correlated with differences in the thickness of the carbon coating on the surface of the particles — could lead to improvements in the efficiency of charging and discharging such batteries.

    “What we learned from this study is that it’s the interfaces that really control the dynamics of the battery, especially in today’s modern batteries made from nanoparticles of the active material. That means that our focus should really be on engineering that interface,” says Martin Bazant, the E.G. Roos Professor of Chemical Engineering and a professor of mathematics at MIT, who is the senior author of the study.

    This approach to discovering the physics behind complex patterns in images could also be used to gain insights into many other materials, not only other types of batteries but also biological systems, such as dividing cells in a developing embryo.

    “What I find most exciting about this work is the ability to take images of a system that’s undergoing the formation of some pattern, and learning the principles that govern that,” Bazant says.

    Hongbo Zhao PhD ’21, a former MIT graduate student who is now a postdoc at Princeton University, is the lead author of the new study, which appears today in Nature. Other authors include Richard Bratz, the Edwin R. Gilliland Professor of Chemical Engineering at MIT; William Chueh, an associate professor of materials science and engineering at Stanford and director of the SLAC-Stanford Battery Center; and Brian Storey, senior director of Energy and Materials at the Toyota Research Institute.

    “Until now, we could make these beautiful X-ray movies of battery nanoparticles at work, but it was challenging to measure and understand subtle details of how they function because the movies were so information-rich,” Chueh says. “By applying image learning to these nanoscale movies, we can extract insights that were not previously possible.”

    Modeling reaction rates

    Lithium iron phosphate battery electrodes are made of many tiny particles of lithium iron phosphate, surrounded by an electrolyte solution. A typical particle is about 1 micron in diameter and about 100 nanometers thick. When the battery discharges, lithium ions flow from the electrolyte solution into the material by an electrochemical reaction known as ion intercalation. When the battery charges, the intercalation reaction is reversed, and ions flow in the opposite direction.

    “Lithium iron phosphate (LFP) is an important battery material due to low cost, a good safety record, and its use of abundant elements,” Storey says. “We are seeing an increased use of LFP in the EV market, so the timing of this study could not be better.”

    Before the current study, Bazant had done a great deal of theoretical modeling of patterns formed by lithium-ion intercalation. Lithium iron phosphate prefers to exist in one of two stable phases: either full of lithium ions or empty. Since 2005, Bazant has been working on mathematical models of this phenomenon, known as phase separation, which generates distinctive patterns of lithium-ion flow driven by intercalation reactions. In 2015, while on sabbatical at Stanford, he began working with Chueh to try to interpret images of lithium iron phosphate particles from scanning tunneling X-ray microscopy.

    Using this type of microscopy, the researchers can obtain images that reveal the concentration of lithium ions, pixel-by-pixel, at every point in the particle. They can scan the particles several times as the particles charge or discharge, allowing them to create movies of how lithium ions flow in and out of the particles.

    In 2017, Bazant and his colleagues at SLAC received funding from the Toyota Research Institute to pursue further studies using this approach, along with other battery-related research projects.

    By analyzing X-ray images of 63 lithium iron phosphate particles as they charged and discharged, the researchers found that the movement of lithium ions within the material could be nearly identical to the computer simulations that Bazant had created earlier. Using all 180,000 pixels as measurements, the researchers trained the computational model to produce equations that accurately describe the nonequilibrium thermodynamics and reaction kinetics of the battery material.
    By analyzing X-ray images of lithium iron phosphate particles as they charged and discharged, researchers have shown that the movement of lithium ions within the material was nearly identical to computer simulations they had created earlier.  In each pair, the actual particles are on the left and the simulations are on the right.Courtesy of the researchers

    “Every little pixel in there is jumping from full to empty, full to empty. And we’re mapping that whole process, using our equations to understand how that’s happening,” Bazant says.

    The researchers also found that the patterns of lithium-ion flow that they observed could reveal spatial variations in the rate at which lithium ions are absorbed at each location on the particle surface.

    “It was a real surprise to us that we could learn the heterogeneities in the system — in this case, the variations in surface reaction rate — simply by looking at the images,” Bazant says. “There are regions that seem to be fast and others that seem to be slow.”

    Furthermore, the researchers showed that these differences in reaction rate were correlated with the thickness of the carbon coating on the surface of the lithium iron phosphate particles. That carbon coating is applied to lithium iron phosphate to help it conduct electricity — otherwise the material would conduct too slowly to be useful as a battery.

    “We discovered at the nano scale that variation of the carbon coating thickness directly controls the rate, which is something you could never figure out if you didn’t have all of this modeling and image analysis,” Bazant says.

    The findings also offer quantitative support for a hypothesis Bazant formulated several years ago: that the performance of lithium iron phosphate electrodes is limited primarily by the rate of coupled ion-electron transfer at the interface between the solid particle and the carbon coating, rather than the rate of lithium-ion diffusion in the solid.

    Optimized materials

    The results from this study suggest that optimizing the thickness of the carbon layer on the electrode surface could help researchers to design batteries that would work more efficiently, the researchers say.

    “This is the first study that’s been able to directly attribute a property of the battery material with a physical property of the coating,” Bazant says. “The focus for optimizing and designing batteries should be on controlling reaction kinetics at the interface of the electrolyte and electrode.”

    “This publication is the culmination of six years of dedication and collaboration,” Storey says. “This technique allows us to unlock the inner workings of the battery in a way not previously possible. Our next goal is to improve battery design by applying this new understanding.”  

    In addition to using this type of analysis on other battery materials, Bazant anticipates that it could be useful for studying pattern formation in other chemical and biological systems.

    This work was supported by the Toyota Research Institute through the Accelerated Materials Design and Discovery program. More

  • in

    A new dataset of Arctic images will spur artificial intelligence research

    As the U.S. Coast Guard (USCG) icebreaker Healy takes part in a voyage across the North Pole this summer, it is capturing images of the Arctic to further the study of this rapidly changing region. Lincoln Laboratory researchers installed a camera system aboard the Healy while at port in Seattle before it embarked on a three-month science mission on July 11. The resulting dataset, which will be one of the first of its kind, will be used to develop artificial intelligence tools that can analyze Arctic imagery.

    “This dataset not only can help mariners navigate more safely and operate more efficiently, but also help protect our nation by providing critical maritime domain awareness and an improved understanding of how AI analysis can be brought to bear in this challenging and unique environment,” says Jo Kurucar, a researcher in Lincoln Laboratory’s AI Software Architectures and Algorithms Group, which led this project.

    As the planet warms and sea ice melts, Arctic passages are opening up to more traffic, both to military vessels and ships conducting illegal fishing. These movements may pose national security challenges to the United States. The opening Arctic also leaves questions about how its climate, wildlife, and geography are changing.

    Today, very few imagery datasets of the Arctic exist to study these changes. Overhead images from satellites or aircraft can only provide limited information about the environment. An outward-looking camera attached to a ship can capture more details of the setting and different angles of objects, such as other ships, in the scene. These types of images can then be used to train AI computer-vision tools, which can help the USCG plan naval missions and automate analysis. According to Kurucar, USCG assets in the Arctic are spread thin and can benefit greatly from AI tools, which can act as a force multiplier.

    The Healy is the USCG’s largest and most technologically advanced icebreaker. Given its current mission, it was a fitting candidate to be equipped with a new sensor to gather this dataset. The laboratory research team collaborated with the USCG Research and Development Center to determine the sensor requirements. Together, they developed the Cold Region Imaging and Surveillance Platform (CRISP).

    “Lincoln Laboratory has an excellent relationship with the Coast Guard, especially with the Research and Development Center. Over a decade, we’ve established ties that enabled the deployment of the CRISP system,” says Amna Greaves, the CRISP project lead and an assistant leader in the AI Software Architectures and Algorithms Group. “We have strong ties not only because of the USCG veterans working at the laboratory and in our group, but also because our technology missions are complementary. Today it was deploying infrared sensing in the Arctic; tomorrow it could be operating quadruped robot dogs on a fast-response cutter.”

    The CRISP system comprises a long-wave infrared camera, manufactured by Teledyne FLIR (for forward-looking infrared), that is designed for harsh maritime environments. The camera can stabilize itself during rough seas and image in complete darkness, fog, and glare. It is paired with a GPS-enabled time-synchronized clock and a network video recorder to record both video and still imagery along with GPS-positional data.  

    The camera is mounted at the front of the ship’s fly bridge, and the electronics are housed in a ruggedized rack on the bridge. The system can be operated manually from the bridge or be placed into an autonomous surveillance mode, in which it slowly pans back and forth, recording 15 minutes of video every three hours and a still image once every 15 seconds.

    “The installation of the equipment was a unique and fun experience. As with any good project, our expectations going into the install did not meet reality,” says Michael Emily, the project’s IT systems administrator who traveled to Seattle for the install. Working with the ship’s crew, the laboratory team had to quickly adjust their route for running cables from the camera to the observation station after they discovered that the expected access points weren’t in fact accessible. “We had 100-foot cables made for this project just in case of this type of scenario, which was a good thing because we only had a few inches to spare,” Emily says.

    The CRISP project team plans to publicly release the dataset, anticipated to be about 4 terabytes in size, once the USCG science mission concludes in the fall.

    The goal in releasing the dataset is to enable the wider research community to develop better tools for those operating in the Arctic, especially as this region becomes more navigable. “Collecting and publishing the data allows for faster and greater progress than what we could accomplish on our own,” Kurucar adds. “It also enables the laboratory to engage in more advanced AI applications while others make more incremental advances using the dataset.”

    On top of providing the dataset, the laboratory team plans to provide a baseline object-detection model, from which others can make progress on their own models. More advanced AI applications planned for development are classifiers for specific objects in the scene and the ability to identify and track objects across images.

    Beyond assisting with USCG missions, this project could create an influential dataset for researchers looking to apply AI to data from the Arctic to help combat climate change, says Paul Metzger, who leads the AI Software Architectures and Algorithms Group.

    Metzger adds that the group was honored to be a part of this project and is excited to see the advances that come from applying AI to novel challenges facing the United States: “I’m extremely proud of how our group applies AI to the highest-priority challenges in our nation, from predicting outbreaks of Covid-19 and assisting the U.S. European Command in their support of Ukraine to now employing AI in the Arctic for maritime awareness.”

    Once the dataset is available, it will be free to download on the Lincoln Laboratory dataset website. More

  • in

    Exploring the nanoworld of biogenic gems

    A new research collaboration with The Bahrain Institute for Pearls and Gemstones (DANAT) will seek to develop advanced characterization tools for the analysis of the properties of pearls and to explore technologies to assign unique identifiers to individual pearls.

    The three-year project will be led by Admir Mašić, associate professor of civil and environmental engineering, in collaboration with Vladimir Bulović, the Fariborz Maseeh Chair in Emerging Technology and professor of electrical engineering and computer science.

    “Pearls are extremely complex and fascinating hierarchically ordered biological materials that are formed by a wide range of different species,” says Mašić. “Working with DANAT provides us a unique opportunity to apply our lab’s multi-scale materials characterization tools to identify potentially species-specific pearl fingerprints, while simultaneously addressing scientific research questions regarding the underlying biomineralization processes that could inform advances in sustainable building materials.”

    DANAT is a gemological laboratory specializing in the testing and study of natural pearls as a reflection of Bahrain’s pearling history and desire to protect and advance Bahrain’s pearling heritage. DANAT’s gemologists support clients and students through pearl, gemstone, and diamond identification services, as well as educational courses.

    Like many other precious gemstones, pearls have been human-made through scientific experimentation, says Noora Jamsheer, chief executive officer at DANAT. Over a century ago, cultured pearls entered markets as a competitive product to natural pearls, similar in appearance but different in value.

    “Gemological labs have been innovating scientific testing methods to differentiate between natural pearls and all other pearls that exist because of direct or indirect human intervention. Today the world knows natural pearls and cultured pearls. However, there are also pearls that fall in between these two categories,” says Jamsheer. “DANAT has the responsibility, as the leading gemological laboratory for pearl testing, to take the initiative necessary to ensure that testing methods keep pace with advances in the science of pearl cultivation.”

    Titled “Exploring the Nanoworld of Biogenic Gems,” the project will aim to improve the process of testing and identifying pearls by identifying morphological, micro-structural, optical, and chemical features sufficient to distinguish a pearl’s area of origin, method of growth, or both. MIT.nano, MIT’s open-access center for nanoscience and nanoengineering will be the organizational home for the project, where Mašić and his team will utilize the facility’s state-of-the-art characterization tools.

    In addition to discovering new methodologies for establishing a pearl’s origin, the project aims to utilize machine learning to automate pearl classification. Furthermore, researchers will investigate techniques to create a unique identifier associated with an individual pearl.

    The initial sponsored research project is expected to last three years, with potential for continued collaboration based on key findings or building upon the project’s success to open new avenues for research into the structure, properties, and growth of pearls. More