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      in Energy

      Overcoming a bottleneck in carbon dioxide conversion

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      If researchers could find a way to chemically convert carbon dioxide into fuels or other products, they might make a major dent in greenhouse gas emissions. But many such processes that have seemed promising in the lab haven’t performed as expected in scaled-up formats that would be suitable for use with a power plant or other emissions sources.

      Now, researchers at MIT have identified, quantified, and modeled a major reason for poor performance in such conversion systems. The culprit turns out to be a local depletion of the carbon dioxide gas right next to the electrodes being used to catalyze the conversion. The problem can be alleviated, the team found, by simply pulsing the current off and on at specific intervals, allowing time for the gas to build back up to the needed levels next to the electrode.

      The findings, which could spur progress on developing a variety of materials and designs for electrochemical carbon dioxide conversion systems, were published today in the journal Langmuir, in a paper by MIT postdoc Álvaro Moreno Soto, graduate student Jack Lake, and professor of mechanical engineering Kripa Varanasi.

      “Carbon dioxide mitigation is, I think, one of the important challenges of our time,” Varanasi says. While much of the research in the area has focused on carbon capture and sequestration, in which the gas is pumped into some kind of deep underground reservoir or converted to an inert solid such as limestone, another promising avenue has been converting the gas into other carbon compounds such as methane or ethanol, to be used as fuel, or ethylene, which serves as a precursor to useful polymers.

      There are several ways to do such conversions, including electrochemical, thermocatalytic, photothermal, or photochemical processes. “Each of these has problems or challenges,” Varanasi says. The thermal processes require very high temperature, and they don’t produce very high-value chemical products, which is a challenge with the light-activated processes as well, he says. “Efficiency is always at play, always an issue.”

      The team has focused on the electrochemical approaches, with a goal of getting “higher-C products” — compounds that contain more carbon atoms and tend to be higher-value fuels because of their energy per weight or volume. In these reactions, the biggest challenge has been curbing competing reactions that can take place at the same time, especially the splitting of water molecules into oxygen and hydrogen.

      The reactions take place as a stream of liquid electrolyte with the carbon dioxide dissolved in it passes over a metal catalytic surface that is electrically charged. But as the carbon dioxide gets converted, it leaves behind a region in the electrolyte stream where it has essentially been used up, and so the reaction within this depleted zone turns toward water splitting instead. This unwanted reaction uses up energy and greatly reduces the overall efficiency of the conversion process, the researchers found.

      “There’s a number of groups working on this, and a number of catalysts that are out there,” Varanasi says. “In all of these, I think the hydrogen co-evolution becomes a bottleneck.”

      One way of counteracting this depletion, they found, can be achieved by a pulsed system — a cycle of simply turning off the voltage, stopping the reaction and giving the carbon dioxide time to spread back into the depleted zone and reach usable levels again, and then resuming the reaction.

      Often, the researchers say, groups have found promising catalyst materials but haven’t run their lab tests long enough to observe these depletion effects, and thus have been frustrated in trying to scale up their systems. Furthermore, the concentration of carbon dioxide next to the catalyst dictates the products that are made. Hence, depletion can also change the mix of products that are produced and can make the process unreliable. “If you want to be able to make a system that works at industrial scale, you need to be able to run things over a long period of time,” Varanasi says, “and you need to not have these kinds of effects that reduce the efficiency or reliability of the process.”

      The team studied three different catalyst materials, including copper, and “we really focused on making sure that we understood and can quantify the depletion effects,” Lake says. In the process they were able to develop a simple and reliable way of monitoring the efficiency of the conversion process as it happens, by measuring the changing pH levels, a measure of acidity, in the system’s electrolyte.

      In their tests, they used more sophisticated analytical tools to characterize reaction products, including gas chromatography for analysis of the gaseous products, and nuclear magnetic resonance characterization for the system’s liquid products. But their analysis showed that the simple pH measurement of the electrolyte next to the electrode during operation could provide a sufficient measure of the efficiency of the reaction as it progressed.

      This ability to easily monitor the reaction in real-time could ultimately lead to a system optimized by machine-learning methods, controlling the production rate of the desired compounds through continuous feedback, Moreno Soto says.

      Now that the process is understood and quantified, other approaches to mitigating the carbon dioxide depletion might be developed, the researchers say, and could easily be tested using their methods.

      This work shows, Lake says, that “no matter what your catalyst material is” in such an electrocatalytic system, “you’ll be affected by this problem.” And now, by using the model they developed, it’s possible to determine exactly what kind of time window needs to be evaluated to get an accurate sense of the material’s overall efficiency and what kind of system operations could maximize its effectiveness.

      The research was supported by Shell, through the MIT Energy Initiative. More

    • 63 Shares149 Views

      in Environment

      Expanding the conversation about sustainability

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      Stacy Godfreey-Igwe sat in her dorm room at MIT, staring frantically at her phone. An unprecedented snowstorm had hit her hometown of Richardson, Texas, and she was having difficulty contacting her family. She felt worried and frustrated, aware that nearby neighborhoods hadn’t lost power during the storm but that her family home had suffered significant damage. She finally got a hold of her parents, who had taken refuge in a nearby office building, but the experience left her shaken and more determined than ever to devote herself to addressing climate injustice.

      Godfreey-Igwe, the daughter of Nigerian immigrants, has long been concerned about how marginalized communities can shoulder a disproportionately heavy environmental burden. At MIT, she chose a double major in mechanical engineering with a concentration in global and sustainable development, and in African and African diaspora studies, a major she helped establish and became the first student to declare. Initially seeing the two fields as separate, she now embraces their intersectionality in her work in and out of the classroom.

      Through an Undergraduate Research Opportunity Program (UROP) project with Amah Edoh, the Homer A. Burnell Assistant Professor of Anthropology and African Studies at MIT, Godfreey-Igwe has learned more about her Igbo cultural heritage and hopes to understand what the future of climate change poses for the culture’s sustainability. Godfreey-Igwe herself is the “Ada” – or eldest child – in her family, a role that carries a responsibility for keeping her family’s culture alive. That sense of responsibility, to her community and to future generations, has stayed with her at MIT.

      For Independent Activities Period during her first year at the Institute, Godfreey-Igwe traveled to Kazakhstan through MIT’s Global Teaching Labs. As a student teacher, she taught Kazakh high school chemistry students about polymers and the impact plastic materials can have on the Earth’s climate. She was also an MIT International Science and Technology Initiatives (MISTI) Identity X Ambassador during her time there, blogging about her experiences as a Black woman in the country. She saw the role as an opportunity to shed light on the challenges of navigating her identity abroad, with hopes of fostering community through her posts.

      The following summer, Godfreey-Igwe interned for the Saathi Biodegradable Sanitary Napkins Startup in Ahmedabad, India. During her time there, she researched and wrote articles focused on educating the public about the benefits eco-friendly sanitary pads posed to public health and the environment. She also interviewed a director for the city’s Center for Environmental Education, about the importance of uplifting and supporting marginalized communities hit hardest by climate change. The conversation was eye-opening for Godfreey-Igwe; she saw not only how complex the process of mitigating climate change was, but also how diverse the solutions needed to be.

      She has also pursued her interest in plastics and sustainability through summer research projects. In of the summer of 2020, Godfreey-Igwe worked under a lab in Stanford University’s civil and environmental engineering department to create and design models maximizing the efficiency of bacterial processes leading to the creation of bioplastics. The project’s goal was to find a sustainable form of plastic breakdown for future applications in the environment.  She presented her research at the Harvard National Collegiate Research Conference and received a presentation award during the MIT Mechanical Engineering Research Exhibition. This past summer, she was awarded a grant through the NSF Center for Sustainable Polymers at the University of Minnesota to work on a research project seeking to understand microplastic generation.

      Ultimately, Godfreey-Igwe recognizes that to propose thoughtful solutions to climate issues, the people hit hardest must be a part of the conversation. For her, a key way to bring more people into conversations about sustainability and inclusion is through mentorship. This role is especially meaningful to Godfreey-Igwe because she knows firsthand how important for members of underrepresented groups to feel supported at a place like MIT. “The experience of coming to an institution like MIT, as someone who is low-income or of color, can be isolating. Especially if you feel like there are people who can’t relate to your background,” she says.

      Godfreey-Igwe is a member of Active Community Engagement FPOP (ACE), a social action group on campus that engages with local communities through public service work. Initially joining as a participant, Godfreey-Igwe became a counselor and then coordinator; she facilitates social action workshops and introduces students to service opportunities both at MIT and around Boston. She says her time in ACE has helped build her confidence in her abilities as a leader, mentor, and cultivator of inclusionary spaces. She is also a member of iHouse (International Development House), where she served for three years as the housing and service co-chair.

      Godfreey-Igwe also tutors one-on-one for Tutoring Plus in Cambridge, where since her first year she has provided mentorship and STEM tutoring to a low-income, high school student of color. Last spring, she was awarded the Tutoring Plus of Cambridge Unwavering Service Award for her service and commitment to the program.

      Looking ahead, Godfreey-Igwe hopes to use the skills learned from her mentorship and leadership roles to establish greater structures for collaboration on climate mitigation technologies, ideas, and practices. Focusing on mentoring young scientists of color, she wants to build up underprivileged groups and institutions for sustainable climate change research, ensuring everyone has a voice in the ongoing conversation.

      “In all this work, I’m hoping to make sure that globally marginalized communities are more visible in climate-related spaces, both in terms of who is doing the engineering and who the engineering works for,” she says. More

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      in Energy

      An energy-storage solution that flows like soft-serve ice cream

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      Batteries made from an electrically conductive mixture the consistency of molasses could help solve a critical piece of the decarbonization puzzle. An interdisciplinary team from MIT has found that an electrochemical technology called a semisolid flow battery can be a cost-competitive form of energy storage and backup for variable renewable energy (VRE) sources such as wind and solar. The group’s research is described in a paper published in Joule.

      “The transition to clean energy requires energy storage systems of different durations for when the sun isn’t shining and the wind isn’t blowing,” says Emre Gençer, a research scientist with the MIT Energy Initiative (MITEI) and a member of the team. “Our work demonstrates that a semisolid flow battery could be a lifesaving as well as economical option when these VRE sources can’t generate power for a day or longer — in the case of natural disasters, for instance.”

      The rechargeable zinc-manganese dioxide (Zn-MnO2) battery the researchers created beat out other long-duration energy storage contenders. “We performed a comprehensive, bottom-up analysis to understand how the battery’s composition affects performance and cost, looking at all the trade-offs,” says Thaneer Malai Narayanan SM ’18, PhD ’21. “We showed that our system can be cheaper than others, and can be scaled up.”

      Narayanan, who conducted this work at MIT as part of his doctorate in mechanical engineering, is the lead author of the paper. Additional authors include Gençer, Yunguang Zhu, a postdoc in the MIT Electrochemical Energy Lab; Gareth McKinley, the School of Engineering Professor of Teaching Innovation and professor of mechanical engineering at MIT; and Yang Shao-Horn, the JR East Professor of Engineering, a professor of mechanical engineering and of materials science and engineering, and a member of the Research Laboratory of Electronics (RLE), who directs the MIT Electrochemical Energy Lab.

      Going with the flow

      In 2016, Narayanan began his graduate studies, joining the Electrochemical Energy Lab, a hotbed of research and exploration of solutions to mitigate climate change, which is centered on innovative battery chemistry and decarbonizing fuels and chemicals. One exciting opportunity for the lab: developing low- and no-carbon backup energy systems suitable for grid-scale needs when VRE generation flags.                                                  

      While the lab cast a wide net, investigating energy conversion and storage using solid oxide fuel cells, lithium-ion batteries, and metal-air batteries, among others, Narayanan took a particular interest in flow batteries. In these systems, two different chemical (electrolyte) solutions with either negative or positive ions are pumped from separate tanks, meeting across a membrane (called the stack). Here, the ion streams react, converting electrical energy to chemical energy — in effect, charging the battery. When there is demand for this stored energy, the solution gets pumped back to the stack to convert chemical energy into electrical energy again.

      The duration of time that flow batteries can discharge, releasing the stored electricity, is determined by the volume of positively and negatively charged electrolyte solutions streaming through the stack. In theory, as long as these solutions keep flowing, reacting, and converting the chemical energy to electrical energy, the battery systems can provide electricity.

      “For backup lasting more than a day, the architecture of flow batteries suggests they can be a cheap option,” says Narayanan. “You recharge the solution in the tanks from sun and wind power sources.” This renders the entire system carbon free.

      But while the promise of flow battery technologies has beckoned for at least a decade, the uneven performance and expense of materials required for these battery systems has slowed their implementation. So, Narayanan set out on an ambitious journey: to design and build a flow battery that could back up VRE systems for a day or more, storing and discharging energy with the same or greater efficiency than backup rivals; and to determine, through rigorous cost analysis, whether such a system could prove economically viable as a long-duration energy option.

      Multidisciplinary collaborators

      To attack this multipronged challenge, Narayanan’s project brought together, in his words, “three giants, scientists all well-known in their fields”:  Shao-Horn, who specializes in chemical physics and electrochemical science, and design of materials; Gençer, who creates detailed economic models of emergent energy systems at MITEI; and McKinley, an expert in rheology, the physics of flow. These three also served as his thesis advisors.

      “I was excited to work in such an interdisciplinary team, which offered a unique opportunity to create a novel battery architecture by designing charge transfer and ion transport within flowable semi-solid electrodes, and to guide battery engineering using techno-economics of such flowable batteries,” says Shao-Horn.

      While other flow battery systems in contention, such as the vanadium redox flow battery, offer the storage capacity and energy density to back up megawatt and larger power systems, they depend on expensive chemical ingredients that make them bad bets for long duration purposes. Narayanan was on the hunt for less-pricey chemical components that also feature rich energy potential.

      Through a series of bench experiments, the researchers came up with a novel electrode (electrical conductor) for the battery system: a mixture containing dispersed manganese dioxide (MnO2) particles, shot through with an electrically conductive additive, carbon black. This compound reacts with a conductive zinc solution or zinc plate at the stack, enabling efficient electrochemical energy conversion. The fluid properties of this battery are far removed from the watery solutions used by other flow batteries.

      “It’s a semisolid — a slurry,” says Narayanan. “Like thick, black paint, or perhaps a soft-serve ice cream,” suggests McKinley. The carbon black adds the pigment and the electric punch. To arrive at the optimal electrochemical mix, the researchers tweaked their formula many times.

      “These systems have to be able to flow under reasonable pressures, but also have a weak yield stress so that the active MnO2 particles don’t sink to the bottom of the flow tanks when the system isn’t being used, as well as not separate into a battery/oily clear fluid phase and a dense paste of carbon particles and MnO2,” says McKinley.

      This series of experiments informed the technoeconomic analysis. By “connecting the dots between composition, performance, and cost,” says Narayanan, he and Gençer were able to make system-level cost and efficiency calculations for the Zn-MnO2 battery.

      “Assessing the cost and performance of early technologies is very difficult, and this was an example of how to develop a standard method to help researchers at MIT and elsewhere,” says Gençer. “One message here is that when you include the cost analysis at the development stage of your experimental work, you get an important early understanding of your project’s cost implications.”

      In their final round of studies, Gençer and Narayanan compared the Zn-MnO2 battery to a set of equivalent electrochemical battery and hydrogen backup systems, looking at the capital costs of running them at durations of eight, 24, and 72 hours. Their findings surprised them: For battery discharges longer than a day, their semisolid flow battery beat out lithium-ion batteries and vanadium redox flow batteries. This was true even when factoring in the heavy expense of pumping the MnO2 slurry from tank to stack. “I was skeptical, and not expecting this battery would be competitive, but once I did the cost calculation, it was plausible,” says Gençer.

      But carbon-free battery backup is a very Goldilocks-like business: Different situations require different-duration solutions, whether an anticipated overnight loss of solar power, or a longer-term, climate-based disruption in the grid. “Lithium-ion is great for backup of eight hours and under, but the materials are too expensive for longer periods,” says Gençer. “Hydrogen is super expensive for very short durations, and good for very long durations, and we will need all of them.” This means it makes sense to continue working on the Zn-MnO2 system to see where it might fit in.

      “The next step is to take our battery system and build it up,” says Narayanan, who is working now as a battery engineer. “Our research also points the way to other chemistries that could be developed under the semi-solid flow battery platform, so we could be seeing this kind of technology used for energy storage in our lifetimes.”

      This research was supported by Eni S.p.A. through MITEI. Thaneer Malai Narayanan received an Eni-sponsored MIT Energy Fellowship during his work on the project. More

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      in Energy

      Design’s new frontier

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      In the 1960s, the advent of computer-aided design (CAD) sparked a revolution in design. For his PhD thesis in 1963, MIT Professor Ivan Sutherland developed Sketchpad, a game-changing software program that enabled users to draw, move, and resize shapes on a computer. Over the course of the next few decades, CAD software reshaped how everything from consumer products to buildings and airplanes were designed.

      “CAD was part of the first wave in computing in design. The ability of researchers and practitioners to represent and model designs using computers was a major breakthrough and still is one of the biggest outcomes of design research, in my opinion,” says Maria Yang, Gail E. Kendall Professor and director of MIT’s Ideation Lab.

      Innovations in 3D printing during the 1980s and 1990s expanded CAD’s capabilities beyond traditional injection molding and casting methods, providing designers even more flexibility. Designers could sketch, ideate, and develop prototypes or models faster and more efficiently. Meanwhile, with the push of a button, software like that developed by Professor Emeritus David Gossard of MIT’s CAD Lab could solve equations simultaneously to produce a new geometry on the fly.

      In recent years, mechanical engineers have expanded the computing tools they use to ideate, design, and prototype. More sophisticated algorithms and the explosion of machine learning and artificial intelligence technologies have sparked a second revolution in design engineering.

      Researchers and faculty at MIT’s Department of Mechanical Engineering are utilizing these technologies to re-imagine how the products, systems, and infrastructures we use are designed. These researchers are at the forefront of the new frontier in design.

      Computational design

      Faez Ahmed wants to reinvent the wheel, or at least the bicycle wheel. He and his team at MIT’s Design Computation & Digital Engineering Lab (DeCoDE) use an artificial intelligence-driven design method that can generate entirely novel and improved designs for a range of products — including the traditional bicycle. They create advanced computational methods to blend human-driven design with simulation-based design.

      “The focus of our DeCoDE lab is computational design. We are looking at how we can create machine learning and AI algorithms to help us discover new designs that are optimized based on specific performance parameters,” says Ahmed, an assistant professor of mechanical engineering at MIT.

      For their work using AI-driven design for bicycles, Ahmed and his collaborator Professor Daniel Frey wanted to make it easier to design customizable bicycles, and by extension, encourage more people to use bicycles over transportation methods that emit greenhouse gases.

      To start, the group gathered a dataset of 4,500 bicycle designs. Using this massive dataset, they tested the limits of what machine learning could do. First, they developed algorithms to group bicycles that looked similar together and explore the design space. They then created machine learning models that could successfully predict what components are key in identifying a bicycle style, such as a road bike versus a mountain bike.

      Once the algorithms were good enough at identifying bicycle designs and parts, the team proposed novel machine learning tools that could use this data to create a unique and creative design for a bicycle based on certain performance parameters and rider dimensions.

      Ahmed used a generative adversarial network — or GAN — as the basis of this model. GAN models utilize neural networks that can create new designs based on vast amounts of data. However, using GAN models alone would result in homogeneous designs that lack novelty and can’t be assessed in terms of performance. To address these issues in design problems, Ahmed has developed a new method which he calls “PaDGAN,” performance augmented diverse GAN.

      “When we apply this type of model, what we see is that we can get large improvements in the diversity, quality, as well as novelty of the designs,” Ahmed explains.

      Using this approach, Ahmed’s team developed an open-source computational design tool for bicycles freely available on their lab website. They hope to further develop a set of generalizable tools that can be used across industries and products.

      Longer term, Ahmed has his sights set on loftier goals. He hopes the computational design tools he develops could lead to “design democratization,” putting more power in the hands of the end user.

      “With these algorithms, you can have more individualization where the algorithm assists a customer in understanding their needs and helps them create a product that satisfies their exact requirements,” he adds.

      Using algorithms to democratize the design process is a goal shared by Stefanie Mueller, an associate professor in electrical engineering and computer science and mechanical engineering.

      Personal fabrication

      Platforms like Instagram give users the freedom to instantly edit their photographs or videos using filters. In one click, users can alter the palette, tone, and brightness of their content by applying filters that range from bold colors to sepia-toned or black-and-white. Mueller, X-Window Consortium Career Development Professor, wants to bring this concept of the Instagram filter to the physical world.

      “We want to explore how digital capabilities can be applied to tangible objects. Our goal is to bring reprogrammable appearance to the physical world,” explains Mueller, director of the HCI Engineering Group based out of MIT’s Computer Science and Artificial Intelligence Laboratory.

      Mueller’s team utilizes a combination of smart materials, optics, and computation to advance personal fabrication technologies that would allow end users to alter the design and appearance of the products they own. They tested this concept in a project they dubbed “Photo-Chromeleon.”

      First, a mix of photochromic cyan, magenta, and yellow dies are airbrushed onto an object — in this instance, a 3D sculpture of a chameleon. Using software they developed, the team sketches the exact color pattern they want to achieve on the object itself. An ultraviolet light shines on the object to activate the dyes.

      To actually create the physical pattern on the object, Mueller has developed an optimization algorithm to use alongside a normal office projector outfitted with red, green, and blue LED lights. These lights shine on specific pixels on the object for a given period of time to physically change the makeup of the photochromic pigments.

      “This fancy algorithm tells us exactly how long we have to shine the red, green, and blue light on every single pixel of an object to get the exact pattern we’ve programmed in our software,” says Mueller.

      Giving this freedom to the end user enables limitless possibilities. Mueller’s team has applied this technology to iPhone cases, shoes, and even cars. In the case of shoes, Mueller envisions a shoebox embedded with UV and LED light projectors. Users could put their shoes in the box overnight and the next day have a pair of shoes in a completely new pattern.

      Mueller wants to expand her personal fabrication methods to the clothes we wear. Rather than utilize the light projection technique developed in the PhotoChromeleon project, her team is exploring the possibility of weaving LEDs directly into clothing fibers, allowing people to change their shirt’s appearance as they wear it. These personal fabrication technologies could completely alter consumer habits.

      “It’s very interesting for me to think about how these computational techniques will change product design on a high level,” adds Mueller. “In the future, a consumer could buy a blank iPhone case and update the design on a weekly or daily basis.”

      Computational fluid dynamics and participatory design

      Another team of mechanical engineers, including Sili Deng, the Brit (1961) & Alex (1949) d’Arbeloff Career Development Professor, are developing a different kind of design tool that could have a large impact on individuals in low- and middle-income countries across the world.

      As Deng walked down the hallway of Building 1 on MIT’s campus, a monitor playing a video caught her eye. The video featured work done by mechanical engineers and MIT D-Lab on developing cleaner burning briquettes for cookstoves in Uganda. Deng immediately knew she wanted to get involved.

      “As a combustion scientist, I’ve always wanted to work on such a tangible real-world problem, but the field of combustion tends to focus more heavily on the academic side of things,” explains Deng.

      After reaching out to colleagues in MIT D-Lab, Deng joined a collaborative effort to develop a new cookstove design tool for the 3 billion people across the world who burn solid fuels to cook and heat their homes. These stoves often emit soot and carbon monoxide, leading not only to millions of deaths each year, but also worsening the world’s greenhouse gas emission problem.

      The team is taking a three-pronged approach to developing this solution, using a combination of participatory design, physical modeling, and experimental validation to create a tool that will lead to the production of high-performing, low-cost energy products.

      Deng and her team in the Deng Energy and Nanotechnology Group use physics-based modeling for the combustion and emission process in cookstoves.

      “My team is focused on computational fluid dynamics. We use computational and numerical studies to understand the flow field where the fuel is burned and releases heat,” says Deng.

      These flow mechanics are crucial to understanding how to minimize heat loss and make cookstoves more efficient, as well as learning how dangerous pollutants are formed and released in the process.

      Using computational methods, Deng’s team performs three-dimensional simulations of the complex chemistry and transport coupling at play in the combustion and emission processes. They then use these simulations to build a combustion model for how fuel is burned and a pollution model that predicts carbon monoxide emissions.

      Deng’s models are used by a group led by Daniel Sweeney in MIT D-Lab to test the experimental validation in prototypes of stoves. Finally, Professor Maria Yang uses participatory design methods to integrate user feedback, ensuring the design tool can actually be used by people across the world.

      The end goal for this collaborative team is to not only provide local manufacturers with a prototype they could produce themselves, but to also provide them with a tool that can tweak the design based on local needs and available materials.

      Deng sees wide-ranging applications for the computational fluid dynamics her team is developing.

      “We see an opportunity to use physics-based modeling, augmented with a machine learning approach, to come up with chemical models for practical fuels that help us better understand combustion. Therefore, we can design new methods to minimize carbon emissions,” she adds.

      While Deng is utilizing simulations and machine learning at the molecular level to improve designs, others are taking a more macro approach.

      Designing intelligent systems

      When it comes to intelligent design, Navid Azizan thinks big. He hopes to help create future intelligent systems that are capable of making decisions autonomously by using the enormous amounts of data emerging from the physical world. From smart robots and autonomous vehicles to smart power grids and smart cities, Azizan focuses on the analysis, design, and control of intelligent systems.

      Achieving such massive feats takes a truly interdisciplinary approach that draws upon various fields such as machine learning, dynamical systems, control, optimization, statistics, and network science, among others.

      “Developing intelligent systems is a multifaceted problem, and it really requires a confluence of disciplines,” says Azizan, assistant professor of mechanical engineering with a dual appointment in MIT’s Institute for Data, Systems, and Society (IDSS). “To create such systems, we need to go beyond standard approaches to machine learning, such as those commonly used in computer vision, and devise algorithms that can enable safe, efficient, real-time decision-making for physical systems.”

      For robot control to work in the complex dynamic environments that arise in the real world, real-time adaptation is key. If, for example, an autonomous vehicle is going to drive in icy conditions or a drone is operating in windy conditions, they need to be able to adapt to their new environment quickly.

      To address this challenge, Azizan and his collaborators at MIT and Stanford University have developed a new algorithm that combines adaptive control, a powerful methodology from control theory, with meta learning, a new machine learning paradigm.

      “This ‘control-oriented’ learning approach outperforms the existing ‘regression-oriented’ methods, which are mostly focused on just fitting the data, by a wide margin,” says Azizan.

      Another critical aspect of deploying machine learning algorithms in physical systems that Azizan and his team hope to address is safety. Deep neural networks are a crucial part of autonomous systems. They are used for interpreting complex visual inputs and making data-driven predictions of future behavior in real time. However, Azizan urges caution.

      “These deep neural networks are only as good as their training data, and their predictions can often be untrustworthy in scenarios not covered by their training data,” he says. Making decisions based on such untrustworthy predictions could lead to fatal accidents in autonomous vehicles or other safety-critical systems.

      To avoid these potentially catastrophic events, Azizan proposes that it is imperative to equip neural networks with a measure of their uncertainty. When the uncertainty is high, they can then be switched to a “safe policy.”

      In pursuit of this goal, Azizan and his collaborators have developed a new algorithm known as SCOD — Sketching Curvature of Out-of-Distribution Detection. This framework could be embedded within any deep neural network to equip them with a measure of their uncertainty.

      “This algorithm is model-agnostic and can be applied to neural networks used in various kinds of autonomous systems, whether it’s drones, vehicles, or robots,” says Azizan.

      Azizan hopes to continue working on algorithms for even larger-scale systems. He and his team are designing efficient algorithms to better control supply and demand in smart energy grids. According to Azizan, even if we create the most efficient solar panels and batteries, we can never achieve a sustainable grid powered by renewable resources without the right control mechanisms.

      Mechanical engineers like Ahmed, Mueller, Deng, and Azizan serve as the key to realizing the next revolution of computing in design.

      “MechE is in a unique position at the intersection of the computational and physical worlds,” Azizan says. “Mechanical engineers build a bridge between theoretical, algorithmic tools and real, physical world applications.”

      Sophisticated computational tools, coupled with the ground truth mechanical engineers have in the physical world, could unlock limitless possibilities for design engineering, well beyond what could have been imagined in those early days of CAD. More

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      in Security

      Q&A: Options for the Diablo Canyon nuclear plant

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      The Diablo Canyon nuclear plant in California, the only one still operating in the state, is set to close in 2025. A team of researchers at MIT’s Center for Advanced Nuclear Energy Systems, Abdul Latif Jameel Water and Food Systems Lab, and Center for Energy and Environmental Policy Research; Stanford’s Precourt Energy Institute; and energy analysis firm LucidCatalyst LLC have analyzed the potential benefits the plant could provide if its operation were extended to 2030 or 2045.

      They found that this nuclear plant could simultaneously help to stabilize the state’s electric grid, provide desalinated water to supplement the state’s chronic water shortages, and provide carbon-free hydrogen fuel for transportation. MIT News asked report co-authors Jacopo Buongiorno, the TEPCO Professor of Nuclear Science and Engineering, and John Lienhard, the Jameel Professor of Water and Food, to discuss the group’s findings.

      Q: Your report suggests co-locating a major desalination plant alongside the existing Diablo Canyon power plant. What would be the potential benefits from operating a desalination plant in conjunction with the power plant?

      Lienhard: The cost of desalinated water produced at Diablo Canyon would be lower than for a stand-alone plant because the cost of electricity would be significantly lower and you could take advantage of the existing infrastructure for the intake of seawater and the outfall of brine. Electricity would be cheaper because the location takes advantage of Diablo Canyon’s unique capability to provide low cost, zero-carbon baseload power.

      Depending on the scale at which the desalination plant is built, you could make a very significant impact on the water shortfalls of state and federal projects in the area. In fact, one of the numbers that came out of this study was that an intermediate-sized desalination plant there would produce more fresh water than the highest estimate of the net yield from the proposed Delta Conveyance Project on the Sacramento River. You could get that amount of water at Diablo Canyon for an investment cost less than half as large, and without the associated impacts that would come with the Delta Conveyance Project.

      And the technology envisioned for desalination here, reverse osmosis, is available off the shelf. You can buy this equipment today. In fact, it’s already in use in California and thousands of other places around the world.

      Q: You discuss in the report three potential products from the Diablo Canyon plant:  desalinatinated water, power for the grid, and clean hydrogen. How well can the plant accommodate all of those efforts, and are there advantages to combining them as opposed to doing any one of them separately?

      Buongiorno: California, like many other regions in the world, is facing multiple challenges as it seeks to reduce carbon emissions on a grand scale. First, the wide deployment of intermittent energy sources such as solar and wind creates a great deal of variability on the grid that can be balanced by dispatchable firm power generators like Diablo. So, the first mission for Diablo is to continue to provide reliable, clean electricity to the grid.

      The second challenge is the prolonged drought and water scarcity for the state in general. And one way to address that is water desalination co-located with the nuclear plant at the Diablo site, as John explained.

      The third challenge is related to decarbonization the transportation sector. A possible approach is replacing conventional cars and trucks with vehicles powered by fuel cells which consume hydrogen. Hydrogen has to be produced from a primary energy source. Nuclear power, through a process called electrolysis, can do that quite efficiently and in a manner that is carbon-free.

      Our economic analysis took into account the expected revenue from selling these multiple products — electricity for the grid, hydrogen for the transportation sector, water for farmers or other local users — as well as the costs associated with deploying the new facilities needed to produce desalinated water and hydrogen. We found that, if Diablo’s operating license was extended until 2035, it would cut carbon emissions by an average of 7 million metric tons a year — a more than 11 percent reduction from 2017 levels — and save ratepayers $2.6 billion in power system costs.

      Further delaying the retirement of Diablo to 2045 would spare 90,000 acres of land that would need to be dedicated to renewable energy production to replace the facility’s capacity, and it would save ratepayers up to $21 billion in power system costs.

      Finally, if Diablo was operated as a polygeneration facility that provides electricity, desalinated water, and hydrogen simultaneously, its value, quantified in terms of dollars per unit electricity generated, could increase by 50 percent.

      Lienhard: Most of the desalination scenarios that we considered did not consume the full electrical output of that plant, meaning that under most scenarios you would continue to make electricity and do something with it, beyond just desalination. I think it’s also important to remember that this power plant produces 15 percent of California’s carbon-free electricity today and is responsible for 8 percent of the state’s total electrical production. In other words, Diablo Canyon is a very large factor in California’s decarbonization. When or if this plant goes offline, the near-term outcome is likely to be increased reliance on natural gas to produce electricity, meaning a rise in California’s carbon emissions.

      Q: This plant in particular has been highly controversial since its inception. What’s your assessment of the plant’s safety beyond its scheduled shutdown, and how do you see this report as contributing to the decision-making about that shutdown?

      Buongiorno: The Diablo Canyon Nuclear Power Plant has a very strong safety record. The potential safety concern for Diablo is related to its proximity to several fault lines. Being located in California, the plant was designed to withstand large earthquakes to begin with. Following the Fukushima accident in 2011, the Nuclear Regulatory Commission reviewed the plant’s ability to withstand external events (e.g., earthquakes, tsunamis, floods, tornadoes, wildfires, hurricanes) of exceptionally rare and severe magnitude. After nine years of assessment the NRC’s conclusion is that “existing seismic capacity or effective flood protection [at Diablo Canyon] will address the unbounded reevaluated hazards.” That is, Diablo was designed and built to withstand even the rarest and strongest earthquakes that are physically possible at this site.

      As an additional level of protection, the plant has been retrofitted with special equipment and procedures meant to ensure reliable cooling of the reactor core and spent fuel pool under a hypothetical scenario in which all design-basis safety systems have been disabled by a severe external event.

      Lienhard: As for the potential impact of this report, PG&E [the California utility] has already made the decision to shut down the plant, and we and others hope that decision will be revisited and reversed. We believe that this report gives the relevant stakeholders and policymakers a lot of information about options and value associated with keeping the plant running, and about how California could benefit from clean water and clean power generated at Diablo Canyon. It’s not up to us to make the decision, of course — that is a decision that must be made by the people of California. All we can do is provide information.

      Q: What are the biggest challenges or obstacles to seeing these ideas implemented?

      Lienhard: California has very strict environmental protection regulations, and it’s good that they do. One of the areas of great concern to California is the health of the ocean and protection of the coastal ecosystem. As a result, very strict rules are in place about the intake and outfall of both power plants and desalination plants, to protect marine life. Our analysis suggests that this combined plant can be implemented within the parameters prescribed by the California Ocean Plan and that it can meet the regulatory requirements.

      We believe that deeper analysis would be needed before you could proceed. You would need to do site studies and really get out into the water and look in detail at what’s there. But the preliminary analysis is positive. A second challenge is that the discourse in California around nuclear power has generally not been very supportive, and similarly some groups in California oppose desalination. We expect that that both of those points of view would be part of the conversation about whether or not to procede with this project.

      Q: How particular is this analysis to the specifics of this location? Are there aspects of it that apply to other nuclear plants, domestically or globally?

      Lienhard: Hundreds of nuclear plants around the world are situated along the coast, and many are in water stressed regions. Although our analysis focused on Diablo Canyon, we believe that the general findings are applicable to many other seaside nuclear plants, so that this approach and these conclusions could potentially be applied at hundreds of sites worldwide. More

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      in Energy

      MIT Energy Night 2021: Connecting global innovators to local talent

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      On Oct. 29, leading clean technology innovators from around the world convened virtually and in-person on the MIT campus for the MIT Energy and Climate (MITEC) Club’s Energy Night 2021.

      The event featured an array of participants and attendees — from MIT students and faculty to investors, engineers, and established and early-stage companies — all committed to developing cutting-edge technologies to address climate and energy challenges.   

      The event began with a series of virtual presentations and panels that featured speakers from premier players in the climate and technology spheres. Those presenting included policymakers and market enablers, such as ARPA-E and Actuate, investors and accelerators, like TDK Ventures and Prime Coalition, along with numerous startups, including Commonwealth Fusion Systems and Infinite Cooling. The goal was to discuss how nascent technologies could crystalize into viable solutions.

      “A lot of project ideas have the potential to be commercialized,” explains Anne Liu, a research assistant at the MIT Materials Systems Lab and the event’s co-managing director. “So, the goal of our virtual session was to explore the business side of the energy ecosystem by inviting leaders to discuss how to turn ideas into successful companies.”

      While the virtual session explored commercialization, the poster session presented early-stage innovation. It featured more than 70 posters by scientists, startups, and engineers from across the MIT community and far beyond.

      “The poster session is one of the most exciting parts of Energy Night,” says Naomi Lutz, a fourth-year undergraduate in the Department of Mechanical Engineering. “It provides a great opportunity to step back and learn more about what others are doing in specific areas of energy.”

      The work featured spanned the climate and energy sphere, ranging from nuclear fusion to carbon capture — and even included a proposal for solar smokestacks.

      “There are so many topics in energy and climate. And, yet it’s common to only connect with those in your specific track,” says Alexandra Steckmest, one of the event’s organizers and an MBA candidate at MIT Sloan School of Management. “So, we designed the poster session as a platform for people to connect with those from different realms of the energy sector.”

      To the MITEC team, presenting this broader spectrum of research isn’t just exciting — it’s necessary.

      “This is such a rapidly changing industry,” says Steckmest. “So, it’s important to have so many industry experts share information about the changes that are going on in it.”

      The event’s hybrid format, therefore, responded to more than just the Covid-19 pandemic: it also catered to the global, collaborative, and continuously evolving nature of the energy and cleantech industries.

      “After some discussion, we decided on this hybrid format,” explains Liu. “We wanted to ensure that we could have the interactivity of an in-person event while also reaching the much broader audience we had cultivated during last year’s entirely remote format.”

      The new hybrid format helped the team cast a wide net. In total, 400 people attended the in-person poster session while nearly an additional 400 people attended virtually from around the world.

      Yet, despite an increasingly global scope, Energy Night still retained a distinctly local composition. Numerous companies present at the virtual session hailed from across Greater Boston, and, quite often, near MIT: Commonwealth Fusion Systems and Infinite Cooling retain offices within Somerville or Cambridge, and each spawned from MIT.

      “There are so many companies coming out of [MIT] that go on to establish themselves in Boston and Cambridge,” notes Steckmest. “That makes [Energy Night] well-positioned to build connections and generate value for local accelerators.”

      MITEC continues to cultivate these local connections while also contributing to Boston’s unique cleantech culture.

      “What sets Boston apart is its emphasis on long-term solutions that are not always easily achievable through conventional venture capital,” says Liu.

      When planning Energy Night, she and her team sought to invite both short- and long-term solutions to showcase Boston’s aspirational culture while also offering a venue for established investors to seek new, more readily deployable technologies.

      Perhaps the greatest testament to Energy Night’s ongoing success is its tendency to come full circle.

      “Over the past few years, we’ve featured serial presenters from MIT that have gone on to found their own companies,” explains Liu. “So, for a lot of projects, we see a transition from an idea to a successful business.”

      Form Energy, for instance, is an MIT spinoff founded in 2017 with the mission of creating low-cost, long-term energy storage. Its stature grew greatly following its presence at Energy Night in 2019, after which it attracted $40 million in venture capital funding.

      “Whether you’re a first-year undergraduate or a long-time member of the energy and cleantech industries, we want Energy Night to generate these driving connections that lead to professional growth, as well as successful partnerships,” says Steckmest. More

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      in Security

      Saving seaweed with machine learning

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      Last year, Charlene Xia ’17, SM ’20 found herself at a crossroads. She was finishing up her master’s degree in media arts and sciences from the MIT Media Lab and had just submitted applications to doctoral degree programs. All Xia could do was sit and wait. In the meantime, she narrowed down her career options, regardless of whether she was accepted to any program.

      “I had two thoughts: I’m either going to get a PhD to work on a project that protects our planet, or I’m going to start a restaurant,” recalls Xia.

      Xia poured over her extensive cookbook collection, researching international cuisines as she anxiously awaited word about her graduate school applications. She even looked into the cost of a food truck permit in the Boston area. Just as she started hatching plans to open a plant-based skewer restaurant, Xia received word that she had been accepted into the mechanical engineering graduate program at MIT.

      Shortly after starting her doctoral studies, Xia’s advisor, Professor David Wallace, approached her with an interesting opportunity. MathWorks, a software company known for developing the MATLAB computing platform, had announced a new seed funding program in MIT’s Department of Mechanical Engineering. The program encouraged collaborative research projects focused on the health of the planet.

      “I saw this as a super-fun opportunity to combine my passion for food, my technical expertise in ocean engineering, and my interest in sustainably helping our planet,” says Xia.

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      From MIT Mechanical Engineering: “Saving Seaweed with Machine Learning”

      Wallace knew Xia would be up to the task of taking an interdisciplinary approach to solve an issue related to the health of the planet. “Charlene is a remarkable student with extraordinary talent and deep thoughtfulness. She is pretty much fearless, embracing challenges in almost any domain with the well-founded belief that, with effort, she will become a master,” says Wallace.

      Alongside Wallace and Associate Professor Stefanie Mueller, Xia proposed a project to predict and prevent the spread of diseases in aquaculture. The team focused on seaweed farms in particular.

      Already popular in East Asian cuisines, seaweed holds tremendous potential as a sustainable food source for the world’s ever-growing population. In addition to its nutritive value, seaweed combats various environmental threats. It helps fight climate change by absorbing excess carbon dioxide in the atmosphere, and can also absorb fertilizer run-off, keeping coasts cleaner.

      As with so much of marine life, seaweed is threatened by the very thing it helps mitigate against: climate change. Climate stressors like warm temperatures or minimal sunlight encourage the growth of harmful bacteria such as ice-ice disease. Within days, entire seaweed farms are decimated by unchecked bacterial growth.

      To solve this problem, Xia turned to the microbiota present in these seaweed farms as a predictive indicator of any threat to the seaweed or livestock. “Our project is to develop a low-cost device that can detect and prevent diseases before they affect seaweed or livestock by monitoring the microbiome of the environment,” says Xia.

      The team pairs old technology with the latest in computing. Using a submersible digital holographic microscope, they take a 2D image. They then use a machine learning system known as a neural network to convert the 2D image into a representation of the microbiome present in the 3D environment.

      “Using a machine learning network, you can take a 2D image and reconstruct it almost in real time to get an idea of what the microbiome looks like in a 3D space,” says Xia.

      The software can be run in a small Raspberry Pi that could be attached to the holographic microscope. To figure out how to communicate these data back to the research team, Xia drew upon her master’s degree research.

      In that work, under the guidance of Professor Allan Adams and Professor Joseph Paradiso in the Media Lab, Xia focused on developing small underwater communication devices that can relay data about the ocean back to researchers. Rather than the usual $4,000, these devices were designed to cost less than $100, helping lower the cost barrier for those interested in uncovering the many mysteries of our oceans. The communication devices can be used to relay data about the ocean environment from the machine learning algorithms.

      By combining these low-cost communication devices along with microscopic images and machine learning, Xia hopes to design a low-cost, real-time monitoring system that can be scaled to cover entire seaweed farms.

      “It’s almost like having the ‘internet of things’ underwater,” adds Xia. “I’m developing this whole underwater camera system alongside the wireless communication I developed that can give me the data while I’m sitting on dry land.”

      Armed with these data about the microbiome, Xia and her team can detect whether or not a disease is about to strike and jeopardize seaweed or livestock before it is too late.

      While Xia still daydreams about opening a restaurant, she hopes the seaweed project will prompt people to rethink how they consider food production in general.

      “We should think about farming and food production in terms of the entire ecosystem,” she says. “My meta-goal for this project would be to get people to think about food production in a more holistic and natural way.” More

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      in Security

      A robot that finds lost items

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      A busy commuter is ready to walk out the door, only to realize they’ve misplaced their keys and must search through piles of stuff to find them. Rapidly sifting through clutter, they wish they could figure out which pile was hiding the keys.

      Researchers at MIT have created a robotic system that can do just that. The system, RFusion, is a robotic arm with a camera and radio frequency (RF) antenna attached to its gripper. It fuses signals from the antenna with visual input from the camera to locate and retrieve an item, even if the item is buried under a pile and completely out of view.

      The RFusion prototype the researchers developed relies on RFID tags, which are cheap, battery-less tags that can be stuck to an item and reflect signals sent by an antenna. Because RF signals can travel through most surfaces (like the mound of dirty laundry that may be obscuring the keys), RFusion is able to locate a tagged item within a pile.

      Using machine learning, the robotic arm automatically zeroes-in on the object’s exact location, moves the items on top of it, grasps the object, and verifies that it picked up the right thing. The camera, antenna, robotic arm, and AI are fully integrated, so RFusion can work in any environment without requiring a special set up.

      While finding lost keys is helpful, RFusion could have many broader applications in the future, like sorting through piles to fulfill orders in a warehouse, identifying and installing components in an auto manufacturing plant, or helping an elderly individual perform daily tasks in the home, though the current prototype isn’t quite fast enough yet for these uses.

      “This idea of being able to find items in a chaotic world is an open problem that we’ve been working on for a few years. Having robots that are able to search for things under a pile is a growing need in industry today. Right now, you can think of this as a Roomba on steroids, but in the near term, this could have a lot of applications in manufacturing and warehouse environments,” said senior author Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science and director of the Signal Kinetics group in the MIT Media Lab.

      Co-authors include research assistant Tara Boroushaki, the lead author; electrical engineering and computer science graduate student Isaac Perper; research associate Mergen Nachin; and Alberto Rodriguez, the Class of 1957 Associate Professor in the Department of Mechanical Engineering. The research will be presented at the Association for Computing Machinery Conference on Embedded Networked Senor Systems next month.

      Play video

      Sending signals

      RFusion begins searching for an object using its antenna, which bounces signals off the RFID tag (like sunlight being reflected off a mirror) to identify a spherical area in which the tag is located. It combines that sphere with the camera input, which narrows down the object’s location. For instance, the item can’t be located on an area of a table that is empty.

      But once the robot has a general idea of where the item is, it would need to swing its arm widely around the room taking additional measurements to come up with the exact location, which is slow and inefficient.

      The researchers used reinforcement learning to train a neural network that can optimize the robot’s trajectory to the object. In reinforcement learning, the algorithm is trained through trial and error with a reward system.

      “This is also how our brain learns. We get rewarded from our teachers, from our parents, from a computer game, etc. The same thing happens in reinforcement learning. We let the agent make mistakes or do something right and then we punish or reward the network. This is how the network learns something that is really hard for it to model,” Boroushaki explains.

      In the case of RFusion, the optimization algorithm was rewarded when it limited the number of moves it had to make to localize the item and the distance it had to travel to pick it up.

      Once the system identifies the exact right spot, the neural network uses combined RF and visual information to predict how the robotic arm should grasp the object, including the angle of the hand and the width of the gripper, and whether it must remove other items first. It also scans the item’s tag one last time to make sure it picked up the right object.

      Cutting through clutter

      The researchers tested RFusion in several different environments. They buried a keychain in a box full of clutter and hid a remote control under a pile of items on a couch.

      But if they fed all the camera data and RF measurements to the reinforcement learning algorithm, it would have overwhelmed the system. So, drawing on the method a GPS uses to consolidate data from satellites, they summarized the RF measurements and limited the visual data to the area right in front of the robot.

      Their approach worked well — RFusion had a 96 percent success rate when retrieving objects that were fully hidden under a pile.

      “Sometimes, if you only rely on RF measurements, there is going to be an outlier, and if you rely only on vision, there is sometimes going to be a mistake from the camera. But if you combine them, they are going to correct each other. That is what made the system so robust,” Boroushaki says.

      In the future, the researchers hope to increase the speed of the system so it can move smoothly, rather than stopping periodically to take measurements. This would enable RFusion to be deployed in a fast-paced manufacturing or warehouse setting.

      Beyond its potential industrial uses, a system like this could even be incorporated into future smart homes to assist people with any number of household tasks, Boroushaki says.

      “Every year, billions of RFID tags are used to identify objects in today’s complex supply chains, including clothing and lots of other consumer goods. The RFusion approach points the way to autonomous robots that can dig through a pile of mixed items and sort them out using the data stored in the RFID tags, much more efficiently than having to inspect each item individually, especially when the items look similar to a computer vision system,” says Matthew S. Reynolds, CoMotion Presidential Innovation Fellow and associate professor of electrical and computer engineering at the University of Washington, who was not involved in the research. “The RFusion approach is a great step forward for robotics operating in complex supply chains where identifying and ‘picking’ the right item quickly and accurately is the key to getting orders fulfilled on time and keeping demanding customers happy.”

      The research is sponsored by the National Science Foundation, a Sloan Research Fellowship, NTT DATA, Toppan, Toppan Forms, and the Abdul Latif Jameel Water and Food Systems Lab. More