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      in Environment

      Exploring the nanoworld of biogenic gems

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      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

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      in Energy

      A simple way to significantly increase lifetimes of fuel cells and other devices

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      In research that could jump-start work on a range of technologies including fuel cells, which are key to storing solar and wind energy, MIT researchers have found a relatively simple way to increase the lifetimes of these devices: changing the pH of the system.

      Fuel and electrolysis cells made of materials known as solid metal oxides are of interest for several reasons. For example, in the electrolysis mode, they are very efficient at converting electricity from a renewable source into a storable fuel like hydrogen or methane that can be used in the fuel cell mode to generate electricity when the sun isn’t shining or the wind isn’t blowing. They can also be made without using costly metals like platinum. However, their commercial viability has been hampered, in part, because they degrade over time. Metal atoms seeping from the interconnects used to construct banks of fuel/electrolysis cells slowly poison the devices.

      “What we’ve been able to demonstrate is that we can not only reverse that degradation, but actually enhance the performance above the initial value by controlling the acidity of the air-electrode interface,” says Harry L. Tuller, the R.P. Simmons Professor of Ceramics and Electronic Materials in MIT’s Department of Materials Science and Engineering (DMSE).

      The research, initially funded by the U.S. Department of Energy through the Office of Fossil Energy and Carbon Management’s (FECM) National Energy Technology Laboratory, should help the department meet its goal of significantly cutting the degradation rate of solid oxide fuel cells by 2035 to 2050.

      “Extending the lifetime of solid oxide fuels cells helps deliver the low-cost, high-efficiency hydrogen production and power generation needed for a clean energy future,” says Robert Schrecengost, acting director of FECM’s Division of Hydrogen with Carbon Management. “The department applauds these advancements to mature and ultimately commercialize these technologies so that we can provide clean and reliable energy for the American people.”

      “I’ve been working in this area my whole professional life, and what I’ve seen until now is mostly incremental improvements,” says Tuller, who was recently named a 2022 Materials Research Society Fellow for his career-long work in solid-state chemistry and electrochemistry. “People are normally satisfied with seeing improvements by factors of tens-of-percent. So, actually seeing much larger improvements and, as importantly, identifying the source of the problem and the means to work around it, issues that we’ve been struggling with for all these decades, is remarkable.”

      Says James M. LeBeau, the John Chipman Associate Professor of Materials Science and Engineering at MIT, who was also involved in the research, “This work is important because it could overcome [some] of the limitations that have prevented the widespread use of solid oxide fuel cells. Additionally, the basic concept can be applied to many other materials used for applications in the energy-related field.”

      A report describing the work was reported Aug. 11, in Energy & Environmental Science. Additional authors of the paper are Han Gil Seo, a DMSE postdoc; Anna Staerz, formerly a DMSE postdoc, now at Interuniversity Microelectronics Centre (IMEC) Belgium and soon to join the Colorado School of Mines faculty; Dennis S. Kim, a DMSE postdoc; Dino Klotz, a DMSE visiting scientist, now at Zurich Instruments; Michael Xu, a DMSE graduate student; and Clement Nicollet, formerly a DMSE postdoc, now at the Université de Nantes. Seo and Staerz contributed equally to the work.

      Changing the acidity

      A fuel/electrolysis cell has three principal parts: two electrodes (a cathode and anode) separated by an electrolyte. In the electrolysis mode, electricity from, say, the wind, can be used to generate storable fuel like methane or hydrogen. On the other hand, in the reverse fuel cell reaction, that storable fuel can be used to create electricity when the wind isn’t blowing.

      A working fuel/electrolysis cell is composed of many individual cells that are stacked together and connected by steel metal interconnects that include the element chrome to keep the metal from oxidizing. But “it turns out that at the high temperatures that these cells run, some of that chrome evaporates and migrates to the interface between the cathode and the electrolyte, poisoning the oxygen incorporation reaction,” Tuller says. After a certain point, the efficiency of the cell has dropped to a point where it is not worth operating any longer.

      “So if you can extend the life of the fuel/electrolysis cell by slowing down this process, or ideally reversing it, you could go a long way towards making it practical,” Tuller says.

      The team showed that you can do both by controlling the acidity of the cathode surface. They also explained what is happening.

      To achieve their results, the team coated the fuel/electrolysis cell cathode with lithium oxide, a compound that changes the relative acidity of the surface from being acidic to being more basic. “After adding a small amount of lithium, we were able to recover the initial performance of a poisoned cell,” Tuller says. When the engineers added even more lithium, the performance improved far beyond the initial value. “We saw improvements of three to four orders of magnitude in the key oxygen reduction reaction rate and attribute the change to populating the surface of the electrode with electrons needed to drive the oxygen incorporation reaction.”

      The engineers went on to explain what is happening by observing the material at the nanoscale, or billionths of a meter, with state-of-the-art transmission electron microscopy and electron energy loss spectroscopy at MIT.nano. “We were interested in understanding the distribution of the different chemical additives [chromium and lithium oxide] on the surface,” says LeBeau.

      They found that the lithium oxide effectively dissolves the chromium to form a glassy material that no longer serves to degrade the cathode performance.

      Applications for sensors, catalysts, and more

      Many technologies like fuel cells are based on the ability of the oxide solids to rapidly breathe oxygen in and out of their crystalline structures, Tuller says. The MIT work essentially shows how to recover — and speed up — that ability by changing the surface acidity. As a result, the engineers are optimistic that the work could be applied to other technologies including, for example, sensors, catalysts, and oxygen permeation-based reactors.

      The team is also exploring the effect of acidity on systems poisoned by different elements, like silica.

      Concludes Tuller: “As is often the case in science, you stumble across something and notice an important trend that was not appreciated previously. Then you test that concept further, and you discover that it is really very fundamental.”

      In addition to the DOE, this work was also funded by the National Research Foundation of Korea, the MIT Department of Materials Science and Engineering via Tuller’s appointment as the R.P. Simmons Professor of Ceramics and Electronic Materials, and the U.S. Air Force Office of Scientific Research. More

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      in Energy

      New hardware offers faster computation for artificial intelligence, with much less energy

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      As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage.

      Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analog artificial “neurons” and “synapses” that execute computations just like a digital neural network. This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.

      A multidisciplinary team of MIT researchers set out to push the speed limits of a type of human-made analog synapse that they had previously developed. They utilized a practical inorganic material in the fabrication process that enables their devices to run 1 million times faster than previous versions, which is also about 1 million times faster than the synapses in the human brain.

      Moreover, this inorganic material also makes the resistor extremely energy-efficient. Unlike materials used in the earlier version of their device, the new material is compatible with silicon fabrication techniques. This change has enabled fabricating devices at the nanometer scale and could pave the way for integration into commercial computing hardware for deep-learning applications.

      “With that key insight, and the very powerful nanofabrication techniques we have at MIT.nano, we have been able to put these pieces together and demonstrate that these devices are intrinsically very fast and operate with reasonable voltages,” says senior author Jesús A. del Alamo, the Donner Professor in MIT’s Department of Electrical Engineering and Computer Science (EECS). “This work has really put these devices at a point where they now look really promising for future applications.”

      “The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime,” explains senior author Bilge Yildiz, the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering.

      “The action potential in biological cells rises and falls with a timescale of milliseconds, since the voltage difference of about 0.1 volt is constrained by the stability of water,” says senior author Ju Li, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and professor of materials science and engineering, “Here we apply up to 10 volts across a special solid glass film of nanoscale thickness that conducts protons, without permanently damaging it. And the stronger the field, the faster the ionic devices.”

      These programmable resistors vastly increase the speed at which a neural network is trained, while drastically reducing the cost and energy to perform that training. This could help scientists develop deep learning models much more quickly, which could then be applied in uses like self-driving cars, fraud detection, or medical image analysis.

      “Once you have an analog processor, you will no longer be training networks everyone else is working on. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft,” adds lead author and MIT postdoc Murat Onen.

      Co-authors include Frances M. Ross, the Ellen Swallow Richards Professor in the Department of Materials Science and Engineering; postdocs Nicolas Emond and Baoming Wang; and Difei Zhang, an EECS graduate student. The research is published today in Science.

      Accelerating deep learning

      Analog deep learning is faster and more energy-efficient than its digital counterpart for two main reasons. “First, computation is performed in memory, so enormous loads of data are not transferred back and forth from memory to a processor.” Analog processors also conduct operations in parallel. If the matrix size expands, an analog processor doesn’t need more time to complete new operations because all computation occurs simultaneously.

      The key element of MIT’s new analog processor technology is known as a protonic programmable resistor. These resistors, which are measured in nanometers (one nanometer is one billionth of a meter), are arranged in an array, like a chess board.

      In the human brain, learning happens due to the strengthening and weakening of connections between neurons, called synapses. Deep neural networks have long adopted this strategy, where the network weights are programmed through training algorithms. In the case of this new processor, increasing and decreasing the electrical conductance of protonic resistors enables analog machine learning.

      The conductance is controlled by the movement of protons. To increase the conductance, more protons are pushed into a channel in the resistor, while to decrease conductance protons are taken out. This is accomplished using an electrolyte (similar to that of a battery) that conducts protons but blocks electrons.

      To develop a super-fast and highly energy efficient programmable protonic resistor, the researchers looked to different materials for the electrolyte. While other devices used organic compounds, Onen focused on inorganic phosphosilicate glass (PSG).

      PSG is basically silicon dioxide, which is the powdery desiccant material found in tiny bags that come in the box with new furniture to remove moisture. It is studied as a proton conductor under humidified conditions for fuel cells. It is also the most well-known oxide used in silicon processing. To make PSG, a tiny bit of phosphorus is added to the silicon to give it special characteristics for proton conduction.

      Onen hypothesized that an optimized PSG could have a high proton conductivity at room temperature without the need for water, which would make it an ideal solid electrolyte for this application. He was right.

      Surprising speed

      PSG enables ultrafast proton movement because it contains a multitude of nanometer-sized pores whose surfaces provide paths for proton diffusion. It can also withstand very strong, pulsed electric fields. This is critical, Onen explains, because applying more voltage to the device enables protons to move at blinding speeds.

      “The speed certainly was surprising. Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before. And this movement doesn’t damage anything, thanks to the small size and low mass of protons. It is almost like teleporting,” he says.

      “The nanosecond timescale means we are close to the ballistic or even quantum tunneling regime for the proton, under such an extreme field,” adds Li.

      Because the protons don’t damage the material, the resistor can run for millions of cycles without breaking down. This new electrolyte enabled a programmable protonic resistor that is a million times faster than their previous device and can operate effectively at room temperature, which is important for incorporating it into computing hardware.

      Thanks to the insulating properties of PSG, almost no electric current passes through the material as protons move. This makes the device extremely energy efficient, Onen adds.

      Now that they have demonstrated the effectiveness of these programmable resistors, the researchers plan to reengineer them for high-volume manufacturing, says del Alamo. Then they can study the properties of resistor arrays and scale them up so they can be embedded into systems.

      At the same time, they plan to study the materials to remove bottlenecks that limit the voltage that is required to efficiently transfer the protons to, through, and from the electrolyte.

      “Another exciting direction that these ionic devices can enable is energy-efficient hardware to emulate the neural circuits and synaptic plasticity rules that are deduced in neuroscience, beyond analog deep neural networks. We have already started such a collaboration with neuroscience, supported by the MIT Quest for Intelligence,” adds Yildiz.

      “The collaboration that we have is going to be essential to innovate in the future. The path forward is still going to be very challenging, but at the same time it is very exciting,” del Alamo says.

      “Intercalation reactions such as those found in lithium-ion batteries have been explored extensively for memory devices. This work demonstrates that proton-based memory devices deliver impressive and surprising switching speed and endurance,” says William Chueh, associate professor of materials science and engineering at Stanford University, who was not involved with this research. “It lays the foundation for a new class of memory devices for powering deep learning algorithms.”

      “This work demonstrates a significant breakthrough in biologically inspired resistive-memory devices. These all-solid-state protonic devices are based on exquisite atomic-scale control of protons, similar to biological synapses but at orders of magnitude faster rates,” says Elizabeth Dickey, the Teddy & Wilton Hawkins Distinguished Professor and head of the Department of Materials Science and Engineering at Carnegie Mellon University, who was not involved with this work. “I commend the interdisciplinary MIT team for this exciting development, which will enable future-generation computational devices.”

      This research is funded, in part, by the MIT-IBM Watson AI Lab. More

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      in Energy

      MIT engineers design surfaces that make water boil more efficiently

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      The boiling of water or other fluids is an energy-intensive step at the heart of a wide range of industrial processes, including most electricity generating plants, many chemical production systems, and even cooling systems for electronics.

      Improving the efficiency of systems that heat and evaporate water could significantly reduce their energy use. Now, researchers at MIT have found a way to do just that, with a specially tailored surface treatment for the materials used in these systems.

      The improved efficiency comes from a combination of three different kinds of surface modifications, at different size scales. The new findings are described in the journal Advanced Materials in a paper by recent MIT graduate Youngsup Song PhD ’21, Ford Professor of Engineering Evelyn Wang, and four others at MIT. The researchers note that this initial finding is still at a laboratory scale, and more work is needed to develop a practical, industrial-scale process.

      There are two key parameters that describe the boiling process: the heat transfer coefficient (HTC) and the critical heat flux (CHF). In materials design, there’s generally a tradeoff between the two, so anything that improves one of these parameters tends to make the other worse. But both are important for the efficiency of the system, and now, after years of work, the team has achieved a way of significantly improving both properties at the same time, through their combination of different textures added to a material’s surface.

      “Both parameters are important,” Song says, “but enhancing both parameters together is kind of tricky because they have intrinsic trade off.” The reason for that, he explains, is “because if we have lots of bubbles on the boiling surface, that means boiling is very efficient, but if we have too many bubbles on the surface, they can coalesce together, which can form a vapor film over the boiling surface.” That film introduces resistance to the heat transfer from the hot surface to the water. “If we have vapor in between the surface and water, that prevents the heat transfer efficiency and lowers the CHF value,” he says.

      Song, who is now a postdoc at Lawrence Berkeley National Laboratory, carried out much of the research as part of his doctoral thesis work at MIT. While the various components of the new surface treatment he developed had been previously studied, the researchers say this work is the first to show that these methods could be combined to overcome the tradeoff between the two competing parameters.

      Adding a series of microscale cavities, or dents, to a surface is a way of controlling the way bubbles form on that surface, keeping them effectively pinned to the locations of the dents and preventing them from spreading out into a heat-resisting film. In this work, the researchers created an array of 10-micrometer-wide dents separated by about 2 millimeters to prevent film formation. But that separation also reduces the concentration of bubbles at the surface, which can reduce the boiling efficiency. To compensate for that, the team introduced a much smaller-scale surface treatment, creating tiny bumps and ridges at the nanometer scale, which increases the surface area and promotes the rate of evaporation under the bubbles.

      In these experiments, the cavities were made in the centers of a series of pillars on the material’s surface. These pillars, combined with nanostructures, promote wicking of liquid from the base to their tops, and this enhances the boiling process by providing more surface area exposed to the water. In combination, the three “tiers” of the surface texture — the cavity separation, the posts, and the nanoscale texturing — provide a greatly enhanced efficiency for the boiling process, Song says.

      “Those micro cavities define the position where bubbles come up,” he says. “But by separating those cavities by 2 millimeters, we separate the bubbles and minimize the coalescence of bubbles.” At the same time, the nanostructures promote evaporation under the bubbles, and the capillary action induced by the pillars supplies liquid to the bubble base. That maintains a layer of liquid water between the boiling surface and the bubbles of vapor, which enhances the maximum heat flux.

      Although their work has confirmed that the combination of these kinds of surface treatments can work and achieve the desired effects, this work was done under small-scale laboratory conditions that could not easily be scaled up to practical devices, Wang says. “These kinds of structures we’re making are not meant to be scaled in its current form,” she says, but rather were used to prove that such a system can work. One next step will be to find alternative ways of creating these kinds of surface textures so these methods could more easily be scaled up to practical dimensions.

      “Showing that we can control the surface in this way to get enhancement is a first step,” she says. “Then the next step is to think about more scalable approaches.” For example, though the pillars on the surface in these experiments were created using clean-room methods commonly used to produce semiconductor chips, there are other, less demanding ways of creating such structures, such as electrodeposition. There are also a number of different ways to produce the surface nanostructure textures, some of which may be more easily scalable.

      There may be some significant small-scale applications that could use this process in its present form, such as the thermal management of electronic devices, an area that is becoming more important as semiconductor devices get smaller and managing their heat output becomes ever more important. “There’s definitely a space there where this is really important,” Wang says.

      Even those kinds of applications will take some time to develop because typically thermal management systems for electronics use liquids other than water, known as dielectric liquids. These liquids have different surface tension and other properties than water, so the dimensions of the surface features would have to be adjusted accordingly. Work on these differences is one of the next steps for the ongoing research, Wang says.

      This same multiscale structuring technique could also be applied to different liquids, Song says, by adjusting the dimensions to account for the different properties of the liquids. “Those kinds of details can be changed, and that can be our next step,” he says.

      The team also included Carlos Diaz-Martin, Lenan Zhang, Hyeongyun Cha, and Yajing Zhao, all at MIT. The work was supported by the Advanced Research Projects Agency-Energy (ARPA-E), the Air Force Office of Scientific Research, and the Singapore-MIT Alliance for Research and Technology, and made use of the MIT.nano facilities. 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

      Chemistry Undergraduate Teaching Lab hibernates fume hoods, drastically reducing energy costs

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      The Department of Chemistry’s state-of-the-art Undergraduate Teaching Lab (UGTL), which opened on the fifth floor of MIT.nano in fall 2018, is home to 69 fume hoods. The hoods, ranging from four to seven feet wide, protect students and staff from potential exposure to hazardous materials while working in the lab. Fume hoods represent a tremendous energy consumption on the MIT campus; in addition to the energy required to operate them, the air that replaces what is exhausted must be heated or cooled. Thus, any lab with a large number of fume hoods is destined to be faced with high operational energy cost.

      “When the UGTL’s fume hoods are in use, the air-change rates — the number of times fresh air is exchanged in the space in a given time frame — averages between 25 and 30 air changes per hour (ACH),” says Nicole Imbergamo, senior sustainability project manager in MIT Campus Construction. “When the lab is unoccupied, that air-change rate averages 11 ACH. For context, in a laboratory with a single fume hood, typically MIT’s EHS [Environment, Health, and Safety] department would require six ACH when occupied and four ACH when unoccupied. Hibernation of the fume hoods allowed us to close the gap between the current unoccupied air-change rate and what is typical on campus in a non-teaching lab environment.”

      Fifty-eight of the 69 fume hoods in the UGTL are consistently unused between the hours of 6:30 p.m. and 12 p.m., as well as all weekend long, totaling 135 hours per week. Based on these numbers, the team determined it was safe to “hibernate” the fume hoods during the off hours, saving the Institute on fan energy and the cost of heating and cooling the air that gets flushed into each hood.

      John Dolhun PhD ’73 is the director of the UGTL. “The project started when MIT Green Labs — a division of the Environment, Health, and Safety Office now known as the Safe & Sustainable Labs Program — contacted the UGTL in October 2018, followed by an initial meeting in November 2018 with all the key players, including Safe and Sustainable Labs, the EHS Office, the Department of Facilities, and the Department of Chemistry,” says Dolhun. “It was during these initial discussions that the UGTL recognized this was something we had to do. The project was completed in April 2021.”

      Now, through a scheduled time clock in the Building Management System (BMS), the 58 fume hoods are flipped into hibernation mode at the end of each day. “In hibernation mode, the exhaust air valves go to their minimum airflow, which is lower than a fume hood minimum required when in use,” says Imbergamo. “As a safety feature, if the sash of a fume hood is opened while it is in standby mode, the valve and hood are automatically released from hibernation until the next scheduled time.” The BMS allows Dolhun and all with access to instantly view the hibernation status of every hood online, at any time, from any location. As an additional safety measure, the lab is equipped with an emergency kill switch that, when activated, instantly takes all 58 fume hoods out of hibernation, increasing the air changes per hour by about 37 percent, at one touch.

      The MIT operations team worked with the building controls vendor to create graphics that allow the UGTL users to easily see the hood sash positions and their current status as either hibernated or in normal operating mode. This virtual visibility allows the UGTL team to confirm the hoods are all closed before leaving the lab at the end of each day, and to confirm the energy reductions. This visual access also lends itself to educating the students on the importance of closing the sash at the end of their lab work, and gives an opportunity for educating the students on relevant fume hood management best practices that will serve them far beyond their undergraduate chemistry classes.

      Since employing the use of hibernation mode, the unoccupied UGTL air change rate has plummeted from 11 ACH to seven ACH, drastically shrinking unnecessary energy outflow, saving MIT an estimated $21,000 per year. The annual utility cost savings of both reduced supply and exhaust fan energy, as well as the heating and cooling required of the supply air to the space, will result in a less-than three-year payback for MIT. The overall success of the hood hibernation program, and the savings that it has afforded the UGTL, is very motivational for the Green Initiative. The highlights of this system will be shared with other labs, both at MIT and beyond, that may also benefit from similar adjustments. More