A new way to make steel could reduce America’s reliance on imports

America has been making steel from iron ore the same way for hundreds of years. Unfortunately, it hasn’t been making enough of it. Today the U.S. is the world’s largest steel importer, relying on other countries to produce a material that serves as the backbone of our society.

That’s not to say the U.S. is alone: Globally, most steel today is made in enormous, multi-billion-dollar plants using a coal-based process that hasn’t changed much in 300 years.

Now Hertha Metals, founded by CEO Laureen Meroueh SM ’18, PhD ’20, is scaling up a new steel production system powered by natural gas and electricity. The process, which can also run on hydrogen, uses a continuous electric arc furnace within which iron ore of any grade and format is reduced and carburized into molten steel in a single step. It also eliminates the need for coking and sintering plants, along with other dangerous and expensive components of traditional systems. As a result, the company says its process uses 30 percent less energy and costs less to operate than conventional steel mills in America.

“The real headline is the fact that we can make steel from iron ore more cost-competitive by 25 percent in the United States, while also reducing emissions.” Meroueh says. “The United States hasn’t been competitive in steelmaking in decades. Now we’re enabling that.”

Since late 2024, Hertha has been operating a 1-tonne-per-day pilot plant at its first production facility outside Houston, Texas. The company calls it the world’s largest demonstration of a single-step steelmaking process. This year, the company will begin construction of a plant that will be able to produce 10,000 tons of steel each year. That plant, which Hertha expects to reach full production capacity at the end 2027, will also produce high-purity iron for the magnet industry, helping America onshore another critical material.

“By importing so much of our pig iron and steel, we are completely reliant on global trade mechanisms and geopolitics remaining the way they are today for us to continue making the materials that are critical for our infrastructure, our defense systems, and our energy systems,” Meroueh says. “Steel is the most foundational material to our society. It is simply irreplaceable.”

Streamlining steelmaking

Meroueh earned her master’s degree in the lab of Gang Chen, MIT’s Carl Richard Soderberg Professor of Power Engineering. She studied thermal energy storage and the fundamental physics of heat transfer, eventually getting her first taste of entrepreneurship when she explored commercializing some of that research. Meroueh received a grant from the MIT Sandbox Innovation Fund and considers Executive Director Jinane Abounadi a close mentor today.

The experience taught Meroueh a lot about startups, but she ultimately decided to stay at MIT to pursue her PhD in metallurgy and hydrogen production in the lab of Douglas Hart, MIT professor of mechanical engineering. After earning her PhD in 2020, she was recruited to lead a hydrogen production startup for a year and a half.

“After that experience, I was looking at all of the hard-to-abate, high-emissions sectors of the economy to find the one receiving the least attention,” Meroueh says. “I stumbled onto steel and fell in love.”

Meroueh became an Innovators Fellow at the climate and energy startup investment firm Breakthrough Energy and officially founded Hertha Metals in 2022.

The company is named after Hertha Ayrton, a 19th-century physicist and inventor who advanced our understanding of electric arcs, which the company uses in its furnaces.

Globally, most steel today is made by combining iron ore with coke (from coal) and limestone in a blast furnace to make molten iron. That “pig iron” is then sent to another furnace to burn off excess carbon and impurities. Alloying elements are then added, and the steel is sent for casting and finishing, requiring additional machinery.

The U.S. makes most of its steel from recycled scrap metal, but it still must import iron made from a blast furnace to reach useful grades of steel.

“The United States has a massive need to make steel from iron ore, not just scrap, so we can stop relying on importing so much,” Meroueh explains. “We only have about 11 operational blast furnaces in the U.S., so we end up importing about 90 percent of the pig iron needed to feed into domestic scrap steel furnaces.”

To solve the problem, Meroueh leveraged a fuel America has in abundance: natural gas. Hertha’s system uses natural gas (the process also works with hydrogen) to reduce iron ore while using electricity to melt it in a single step. She says the closest competing technology requires scarce and expensive pelletized, high-grade iron ore and multiple furnaces to produce liquid steel. Meroueh’s process uses iron ore of any format or grade, producing refined liquid steel in a single furnace, cutting both cost and emissions.

“Many reactions that were previously run sequentially though a conventional steelmaking process are now occurring simultaneously, within a single furnace,” Meroueh explains. “We’re melting, we’re reducing, and we’re carburizing the steel to the exact amount we need. What exits our furnace is a refined molten steel. We can process any grade and format of iron ore because everything is occurring in the molten phase. It doesn’t matter whether the ore came in as a pellet or clumps and fines out of the ground.”

Meroueh says the company’s biggest innovation is performing the gaseous reduction when the iron oxide is a molten liquid using proprietary gas technologies.     

“All of the conventional steelmaking technologies perform reduction while the iron ore is in a solid state, and they use gas — whether that’s combusted coke or natural gas — to perform that reduction,” Meroueh says. “We saw the inefficiency in doing that and how it restricted the grade and form of usable iron ore, because at the end of the day you have to melt the ore anyway.”

Hertha’s system is modular and uses standard off-gas handling equipment, steam turbines, and heat exchangers. It also recycles natural gas to regenerate electricity from the hot off-gas leaving the furnace.

“Our steel mill has its own little power plant attached that leads to 35 percent recovery in energy and minimizes grid power demand in an age in which we are competing with data centers,” Meroueh says.

Onshoring critical materials

Today’s steel mills are the result of enormous investments and are designed to run for at least 50 years. Hertha Metals doesn’t envision replacing those entirely — at least not anytime soon.

“You’re not just going to shut off a steel mill in the middle of its life,” Meroueh says. “Sure, you can build new steel mills, but we really want to be able to displace the blast furnace and the basic oxygen furnace while still utilizing all the mill’s downstream equipment.”

The company’s Houston plant began producing one ton of steel per day just two years after Hertha’s founding and less than one year after Meroueh opened up Hertha’s headquarters. She calls it an important first step.

“This is the largest-scale demonstration of a single-step steelmaking company,” Meroueh says. “It’s a true breakthrough in terms of scalability, pace of progress, and capital efficiency.”

The company’s next plant, which will be capable of producing 10,000 tons of steel each year, will also be producing high-purity iron for permanent magnets, which are used in electric motors, robotics, consumer electronics, aerospace and military hardware.

“It’s insane that we don’t make rare earth magnets domestically,” Meroueh says. “It’s insane that any country doesn’t make their own rare earth magnets. Most rare earth magnets are permanent magnets, so neodymium magnets. What’s interesting is that by weight, 70 percent of that magnet is not a rare earth, it’s high-purity iron. America doesn’t currently make any high-purity iron, but Hertha has already made it in our pilot plant.”

Hertha plans to quickly scale up its production of high-purity iron so that, by 2030, it will be able to meet about a quarter of total projected demand for magnets in the U.S.

After that, the company plans to run a full-scale commercial steel plant in partnership with a steel manufacturer in America. Meroueh says that plant, which will be able to produce around half a million tons of steel each year, should be operational by 2030.

“We are eager to partner with today’s steel producers so that we can collectively leverage the existing infrastructure alongside Hertha’s innovation,” Meroueh says. “That includes the $1.5 billion of capital downstream of a melt shop that Hertha’s process can integrate into. The melt shop is the ore-to-liquid steel portion of the steel mill. That’s just the start.  It’s a smaller scale than a conventional plant in which we still economically out compete traditional production processes. Then we’re going to scale to 2 million tons per year once we build up our balance sheet.”

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New J-PAL research and policy initiative to test and scale AI innovations to fight poverty

The Abdul Latif Jameel Poverty Action Lab (J-PAL) at MIT has awarded funding to eight new research studies to understand how artificial intelligence innovations can be used in the fight against poverty through its new Project AI Evidence.

The age of AI has brought wide-ranging optimism and skepticism about its effects on society. To realize AI’s full potential, Project AI Evidence (PAIE) will identify which AI solutions work and for whom, and scale only the most effective, inclusive, and responsible solutions — while scaling down those that may potentially cause harm.

PAIE will generate evidence on what works by connecting governments, tech companies, and nonprofits with world-class economists at MIT and across J-PAL’s global network to evaluate and improve AI solutions to entrenched social challenges.

The new initiative is prioritizing questions policymakers are already asking: Do AI-assisted teaching tools help all children learn? How can early-warning flood systems help people affected by natural disasters? Can machine learning algorithms help reduce deforestation in the Amazon? Can AI-powered chatbots help improve people’s health? In the coming years, PAIE will run a series of funding competitions to invite proposals for evaluations of AI tools that address questions like these, and many more.

PAIE is financially supported by a grant from Google.org, philanthropic support from Community Jameel, a grant from Canada’s International Development Research Centre and UK International Development, and a collaboration agreement with Amazon Web Services. Through a grant from Eric and Wendy Schmidt, awarded by recommendation of Schmidt Sciences, the initiative will also study generative AI in the workplace, particularly in low- and middle-income countries.

Alex Diaz, head of AI for social good at Google.org, says, “we’re thrilled to collaborate with MIT and J-PAL, already leaders in this space, on Project AI Evidence. AI has great potential to benefit all people, but we urgently need to study what works, what doesn’t, and why, if we are to realize this potential.”

“Artificial intelligence holds extraordinary potential, but only if the tools, knowledge, and power to shape it are accessible to all — that includes contextually grounded research and evidence on what works and what does not,” adds Maggie Gorman-Velez, vice president of strategy, regions, and policies at IDRC. “That is why IDRC is proud to be supporting this new evaluation work as part of our ongoing commitment to the responsible scaling of proven safe, inclusive, and locally relevant AI innovations.”

J-PAL is uniquely positioned to help understand AI’s effects on society: Since its inception in 2003, J-PAL’s network of researchers has led over 2,500 rigorous evaluations of social policies and programs around the world. Through PAIE, J-PAL will bring together leading experts in AI technology, research, and social policy, in alignment with MIT president Sally Kornbluth’s focus on generative AI as a strategic priority.

PAIE is chaired by Professor Joshua Blumenstock of the University of California at Berkeley; J-PAL Global Executive Director Iqbal Dhaliwal; and Professor David Yanagizawa-Drott of the University of Zurich.

New evaluations of urgent policy questions

The studies funded in PAIE’s first round of competition explore urgent questions in key sectors like education, health, climate, and economic opportunity.

How can AI be most effective in classrooms, helping both students and teachers?

Existing research shows that personalized learning is important for students, but challenging to implement with limited resources. In Kenya, education social enterprise EIDU has developed an AI tool that helps teachers identify learning gaps and adapt their daily lesson plans. In India, the nongovernmental organization (NGO) Pratham is developing an AI tool to increase the impact and scale of the evidence-informed Teaching at the Right Level approach. J-PAL researchers Daron Acemoglu, Iqbal Dhaliwal, and Francisco Gallego will work with both organizations to study the effects and potential of these different use cases on teachers’ productivity and students’ learning.

Can AI tools reduce gender bias in schools?

Researchers are collaborating with Italy’s Ministry of Education to evaluate whether AI tools can help close gender gaps in students’ performance by addressing teachers’ unconscious biases. J-PAL affiliates Michela Carlana and Will Dobbie, along with Francesca Miserocchi and Eleonora Patacchini, will study the impacts of two AI tools, one that helps teachers predict performance and a second that gives real-time feedback on the diversity of their decisions.

Can AI help career counselors uncover more job opportunities?

In Kenya, researchers are evaluating if an AI tool can identify overlooked skills and unlock employment opportunities, particularly for youth, women, and those without formal education. In collaboration with NGOs Swahilipot and Tabiya, Jasmin Baier and J-PAL researcher Christian Meyer will evaluate how the tool changes people’s job search strategies and employment. This study will shed light on AI as a complement, rather than a substitute, for human expertise in career guidance.

Looking forward

As use of AI in the social sector evolves, these evaluations are a first step in discovering effective, responsible solutions that will go the furthest in alleviating poverty and inequality.

J-PAL’s Dhaliwal notes, “J-PAL has a long history of evaluating innovative technology and its ability to improve people’s lives. While AI has incredible potential, we need to maximize its benefits and minimize possible harms. We’re grateful to our donors, sponsors, and collaborators for their catalytic support in launching PAIE, which will help us do exactly that by continuing to expand evidence on the impacts of AI innovations.”

J-PAL is also seeking new collaborators who share its vision of discovering and scaling up real-world AI solutions. It aims to support more governments and social sector organizations that want to adopt AI responsibly, and will continue to expand funding for new evaluations and provide policy guidance based on the latest research.

To learn more about Project AI Evidence, subscribe to J-PAL's newsletter or contact paie@povertyactionlab.org.

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Maria Yang named vice provost for faculty

Maria Yang ’91, the William E. Leonhard (1940) Professor in the Department of Mechanical Engineering, has been appointed vice provost for faculty at MIT, a role in which she will oversee programs and strategies to recruit and retain faculty members and support them throughout their careers.

Provost Anantha Chandrakasan announced Yang’s appointment, which is effective Feb. 16, in an email to MIT faculty and staff today.

“In the nearly two decades since Maria joined the MIT faculty, she has exemplified dedicated service to the Institute and deep interdisciplinary collaboration,” Chandrakasan wrote. He added that, in a series of leadership positions within the School of Engineering, Yang “consistently demonstrated her skill as a leader, her empathy as a colleague, and her values-driven decision-making.”

As vice provost for faculty, Yang will play a pivotal role in creating an environment where MIT’s faculty members are able to do their best work, “pursuing bold ideas with excellence and creativity,” according to Chandrakasan’s letter. She will partner with school and department leaders on faculty recruitment and retention, mentorship, and strategic planning, and she will oversee programs to support faculty members’ professional development at every stage of their careers.

“Part of what makes MIT unique is the way it provides faculty the room and the encouragement to do work that they think is important, impactful, and sometimes unexpected,” says Yang. “I think it’s vital to foster a culture and a sense of community that really enables our faculty to perform at their best — as researchers, of course, but also as educators and mentors, and as citizens of MIT.”

In addition to her role supporting MIT faculty, Yang will also handle oversight and planning responsibilities for campus academic and research spaces, in partnership with the Office of the Executive Vice President and Treasurer. She will also serve as the principal investigator for the National Science Foundation’s New England Innovation Corps Hub, oversee MIT Solve, and represent the provost on various boards and committees, such as MIT International and the Axim Collaborative.

Yang, who attended MIT as an undergraduate in mechanical engineering as part of the Class of 1991 before earning her master’s and PhD degrees from the design division of the mechanical engineering department at Stanford University, returned to MIT in 2007 as an assistant professor. She has held a number of leadership positions at MIT, including associate dean, deputy dean, and interim dean of the School of Engineering. 

In 2021, Yang co-chaired an Institute-wide committee on the future of design, which recommended the creation of a center to support design opportunities at MIT. Through a generous gift from the Morningside Foundation, the recommendation came to life as the interdisciplinary Morningside Academy for Design (MAD), where Yang has served as associate director since inception. Yang has been instrumental in the development of several new programs at MAD, including design-focused graduate fellowships open to students across MIT and a new design-themed first-year learning community.

Since 2017, Yang has also served as academic faculty director for MIT D-Lab, which uses participatory design to collaborate with communities around the world on the development of solutions to poverty challenges. And since 2024, Yang has served as a co-chair of the SHASS+ Connectivity Fund, which funds research projects in which scholars in the School of Humanities, Arts, and Social Sciences collaborate with faculty colleagues from other schools at MIT.

Given Yang’s extensive track record of working across disciplinary lines, Chandrakasan said in his letter that he had “no doubt that in her new role she will be an effective and trusted champion for colleagues across the Institute.”

An internationally recognized leader in design theory and methodology, Yang is currently focused on researching the early-stage processes used to create successful designs for everything from consumer products to complex, large-scale engineering systems, and the role that these early-stage processes play in determining design outcomes.

Yang, a fellow of the American Society of Mechanical Engineers (ASME), received the 2024 ASME Design Theory and Methodology Award, recognizing “sustained and meritorious contributions” in the field. She has also been recognized with a National Science Foundation CAREER award and the American Society of Engineering Education Fred Merryfield Design Award. In 2017 Yang was named a MacVicar Faculty Fellow, one of MIT’s highest teaching honors.

Yang succeeds Institute Professor Paula Hammond, who served in the role from 2023 before being named dean of the School of Engineering, a role she assumed in January.

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Accelerating science with AI and simulations

For more than a decade, MIT Associate Professor Rafael Gómez-Bombarelli has used artificial intelligence to create new materials. As the technology has expanded, so have his ambitions.

Now, the newly tenured professor in materials science and engineering believes AI is poised to transform science in ways never before possible. His work at MIT and beyond is devoted to accelerating that future.

“We’re at a second inflection point,” Gómez-Bombarelli says. “The first one was around 2015 with the first wave of representation learning, generative AI, and high-throughput data in some areas of science. Those are some of the techniques I first brought into my lab at MIT. Now I think we’re at a second inflection point, mixing language and merging multiple modalities into general scientific intelligence. We’re going to have all the model classes and scaling laws needed to reason about language, reason over material structures, and reason over synthesis recipes.”

Gómez Bombarelli’s research combines physics-based simulations with approaches like machine learning and generative AI to discover new materials with promising real-world applications. His work has led to new materials for batteries, catalysts, plastics, and organic light-emitting diodes (OLEDs). He has also co-founded multiple companies and served on scientific advisory boards for startups applying AI to drug discovery, robotics, and more. His latest company, Lila Sciences, is working to build a scientific superintelligence platform for the life sciences, chemical, and materials science industries.

All of that work is designed to ensure the future of scientific research is more seamless and productive than research today.

“AI for science is one of the most exciting and aspirational uses of AI,” Gómez-Bombarelli says. “Other applications for AI have more downsides and ambiguity. AI for science is about bringing a better future forward in time.”

From experiments to simulations

Gómez-Bombarelli grew up in Spain and gravitated toward the physical sciences from an early age. In 2001, he won a Chemistry Olympics competition, setting him on an academic track in chemistry, which he studied as an undergraduate at his hometown college, the University of Salamanca. Gómez-Bombarelli stuck around for his PhD, where he investigated the function of DNA-damaging chemicals.

“My PhD started out experimental, and then I got bitten by the bug of simulation and computer science about halfway through,” he says. “I started simulating the same chemical reactions I was measuring in the lab. I like the way programming organizes your brain; it felt like a natural way to organize one’s thinking. Programming is also a lot less limited by what you can do with your hands or with scientific instruments.”

Next, Gómez-Bombarelli went to Scotland for a postdoctoral position, where he studied quantum effects in biology. Through that work, he connected with Alán Aspuru-Guzik, a chemistry professor at Harvard University, whom he joined for his next postdoc in 2014.

“I was one of the first people to use generative AI for chemistry in 2016, and I was on the first team to use neural networks to understand molecules in 2015,” Gómez-Bombarelli says. “It was the early, early days of deep learning for science.”

Gómez-Bombarelli also began working to eliminate manual parts of molecular simulations to run more high-throughput experiments. He and his collaborators ended up running hundreds of thousands of calculations across materials, discovering hundreds of promising materials for testing.

After two years in the lab, Gómez-Bombarelli and Aspuru-Guzik started a general-purpose materials computation company, which eventually pivoted to focus on producing organic light-emitting diodes. Gómez-Bombarelli joined the company full-time and calls it the hardest thing he’s ever done in his career.

“It was amazing to make something tangible,” he says. “Also, after seeing Aspuru-Guzik run a lab, I didn’t want to become a professor. My dad was a professor in linguistics, and I thought it was a mellow job. Then I saw Aspuru-Guzik with a 40-person group, and he was on the road 120 days a year. It was insane. I didn’t think I had that type of energy and creativity in me.”

In 2018, Aspuru-Guzik suggested Gómez-Bombarelli apply for a new position in MIT’s Department of Materials Science and Engineering. But, with his trepidation about a faculty job, Gómez-Bombarelli let the deadline pass. Aspuru-Guzik confronted him in his office, slammed his hands on the table, and told him, “You need to apply for this.” It was enough to get Gómez-Bombarelli to put together a formal application.

Fortunately at his startup, Gómez-Bombarelli had spent a lot of time thinking about how to create value from computational materials discovery. During the interview process, he says, he was attracted to the energy and collaborative spirit at MIT. He also began to appreciate the research possibilities.

“Everything I had been doing as a postdoc and at the company was going to be a subset of what I could do at MIT,” he says. “I was making products, and I still get to do that. Suddenly, my universe of work was a subset of this new universe of things I could explore and do.”

It’s been nine years since Gómez Bombarelli joined MIT. Today his lab focuses on how the composition, structure, and reactivity of atoms impact material performance. He has also used high-throughput simulations to create new materials and helped develop tools for merging deep learning with physics-based modeling.

“Physics-based simulations make data and AI algorithms get better the more data you give them,” Gómez Bombarelli’s says. “There are all sorts of virtuous cycles between AI and simulations.”

The research group he has built is solely computational — they don’t run physical experiments.

“It’s a blessing because we can have a huge amount of breadth and do lots of things at once,” he says. “We love working with experimentalists and try to be good partners with them. We also love to create computational tools that help experimentalists triage the ideas coming from AI .”

Gómez-Bombarelli is also still focused on the real-world applications of the materials he invents. His lab works closely with companies and organizations like MIT’s Industrial Liaison Program to understand the material needs of the private sector and the practical hurdles of commercial development.

Accelerating science

As excitement around artificial intelligence has exploded, Gómez-Bombarelli has seen the field mature. Companies like Meta, Microsoft, and Google’s DeepMind now regularly conduct physics-based simulations reminiscent of what he was working on back in 2016. In November, the U.S. Department of Energy launched the Genesis Mission to accelerate scientific discovery, national security, and energy dominance using AI.

“AI for simulations has gone from something that maybe could work to a consensus scientific view,” Gómez-Bombarelli says. “We’re at an inflection point. Humans think in natural language, we write papers in natural language, and it turns out these large language models that have mastered natural language have opened up the ability to accelerate science. We’ve seen that scaling works for simulations. We’ve seen that scaling works for language. Now we’re going to see how scaling works for science.”

When he first came to MIT, Gómez-Bombarelli says he was blown away by how non-competitive things were between researchers. He tries to bring that same positive-sum thinking to his research group, which is made up of about 25 graduate students and postdocs.

“We’ve naturally grown into a really diverse group, with a diverse set of mentalities,” Gomez-Bombarelli says. “Everyone has their own career aspirations and strengths and weaknesses. Figuring out how to help people be the best versions of themselves is fun. Now I’ve become the one insisting that people apply to faculty positions after the deadline. I guess I’ve passed that baton.”

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Using synthetic biology and AI to address global antimicrobial resistance threat

James J. Collins, the Termeer Professor of Medical Engineering and Science at MIT and faculty co-lead of the Abdul Latif Jameel Clinic for Machine Learning in Health, is embarking on a multidisciplinary research project that applies synthetic biology and generative artificial intelligence to the growing global threat of antimicrobial resistance (AMR).

The research project is sponsored by Jameel Research, part of the Abdul Latif Jameel International network. The initial three-year, $3 million research project in MIT’s Department of Biological Engineering and Institute of Medical Engineering and Science focuses on developing and validating programmable antibacterials against key pathogens.

AMR — driven by the overuse and misuse of antibiotics — has accelerated the rise of drug-resistant infections, while the development of new antibacterial tools has slowed. The impact is felt worldwide, especially in low- and middle-income countries, where limited diagnostic infrastructure causes delays or ineffective treatment.

The project centers on developing a new generation of targeted antibacterials using AI to design small proteins to disable specific bacterial functions. These designer molecules would be produced and delivered by engineered microbes, providing a more precise and adaptable approach than traditional antibiotics.

“This project reflects my belief that tackling AMR requires both bold scientific ideas and a pathway to real-world impact,” Collins says. “Jameel Research is keen to address this crisis by supporting innovative, translatable research at MIT.”

Mohammed Abdul Latif Jameel, chair of Abdul Latif Jameel, says, “antimicrobial resistance is one of the most urgent challenges we face today, and addressing it will require ambitious science and sustained collaboration. We are pleased to support this new research, building on our long-standing relationship with MIT and our commitment to advancing research across the world, to strengthen global health and contribute to a more resilient future.”

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Magnetic mixer improves 3D bioprinting

3D bioprinting, in which living tissues are printed with cells mixed into soft hydrogels, or “bio-inks,” is widely used in the field of bioengineering for modeling or replacing the tissues in our bodies. The print quality and reproducibility of tissues, however, can face challenges. One of the most significant challenges is created simply by gravity — cells naturally sink to the bottom of the bioink-extruding printer syringe because the cells are heavier than the hydrogel around them.

“This cell settling, which becomes worse during the long print sessions required to print large tissues, leads to clogged nozzles, uneven cell distribution, and inconsistencies between printed tissues,” explains Ritu Raman, the Eugene Bell Career Development Professor of Tissue Engineering and assistant professor of mechanical engineering at MIT. “Existing solutions, such as manually stirring bioinks before loading them into the printer, or using passive mixers, cannot maintain uniformity once printing begins.”

In a study published Feb. 2 in the journal Device, Raman’s team introduces a new approach that aims to solve this core limitation by actively preventing cell sedimentation within bioinks during printing, allowing for more reliable and biologically consistent 3D printed tissues.

“Precise control over the bioink’s physical and biological properties is essential for recreating the structure and function of native tissues,” says Ferdows Afghah, a postdoc in mechanical engineering at MIT and lead author of the study.

“If we can print tissues that more closely mimic those in our bodies, we can use them as models to understand more about human diseases, or to test the safety and efficacy of new therapeutic drugs,” adds Raman. Such models could help researchers move away from techniques like animal testing, which supports recent interest from the U.S. Food and Drug Administration in developing faster, less expensive, and more informative new approaches to establish the safety and efficacy of new treatment paths.

“Eventually, we are working towards regenerative medicine applications such as replacing diseased or injured tissues in our bodies with 3D printed tissues that can help restore healthy function,” says Raman.

MagMix, a magnetically actuated mixer, is composed of two parts: a small magnetic propeller that fits inside the syringes used by bioprinters to deposit bioinks, layer by layer, into 3D tissues, and a permanent magnet attached to a motor that moves up and down near the syringe, controlling the movement of the propeller inside. Together, this compact system can be mounted onto any standard 3D bioprinter, keeping bioinks uniformly mixed during printing without changing the bioink formulation or interfering with the printer’s normal operation. To test the approach, the team used computer simulations to design the optimal mixing propeller geometry and speed and then validated its performance experimentally.

“Across multiple bioink types, MagMix prevented cell settling for more than 45 minutes of continuous printing, reducing clogging and preserving high cell viability,” says Raman. “Importantly, we showed that mixing speeds could be adjusted to balance effective homogenization for different bioinks while inducing minimal stress on the cells. As a proof-of-concept, we demonstrated that MagMix could be used to 3D print cells that could mature into muscle tissues over the course of several days.”

By maintaining uniform cell distribution throughout long or complex print jobs, MagMix enables the fabrication of high-quality tissues with more consistent biological function. Because the device is compact, low-cost, customizable, and easily integrated into existing 3D printers, it offers a broadly accessible solution for laboratories and industries working toward reproducible engineered tissues for applications in human health including disease modeling, drug screening, and regenerative medicine.

This work was supported, in part, by the Safety, Health, and Environmental Discovery Lab (SHED) at MIT, which provides infrastructure and interdisciplinary expertise to help translate biofabrication innovations from lab-scale demonstrations to scalable, reproducible applications.

“At the SHED, we focus on accelerating the translation of innovative methods into practical tools that researchers can reliably adopt,” says Tolga Durak, the SHED’s founding director. “MagMix is a strong example of how the right combination of technical infrastructure and interdisciplinary support can move biofabrication technologies toward scalable, real-world impact.”

The SHED’s involvement reflects a broader vision of strengthening technology pathways that enhance reproducibility and accessibility across engineering and the life sciences by providing equitable access to advanced equipment and fostering cross-disciplinary collaboration.

“As the field advances toward larger-scale and more standardized systems, integrated labs like SHED are essential for building sustainable capacity,” Durak adds. “Our goal is not only to enable discovery, but to ensure that new technologies can be reliably adopted and sustained over time.”

The team is also interested in non-medical applications of engineered tissues, such as using printed muscles to power safer and more efficient “biohybrid” robots.

The researchers believe this work can improve the reliability and scalability of 3D bioprinting, making the potential impacts on the field of 3D bioprinting and on human health significant. Their paper, “Advancing Bioink Homogeneity in Extrusion 3D Bioprinting with Active In Situ Magnetic Mixing,” is available now from the journal Device. 

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3 Questions: Using AI to help Olympic skaters land a quint

Olympic figure skating looks effortless. Athletes sail across the ice, then soar into the air, spinning like a top, before landing on a single blade just 4-5 millimeters wide. To help figure skaters land quadruple axels, Salchows, Lutzes, and maybe even the elusive quintuple without looking the least bit stressed, Jerry Lu MFin ’24 developed an optical tracking system called OOFSkate that uses artificial intelligence to analyze video of a figure skater’s jump and make recommendations on how to improve. Lu, a former researcher at the MIT Sports Lab, has been aiding elite skaters on Team USA with their technical performance and will be working with NBC Sports during the 2026 Winter Olympics to help commentators and TV viewers make better sense of the complex scoring system in figure skating, snowboarding, and skiing. He’ll be applying AI technologies to explain nuanced judging decisions and demonstrate just how technically challenging these sports can be.

Meanwhile, Professor Anette “Peko” Hosoi, co-founder and faculty director of the MIT Sports Lab, is embarking on new research aimed at understanding how AI systems evaluate aesthetic performance in figure skating. Hosoi and Lu recently chatted with MIT News about applying AI to sports, whether AI systems could ever be used to judge Olympic figure skating, and when we might see a skater land a quint.

Q: Why apply AI to figure skating?

Lu: Skaters can always keep pushing, higher, faster, stronger. OOFSkate is all about helping skaters figure out a way to rotate a little bit faster in their jumps or jump a little bit higher. The system helps skaters catch things that perhaps could pass an eye test, but that might allow them to target some high-value areas of opportunity. The artistic side of skating is much harder to evaluate than the technical elements because it’s subjective.

To use mobile training app, you just need to take a video of an athlete’s jump, and it will spit out the physical metrics that drive how many rotations you can do. It tracks those metrics and builds in all of the other current elite and former elite athletes. You can see your data and then see, “This is how an Olympic champion did this element, perhaps I should try that.” You get the comparison and the automated classifier, which shows you if you did this trick at World Championships and it were judged by an international panel, this is approximately the grade of execution score they would give you.

Hosoi: There are a lot of AI tools that are coming online, especially things like pose estimators, where you can approximate skeletal configurations from video. The challenge with these pose estimators is that if you only have one camera angle, they do very well in the plane of the camera, but they do very poorly with depth. For example, if you’re trying to critique somebody’s form in fencing, and they’re moving toward the camera, you get very bad data. But with figure skating, Jerry has found one of the few areas where depth challenges don’t really matter. In figure skating, you need to understand: How high did this person jump, how many times did they go around, and how well did they land? None of those rely on depth. He’s found an application that pose estimators do really well, and that doesn’t pay a penalty for the things they do badly.

Q: Could you ever see a world in which AI is used to evaluate the artistic side of figure skating?

Hosoi: When it comes to AI and aesthetic evaluation, we have new work underway thanks to a MIT Human Insight Collaborative (MITHIC) grant. This work is in collaboration with Professor Arthur Bahr and IDSS graduate student Eric Liu. When you ask an AI platform for an aesthetic evaluation such as “What do you think of this painting?” it will respond with something that sounds like it came from a human. What we want to understand is, to get to that assessment, are the AIs going through the same sort of reasoning pathways or using the same intuitive concepts that humans go through to arrive at, “I like that painting,” or “I don’t like that painting”? Or are they just parrots? Are they just mimicking what they heard a person say? Or is there some concept map of aesthetic appeal? Figure skating is a perfect place to look for this map because skating is aesthetically judged. And there are numbers. You can’t go around a museum and find scores, “This painting is a 35.” But in skating, you’ve got the data.

That brings up another even more interesting question, which is the difference between novices and experts. It’s known that expert humans and novice humans will react differently to seeing the same thing. Somebody who is an expert judge may have a different opinion of a skating performance than a member of the general population. We’re trying to understand differences between reactions from experts, novices, and AI. Do these reactions have some common ground in where they are coming from, or is the AI coming from a different place than both the expert and the novice?

Lu: Figure skating is interesting because everybody working in the field of AI is trying to figure out AGI or artificial general intelligence and trying to build this extremely sound AI that replicates human beings. Working on applying AI to sports like figure skating helps us understand how humans think and approach judging. This has down-the-line impacts for AI research and companies that are developing AI models. By gaining a deeper understanding of how current state-of-the-art AI models work with these sports, and how you need to do training and fine-tuning of these models to make them work for specific sports, it helps you understand how AI needs to advance.

Q: What will you be watching for in the Milan Cortina Olympics figure skating competitions, now that you’ve been studying and working in this area? Do you think someone will land a quint?

Lu: For the winter games, I am working with NBC for the figure skating, ski, and snowboarding competitions to help them tell a data-driven story for the American people. The goal is to make these sports more relatable. Skating looks slow on television, but it’s not. Everything is supposed to look effortless. If it looks hard, you are probably going to get penalized. Skaters need to learn how to spin very fast, jump extremely high, float in the air, and land beautifully on one foot. The data we are gathering can help showcase how hard skating actually is, even though it is supposed to look easy.

I’m glad we are working in the Olympics sports realm because the world watches once every four years, and it is traditionally coaching-intensive and talent-driven sports, unlike a sport like baseball, where if you don’t have an elite-level optical tracking system you are not maximizing the value that you currently have. I’m glad we get to work with these Olympic sports and athletes and make an impact here.

Hosoi: I have always watched Olympic figure skating competitions, ever since I could turn on the TV. They’re always incredible. One of the things that I’m going to be practicing is identifying the jumps, which is very hard to do if you’re an amateur “judge.”

I have also done some back-of-the-envelope calculations to see if a quint is possible. I am now totally convinced it’s possible. We will see one in our lifetime, if not relatively soon. Not in this Olympics, but soon. When I saw we were so close on the quint, I thought, what about six? Can we do six rotations? Probably not. That’s where we start to come up against the limits of human physical capability. But five, I think, is in reach.

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