Who we become when we talk to machines

Brian, a middle-aged financial consultant, spends his day sitting in front of three screens. Two of them involve his job. The third features a chatbot, which he has given a woman’s name and frequently uses. Brian spent years on the road for work earlier in his career, has never married, and recently broke up with a woman who said he was emotionally unavailable. What did Brian do, in response? He asked the chatbot if the woman had a point. 

“It’s an extraordinary moment,” writes MIT Professor Sherry Turkle, who interviewed Brian (not his real name) while conducting her research for her new book about chatbots. “Brian asks an object with no emotions to tell him if he is emotionally withholding: ‘Speak to me about what you cannot experience.’ As soon as he asks the program to talk about intimacy, he’s asking it to punch above its weight.”

And yet, this kind of thing seems to be happening a lot today. 

“People think the empathy of a machine is what empathy is, then turn away from the people in their lives because they’re not empathetic enough,” Turkle says. “We’re getting ourselves in a position where human beings are too much work, right at the moment when we have never needed other people more.”

After all, what is a chatbot? It’s a computer program predicting the most plausible next string of text in a conversation, based on massive amounts of data fed into it. 

“What a chatbot does is offer pretend empathy,” Turkle says. “After it tells you how much it loves you and how much it understands you, it doesn’t care if you kill yourself or cook some pasta.”

Sadly, chatbots have in fact been associated with high-profile cases of teen suicide as well, sometimes after teens get drawn into extensive dialogues with chat tools.

Turkle explores this terrain in a new book, “Artificial Intimacy: Who We Become When We Talk to Machines,” published today by Little, Brown and Company. After surveying evidence and conducting new research, she emphatically concludes that chatbot use, while it may often feel like a short-term salve, is broadly detrimental in terms of human development and social connectivity.

“We’re doing ourselves a tremendous disservice at every moment in the life cycle,” says Turkle, the Abby Rockefeller Mauzé Professor of the Social Studies of Science and Technology at MIT.

The inner history of technology

A sociologist and clinical psychologist by training, Turkle is a longtime faculty member in MIT’s Program in Science, Technology, and Society. In books such as “The Second Self” (1984), “Life on the Screen” (1997), and “Alone Together” (2011), she has evaluated the interplay of technology, psychology, and personal identity. In “Reclaiming Conversation” (2015), she documented the costs of texting and using social media. 

“I feel the story I’m telling is the inner history of technology,” Turkle says. “Not just what it does, but what it does to people.”

Turkle structures “Artificial Intimacy” around the stages of a human life and explores the implications of chatbots for each one. In her interviews with children, Turkle finds a persistent blurring of the line between chatbots, people, and other devices.

One 8-year-old uses the same phone to talk to their grandparents and to ChatGPT, regarding them all as “things you reach on your phone.” A weary graduate-student mother who uses a chatbot for bedtime stories says her daughter thinks the chatbot is “a person in the phone, absolutely.” 

In this sense, chatbots may be interfering with even the most basic childhood processes of distinguishing people from inanimate objects. Turkle finds many additional problems with the use of chatbots as ever-present entertainment for children, noting that a certain amount of time alone helps the development of imagination and inner resources. 

“Children can’t learn trust from a device that not only lies but doesn’t know when it lies,” Turkle writes. “They can’t develop the capacity for solitude that enables both a sense of self and the capacity for mutuality.”

All told, in affecting the ability of children to develop, the dangers of chatbots are “existential,” Turkle believes. 

Facing reality, but understanding the appeal

Adults don’t fare much better when they use chatbots heavily, Turkle says. “Artificial Intimacy” explores case after case of grown-ups who become dependent on chatbots as well: people going through divorces or estrangements who want dialogue, students looking for advice about applying to college or graduate school, workers who enlist ChatGPT to do assignments, and more. They start using chatbots, keep using chatbots, and before long seem less interested in human interaction, and less capable of it. 

“Once people are involved with a connection with a robot, they start to think that it’s alive, that it cares about them, that it loves them,” Turkle says. 

That means people can lose or fail to build their own capacities for new thought, and opt out of dealing with the real world in all its maddening-but-affirming complexity.

“We’re de-skilling ourselves as relational beings,” Turkle says. “We’re also de-skilling ourselves in work. We’re de-skilling ourselves in personal relationships. It’s across the board.” And speaking of a practice multiple people in the book have attempted, she says, “When we build chatbot avatars of dead relatives to keep them alive, we can lose our ability to mourn.”

We are not, in her estimation, doing the hard work of figuring other people out, seeing things from different points of view, and putting in the effort to build on those things and make society better.

“We’re starting to define being human as not doing the work,” Turkle says. 

For all of that, a considerable amount of “Artificial Intimacy” involves understanding why people engage with chatbots. After all, as Turkle writes in the book, “Chatbots always agree with us and affirm us. They always offer us their full, undivided attention.” And once that happens, things seem to just go from there. 

People, meanwhile, can be prickly, demanding, and moody. As Turkle says: “People say they prefer the chatbot because their husband or wife says, ‘Dear, you took out the garbage, but also do the dishes, and clean up the kitchen while you’re at it.’ And a chatbot just says, ‘Oh, you’re so wonderful.’” 

She adds: “This technology is offering something people really want, which is to feel less vulnerable. And it offers it over and over again. You don’t want to have to ask somebody out? Don’t want to offer condolences? At every opportunity, technology says, ‘Why don’t you do this thing that’s less hard.’” 

And as Turkle acknowledges in the book, there is a shortage of services such as mental health care providers in the U.S., where only about half of people have access to one, according to a federal government study she cites. Chatbots are helping to fill this void, for better or worse. 

Don’t fear the friction

Turkle also notes that the age of social media has likely led people to have fewer in-person friendships and interactions, a problem that chatbots are now aiming to address. 

“You take away people’s capacities, then you offer technology as the cure for the problems technology caused in the first place,” Turkle says.

In light of all this, given Turkle’s dim view of chatbots amid the spread of AI, what is actually to be done to restore human interaction? For starters, Turkle thinks, we need to face up to the idea that life is not frictionless — and that’s okay.

“Everything in the life cycle is about facing up to friction and fears and stress and tension, and understanding that friction is not a bad thing,” Turkle emphasizes. Developing the capacity to overcome difficulties is an essential part of life. In trying to sidestep the hard work of being social beings, she thinks, personal technology is enfeebling us.

Beyond that, Turkle envisions pushback against chatbots similar to the movement against, say, having phones in schools or letting young people use social media. 

“I hope my book is part of a larger and larger movement,” Turkle says. “I don’t want to be alone. I want to be part of a movement pushing back.”

Other writers in this domain have praised “Artificial Intimacy.” Journalist Nicholas Carr, author of “The Shallows,” has stated it “will help you avoid the profound but often hidden threats AI poses to you and your relationships.” Psychologist Jonathan Haidt of New York University, author of “The Anxious Generation,” has stated that Turkle’s “groundbreaking research and beautiful writing make her the most qualified member of Team Humanity to call us back to our senses, and to each other.” 

For her part, Turkle says, “I wanted to write a book that college students would read, that high school seniors, juniors would be able to read, that parents would pick up and not be intimidated by, that teachers would read.” 

And “Artificial Intimacy” offers a recurring question for those readers.

“If not a richer life in the real,” Turkle writes, “what’s our endgame?” The purpose of life, she underlines, is not to avoid it, but to tackle it head on. 

“It’s one thing if you say, my teen is texting too much,” Turkle says. “It’s very different if your 2-year-old is thinking a plushy toy with a chatbot inside is their best friend. Because you are getting into the intimate infrastructure of what makes a person develop. Social media came for our attention. Chatbots come for our capacity for attachment. That’s toxic on another level. What is the endgame? That the baby will prefer chatbots to people who are much more complicated and hard? Is that who we want to be?”

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Pressurized experiments could help wind farms generate more power

The world needs more wind energy. But anyone designing new wind turbines or trying to squeeze more power out of existing ones faces a stiff challenge testing new approaches. That’s because the atmosphere is a tough place for a controlled experiment.

Some researchers use wind tunnels to conduct tests, but scaled-down wind turbines in traditional wind tunnels can differ widely from conditions in the field. (Wind turbines are the largest rotating machines ever made.) The problem hampers not only development of better wind turbines, but also our understanding of basic questions like how much power to expect from a turbine when winds change direction.

In a new open access paper published in PNAS Nexus, researchers closed the gap between experiments in the field and the lab by using a wind tunnel that features high pressurization to simulate the flow physics in the atmosphere. With this approach, the researchers determined how the alignment of the turbine and its tip speed relative to the wind influence the power it generates, offering new insights into how to get more power from existing wind farms.

They also used the approach to validate a computationally lightweight model that engineers can use to test different turbine designs and wind farm control strategies.

Together, the researchers estimate that optimizing the turbine alignment relative to wind, the blade pitch angles, which control the airfoil’s angle of attack, and the tip speed relative to the wind could potentially result in tens of thousands of dollars per turbine every year in additional revenue.

“The immediate impact of this study is that we’ve now both improved and validated models that go into wind turbine control protocols for existing farms,” says Michael Howland, MIT’s Jeffrey Cheah Career Development Professor. “The bigger, medium-term impact, with a much larger upside, is this new experimental paradigm to rapidly prototype, validate simulation models, and test hypotheses about better designs and control strategies much faster than has been possible before.”

Joining Howland on the paper are first author John Kurelek, an assistant professor at Queen’s University; MIT PhD candidates Ilan Upfal and Kirby Heck; Queen’s University postdoc Supun Pieris; Penn State University researcher Alexander Piqué; and Princeton University Professor Marcus Hultmark.

Answers in the wind

Howland has spent years developing models to simulate wind farm performance and developing new techniques to increase their power output. In 2022, he showed that accounting for the wake of individual turbines when controlling the entire wind farm could significantly increase power output.

But that work required his research team to first conduct a lengthy field experiment that temporarily resulted in lowering a real wind farm’s power output by intentionally misaligning turbines from the wind for months to better understand their performance in misalignment.

“Wind energy is a uniquely challenging problem to study experimentally,” Howland says. “We want to test the effect of a certain change in isolation, but wind farms operate in chaotic, turbulent environments where the weather is constantly evolving. Wind turbines have to react to weather conditions that we have no control over, and that introduces complexities in identifying the impact of the imposed change we are studying. The field sits at this unique intersection between environmental flow, mechanics, aerodynamics, and meteorology.”

The difficulty of running experiments at real wind farms has left researchers and engineers unsure of how changes in the alignment between the wind and turbine or factors like the turbine’s tip speed relative to the wind change power output.

In fact, the researchers say many predictive models people use are built on the assumption that turbines are always perfectly perpendicular to the wind. That’s rarely the case in the real world, even with modern turbines that gradually adjust their angle in response to the wind’s rapid directional changes.

“People have been debating which models are best for understanding the output from these wind farms, but if you have nothing to compare them against, it’s very difficult to advance the field,” Hultmark says. “This paper tries to do both of those things.”

Hultmark’s research lab at Princeton has pioneered the study of scaled-down wind turbines in pressurized wind tunnels, which, as previous studies have shown, better reproduce large-scale turbines in the atmosphere because pressure makes air more dense, resulting in more inertia within the scaled laboratory environment. For the new study, the researchers used a turbine measuring 15 centimeters in diameter at varying pressures of up to 240 atmospheres.

“By pressurizing the chamber, we’re testing a turbine that is, all else being equal, 15 to 20 meters in diameter, with the ability to go up to 35 meters in diameter,” lead-author Kurelek explains. “That’s because we’re increasing the density by a factor of 100 to 220 times,” 

Kurelek sent the dimensions of the wind tunnel and wind turbine setup to Howland, who used them to calculate the aerodynamics, forces, and power production using a newly developed unified wind turbine model, which builds on previous work that developed a more general aerodynamic theory for wind turbines. The new model enables the researchers to simulate wind turbine performance across operating conditions without relying on empirical corrections that have historically been used in wind power models.

The researchers then ran a series of experiments in the tunnel over the course of several weeks, testing the turbine’s performance at different wind alignments and with different control strategies, to isolate how each factor affects performance.

They found power output could be significantly increased by adjusting the turbine’s tip speed based on its misalignment angle with the wind — a control strategy that is rarely employed in wind farms today but could offer a way to boost performance with minimal added costs.

“The big output of the experiments was clearly showing that new power maximums can be achieved when the turbine becomes misaligned with the wind through only changes to the tip speed,” Kurelek says.

Scaling the approach

The study served as validation for Howland’s model, which is fast enough to be run by engineers designing and operating wind turbines around the world using regular laptop computers.

“What we really want to know is if the turbines are always operating in some degree of misalignment with the wind, how should we control the turbine to get the maximum achievable power production?” Howland explains. “Our unified momentum model was able to make predictions of how to do this control a few years ago, and this is the first time we’e able to experimentally validate that model.”

Howland says validating models is only one part of the paper’s potential impact.

“This study also shows the huge opportunity to perform these high-throughput, controlled experiments in the pressurized facilities that Marcus and John work with, enabling us to achieve the right physics but in a time efficient and low-cost manner,” Howland says. “Right now, there’s a massive gap between idealized theoretical and simulation models and full-scale testing in extremely complicated field environments. Nothing is filling that gap except for these pressurized experiments. I hope this can be an enabler to investigate a huge range of unanswered wind energy questions in controlled environments.”

The work was supported in part by the Natural Sciences and Engineering Research Council of Canada; the National Science Foundation; and the MIT-GE Vernova Alliance.

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New formulation helps RNA vaccines withstand high temperatures

RNA vaccines, which have been proven effective against Covid-19, are now being developed for many other diseases, including cancer. One of the drawbacks to these vaccines is that they require ultracold storage, but researchers from MIT have found a promising way to overcome that limitation.

With help from an AI algorithm, the researchers tweaked the formulation surrounding the lipid nanoparticles that are typically used to deliver mRNA vaccines, making the vaccines more heat-resistant. Using this approach, they formulated vaccines that could remain stable even when stored at room temperature for up to a year, or at nearly 100 degrees Fahrenheit for two months.

When Covid-19 vaccines carried by these particles were administered to mice, they generated just as strong an immune response as an RNA Covid-19 vaccine similar to one developed by Moderna. By using the AI algorithm to predict the optimal formulations for the particles, the researchers were able to cut down the number of experiments they needed to do, which rapidly sped up the development process.

“The real beauty of this algorithm is that we can use it with small data sets,” says Ana Jaklenec, a principal investigator in MIT’s Koch Institute for Integrative Cancer Research. “It’s really hard to run thousands of experiments, so this algorithm allows us to more easily achieve formulations with features that we want — in this case, stability.”

Jaklenec and Robert Langer, the David H. Koch Institute Professor, are the senior authors of the paper, which appears today in Nature Biotechnology. Graduate student Jinbi Tian and postdoc Khanh Tran are the lead authors of the paper.

Stable vaccines

RNA is a highly fragile molecule, so researchers stabilize it with lipid nanoparticles (LNPs) that protect the RNA from degradation and help it get into cells. However, these RNA-LNP vaccines still need to be kept cold (-20 to -80 degrees Celsius), which makes it difficult to ship them to regions that don’t have cold-storage facilities available.

Making these vaccines more heat-tolerant would not only enable them to be distributed more widely, but could also help researchers develop new vaccines that could be administered through novel methods such as microneedle patches. These patches contain hundreds of vaccine-filled microneedles, which dissolve when the patch is applied to the skin, releasing the vaccine.

To create more stable RNA vaccines, researchers have experimented with adding a variety of excipients — sugars, salts, or polymers — to the LNPs. Jaklenec and Langer recently developed polymer-stabilized LNPs that can withstand higher temperatures, but those LNPs were slightly different from the FDA-approved formulations that were used for the Moderna and Pfizer Covid-19 vaccines. 

In their new paper, the researchers wanted to see if they could find a way to make those FDA-approved formulations more stable at high temperatures.

They began by reusing some of the excipients that had worked in their earlier efforts, but they were “really getting stuck,” Jaklenec says. “We were trying to use and screen excipients that we’ve previously used successfully to stabilize LNPs, but it just wasn’t working. It was really frustrating for the team.” 

To speed up their progress, the researchers decided to try a machine-learning approach. Working with researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), they developed an algorithm that can make predictions based on very small datasets.

“We’d used our algorithms for various automated experimental design applications before, but never on a biological problem like vaccine stability,” says Mina Konaković Luković, an assistant professor of electrical engineering and computer science in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), who is also an author of the paper. “It was surprising to see how quickly the algorithm converged on a stable formulation — getting there in just a handful of iterations, rather than the exhaustive search that would normally be required.” 

The researchers used this algorithm to analyze nearly 50 FDA-approved excipients. For each excipient, the researchers first measured how well it stabilized RNA when incorporated into an LNP. They used these particles to deliver mRNA encoding a protein called firefly luciferase, which produces bioluminescence, into cells. By measuring how much light was emitted by the cells, the researchers could determine how effectively each excipient protected the mRNA.

The researchers chose five of the most promising excipients and used their AI algorithm to predict ratios of those excipients that would best stabilize LNPs similar to those used by Moderna. Based on those predictions, the researchers tested two formulations at a time in cells, fed those results back into the algorithm, and generated more predictions. After several rounds, they chose one formulation that appeared promising enough to test in animal studies.

This process took only a few weeks, much less than it would have taken without guidance from the AI algorithm.

“Before we implemented the AI algorithm, we spent several months testing different combinations and also doing the prescreening of all the excipients that we could find, but nothing would get us to 100 percent stability,” Tran says.

Robust immune responses

To test the heat resistance of their new LNP formulation, the researchers used the particles to package Covid-19 mRNA antigens, then dehydrated them using a process called vacuum drying. These particles were then stored at 37 degrees Celsius (98 degrees Fahrenheit) for two months, or at room temperature for one year. Mice that were vaccinated with these particles, even after long-term storage, showed equivalent immune responses to mice that received vaccines carried by LNPs similar to the original Moderna formulation.

The researchers also used their new heat-resistant formulation to create solid microneedle patches that could deliver a SARS-CoV-2 antigen. These patches generated an immune response similar to that produced by the injectable RNA vaccines.  

“Our approach broadens the application of not only mRNA vaccines, but also therapeutics or advanced drug-delivery platforms like controlled-release particles or microneedle patches, which requires the formulation to either be in solid state or to be stable at higher temperature,” Tian says. 

The researchers also showed that they could use their algorithm to stabilize other LNP formulations, including one similar to those used by Pfizer to deliver its Covid-19 vaccine. This formulation uses the same excipients as the one the MIT team developed for the Moderna LNP, but in a different ratio. For each LNP, once a heat-resistant formulation has been developed, it could be adapted to deliver any type of mRNA payload, the researchers say. 

The research was, in part, funded by the Gates Foundation.

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MIT students gain a humanist lens on technical innovation in Tulsa, Oklahoma

When MIT mechanical engineering student Daphne Wang arrived for her internship at the Muscogee Creek Nation Department of Health in Tulsa, Oklahoma last summer, she expected to be writing code. To her surprise, she found herself analyzing tribal history and the implications for technical innovation. 

“I learned so much that I think should be required curriculum for every single person in this country,” says Wang. “I think tech for good is genuinely getting out into communities and learning about people who aren't you, creating worldly perspectives that you ultimately can take into everything else that you do.”

Wang and fellow intern Lucy Sun, who is majoring in artificial intelligence and decision making, spent the summer supporting the development of the Muscogee Nation's first Pregnancy Risk Assessment Monitoring System (PRAMS) survey to capture maternal health data specific to Muscogee women. The project required completing literature reviews, analyzing datasets, and drafting survey questions, while considering issues of data sovereignty and governance structures specific to the Muscogee.

“It was this change of pace — 180 [degrees] from MIT, completely,” Wang reflects. At MIT, she says, course instructors routinely prepare students for big technical challenges, but there are fewer opportunities to “work with communities, [learn] how to think about the cultural and historical dimensions of the work, or how to consider the people who will be affected.” 

Wang’s supervisor, epidemiologist Breanna McNaughton-Long, was effusive about Wang and Sun’s engagement. “They were instrumental. They did so much work that I don't think we would have been able to do as quickly,” she says. “And they brought this sense that we were doing something that mattered.”

Experiential learning at the confluence of humanities and engineering 

Wang was one of 10 MIT undergraduates to participate in the PKG Center for Social Impact’s 2026 Code.Tulsa program. Now in its second year, Code.Tulsa is made possible by support from the Patrick J. McGovern Foundation and the George Kaiser Family Foundation. 

Students live at the University of Tulsa for the summer. They intern with the Muscogee and Cherokee nations, as well as local nonprofits Black Tech Street and Urban Coders Guild, completing AI, data science, and other technical projects. 

The PKG Center’s assistant dean for community-based programs, Vippy Yee, complements students’ professional experience with education on the historical and cultural significance of tech-based development in the Tulsa region from the perspective of local leaders. 

“As with all PKG Center programming, our aim is to help students integrate an engineer’s approach to problem-solving with a humanist lens on the nature of social challenges, and by extension interventions,” says Alison Badgett, the PKG Center’s director. 

“Getting to speak to people who were either very educated on the issues or have experienced them has been a massive help in reshaping the way that I think about social issues,” says Chenise Harper, an electrical engineering with computing major who interned with the Urban Coders Guild. 

Harper puts this dynamic candidly: “I hated history, but I learned a lot about why it is useful for making social change and how to go about researching it. I now feel a lot more confident that I can make an impact.”

A student’s vision for increasing STEM access

Code.Tulsa was the brainchild of sophomore Jack Carson, an electrical engineering and computer science (EECS) student who approached the PKG Center with the idea for Code.Tulsa as an incoming first-year student. Carson, who is from Tulsa and a member of the Cherokee Nation, saw firsthand that rural Native students who could benefit greatly from STEM education were unlikely to have access to it. Carson proposed developing a weeklong STEM camp for members of federally recognized tribes held at the University of Tulsa, with he and Code.Tulsa interns serving as instructors. 

Carson devised a nomination system to attract promising high school students, selecting 25 campers from 10 tribes out of 160 applications representing 25 Native nations. 

To develop the curriculum, Carson enlisted Harvard University student Allie Zong, whom he had met at Campus Preview Weekend. The Tulsa Advanced Sciences Camp (TASC) offers intensive, interactive track time in AI engineering, DNA technology, physics, and chemistry and energy. This is complemented by philosophical workshops to help students think more ambitiously and deliberately about what they can and should achieve in the near and long term. As Zong explains, “We teach them not knowledge, but curiosity, and the skills to teach themselves."

The TASC experience “kind of propelled me to self-study calculus,” says Alyssa Theofanidis, a rising high school senior from Houston who is a citizen of the Cherokee Nation. Theofanidis participated in the camp’s first year, returning this summer as a teaching assistant. Like many TASC campers, Theofanidis is eager to use her developing STEM expertise to benefit her Native community. 

TASC “got us to think 1,000 times more ambitiously about the impact we could have,” said another camper, who was inspired by guest speakers “like the nuclear fusion person at the top of their field,” referring to physicist Alexandra LeViness of MIT spinout Commonwealth Fusion Systems, who helped develop TASC’s chemistry and energy track. Campers were also inspired by “the different worldviews” of MIT interns. “I thought, maybe this is what MIT looks like,” said one camper, with several expressing the intent to apply to MIT as a result of the camp. 

Cherokee Nation Principal Chief Chuck Hoskin Jr. also delivered remarks at TASC, encouraging students as future leaders to take a public interest in technology. 

“Technology can be used for good, or it can be used for harm. Your generation has the opportunity to bend that arc toward something good,” Hoskin said. “It's a very special relationship that the Cherokee Nation has with MIT. As we reach out a hand in friendship, we have a hand reaching back. We are thankful for Cherokee citizen Jack Carson, a former secretary for the Cherokee Nation tribal youth council, for his leadership role in organizing this effort.”

Making a positive long-term social impact 

Like TASC campers, Code.Tulsa interns came away from the experience motivated to make a positive impact. While most MIT students won’t go on to full-time professional roles traditionally associated with social impact, PKG Center programming like Code.Tulsa helps students recognize they can promote the public interest no matter their career. As Elvis Chipiro, a junior in computer science and engineering, reflected after interning with the Cherokee Nation, “Social impact is not separate from mainstream technology; rather, it is embedded in the choices engineers make every day … Ultimately, meaningful social change is not driven by technology alone, but by people willing to design systems with empathy, responsibility, and inclusion at the center.”

For others, Code.Tulsa helps them reconnect with a sense of public purpose. “Remembering that … I could use my MIT education to help others was a big reason I applied to MIT in the first place,” says Harper. “But I forgot my own mission in the stress of school. Code.Tulsa really re-opened my eyes to why I am here.”

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A new technique could accelerate the development of RNA therapies

RNA vaccines and other nucleic acid therapeutics are typically packaged within fatty molecules known as lipid nanoparticles (LNPs). MIT researchers have now come up with a way to produce these particles much more quickly, and with more precise control over their size and shape.

This new process, which can be run automatically without any human intervention, could greatly speed up the development of new RNA and DNA therapeutics, the researchers say. 

“This method can help you determine what are the parameters that will generate specific size and shape attributes, before you take those particles and see which one will perform best,” says Cedric Devos, an MIT postdoc and one of the lead authors of the study. “It could be a quite powerful development tool.”

Current methods for designing new lipid nanoparticles often require time-consuming trial-and-error experiments, with limited investigation of desired particle size and shape. Tuning the size and shape of LNPs can open the possibility of targeting different organs and tissues.

“The size and shape of LNPs could not be reliably controlled by any previous production method. The problem may appear simple at first glance, but in reality it requires a deep understanding of lipid nanoparticle assembly,” says Allan Myerson, a professor of the practice in MIT’s Department of Chemical Engineering and the senior author of the new study.

MIT postdocs Aniket Udepurkar and Peter Sagmeister are also lead authors of the paper, which appears today in ACS Nano. 

More precise control

Vaccines based on mRNA work by delivering instructions to cells to produce a harmless version of a viral protein, which prompts the immune system to generate a response. However, if mRNA is injected on its own, it will be quickly broken down in the body.

“These are really a revolutionary type of therapeutics, but they need some kind of delivery vehicle to bring them to the right cells in the body,” Devos says.

For the mRNA Covid-19 vaccines and other mRNA therapeutics, scientists have used lipid nanoparticles for that packaging. LNPs usually consist of four components — an ionizable lipid, a phospholipid, cholesterol, and a lipid attached to a molecule of polyethylene glycol (PEG), which helps to stabilize the LNP.

To make the particles, two streams of fluid are mixed together at high speed. One consists of lipid molecules suspended in ethanol, and the other contains mRNA dissolved in an acidic buffer.

Those solutions aren’t mixed at equal flow rates, however. Instead, there is about three times more of the mRNA solution than the lipid solution. This unequal ratio helps promote the formation of lipid nanoparticles containing mRNA, but it doesn’t offer precise control over the size or shapes of the particles.

In a study published last year in ACS Nano, the MIT team showed that they could enable much better control of particle size by breaking the mixing process down into two steps. 

In the first step, mRNA and lipids are mixed together at equal flow rates. Then, after a short delay, more of the buffer is added, which halts the growth of the particles. Longer delays produce larger particles. 

“This gives you the ability to play around with the residence time, which is the time it takes between the first mixer and the second mixer. If you keep that residence time really long, it means your particles will grow a lot. If you keep it really short, you can keep them really small,” Devos says. “It gives you a lever over lipid nanoparticle manufacturing that wasn't available before.”

In that study, the researchers also showed that they could alter the shape of the particles. By changing the concentration of the buffer that they add in the second step, they were able to transform the particles from spheres to elongated particles that resemble avocados.

Both of these interventions — changing the delay residence time and changing the buffer composition — allow the researchers to control particle size and shape without otherwise altering the composition of the LNPs.

An automatic process

In the new ACS Nano paper, the researchers developed a way to automate the two-step mixing process. They also incorporated a commercially available dynamic light scattering device that can measure the sizes of the particles as they are formed.

The project also highlights the impact of MIT’s Undergraduate Research Opportunities Program (UROP). “Through this program, applied mathematics and computer science undergraduates Joy Ren, Sofiya Chubich, and Dylan Nguyen gained hands-on experience, learning how advanced software engineering can be integrated with chemical engineering to develop an automated platform for LNP process development,” Sagmeister says.

With this automated system, the researchers can specify a particle size, and the system will generate that size. It will also measure the resulting particles to make sure they’re the right size, and if not, adjust the delay time and other factors to steer them to the right size.

“The first paper really unlocked the new methodology to make lipid nanoparticles, to truly engineer them by size and shape,” Sagmeister says. “With the second study, we automate the whole process.”

The system can also produce particles of different shapes, but measuring those shapes has to be done outside of the automated system. 

Using this approach, the researchers were able to investigate how changing the inputs to the system affects the size and shapes of the particles, on a much faster timescale than currently possible. The researchers then used the data they gathered from these experiments to train a machine-learning model that can predict the combination of factors that will generate a particular size or shape.

With this method, it could be much easier for developers of RNA therapeutics to generate different sizes of particles to test them for a particular application. Controlling the size of a lipid nanoparticle is critical because the particle’s size determines where in the body it is most likely to end up. 

“If you make an LNP-based therapeutic with a target size of 150 nanometers, and one that is 70 nanometers, and everything else is the same, they will behave very differently,” Devos says.

The researchers have filed for a patent on their technology and are now working to commercialize it through a new company called BIZON Labs. Since receiving initial support through the Martin Trust Center for MIT Entrepreneurship’s Researcher 2 Entrepreneur (R2E) program, the team has also been accepted into MIT’s flagship accelerator program, delta v. The research was funded by the U.S. Food and Drug Administration and the Koch Institute Support (core) Grant from the National Cancer Institute. The work was carried out, in part, through the use of MIT.nano’s facilities.

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Scattering neutrinos to probe the fundamental laws of the universe

Many people who are successful in STEM fields were lucky to have someone who turned them on to a topic and encouraged their interest. For Faith Reyes, that person was her high school physics teacher, Ms. Bolster. 

Whereas Reyes had earlier studied math and science without seeing how those subjects could be applied outside the classroom, her teacher helped her connect those dots and experience the excitement of investigating the physical world. With Ms. Bolster’s help, Reyes started a physics club where students would gather before school and conduct experiments. 

“I just sort of fell in love with physics then,” Reyes says. “And luckily, as I began to study it more and more, I found I landed exactly where I wanted to be.”

Reyes has carried that enthusiasm for physics into not only her research but her role as a personal tutor and teaching assistant at MIT, where she works to foster the same love of the subject in first- and second-year undergraduate students.

As an experimental particle physicist and sixth-year PhD student in the Formaggio Group in the Laboratory for Nuclear Science, Reyes studies neutrinos, elementary particles that have vanishingly little mass and rarely interact with other matter. Neutrinos are produced during radioactive decay, including the processes taking place inside nuclear reactors. Because they interact so infrequently, detecting them can be difficult. (We can’t feel them, but neutrinos from the sun are streaming through our bodies every minute.) Their unusual behavior makes them valuable to physicists seeking to understand what lies beyond the Standard Model, the framework that describes many of the fundamental particles and forces in nature.

“The Standard Model is extremely accurate and describes most of everything that we see,” Reyes says. “But it’s not complete.”

Reyes is a member of the Ricochet neutrino experiment, an international collaboration studying neutrinos produced by a nuclear reactor at the Institut Laue-Langevin in Grenoble, France. The experiment seeks to observe coherent elastic neutrino-nucleus scattering, a low-energy interaction in which a neutrino scatters off an atomic nucleus.

Through Ricochet, scientists aim to investigate some properties of these elusive particles, and contribute to, as Reyes puts it, “just fundamentally understanding the world in which we live.”

When looking back at her time in graduate school, Reyes’ path has not always followed the plan she initially envisioned.

When she joined Ricochet, she expected to work on a particular project located at MIT. But a few years into her PhD, it was clear that the project would not be ready within her timeline. Reyes instead shifted her focus to work taking place in France, where she began learning the technical details of the experiment’s detectors.

Her first visit lasted three months. At the time, Ricochet had two detectors, and Reyes spent much of her time performing the routine work required to understand how they operated.

As the experiment expanded to nine detectors and eventually 18, the amount of work required to manage the system grew substantially. Reyes and a colleague recognized that many of the repetitive tasks could be automated.

Together, they developed a software framework that could perform much of the low-level analysis and detector monitoring that Reyes had initially carried out manually.

The project became an important part of her development as a physicist. By working closely with the detectors and helping build tools to manage them, Reyes gained a detailed understanding of the experiment’s operations.

“It’s sort of like you’re building your own stuff to replace yourself,” she says. “Which is nice in a way because you can save yourself a lot of time.”

The opportunity was both validating and humbling. As a graduate student, she had moved from learning the basics of the experiment to helping guide the work of other scientists.

“It felt like my collaborators trusted me, and I had something of value to give to the collaboration,” Reyes says.

The people she has met through MIT and the Ricochet collaboration have been among the most rewarding parts of her graduate experience. Students, mentors, and collaborators have helped her think critically and become a better physicist, she says.

Her increasing leadership responsibilities have also changed how she approaches research.

Earlier in her academic career, Reyes says, she was more comfortable being told what to do than proposing her own scientific ideas. Over time, leading projects and coordinating groups pushed her to become more confident in her judgment.

“I think I was sometimes not really standing up for myself,” she says. “But now I feel more confident in my position and my prowess as a physicist.”

That confidence has become one of the most important lessons of her PhD.

After completing her doctorate, Reyes hopes to continue conducting research. She is considering a postdoctoral position, which would allow her to continue working in physics at another institution.

Her time in France has also influenced her vision of the future. Reyes spent nine months there through the Chateaubriand Fellowship, following two earlier three-month visits. While the latest trip was primarily focused on research, working in the same office as her collaborators made it easier to coordinate across time zones and strengthened her connection to the experiment. She also grew fond of the country’s culture and work-life balance and would even consider living there. 

“I fell in love with France and the people,” she says. “And also, the work culture.”

Outside the lab, Reyes enjoys playing video games and crocheting, a hobby she picked up during her time in France. She often crochets while watching movies or television, appreciating the opportunity to work with her hands while thinking about other things.

For a physicist whose work involves investigating some of the universe’s smallest and most elusive particles, the hobby offers a different kind of satisfaction: creating something tangible.

As Reyes moves toward the next stage of her career, she hopes to continue pursuing the questions that first drew her to physics. Her research may help reveal what lies beyond the Standard Model, but her experience at MIT has also shown her how much science depends on collaboration, adaptability, and the confidence to lead.

“I’ve learned a lot of lessons,” Reyes says. “Especially about wrangling people.”

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Estimating suicide risk from text

When people reach out during a mental health crisis, a top priority for counselors is identifying those with a high risk of suicide. The distressed person’s language holds critical clues, and a new tool developed by scientists at MIT’s McGovern Institute for Brain Research is designed to pick up on and rapidly evaluate those signals.

The language-processing tool was developed by Daniel Low, a former graduate student in Senior Research Scientist Satra Ghosh’s Senseable Intelligence Group who is now a research scientist at the Child Mind Institute, where he leads its AI, Risk, and Contemplative Science Lab, as well as a visiting scholar at Harvard University. It uses a custom-built list of words and phrases linked to 49 suicide risk factors, searching text for these and using them to estimate an individual’s risk.

Ghosh, Low, and colleagues report today in the Journal of Psychopathology and Clinical Science that their tool accurately predicts suicide risk from text conversations with crisis counselors. It is already helping to clarify which suicide risk factors matter most in times of crisis. With more validation, it could help with risk assessment in clinical settings and crisis-support situations.

Identifying key risk factors

Suicide attempts are notoriously difficult to predict. Dozens of risk factors have been linked to suicide, and even trained clinicians struggle to identify who will make an attempt among those who have some form of suicidal ideation. Among the factors that can make suicidal thoughts and behaviors more likely are certain psychiatric symptoms and disorders, like depression, borderline personality disorder, and post-traumatic stress disorder, as well as environmental and social stressors, like poverty, incarceration, discrimination, and loneliness.

“You see all these 50 risk factors, and they're all interacting in ways we don't really understand,” Low says. “Many different pathways could lead to someone feeling they want to escape their internal pain,” he says — and it’s challenging to know whose path will lead to a suicide attempt or death.

Ghosh and Low wanted to understand which risk factors counselors and clinicians should most look out for during a mental health crisis. To do that, they collaborated with the Crisis Text Line, whose trained volunteers provide confidential text-based support to people in distress.

Crisis Text Line, a global mental health nonprofit that provides free, 24/7, confidential mental health support for people in need, provided specialized training and controlled access to this restricted dataset. The researchers analyzed de-identified texts from approximately 16,000 conversations with Crisis Text Line’s volunteer crisis counselors. Based on Crisis Text Line’s assessments, those conversations were grouped into three different risk levels: non-suicidal, suicidal ideation without imminent risk, and imminent risk. It was this imminent risk group — those with a plan for suicide, or who have an intent to die within the next 48 hours — that the researchers most wanted to understand.

“We wanted to know what type of symptoms predict the highest suicide risk,” Low says. This question has been studied before, he says — but typically through epidemiological surveys that ask a person to recall their symptoms and experiences, often after their mental health crisis has passed. In contrast, he says, “Crisis Text Line gives us an opportunity to assess many different symptoms and potential risk factors as people are having the crises.”

Reading between the lines

Before analyzing the crisis line texts, the research team built a suicide-risk lexicon. They turned to artificial intelligence to generate a preliminary list of words and phrases tied to established suicide risk factors, including factors associated with suicidal ideation, suicide attempt, and suicide death. Then they manually reviewed and curated that list. Their final lexicon includes about 60 words or phrases for each of 49 risk factors, with the relevance of each one confirmed by expert clinicians.

Then they trained a machine learning model to search the crisis conversations for words and phrases in their lexicon and use these to predict suicide risk. Because the lexicon links each word or phrase to a specific risk factor, they could use these data to determine which risk factors are most closely tied to imminent risk among people in crisis.

What they found was consistent with patterns found in previous research, although not always intuitive. For example, depression is a well-known risk factor for suicidal ideation, but their model found that mentions of lethal means and substance use were more likely to be expressed by the highest-risk group than depressed mood or fatigue. Expressions of active suicidal ideation and self-injury were also strong predictors. Intermediate predictors included anxiety, post-traumatic stress disorder, and emotional pain.

The predictive model assigns a weight to each risk factor based on its contribution to risk. For example, mentions of lethal means for suicide, like “cut” or “pills,” are weighed heavily, whereas terms related to hopelessness, like “don’t know what to do” or “hopeless,” contribute to a lesser degree. After training their model, the team found they could use it to accurately predict risk severity in new conversations the model had not previously seen.

One limitation of lexicons, the researchers note, is that they do not consider the context of terms, and they can miss terms that are similar to those in the lexicon, but not explicitly included. Large language models have reasoning abilities, and Low and colleagues have developed ways of using large language models to detect suicide risk in other projects. However, they say they often use their lexicon in parallel to guarantee flagging certain terms, as well as to maintain data privacy.

Low stresses that while the team used the power of a large language model to develop its lexicon, its prediction model is a simpler, “lightweight” model. Unlike large language models, which require massive computational power, it can be run easily on a personal computer, reducing both cost and privacy concerns. Just as importantly, it is interpretable: Rather than merely generating a risk estimate like some deep learning models can do more effectively, it tells users how it got there. Words of concern can be flagged so users understand the basis for each assessment and act on that information. They are working on similar explainability approaches with large language models.

That’s critical, because the stakes are so high. “This is such a complex space that having a human in the loop is, I think, going to be critical for a long, long time,” says Ghosh, who is the director of the Open Data in Neuroscience Initiative at the McGovern Institute. Likewise, the researchers add that any predictive model must be thoroughly validated before clinical use, and might need to be continually refined to keep up with changes in language use or target populations.

Because a reliable lexicon opens doors to new ways of understanding mental health, Ghosh and Low are widely sharing not just their suicide risk lexicon, but also the software package they developed to build it. Researchers can use that tool to efficiently build lexicons for other mental health conditions. Meanwhile, Low says, the suicide risk lexicon is already being used to explore how text data from a variety of sources, from social media to electronic health records, might help researchers and clinicians better estimate risk.

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