Carbon nanotube-based sensor can detect SARS-CoV-2 proteins

Using specialized carbon nanotubes, MIT engineers have designed a novel sensor that can detect SARS-CoV-2 without any antibodies, giving a result within minutes. Their new sensor is based on technology that can quickly generate rapid and accurate diagnostics, not just for Covid-19 but for future pandemics, the researchers say.

“A rapid test means that you can open up travel much earlier in a future pandemic. You can screen people getting off of an airplane and determine whether they should quarantine or not. You could similarly screen people entering their workplace and so forth,” says Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering at MIT and the senior author of the study. “We do not yet have technology that can develop and deploy such sensors fast enough to prevent economic loss.”

The diagnostic is based on carbon nanotube sensor technology that Strano’s lab has previously developed. Once the researchers began working on a Covid-19 sensor, it took them just 10 days to identify a modified carbon nanotube capable of selectively detecting the viral proteins they were looking for, and then test it and incorporate it into a working prototype. This approach also eliminates the need for antibodies or other reagents that are time-consuming to generate, purify, and make widely available.

MIT postdoc Sooyeon Cho and graduate student Xiaojia Jin are the lead authors of the paper, which appears today in Analytical Chemistry. Other authors include MIT graduate students Sungyun Yang and Jianqiao Cui, and postdoc Xun Gong.

Molecular recognition

Several years ago, Strano’s lab developed a novel approach to designing sensors for a variety of molecules. Their technique relies on carbon nanotubes — hollow, nanometer-thick cylinders made of carbon that naturally fluoresce when exposed to laser light. They have shown that by wrapping such tubes in different polymers, they can create sensors that respond to specific target molecules by chemically recognizing them.

Their approach, known as Corona Phase Molecular Recognition (CoPhMoRe), takes advantage of a phenomenon that occurs when certain types of polymers bind to a nanoparticle. Known as amphiphilic polymers, these molecules have hydrophobic regions that latch onto the tubes like anchors and hydrophilic regions that form a series of loops extending away from the tubes.

Those loops form a layer called a corona surrounding the nanotube. Depending on the arrangement of the loops, different types of target molecules can wedge into the spaces between the loops, and this binding of the target alters the intensity or peak wavelength of fluorescence produced by the carbon nanotube.

Earlier this year, Strano and InnoTech Precision Medicine, a Boston-based diagnostics developer, received a National Institutes of Health grant to create a CoPhMoRe sensor for SARS-CoV-2 proteins. Researchers in Strano’s lab had already developed strategies that allow them to predict which amphiphilic polymers will interact best with a particular target molecule, so they were able to quickly generate a set of 11 strong candidates for SARS-CoV-2.

Within about 10 days of starting the project, the researchers had identified accurate sensors for both the nucleocapsid and the spike protein of the SARS-CoV-2 virus. During that time, they also were able to incorporate the sensors into a prototype device with a fiber optic tip that can detect fluorescence changes of the biofluid sample in real time. This eliminates the need to send the sample to a lab, which is required for the gold-standard PCR diagnostic test for Covid-19.

This device produces a result within about five minutes, and can detect concentrations as low as 2.4 picograms of viral protein per milliliter of sample. In more recent experiments done after this paper was submitted, the researchers have achieved a limit of detection lower than the rapid tests that are now commercially available.

The researchers also showed that the device could detect the SARS-CoV-2 nucleocapsid protein (but not the spike protein) when it was dissolved in saliva. Detecting viral proteins in saliva is usually difficult because saliva contains sticky carbohydrate and digestive enzyme molecules that interfere with protein detection, which is why most Covid-19 diagnostics require nasal swabs.

“This sensor shows the highest range of limit of detection, response time, and saliva compatibility even without any antibody and receptor design,” Cho says. “It is a unique feature of this type of molecular recognition scheme that rapid design and testing is possible, unhindered by the development time and supply chain requirements of a conventional antibody or enzymatic receptor.”

Quick response

The speed with which the researchers were able to develop a working prototype suggests that this approach could prove useful for developing diagnostics more quickly during future pandemics, Strano says.

“We’re able to go from someone handing us viral markers to a working fiber optic sensor in an extremely short amount of time,” he says.

Sensors that rely on antibodies to detect viral proteins, which form the basis of many of the rapid Covid-19 tests now available, take much longer to develop because the process of designing the right protein antibody is so time-consuming.

The researchers have filed for a patent on the technology in hopes that it could be commercialized for use as a Covid-19 diagnostic. Strano also hopes to further develop the technology so that it could be deployed quickly in response to future pandemics.

The research was funded by a National Institutes of Health Rapid Acceleration of Diagnostics (RADx) grant.

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Dragging your feet? Lack of sleep affects your walk, new study finds

Good sleep can be hard to come by. But a new study finds that if you can make up for lost sleep, even for just a few weekend hours, the extra zzz’s could help reduce fatigue-induced clumsiness, at least in how you walk.

There’s plenty of evidence to show sleep, and how much we get of it, can affect how well we do on cognitive tasks such as solving a math problem, holding a conversation, or even reading this article. Less explored is the question of whether sleep influences the way we walk or carry out other activities that are assumed to be less mentally taxing.

The new study, by researchers at MIT and the University of São Paulo in Brazil, reports that walking — and specifically, how well we can control our stride, or gait — can indeed be affected by lack of sleep.

In experiments with student volunteers, the team found that overall, the less sleep students got, the less control they had when walking during a treadmill test. For students who pulled an all-nighter before the test, this gait control plummeted even further.

Interestingly, for those who didn’t stay up all night before the test, but who generally had less-than-ideal sleep during the week, those who slept in on weekends performed better than those who didn’t.

“Scientifically, it wasn’t clear that almost automatic activities like walking would be influenced by lack of sleep,” says Hermano Krebs, principal research scientist in MIT’s Department of Mechanical Engineering. “We also find that compensating for sleep could be an important strategy. For instance, for those who are chronically sleep-deprived, like shift workers, clinicians, and some military personnel, if they build in regular sleep compensation, they might have better control over their gait.”

Krebs and his co-authors, including lead author Arturo Forner-Cordero of the University of São Paulo, have published the study today in the journal Scientific Reports.

Brainy influence

The act of walking was once seen as an entirely automatic process, involving very little conscious, cognitive control. Animal experiments with a treadmill suggested that walking appeared to be an automatic process, governed mainly by reflexive, spinal activity, rather than more cognitive processes involving the brain.

“This is the case with quadrupeds, but the idea was more controversial in humans,” Krebs says.

Indeed, since those experiments, scientists including Krebs have showed that the act of walking is slightly more involved than once thought. Over the last decade, Krebs has extensively studied gait control and the mechanics of walking, in order to develop strategies and assistive robotics for patients who have suffered strokes and other motion-limiting conditions.

In previous experiments, he has shown, for instance, that healthy subjects can adjust their gait to match subtle changes in visual stimuli, without realizing they are doing so. These results suggested that walking involves some subtle, conscious influence, in addition to more automatic processes.

In 2013, he struck up a collaboration with Forner-Cordero through a grant from the MIT-Brazil MISTI program, and the team began to explore whether more subtle stimuli, such as auditory cues, might influence walking. In these initial experiments, volunteers were asked to walk on a treadmill as researchers played and slowly shifted the frequency of a metronome. The volunteers, without realizing it, matched their steps to the subtly changing beat.

“That suggested the concept of gait being only an automatic process is not a complete story,” Krebs says. “There’s a lot of influence coming from the brain.”

Sleep and walking

Forner-Cordero and Krebs continued to investigate the mechanics of walking and general motor control, mostly enlisting student volunteers in their experiments. Cordero in particular noticed that, toward the end of the semester, when students faced multiple exams and project deadlines, they were more sleep-deprived and happened to do worse in the team’s experiments.

“So, we decided to embrace the situation,” Forner-Cordero says.

In their new study, the team enlisted students from the University of São Paulo to take part in an experiment focused on the effects of sleep deprivation on gait control.

The students were each given a watch to track their activity over 14 days. This information gave researchers an idea of when and how long students were sleeping and active each day. The students were given no instruction on how much to sleep, so that the researchers could record their natural sleep patterns. On average, each student slept about six hours per day, although some students compensated, catching up on sleep over the two weekends during the 14-day period.

On the evening before the 14th day, one group of students stayed awake all night in the team’s sleep lab. This group was designated the Sleep Acute Deprivation group, or SAD. On the morning of the 14th day, all students went to the lab to perform a walking test.

Each student walked on a treadmill set at the same speed, as researchers played a metronome. The students were asked to keep step with the beat, as the researchers slowly and subtly raised and lowered the metronome’s speed, without telling the students they were doing so. Cameras captured the students’ walking, and specifically, the moment their heel struck the treadmill, compared with the beat of the metronome.

“They had to synchronize their heel strike to the beat, and we found the errors were larger in people with acute sleep deprivation,” Forner-Cordero says. “They were off the rhythm, they missed beeps, and were performing in general, worse.”

This in itself may not be entirely surprising. But in comparing students who did not pull an all-nighter prior to the test, the researchers found an unexpected difference: The students who did slightly better were those who compensated and got slightly more sleep on the weekends, even when they performed the test at the tail end of the week.

“That’s paradoxical,” Forner-Cordero says. “Even at the peak of when most people would be tired, this compensating group did better, which we didn’t expect.”

“The results show that gait is not an automatic process, and that it can be affected by sleep deprivation,” Krebs says. “They also suggest strategies for mitigating effects of sleep deprivation. Ideally, everyone should sleep eight hours a night. But if we can’t, then we should compensate as much and as regularly as possible.”

This research was supported, in part, by the Office of Naval Research Global.

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Solid, liquid, or gas? Technique quickly identifies physical state of tissues and tumors

As an organism grows, the feel of it changes too. In the initial stages, an embryo takes on an almost fluid-like state that allows its cells to divide and expand. As it matures, its tissues and organs firm up into their final form. In certain species, this physical state of an organism can be an indicator of its developmental stage, and even the general state of its health.

Now, researchers at MIT have found that the way in which a tissue’s cells are arranged can serve as a fingerprint for the tissue’s “phase.” They have developed a method to decode images of cells in a tissue to quickly determine whether that tissue is more like a solid, liquid, or even a gas. Their findings are reported this week in the Proceedings of the National Academy of Sciences.

The team hopes that their method, which they’ve dubbed “configurational fingerprinting,” can help scientists track physical changes in an embryo as it develops. More immediately, they are applying their method to study and eventually diagnose a specific type of tissue: tumors.

In cancer, there has been evidence to suggest that, like an embryo, a tumor’s physical state may indicate its stage of growth. Tumors that are more solid may be relatively stable, whereas more fluid-like growths could be more prone to mutate and metastasize.

The MIT researchers are analyzing images of tumors, both grown in the lab and biopsied from patients, to identify cellular fingerprints that indicate whether a tumor is more like a solid, liquid, or gas. They envision that doctors can one day match an image of a tumor’s cells with a cellular fingerprint to quickly determine a tumor’s phase, and ultimately a cancer’s progression.

“Our method would allow a very easy diagnosis of the states of cancer, simply by examining the positions of cells in a biopsy,” says Ming Guo, associate professor of mechanical engineering at MIT. “We hope that, by simply looking at where the cells are, doctors can directly tell if a tumor is very solid, meaning it can’t metastasize yet, or if it’s more fluid-like, and a patient is in danger.”

Guo’s co-authors are Haiqian Yang, Yulong Han, Wenhui Tang, and Rohan Abeyaratne of MIT, Adrian Pegoraro of the University of Ottawa, and Dapeng Bi of Northeastern University.

Triangular order

In a perfect solid, the material’s individual constituents are configured as an orderly lattice, such as the atoms in a cube of crystal. If you were to cut a slice of the crystal and lay it on a table, you would see that the atoms are arranged such that you could connect them in a pattern of repeating triangles. In a perfect solid, as the spacing between atoms would be exactly the same, the triangles that connect them would typically be equilateral in shape.

Guo took this construct as a template for a perfectly solid structure, with the idea that it could serve as a reference for comparing the cell configurations of actual, less-than-perfectly-solid tissues and tumors.

“Real tissues are never perfectly ordered,” Guo says. “They are mostly disordered. But still, there are subtle differences in how much they are disordered.”

Following this idea, the team started with images of various types of tissues and used software to map triangular connections between a tissue’s cells. In contrast to the equilateral triangles in a perfect solid, the maps produced triangles of various shapes and sizes, indicating cells with a range of spatial order (and disorder).

For each triangle in an image, they measured two key parameters: volumetric order, or the space within a triangle; and shear order, or how far a triangle’s shape is from equilateral. The first parameter indicates a material’s density fluctuation, while the second illustrates how prone the material is to deforming. These two parameters, they found, were enough to characterize whether a tissue was more like a solid, liquid, or gas.

“We’re directly calculating the exact value of both parameters, compared to those of a perfect solid, and using those exact values as our fingerprints,” Guo explains.

Vapor tendrils

The team tested its new fingerprinting technique in several different scenarios. The first was a simulation in which they modeled the mixing of two types of molecules, the concentration of which they increased gradually. For each concentration, they mapped the molecules into triangles, then measured each triangle’s two parameters. From these measurements, they characterized the phase of the molecules and were able to reproduce the transitions between gas, liquid, and solid, that was expected.

“People know what to expect in this very simple system, and this is what we see exactly,” Guo says. “This demonstrated the capability of our method.”

The researchers then went on to apply their method in systems with cells rather than molecules. For instance, they looked at videos, taken by other researchers, of a growing fruitfly wing. Applying their method, they could identify regions in the developing wing that morphed from solid to a more fluid state.

“As a fluid, this may help with growth,” Guo says. “How exactly that happens is still under investigation.”

He and his team also grew small tumors from cells of human breast tissue and watched as the tumors grew appendage-like tendrils — signs of early metastasis. When they mapped the configuration of cells in the tumors, they found that the noninvasive tumors resembled something between a solid and a liquid, and the invasive tumors were more gas-like, while the tendrils showed an even more disordered state. 

“Invasive tumors were more like vapor, and they want to spread out and go everywhere,” Guo says. “Liquids can barely be compressed. But gases are compressible — they can swell and shrink easily, and that’s what we see here.”

The team is working with samples of human cancer biopsies, which they are imaging and analyzing to hone their cellular fingerprints. Eventually, Guo envisions that mapping a tissue’s phases can be a quick and less invasive way to diagnose multiple types of cancer.

“Doctors typically have to take biopsies, then stain for different markers depending on the cancer type, to diagnose,” Guo says. “Perhaps one day we can use optical tools to look inside the body, without touching the patient, to see the position of cells, and directly tell what stage of cancer a patient is in”

This research was supported in part by the National Institutes of Health, MathWorks, and the Jeptha H. and Emily V. Wade Award at MIT.

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Artificial intelligence sheds light on how the brain processes language

In the past few years, artificial intelligence models of language have become very good at certain tasks. Most notably, they excel at predicting the next word in a string of text; this technology helps search engines and texting apps predict the next word you are going to type.

The most recent generation of predictive language models also appears to learn something about the underlying meaning of language. These models can not only predict the word that comes next, but also perform tasks that seem to require some degree of genuine understanding, such as question answering, document summarization, and story completion. 

Such models were designed to optimize performance for the specific function of predicting text, without attempting to mimic anything about how the human brain performs this task or understands language. But a new study from MIT neuroscientists suggests the underlying function of these models resembles the function of language-processing centers in the human brain.

Computer models that perform well on other types of language tasks do not show this similarity to the human brain, offering evidence that the human brain may use next-word prediction to drive language processing.

“The better the model is at predicting the next word, the more closely it fits the human brain,” says Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience, a member of MIT’s McGovern Institute for Brain Research and Center for Brains, Minds, and Machines (CBMM), and an author of the new study. “It’s amazing that the models fit so well, and it very indirectly suggests that maybe what the human language system is doing is predicting what’s going to happen next.”

Joshua Tenenbaum, a professor of computational cognitive science at MIT and a member of CBMM and MIT’s Artificial Intelligence Laboratory (CSAIL); and Evelina Fedorenko, the Frederick A. and Carole J. Middleton Career Development Associate Professor of Neuroscience and a member of the McGovern Institute, are the senior authors of the study, which appears this week in the Proceedings of the National Academy of Sciences. Martin Schrimpf, an MIT graduate student who works in CBMM, is the first author of the paper.

Making predictions

The new, high-performing next-word prediction models belong to a class of models called deep neural networks. These networks contain computational “nodes” that form connections of varying strength, and layers that pass information between each other in prescribed ways.

Over the past decade, scientists have used deep neural networks to create models of vision that can recognize objects as well as the primate brain does. Research at MIT has also shown that the underlying function of visual object recognition models matches the organization of the primate visual cortex, even though those computer models were not specifically designed to mimic the brain.

In the new study, the MIT team used a similar approach to compare language-processing centers in the human brain with language-processing models. The researchers analyzed 43 different language models, including several that are optimized for next-word prediction. These include a model called GPT-3 (Generative Pre-trained Transformer 3), which, given a prompt, can generate text similar to what a human would produce. Other models were designed to perform different language tasks, such as filling in a blank in a sentence.

As each model was presented with a string of words, the researchers measured the activity of the nodes that make up the network. They then compared these patterns to activity in the human brain, measured in subjects performing three language tasks: listening to stories, reading sentences one at a time, and reading sentences in which one word is revealed at a time. These human datasets included functional magnetic resonance (fMRI) data and intracranial electrocorticographic measurements taken in people undergoing brain surgery for epilepsy.

They found that the best-performing next-word prediction models had activity patterns that very closely resembled those seen in the human brain. Activity in those same models was also highly correlated with measures of human behavioral measures such as how fast people were able to read the text.

“We found that the models that predict the neural responses well also tend to best predict human behavior responses, in the form of reading times. And then both of these are explained by the model performance on next-word prediction. This triangle really connects everything together,” Schrimpf says.

“A key takeaway from this work is that language processing is a highly constrained problem: The best solutions to it that AI engineers have created end up being similar, as this paper shows, to the solutions found by the evolutionary process that created the human brain. Since the AI network didn't seek to mimic the brain directly — but does end up looking brain-like — this suggests that, in a sense, a kind of convergent evolution has occurred between AI and nature,” says Daniel Yamins, an assistant professor of psychology and computer science at Stanford University, who was not involved in the study.

Game changer

One of the key computational features of predictive models such as GPT-3 is an element known as a forward one-way predictive transformer. This kind of transformer is able to make predictions of what is going to come next, based on previous sequences. A significant feature of this transformer is that it can make predictions based on a very long prior context (hundreds of words), not just the last few words.

Scientists have not found any brain circuits or learning mechanisms that correspond to this type of processing, Tenenbaum says. However, the new findings are consistent with hypotheses that have been previously proposed that prediction is one of the key functions in language processing, he says.

“One of the challenges of language processing is the real-time aspect of it,” he says. “Language comes in, and you have to keep up with it and be able to make sense of it in real time.”

The researchers now plan to build variants of these language processing models to see how small changes in their architecture affect their performance and their ability to fit human neural data.

“For me, this result has been a game changer,” Fedorenko says. “It’s totally transforming my research program, because I would not have predicted that in my lifetime we would get to these computationally explicit models that capture enough about the brain so that we can actually leverage them in understanding how the brain works.”

The researchers also plan to try to combine these high-performing language models with some computer models Tenenbaum’s lab has previously developed that can perform other kinds of tasks such as constructing perceptual representations of the physical world.

“If we’re able to understand what these language models do and how they can connect to models which do things that are more like perceiving and thinking, then that can give us more integrative models of how things work in the brain,” Tenenbaum says. “This could take us toward better artificial intelligence models, as well as giving us better models of how more of the brain works and how general intelligence emerges, than we’ve had in the past.”

The research was funded by a Takeda Fellowship; the MIT Shoemaker Fellowship; the Semiconductor Research Corporation; the MIT Media Lab Consortia; the MIT Singleton Fellowship; the MIT Presidential Graduate Fellowship; the Friends of the McGovern Institute Fellowship; the MIT Center for Brains, Minds, and Machines, through the National Science Foundation; the National Institutes of Health; MIT’s Department of Brain and Cognitive Sciences; and the McGovern Institute.

Other authors of the paper are Idan Blank PhD ’16 and graduate students Greta Tuckute, Carina Kauf, and Eghbal Hosseini.

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MIT welcomes Virginia Rometty as its next Visiting Innovation Fellow

Virginia M. “Ginni” Rometty, a business leader and champion of diversity in the workplace, will serve as the next MIT Visiting Innovation Fellow for the coming academic year.

Formerly chairperson, president, and CEO of IBM, Rometty helped streamline the company’s portfolio by transforming it into a leader in AI, hybrid cloud, and quantum computing, both within the company and through acquisition. Under Rometty’s leadership, IBM acquired 65 companies, including Red Hat, a provider of enterprise open-source solutions, which was the largest acquisition in the company’s history.

During her MIT appointment, Rometty will focus on advancing women in STEM and in entrepreneurship, as well as on bolstering ethics and responsibility in a digital age.

"The Visiting Innovation Fellowship brings to campus thoughtful, visionary leaders to inspire and educate the next generation of entrepreneurs and innovators. Ginni Rometty is just such a leader, and I am delighted that our entire community will have this opportunity to learn from her,” says MIT President L. Rafael Reif. “Ginni believes deeply in MIT’s mission, and she has been a wise and insightful advisor to me and others on our campus. We are honored to welcome her to MIT.”

The MIT Visiting Innovation Fellows are part of MIT’s Innovation Initiative, which collaborates with all five of MIT’s schools and the MIT Schwarzman College of Computing. The Innovation Initiative provides the knowledge, tools, and the ecosystem needed for alumni and students to move high-potential ideas “from conception to impact” and generate important local and global innovations.

Visiting Innovation Fellows are drawn from policy, corporate, risk capital, and entrepreneurial settings. In their role, fellows often interact with the MIT community by convening events, lectures, and one-on-one sessions with student entrepreneurs and others seeking to engage more deeply with innovation practitioners.

The human component at the core of tech issues

Describing her interest in the Visiting Innovation Fellowship, Rometty says, “MIT understands that the future lies at the integration of tech and business — but it also understands the human element that’s at the core of these issues. Research in these areas, like AI and cybersecurity, is incredibly urgent for our economy and our society — especially in light of the changes we’ve seen during the pandemic.”

“At IBM, we shared a belief in the fundamental promise of technology: that when we apply science to real-world problems, we can create a tomorrow that is better than today. I’m looking forward to further advancing that mission during my time at MIT,” she adds.

Rometty is already familiar with the Institute after serving as a member of the MIT Presidential CEO Advisory Board. She was also a member of the advisory board for the MIT Task Force on the Work of the Future, a two-and-a-half year effort aimed at understanding how emerging technologies are changing the nature of human work and the skills required, and exploring how we can design and leverage technological innovations for the benefit of everyone in society.

MIT Provost Martin A. Schmidt says Rometty’s background and experience dovetail well with MIT’s innovation goals. “Ginni Rometty embodies the essence of MIT’s innovation mission for the campus and the world, and we are honored to welcome her to our community,” Schmidt says. “Her extensive work to strengthen job readiness, address ethics and privacy challenges in computing, and build a diverse and inclusive workforce will help inform how to efficiently and effectively prepare workers for the future and move innovation from ideas into real-world solutions.”

Diversity efforts and new pathways to work

During her eight years at the helm of IBM, Rometty was also known for her work in championing diversity and inclusion.

“You cannot be what you cannot see. It’s important for women, communities of color, or any group to have role models to see what’s possible,” Rometty says.

One of her proudest accomplishments was taking action around diversity initiatives, she says: “I knew it was my responsibility as CEO to break down systemic barriers that keep people out of the workforce and deny economic opportunity.”

While at IBM, Rometty championed a global movement sometimes referred to as “skills first,” which is a paradigm that hires new workers based on their skills and aptitude, not just their degrees. These “new collar workers” represented 15 percent of IBM’s new U.S. hires during Rometty’s final year at IBM.

“Economic opportunity is the best equalizer. At IBM, we invested in recruiting and training new collar workers to take on valuable, tech-based jobs that don’t require a traditional four-year degree to start, but do require the right mix of skills,” Rometty says.

IBM also saw the growth of its Pathways to Technology Early College High Schools, or P-TECH programs, a global education model co-designed with the New York City Department of Education and the City University of New York that now operates about 200 schools in the United States and another 100 across the globe.

Rometty also launched an extended parental leave policy and encouraged new moms to return to IBM with a “returnship” program that offered hands-on experience in emerging technologies.

“Now, I’m proud to serve as co-chair of OneTen, a coalition that aims to close the opportunity gap for Black talent in America by hiring and advancing 1 million Black Americans, who have skills but not college degrees, into family-sustaining careers over the next 10 years. We are building a skills-first, versus degrees-only movement across the country and the world,” Rometty says.

Fiona Murray, associate dean for innovation and inclusion, says that priorities like these are valuable to MIT’s community. “As CEO, Ms. Rometty deepened and extended IBM’s core capabilities in critical areas of deep technology and also created new workforce training pathways for the 21st century. Her particular commitment to actively supporting leadership roles for those in historically marginalized communities aligns with important ongoing research and Institute priorities. MIT’s students, especially those involved in innovation and entrepreneurial activities, will benefit from her kinds of visionary insights as they launch, scale, and grow their own companies and teams.”

Rometty also serves on several boards including at JPMorgan Chase and the Brookings Institute. She is a member of the Tsinghua University School of Economics and Management Advisory Board.

Rometty has a bachelor of science degree with high honors in computer science and electrical engineering from Northwestern University, where she also awarded an honorary degree.

Eric Schmidt, co-founder of Schmidt Futures and former Google CEO, served as the most recent Visiting Innovation Fellow. Other fellows have included former U.S. Secretary of Defense Ash Carter and former Massachusetts Governor Deval Patrick.

Rometty will give the closing keynote address at the virtual Tough Tech Summit on Oct. 27.

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Neutron star collisions are a “goldmine” of heavy elements, study finds

Most elements lighter than iron are forged in the cores of stars. A star’s white-hot center fuels the fusion of protons, squeezing them together to build progressively heavier elements. But beyond iron, scientists have puzzled over what could give rise to gold, platinum, and the rest of the universe’s heavy elements, whose formation requires more energy than a star can muster.

A new study by researchers at MIT and the University of New Hampshire finds that of two long-suspected sources of heavy metals, one is more of a goldmine than the other.

The study, published today in Astrophysical Journal Letters, reports that in the last 2.5 billion years, more heavy metals were produced in binary neutron star mergers, or collisions between two neutron stars, than in mergers between a neutron star and a black hole.

The study is the first to compare the two merger types in terms of their heavy metal output, and suggests that binary neutron stars are a likely cosmic source for the gold, platinum, and other heavy metals we see today. The findings could also help scientists determine the rate at which heavy metals are produced across the universe.

“What we find exciting about our result is that to some level of confidence we can say binary neutron stars are probably more of a goldmine than neutron star-black hole mergers,” says lead author Hsin-Yu Chen, a postdoc in MIT’s Kavli Institute for Astrophysics and Space Research. 

Chen’s co-authors are Salvatore Vitale, assistant professor of physics at MIT, and Francois Foucart of UNH.

An efficient flash

As stars undergo nuclear fusion, they require energy to fuse protons to form heavier elements. Stars are efficient in churning out lighter elements, from hydrogen to iron. Fusing more than the 26 protons in iron, however, becomes energetically inefficient.

“If you want to go past iron and build heavier elements like gold and platinum, you need some other way to throw protons together,” Vitale says.

Scientists have suspected supernovae might be an answer. When a massive star collapses in a supernova, the iron at its center could conceivably combine with lighter elements in the extreme fallout to generate heavier elements.

In 2017, however, a promising candidate was confirmed, in the form a binary neutron star merger, detected for the first time by LIGO and Virgo, the gravitational-wave observatories in the United States and in Italy, respectively. The detectors picked up gravitational waves, or ripples through space-time, that originated 130 million light years from Earth, from a collision between two neutron stars — collapsed cores of massive stars, that are packed with neutrons and are among the densest objects in the universe.

The cosmic merger emitted a flash of light, which contained signatures of heavy metals.

“The magnitude of gold produced in the merger was equivalent to several times the mass of the Earth,” Chen says. “That entirely changed the picture. The math showed that binary neutron stars were a more efficient way to create heavy elements, compared to supernovae.”

A binary goldmine

Chen and her colleagues wondered: How might neutron star mergers compare to collisions between a neutron star and a black hole? This is another merger type that has been detected by LIGO and Virgo and could potentially be a heavy metal factory. Under certain conditions, scientists suspect, a black hole could disrupt a neutron star such that it would spark and spew heavy metals before the black hole completely swallowed the star.

The team set out to determine the amount of gold and other heavy metals each type of merger could typically produce. For their analysis, they focused on LIGO and Virgo’s detections to date of two binary neutron star mergers and two neutron star – black hole mergers.

The researchers first estimated the mass of each object in each merger, as well as the rotational speed of each black hole, reasoning that if a black hole is too massive or slow, it would swallow a neutron star before it had a chance to produce heavy elements. They also determined each neutron star’s resistance to being disrupted. The more resistant a star, the less likely it is to churn out heavy elements. They also estimated how often one merger occurs compared to the other, based on observations by LIGO, Virgo, and other observatories.

Finally, the team used numerical simulations developed by Foucart, to calculate the average amount of gold and other heavy metals each merger would produce, given varying combinations of the objects’ mass, rotation, degree of disruption, and rate of occurrence.

On average, the researchers found that binary neutron star mergers could generate two to 100 times more heavy metals than mergers between neutron stars and black holes. The four mergers on which they based their analysis are estimated to have occurred within the last 2.5 billion years. They conclude then, that during this period, at least, more heavy elements were produced by binary neutron star mergers than by collisions between neutron stars and black holes.

The scales could tip in favor of neutron star-black hole mergers if the black holes had high spins, and low masses. However, scientists have not yet observed these kinds of black holes in the two mergers detected to date.

Chen and her colleagues hope that, as LIGO and Virgo resume observations next year, more detections will improve the team’s estimates for the rate at which each merger produces heavy elements. These rates, in turn, may help scientists determine the age of distant galaxies, based on the abundance of their various elements.

“You can use heavy metals the same way we use carbon to date dinosaur remains,” Vitale says. “Because all these phenomena have different intrinsic rates and yields of heavy elements, that will affect how you attach a time stamp to a galaxy. So, this kind of study can improve those analyses.”

This research was funded, in part, by NASA, the National Science Foundation, and the LIGO Laboratory.

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At MIT, Nobel laureate Frances Arnold describes innovation by evolution

“As engineers, we want to create things that don’t necessarily exist on the planet, or may have never existed, but that solve real problems,” said Frances H. Arnold at the 2021 Hoyt C. Hottel Lecture in Chemical Engineering on Oct. 1.

Harnessing the process of evolution to optimize and create enzymes, Arnold, the Pauling Professor of Chemical Engineering, Bioengineering and Biochemistry at Caltech, launched a field of engineering with applications in alternative energy, medicine, and diverse industries. Her research earned her the 2018 Nobel Prize in Chemistry, as well as the Charles Stark Draper Prize of the U.S. National Academy of Engineering (2011), the U.S. National Medal of Technology and Innovation (2011), and the Millennium Technology Prize (2016).

Her Hottel presentation, Arnold noted at the start, was the first time she had spoken to a live audience in 18 months — a cause for celebration. In the talk, “Bringing New Chemistry to Life,” Arnold recounted the story of her relentless quest to address urgent global challenges by way of better enzymes — the proteins catalyzing chemical reactions in biology and in a vast array of manufactured products and processes. Her narrative described her decades-long effort to, in her words, “compose” with DNA, utilizing the tools of nature to generate enzymes “that work better than what nature has provided.”

The lecture was sponsored by the Department of Chemical Engineering, and was introduced by department head and Institute Professor Paula T. Hammond.

Incomprehensible possibilities

Arnold was in the vanguard of scientists in the late 1980s eager to leverage the latest innovations in genetics. Researchers had figured out how DNA coded for proteins, and how to edit DNA. But in an era before high throughput computing and massive databases for cataloging proteins, no lab could manipulate genetic sequences to select for desired properties on a realistic time scale. “A typical small protein 300 amino acids long with 20 different amino acids — that space of possible sequences is bigger than anything you can comprehend,” said Arnold.

The challenge facing scientists at the time, said Arnold, reminded her of the Jorge Luis Borges 1941 short story, “The Library of Babel.” In this massive collection of books, order and content are completely random, and “librarians despair of ever finding a book that has a meaningful sentence, much less a work of literature,” she said. “So here I am, an assistant professor at Caltech, in this library of all possible proteins, and I have to find ‘Moby Dick.’”

To escape this quagmire, Arnold drew inspiration from the British biologist John Maynard Smith, who laid out the workings of natural selection in molecules. Mutations that routinely pop up in DNA sequences can either lead to protein failure and the end of the line, or to a fitter protein variant that survives and can engender future generations. “This was a powerful idea for me,” said Arnold. “If I’m the breeder of molecules, I decide who is fit to go on to the next generation.” This was the spark behind directed enzyme evolution — the process developed by Arnold to engineer better catalysts.

Selectively breeding enzymes

To realize her vision, Arnold created a factory in her lab guided by a rigorous methodology. She sampled enzymes of interest, and identified DNA sequences that could lead to enhanced functions. Then she generated mutations in these sequences and, using host bacteria, created enzymes whose properties she would evaluate. Arnold repeated this process again and again until she arrived at an enzyme with the properties she sought.

The result of her first years pursuing directed enzyme evolution was a new breed of subtilisin, an enzyme that can be found in dirt. (“Four billion years of natural selection has given us proteins you can scrape from the bottom of your shoe,” noted Arnold.) The engineered subtilisin could function in a harsh solvent, a property that made it extremely useful for chemical applications. This version also satisfied an overarching goal of Arnold’s research: making biologically based enzymes to replace those synthesized by chemists, which often involve environmentally destructive materials.

“It was simple, good engineering, an algorithmic process that led to products like laundry detergent enzymes, and got me the biggest accolade of my life, and an appearance on the set of ‘The Big Bang Theory’ in 2017.”

Emulating nature

Directed enzyme evolution unleashed a flood of activity on optimized and repurposed enzymes from Arnold’s lab, as well as from labs around the world. Biocatalysis is becoming a transformative industry, with the proliferation of biologically based enzymes to coax the formation of chemical bonds in molecules containing such elements as halogen, fluorine, or chlorine. In 2016, Arnold’s lab designed an enzyme that normally catalyzes important biological reactions in living things to forge a carbon-silicon bond. It was a first. “We can program bacteria to produce these bonds with a mutant that does the job 50 times better than the best human chemist … and without the environmental devastation,” said Arnold.

Molecules built around such chemical bonds are in high demand in the pharmaceutical, agricultural, semiconductor, and renewable energy industries. To meet the need, conventional synthetic chemistry relies on hazardous materials, harsh and often costly manufacturing conditions. Arnold believes her methods offer an environmentally friendlier and less expensive alternative.

By emulating nature “and the powerful process that’s given rise to all life,” she said, “we can use abundant renewable resources to make everything we might want.” Arnold hailed students in the audience: “It’s a wonderful thing to work with; come in with fabulous ideas!” In closing, she said, “If we can learn how to use this process, we can adapt, evolve, and innovate in tandem with our beautiful planet.”

Hoyt C. Hottel served as an MIT faculty member from 1928 to 1968. The Hoyt C. Hottel Lectureship was established in 1985 to recognize his contributions to the Department of Chemical Engineering and its students, and to the establishment and direction of the Fuels Research Laboratory. The lectureship is intended to draw eminent scholars to MIT to stimulate future generations of students. The lectureship resumed this year after a pause in 2020 during the Covid-19 pandemic.

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