School of Engineering

ClimateWire reporter John Fialka writes that MIT engineers have developed a new process to convert carbon dioxide into a powder that can be safely stored for decades. “The MIT process gets closer to an ambitious dream: turning captured CO2 into a feedstock for clean fuel that replaces conventional batteries and stores electricity for months or years,” writes Fialka. “That could fill gaps in the nation's power grids as they transition from fossil fuels to intermittent solar and wind energy.”

MIT researchers are working to uncover new ways to avoid contrails and minimize their impact on global warming, reports Nicolas Rivero for The Washington Post. “Whether [the contrail impact is] exactly 20 percent or 30 percent or 50 percent, I don’t think anybody knows that answer, really,” says research scientist Florian Allroggen “But it also doesn’t really matter. It’s a big contributor and we need to worry about it.”

Forbes contributor William Haseltine spotlights how MIT researchers developed a biosensor ingestible capsule that can gather and transmit information on a patient’s condition to a physician. Haseltine notes that “aside from respiratory and heart rate monitoring, future applications for the pill could come from alterations in its design, leading to other avenues of health monitoring. These may include digestive health, blood sugar monitoring and cancer cell detection.” 

Fast Company reporter Adele Peters spotlights how researchers at MIT have combined cement with carbon black to make concrete that can store energy as one of the climate tech innovations that provide hope “that it’s still possible to avoid the worst impacts of climate change.” With this new technology, “the foundation of your future house could eventually store solar power from your roof,” explains Peters.

Prof. Manolis Kellis speaks with Brian Bergstein of The Boston Globe’s “Say More” podcast about the potential influence of artificial intelligence on biological advances and medicine. “I would say there is no way to answer biological questions without computational science nowadays,” says Kellis. “The genome and all of the technologies that it enabled transformed biology into an information-based discipline.”

Dynamic Labs, co-founded by Itai Turbahn '11 and Yoni Goldberg '09, MEng '10, offers “tech for crypto and non-crypto companies alike to create seamless login experiences backed by digital wallets,” reports Marco Quiroz-Gutierrez for Fortune. “The firm’s wallet-based business is twofold,” explains Quiroz-Gutierrez. “It offers a customizable all-in-one service that can either push the crypto wallet technology to the background for less savvy users or put it front and center for Web3 natives.”

Erika Debenedictis PhD '20 speaks with Nature reporter Rachel Pells about her experience building her own tools to use in her scientific research. “Inventions come from going out on a limb,” says Debenedictis. “The first version might be duct-taped together, but then you make another one, and another one, and the hope is that eventually the pay-off is greater than the pain of putting it together.”

Prof. Manolis Kellis speaks with USA Today reporter Josh Peter about the potential impact of AI in developing undetectable performance-enhancing drugs (PEDs). "The most feasible approach would be using generative AI to alter existing PEDs that trigger drug tests in a way that makes those drugs undetectable by current testing technology,” Kellis notes.

Gizmodo reporter George Dvorsky spotlights the Venus Life Finder mission, developed by researchers from MIT and Rocket Lab, which will be launching no earlier than December 2024. “The mission will send a small probe, equipped with a single science instrument, to analyze organic molecules and potential signs of life in the Venusian atmosphere,” writes Dvorsky.

Researchers from MIT and elsewhere are developing an electronic pill that can “measure heart rate, breathing rate and core temperature – from inside a human stomach,” reports Celia Ford for Wired. “We have a solution that’s relatively simple and enables access broadly,” says Prof. Giovanni. “I think that can be really transformative.”

Prof. Ritu Raman has developed centimeter-scale robots that use biological muscle, reports Liam Drew for Nature. “Raman is now developing muscle systems connected to neurons that can trigger contraction, just as they exist in animals,” writes Drew. “In the longer term, she aims to use networks of biological neurons that can sense external stimuli as well, enabling them to move in response to environmental cues.”

Christian Science Monitor reporter Ira Porter spotlights undergraduate Subin Kim and his experience transferring from community college to MIT through the Transfer Scholars Network, which is aimed at helping community college students find a path to four-year universities. “Every student that we admit, we’re looking for academic excellence and personal excellence,” says Stuart Schmill, dean of MIT admissions and student financial services. “And the students that we’ve brought in from the Transfer Scholar Network and in general from community colleges are remarkable individuals.”

Boston Globe reporters Aaron Pressman and Jon Chesto spotlight Liquid AI, a new startup founded by MIT researchers that is developing an AI system that relies on neural-network models that are “much simpler and require significantly less computer power to train and operate” than generative AI systems. “You need a fraction of the cost of developing generative AI, and the carbon footprint is much lower,” explains Liquid AI CEO Ramin Hasani, a research affiliate at CSAIL. “You get the same capabilities with a much smaller representation.”

Prof. Daniela Rus, director of CSAIL, and research affiliates Ramin Hasani, Mathias Lechner, and Alexander Amini have co-founded Liquid AI, a startup building a general-purpose AI system powered by a liquid neural network, reports Kyle Wiggers for TechCrunch. “Accountability and safety of large AI models is of paramount importance,” says Hasani. “Liquid AI offers more capital efficient, reliable, explainable and capable machine learning models for both domain-specific and generative AI applications." 

Researchers from MIT and elsewhere have developed a new AI technique for teaching robots to pack items into a limited space while adhering to a range of constraints, reports Nick Hilden for Scientific American. “We want to have a learning-based method to solve constraints quickly because learning-based [AI] will solve faster, compared to traditional methods,” says graduate student Zhutian “Skye” Yang.