Waging a two-pronged campaign against climate change
With MIGHTR, PhD student W. Robb Stewart aims to speed construction of new nuclear plants to help decarbonize the economy.
With MIGHTR, PhD student W. Robb Stewart aims to speed construction of new nuclear plants to help decarbonize the economy.
MIT researchers train a neural network to predict a “boiling crisis,” with potential applications for cooling computer chips and nuclear reactors.
Jacopo Buongiorno and others say factory-built microreactors trucked to usage sites could be a safe, efficient option for decarbonizing electricity systems.
Whether testing high-field fusion magnets or his own physical endurance, Theo Mouratidis pushes the limits.
How an MIT engineering course became an incubator for fusion design innovations.
Fifth-year nuclear science and engineering graduate student Arunkumar Seshadri looks to develop materials and fuels that can better withstand the extreme conditions in nuclear reactors.
MIT Energy Fellow Richard Ibekwe finds flaws in high-temperature superconducting tapes so they can be measured, fixed, or embraced.
Within minutes, the earthquake, tsunami, and nuclear meltdown on March 11, 2011, brought an unprecedented wave of death, displacement, and destruction to Japan.
National Academies study says fusion can help decarbonize US energy, calls for public-private approach to pilot plant operation by 2035-40.
Daniel Korsun’s undergraduate career at MIT prepared him to look more deeply into fusion magnet technology and design.
MIT’s Erica Salazar shows that faster detection of thermal shifts can prevent disruptive quench events in the HTS magnets used in tokamak fusion devices.
Manipulating materials at a fundamental level, Ju Li reveals new properties for energy applications.
Associate Professor Michael Short’s innovative approach can be seen in the two nuclear science and engineering courses he’s transformed.
Wide-ranging contributions over a span of seven decades advanced nuclear waste disposal and fuel cycle development.
Researchers show that deep reinforcement learning can be used to design more efficient nuclear reactors.