A noninvasive test to detect cancer cells and pinpoint their location
Diagnostic nanoparticles could be used to monitor tumor recurrence after treatment or to perform routine cancer screenings.
Diagnostic nanoparticles could be used to monitor tumor recurrence after treatment or to perform routine cancer screenings.
MIT researchers train a neural network to predict a “boiling crisis,” with potential applications for cooling computer chips and nuclear reactors.
Researchers could rapidly obtain high-resolution images of blood vessels and neurons within the brain.
FIB-SEM is now available to researchers across the Institute for use in characterization, nanofabrication, and rapid prototyping.
Former naval petty officer Manuel Morales now develops imaging applications to detect cardiac dysfunction in young patients.
Imaging technique could enable new pathways for reducing concrete’s hefty carbon footprint, as well as for 3-D printing of concrete.
Despite construction and a pandemic, MIT Distinctive Collections staff continue their work.
Cutting-edge microscope helps reveal ways to control the electronic properties of atomically thin materials.
Company specializing in atomic force microscopy to advise, collaborate with MIT researchers.
Mechanical engineering students Ivan Goryachev and Ryan Koeppen ’19 are developing a thermal trailer and subsequent kiosks that could be deployed on campus during the Covid-19 pandemic.
Unbiased, high-throughput analysis pipeline improves utility of “minibrains” for understanding development and diseases such as Zika infection.
Animators spend hours adding textures to objects. A new machine-learning system simplifies the process.
The initiation of droplet and bubble formation on surfaces can now be directly imaged, allowing for design of more efficient condensers and boilers.
Machine learning model predicts probability that a particular urinary tract infection can be treated by specific antibiotics.
Many health issues are tied to excess fluid in the lungs. A new algorithm can detect the severity by looking at a single X-ray.