A clearer future: POSTECH research team reduce light noise to push the boundaries of flat optics
Peer-Reviewed Publication
Updates every hour. Last Updated: 6-May-2025 06:09 ET (6-May-2025 10:09 GMT/UTC)
POSTECH Professor Junsuk Rho’s Team Develops a Multidimensional Sampling Theory Spanning the Entire Electromagnetic Spectrum—from Extreme Ultraviolet to Microwaves.
Foams are an essential component of many different drinks and foods: from a frothy head of beer to coffee crema, bread and ice cream. Despite their ubiquity, little is actually known or understood about these highly complex systems. Collaboration between the Institut Laue-Langevin (ILL) and Aarhus University has connected unique capabilities to investigate foam with critically relevant food science challenges, bringing a greener food future a step closer.
Through a multi-university collaboration, researchers at Virginia Tech have discovered a new, solid lubricating mechanism that can reduce friction in machinery at extremely high temperatures, well beyond the breakdown temperature of traditional solid lubricant such as graphite.
By linking theoretical predictions with neutron experiments, researchers have found evidence for quantum spin ice in the material Ce2Sn2O7. Their findings, which may inspire the technology of tomorrow, such as quantum computers, have been published in the journal ‘Nature Physics’. The experiments were conducted at the Institut Laue-Langevin (ILL), taking advantage of the world’s most intense neutron beams and of an unparalleled state-of-the-art instrument suite. This work paves the way towards future unifications of theory and experiments, which is of particular interest for highly complex areas such as quantum physics and exotic states of matter. The findings also offer a wonderful playground for further exploration of quantum phenomena in materials with potential applications in quantum computing.
Texas A&M researchers are developing a hybrid biomechanical physics-informed machine learning model. Their approach combines experimentally measured bone deformation data with governing physics and a robust machine learning framework, enabling precise, personalized predictions of mechanical stress in the bone. This innovation provides an efficient tool for patient-specific dental surgery planning, optimizing bone healing and ensuring long-term implant success.