With the help of artificial intelligence, researchers at Linköping University, Sweden, have found previously overlooked occurrences of the 2D materials MXenes in older databases. The findings have inspired the researchers to create new materials with more sustainable synthesis methods than before. The results, published in the journal Science, open up new applications for MXenes, where magnetism is an important component, such as in energy and data storage.

Metallic materials used in extremely cold environments must maintain high strength, ductility and fracture toughness to support applications in aerospace, clean energy, superconducting systems and deep-space exploration. However, traditional alloys often suffer performance degradation at ultralow temperatures because of the long-standing trade-off among strength, ductility and toughness. Although metals may become stronger at lower temperatures, they can also become more brittle and prone to fracture. The sinking of the Titanic is a well-known historical event associated with low-temperature brittle failure. This vulnerability, known as “cold brittleness”, remains a bottleneck in the development of deep-sea and deep-space exploration technologies.

Event cameras, which mimic aspects of biological vision, can detect changes in light far faster than conventional cameras, but interpreting their data has been a longstanding challenge. Now, Penn engineers have developed Fast Feature Field, a computational tool that could make these cameras far easier to use across a wide range of robotic applications.

An Eye Discovery study combines transcriptomic analysis, machine learning, and corneal epithelial cell experiments to identify IFITM1 and CTSS as candidate genes associated with immune-inflammatory changes in Sjögren’s syndrome-related keratoconjunctivitis sicca.

Researchers from Northeastern University and China Medical University propose a new framework to align deep learning technologies with the practical needs of pathological diagnosis and prognosis, paving the way for more integrated and trustworthy AI in healthcare.