Going to the doctor might make you want to cry, and according to a new study, doctors could someday put those tears to good use. In ACS Nano, researchers report a nanomembrane system that harvests and purifies tiny blobs called exosomes from tears, allowing researchers to quickly analyze them for disease biomarkers. Dubbed iTEARS, the platform could enable more efficient and less invasive molecular diagnoses for many diseases and conditions, without relying solely on symptoms.

Diagnosing diseases often hinges on assessing a patient’s symptoms, which can be unobservable at early stages, or unreliably reported...

Researchers have designed a fuel production system that uses water, carbon dioxide (CO2), and sunlight to produce aviation fuel. They have implemented the system in the field, and the design, publishing July 20 in the journal Joule, could help the aviation industry become carbon neutral.

“We are the first to demonstrate the entire thermochemical process chain from water and CO2 to kerosene in a fully-integrated solar tower system,” says Aldo Steinfeld (@solarfuels), ...

Shockwaves caused by asteroids colliding with Earth create materials with a range of complex carbon structures, which could be used for advancing future engineering applications, according to an international study led by UCL and Hungarian scientists.

Published today in Proceedings of the National Academy of Sciences, the team of researchers has found that diamonds formed during a high-energy shockwave from an asteroid collision around 50,000 years ago have unique and exceptional properties, caused by the short-term high temperatures and extreme pressure.

The researchers say that these structures can be targeted for advanced mechanical and electronic applications, giving us the ability to design materials that are not only ultrahard but also malleable with tunable electronic pro...

It’s been nearly a century since astronomer Fritz Zwicky first calculated the mass of the Coma Cluster, a dense collection of almost 1,000 galaxies located in the nearby universe. But estimating the mass of something so huge and dense, not to mention 320 million light-years away, has its share of problems—then and now. Zwicky’s initial measurements, and the many made since, are plagued by sources of error that bias the mass higher or lower.

Now, using tools from machine learning, a team led by Carnegie Mellon University physicists has developed a deeplearning method that accurately estimates the mass of the Coma Cluster and effectively mitigates the sources of error.

“People have made mass estimates of the Coma Cluster for many, many years...