New research shows how faint radio signals from the early Universe, soon to be detected by Moon missions, could reveal dark matter's secrets. Computer simulations revealed how gas cooled and formed clumps through gravitational interactions with dark matter during the cosmic Dark Ages.

A new study published in Biochar presents a detailed bibliometric analysis showcasing the rapid growth and diverse applications of biochar as a sustainable environmental remediation tool. The research, which examined over 7,000 scientific publications from 2007 to 2023, underscores biochar’s potential in soil improvement, water purification, air quality control, and carbon sequestration.

ORNL will lead two collaborations under the Fusion Innovation Research Engine (FIRE) program and partner on two more to close critical gaps in fusion technology and bridge basic research with growing fusion companies.

Crucial drugs such as crucial drugs such as statins, antibiotics and HIV inhibitors are among some of the most expensive because they require a chiral center ― a property in which a molecule cannot be superimposed with its mirror image. In a new study, University of Maine researchers explore a new, cost-reducing pathway to produce one of the key building blocks for chiral drugs, (S)-3-hydroxy-γ-butyrolactone (HBL), from glucose at high concentrations and yields. Because glucose can be derived from any lignocellulosic feedstock, this process opens a new door for the sustainable production of HBL. 

An “Einstein Cross” is a rarely seen cosmic configuration, in which the light from a distant galaxy is bent by the gravity of galaxies in front of it, creating four images. But scienitsts have found an extra image in an Einstein Cross -- a fifth point of light. This, they found, pointed to “something unusual,” which turned out to be a massive, hidden halo of dark matter. The existence of this invisible structure could only be inferred through careful computer modeling and analysis. The discovery, made by an international team is now being published in The Astrophysical Journal.

Prof. Laura Gagliardi at the University of Chicago Pritzker School of Molecular Engineering (UChicago PME) and Chemistry Department has developed a powerful new tool that harnesses electronic structure theories and machine learning to simulate transition metal catalytic dynamics with both accuracy and speed.