Co-senior authors Jeremy Levy of Pitt and Andrew Daley of Oxford, collaborators from the early days of the Pittsburgh Quantum Institute, wrote an explanatory analysis of their group’s chiral-electron findings publishing in Science Advances Friday. In this study that combines  quantum physics, physics, biology, chemistry and organic systems, they developed in essence a programmable platform that could ultimately provide new ways to explore electrons in chiral systems — such as DNA, amino acids, proteins and more.

A cosmic particle detector in Antarctica has emitted a series of bizarre signals that defy the current understanding of particle physics, according to an international research group that includes scientists from Penn State. The unusual radio pulses were detected by the Antarctic Impulsive Transient Antenna (ANITA) experiment, a range of instruments flown on balloons high above Antarctica that are designed to detect radio waves from cosmic rays hitting the atmosphere.

KSU professor Mahmoud Asmar has received a three-year grant worth $799,800 from the U.S. Department of Energy. His work aims to deepen our understanding of how to generate and stabilize topological out-of-equilibrium quantum states.

A new cooling technology could significantly improve the energy efficiency of data centers and high-powered electronics while reducing water use associated with cooling. By passively removing heat through evaporation, it offers a promising alternative to traditional cooling systems.

University of São Paulo researchers are developing an aircraft equipped with sensors that can detect and measure the concentrations of carbon dioxide and methane in the environment; the project was presented at FAPESP Week France in Toulouse.

Researchers at Tianjin University have developed LineGen, a physics-guided method combining ordinary differential equations and a time-embedded U-Net, to generate super-resolution load data (SRLD) for power distribution systems. Published in Engineering, the approach addresses limitations in high-frequency data collection by achieving a 1000-fold resolution enhancement—significantly surpassing prior deep learning methods—while offering traceable physical interpretability for improved grid management and reliability.