To increase energy efficiency and reduce the carbon footprint of hydrogen fuel production, Fanglin Che, associate professor in the Department of Chemical Engineering at Worcester Polytechnic Institute, is leveraging the power and potential of machine learning and computational modeling. The multi-university team she leads has completed a research study that was just published in Nature Chemical Engineering.

Scientists report the first direct real-time measurement and control of quantum uncertainty dynamics using ultrafast squeezed light pulses. This attosecond-resolved breakthrough not only advances fundamental quantum physics but also demonstrates petahertz-scale secure communication protocols, paving the way for next-generation quantum technologies in secure communication, quantum computing, and ultrafast spectroscopy.

A hundred years before the Event Horizon Telescope Collaboration released the first image of a black hole in 2019 – located at the heart of the galaxy M87 – astronomer Heber Curtis had already discovered a strange jet protruding from the galaxy’s center. Today, we know this to be the jet of the black hole M87*. Such jets are also emitted by other black holes. Theoretical astrophysicists at Goethe University have now developed a numerical code to describe with high mathematical precision how black holes transform their rotational energy into such ultra-fast jets.

A new metal–organic framework (MOF), APF-80, enables the crystalline sponge method to capture and analyze nucleophilic compounds. Alkaloids, a diverse group of biologically active compounds, usually damage MOF crystals and resist study. By incorporating multiple structural motifs, these guests are encapsulated inside APF-80, which allows high-quality crystallographic data collection. This development opens new possibilities for structural analysis, advancing drug development and biochemistry.

Arsenic leaking from abandoned gold mines can harm forest ecosystems by entering soils and affecting soil organisms. In a recent study, researchers tested forest soils with different chemical properties to see how they influence arsenic mobility and toxicity in springtails. Results showed juveniles were more sensitive to mobile arsenic, while adults responded to total arsenic. These findings highlight the importance of soil chemistry and life stage in arsenic risk assessment.

A study of clam shells suggests Atlantic Ocean currents may be approaching a “tipping point”.

Researchers at Harvard and Northwestern have developed a machine learning method that can design intrinsically disordered proteins with custom properties, addressing nearly 30% of all human proteins that are currently out of reach of AI tools like AlphaFold. The new approach uses automatic differentiation, traditionally a deep learning tool, to optimize protein sequences for desired properties.