Engineers at the University of Pennsylvania have developed PeptiVerse, an open-source AI platform that predicts key chemical and biological properties of peptides, including whether they are likely to dissolve, enter cells, avoid toxicity and last long enough in the body to have an effect. The platform could help researchers screen peptide drug candidates earlier and support generative AI systems for drug design.

Rare earth elements like lanthanum, neodymium, and dysprosium are used to build the electric motor in your car, the LED lights in your house, and the MRI machine at your doctor’s office. But first, they have to be mined and separated from each other. Historically, that purification has been a difficult, costly, process, relying on huge amounts of toxic chemicals. Now, researchers in the lab of Assoc. Prof. Chong Liu at the University of Chicago Pritzker School of Molecular Engineering (UChicago PME), working with colleagues at Northwestern University and Argonne National Lab, have discovered a cleaner method to separate rare earth elements from each other. The new approach relies on a layered form of manganese oxide—a mineral material with the right size layers to allow ions to slip in and out and to differentiate rare earth elements.

The University of East London (UEL) is helping shape the future of digital safety, with Professor Julia Davidson OBE contributing to a major new parliamentary White Paper proposing a product safety approach to regulating social media platforms, artificial intelligence and other digital technologies. Developed by Tom Collins MP in collaboration with leading academics, industry experts and civil society organisations, the proposals build on UEL's internationally recognised expertise in child online harms, cybercrime and digital safeguarding. The White Paper aims to embed safety into digital products from the outset, shifting responsibility to technology companies to identify and mitigate risks before products reach the public.

Developed by Renmin University of China, the comprehensive Climate-gOvernance Modeling & Policy ASsessment System (COMPASS) integrates multiple complementary modeling tools to deliver multi-scale, cross-dimensional simulations for energy transition and climate governance. Combining a top-down computable general equilibrium model (CE3-CGE), a bottom-up energy technology model (PECE), an integrated assessment model (GCAM-CHN), and data-driven econometric and machine learning approaches, COMPASS effectively bridges macroeconomic trends and micro-level technological changes, providing robust scientific support for China’s carbon neutrality strategy and global climate policy research.

With the long-term on-orbit operation of the China Space Station, using nanosatellite swarms for cooperative observation of its external structures has become an important technical direction for enhancing in‑orbit monitoring capabilities. However, the space station itself has a non‑spherical complex geometry (e.g., protruding structures such as modules and docking ports) and may perform attitude maneuvers (e.g., sun‑pointing) during orbital flight, making it a dynamic obstacle with both intricate shape and rotational motion relative to the nanosatellites. Traditional artificial potential field obstacle avoidance methods mostly simplify obstacles as spheres or ellipsoids, which cannot accurately characterize the composite geometric features of the space station, and usually ignore the obstacle's own rotational motion. This leads to difficulties in constructing the repulsive potential field, limited maneuvering space, and, more critically, a lack of rigorous theoretical proof to guarantee closed-loop system stability and collision avoidance. Therefore, developing a nanosatellite formation control method that can handle rotating non‑spherical obstacles while providing strict stability guarantees is of significant theoretical importance and engineering value for ensuring safe fly-around and efficient cooperative observation in the vicinity of the space station.

To accelerate the transition to sustainable transport, researchers from Nanjing University of Information Science and Technology and Eindhoven University of Technology investigated what motivates vehicle owners to replace their cars. By applying a Push-Pull-Mooring framework to 803 Chinese drivers, the study reveals that fuel vehicle owners are driven by pragmatic economic factors, while current electric vehicle owners are held by deep-seated habits and social reinforcement.