Graphene is a promising material for gas separation. However, identifying the optimal pore sizes for efficiently filtering different gases remains a challenge. Researchers at Chiba University have found that strategically adding oxygen to graphene improves its ability to separate carbon dioxide from methane while still allowing gases to flow through quickly, a critical requirement for industrial use. This approach could enable real-world applications of graphene membranes as a more energy-efficient technology for gas purification.

Every particle in our universe seems to fit strictly into two categories: bosonic or fermion. Why are there no others? Two new papers have, for the first time, identified and described the theoretical properties of one-dimensional anyons, particles that are neither fermionic nor bosonic, and provide the ‘recipe’ for observing these enigmatic particles using present-day experimental setups. Their work opens an exciting new path to improving our understanding of the fundamental properties of the quantum world.

POSTECH team develops a next-generation secure hologram platform where light wavelength and interlayer spacing act as encryption keys.

Researchers have quantitatively assessed the impact of extreme laser fields, expected to be achievable in the near future, on the half-lives of deformed nuclei undergoing cluster radioactivity. The study reveals that while laser fields only slightly modulate decay probabilities, specific nuclear properties–such as longer tunneling paths– enhance the sensitivity to these external fields, thereby providing new insights for future laser-nucleus experiments.

Life on Earth has long been understood to run on two main energy sources. One comes from sunlight, captured by photosynthesis. The other comes from chemical reactions, such as microbes feeding on reduced compounds in soils, sediments, and oceans. A new review suggests there is a third, largely overlooked energy source that could quietly help drive Earth’s biogeochemical cycles: mechanical force.

Deep learning is increasingly being used to emulate cloud and convection processes in climate models, offering a faster alternative to computationally intensive cloud-resolving simulations. However, because these processes unfold at kilometre scales or smaller — far below the resolution of global climate models — hybrid AI–physics approaches often become unstable when run over long time horizons. Researchers from the National University of Singapore (NUS)’s College of Design and Engineering (CDE), in collaboration with Tsinghua University, NVIDIA and the Centre for Climate Research Singapore, have traced a key instability to a moisture imbalance during condensation. They developed CondensNet, an adaptive, physics-constrained neural-network that can correct physically inconsistent behaviour. Integrated into a global climate model, it enables stable, accurate long-term simulations while running far faster than cloud-resolving approaches.

Harvard researchers have developed a mathematical framework for optimizing the design of rolling contact joints, which are made of rolling surfaces and flexible connectors.

Scientists at Institute of Science Tokyo (Science Tokyo) have successfully realized the highly selective asymmetric 1,6-addition of aliphatic Grignard reagents to α,β,γ,δ-unsaturated carbonyl compounds. This new methodology employs an iron catalyst in combination with a chiral N-heterocyclic carbene ligand, which suppresses undesired side reactions and drives highly regio-, stereo-, and enantioselective alkyl migration. The achievement represents a major advance in organic synthesis, offering new opportunities for drug discovery, materials chemistry, and the fine-chemical sector.