Researchers demonstrated a superconducting qubit sensor that combines quantum criticality and non-equilibrium dynamics to estimate gradient fields with precision approaching the fundamental precision limits using simple measurements.

As part of an international team, researchers from the Karlsruhe Institute of Technology (KIT) have synthesized an extraordinary aromatic cycle. Consisting of three metal atoms, it is the heaviest example of this type of molecule: A triangle made from three bismuth atoms is held between two metal complexes in a structure known as “inverse‑sandwich” complex. This discovery contributes to a better understanding of aromaticity in heavy elements. In addition, the study might pave the way for new functional materials. The results have been published in Nature Chemistry.  (DOI: 10.1038/s41557-026-02123-8)

Scientists from the Dalian Institute of Chemical Physics of the Chinese Academy of Sciences have explained themicrostructure formation mechanism of nonsolvent-induced phase separation (NIPS) by separating the formation processesof different pore structure. Based on the findings, the team developed a free-standing ultrathin porous polymeric membrane with a thickness of only 2.7 μm that simultaneously offers high selectivity and high conductivity. When applied in a vanadium flow battery, the membrane demonstrated outstanding electrochemical performance.

Could wound healing dressings adhere better, and drug delivery patches become more sophisticated? A KAIST research team has developed a technology that leverages natural ingredients derived from plants to increase the strength of seaweed-based hydrogel (a gel material that contains a large amount of water while maintaining its shape) by more than fivefold, while also controlling its adhesiveness and degradation rate.

Machine learning (ML) is reshaping pipeline integrity management (PIM) from physics-based to data-driven paradigms. This systematic review comprehensively surveys ML applications across pipeline reliability design, integrity assessment, inspection, and maintenance; identifies key bottlenecks including data scarcity, poor interpretability, and limited field validation; and proposes a research roadmap toward tractable, trustworthy ML for full-lifecycle pipeline safety.

Researchers from the University of Oxford have demonstrated a new family of quantum superpositions – Schrödinger’s cat- like quantum states – from highly nonclassical building blocks. The work opens a new route towards quantum computing with non-binary systems, sensing and understanding quantum physics at a more fundamental level.

Using a novel simulation model based on machine learning, an international research team at GSI/FAIR has succeeded in gaining a deeper understanding of element formation in stellar events such as neutron star mergers. For the first time, the scientists used deep learning with a neural network to model the energy release during r-process nucleosynthesis in hydrodynamic simulations. The results are published in the journal Physical Review D.