Researchers have developed a membrane-based strategy that separates complex light naphtha into valuable chemical streams at the molecular level. By mildly etching a metal–organic framework membrane with tannic acid, the team created complementary membranes that sort hydrocarbons by size and chemical affinity. The membrane cascade separated a 15-component petroleum mixture into three product streams while potentially reducing energy consumption by about 91% compared with conventional distillation.

Can chemical processes be designed based solely on the properties of the molecules involved? Prof. Dr.-Ing. Joachim Groß, Head of the Institute of Thermodynamics and Thermal Process Engineering (ITT) at the University of Stuttgart believes this is possible. He and his team plan to investigate this question as part of an ERC Advanced Grant, which Groß has now been awarded by the European Research Council (ERC).

The integration of luminescent nanomaterials into scalable semiconductor platforms is vital for on-chip photonics. This study demonstrates an approach to fabricate fluorescent nanostructures by hybridising carbon dots (CDs) with semiconductor nanowires (NWs) grown on Si. By coupling the tuneable chemistry of CDs with the tailored optoelectronics of semiconductor NWs, this study establishes a scalable host–guest architecture for engineering nanoscale light sources to promote applications in integrated photonic circuits and sensing.

A research team found that scent compounds in household cleaning products — conventional and botanical essential oil-based — quickly react in the air, forming invisible nanoparticles that can travel deep into the lungs if inhaled. Several simple changes can reduce a person’s exposure while cleaning. The researchers will present their results at ACS Fall 2026.

Researchers have captured in real time the “birth moment” at which the performance of a next-generation memory material is determined. The key lies in controlling oxygen vacancies, microscopic defects created when oxygen atoms are missing from their normal positions.

Researchers from Zhejiang University and Zhejiang Advanced CNC Machine Tool Technology Innovation Center, China, have developed a Symmetry-Imbued Hamiltonian Neural Operator (SIHNO) for predicting stress fields in porous metamaterials. The proposed architecture combines rotation-equivariant geometric representations with a Hamiltonian-inspired energy structure to improve prediction accuracy and robustness under different structural orientations.

The framework integrates a Rotation-Steerable Lattice Projector (RSLP), a Hamiltonian-Fueled Propagator (HFP), and a slice-aware convolutional decoder to address two challenges in stress field prediction: orientation bias caused by fixed coordinate representations and non-physical stress leakage near void-solid interfaces.

In comprehensive numerical experiments based on finite-element-generated stress fields, SIHNO outperformed several established neural operator and physics-informed models across multiple evaluation metrics. The model also maintained millisecond-level inference speed, highlighting its potential for rapid stress prediction in additive manufacturing applications.