Physicists from Heinrich Heine University Düsseldorf (HHU), the Technical University of Darmstadt, Sapienza University in Rome and the University of Camerino (both Italy) have calculated the fundamental laws of thermodynamics for a gas made up of spinning particles. In the scientific journal Proceedings of the National Academy of Sciences (PNAS), they demonstrate that the pressure of this gas is similar to that of a normal gas, but at an elevated temperature. In addition, localised surface currents arise, which can be utilised for targeted particle transport.

Researchers developed a terbium-doped aluminum gallium nitride LED that produces blue, green, yellow, and red light from the same area at room temperature. The emission wavelengths remained highly stable as the current changed. Increasing the aluminum content improved energy transfer and efficiency, while using an aluminum nitride base layer enhanced crystal quality and device performance. With further improvements, this approach could simplify the production of full-color micro-LED displays and contribute to compact displays, wearable devices, and white-light sources.

 Researchers have developed ACGC+, an enhanced atomic connectivity group contribution method that improves the prediction of nine key phase transition properties for organic compounds, including entropy of fusion, enthalpy of vaporization, boiling point, melting point, and critical properties. By introducing local atomic connectivity factors (ACFs+) that capture both global and local structural influences, the new method outperforms the original ACGC approach, reducing mean absolute errors by 1.44% to 7.91%. External validation and cross-validation confirmed the model's robustness, with R² values ranging from 0.845 (melting point) to 0.998 (critical volume).

Researchers have systematically evaluated 14 polyglycol‑based foam control agents under simulated industrial fermentation conditions, investigating their foam suppression efficiency and microbial compatibility. FCA332 emerged as the superior candidate, combining low viscosity, favorable surface tension, and moderate cloud point with consistent foam control and excellent biocompatibility. A predictive model was developed to estimate foam volume and microbial growth based on FCA physicochemical properties, achieving R² values of 0.97 and 0.99, respectively. This model enables rapid screening of FCAs without extensive biological assays, accelerating product development for fermentation applications.

A new study published in Big Earth Data proposes an operational framework that integrates multiple machine learning and deep learning models with high-resolution Sentinel-2 data to estimate agricultural drought conditions in irrigated and non-irrigated maize fields in Eastern Austria. Results from 2018–2023 show that deep learning models, particularly the Deep Neural Network (DNN), achieved the best performance, while non-irrigated fields exhibited higher prediction accuracy due to clearer drought signals, demonstrating the value of Sentinel-2–based deep learning for operational drought monitoring.

Findings from a new study comparing the effects of different beverages on mouth acidity suggest that unsweetened sparkling water may be better for your teeth than sugar-sweetened soda.

Professor Zheng Yanzhen's team at Xi'an Jiaotong University has discovered a single-molecule magnetic ring whose shape responds to changes in the magnetic field. In this system, the large spin ground state generated by ferromagnetic coupling can rapidly respond to the magnetic field, thereby inducing macroscopic crystal striction, a phenomenon that characterizes a 'single-molecule magnetostrictor'.

• KAIST and Yonsei University uncovered how the MSC regulates LARS1-mediated activation of mTORC1 in response to amino acids

• Discovery pinpoints a key phosphorylation switch that initiates growth signaling

• Findings provide a foundation for developing a next-generation anticancer strategy that block abnormal growth signaling upstream of mTORC1