Hiroshima University research shows that a portable heart monitoring device can detect fetal distress earlier and sharply improve newborn survival rates in low-resource environments.

Catalytic dry reforming of methane (DRM) is a key reaction for the sustainable utilization of major greenhouse gases, CO₂ and CH₄. However, conventional DRM often suffers from severe catalyst deactivation due to high temperature requirements. Applying direct current (DC) to catalyst materials has emerged as a promising strategy to overcome these limitations, yet the underlying DC-enhanced catalytic mechanism remains elusive. Here, we unveil the non-thermal catalytic origin of DC-applied DRM over Pd/CeO₂ through multimodal operando analyses, providing a microscopic physicochemical framework for the rational design of next-generation DRM catalysts beyond the limitations of conventional thermal catalysis.

A research team led by Chang Keke from the Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy of Sciences (CAS), has developed an innovative machine learning framework (PAMCs-MP) for predicting the mechanical properties of particle-reinforced aluminum matrix composites (PAMCs). Despite limited existing datasets, the approach uses extensive pre-training on larger aluminium alloy datasets to guide multi-objective optimization tasks effectively. The model achieves high predictive accuracy, R² values of over 92% for ultimate tensile strength and over 90% for elongation, demonstrating its robustness and reliability. The platform not only accelerates the design cycle but also offers profound insights into material behaviour, facilitating the development of high-strength, ductile aluminum composites tailored to specific application needs.