Nanographenes are organic semiconductor materials used in smartphones, OLED displays, and solar cells. At the molecular level, they are composed of polycyclic aromatic hydrocarbons (PAHs) which are a network of connected benzene rings (hexagon-shaped carbon molecules). Chemists can modify the electronic properties of PAHs by adding more benzene rings to them, changing their size and shape. As such, there is high demand for methods that can selectively extend specific sites of PAH molecules to allow greater versatility in technological applications. New research from Nagoya University introduces a new methodology for developing PAH molecules that has elucidated chemists for years.

Researchers have solved a mystery in fluid dynamics regarding high-speed particle collisions on wet surfaces. They discovered that at high speeds, cavitation (the sudden formation of vapor cavities) changes the liquid shape from a "bridge" to a "dome", releasing the liquid pull-back force. This causes particles to bounce back stronger than they would at lower speeds. Such a vital discovery would drastically improve the safety, design, and durability of ultra-fast motors in the aerospace and automotive industries.

Researchers from The University of Osaka have demonstrated that a wireless electroencephalogram transmission system can operate using energy harvested from the temperature difference between the human body and the ambient air. The low-power device successfully operated outdoors at high temperatures, demonstrating stable performance without external power or airflow. This technology could enable the development of maintenance-free sensing systems for health monitoring and infrastructure applications in the future.

Researchers from The University of Osaka used large-scale simulations and turbulence theory to study how dolphins swim so effectively. The team found that large vortices created by the dolphin’s tail provide most of the propulsion, while smaller vortices contribute little. This discovery improves our mechanical understanding of fast swimming and could guide the design of energy-efficient underwater robots and technologies for controlling turbulence.

Jian Jiang's team at the Institute of Chemistry, Chinese Academy of Sciences, recently published an article that focuses on the core bottlenecks of machine learning force fields (MLFF) in organic systems during long-term molecular dynamics simulations, including molecular structure collapse and low accuracy in macroscopic property calculations, and proposed two physical embedding solutions. Their approach works on two levels, addressing both intramolecular and intermolecular interactions. They develop a physics-guided adaptive bond length sampling method and a top-down model correction method based on physical equation embedding, respectively. The results show that these methods can significantly improve simulation stability under small sample conditions and effectively improve the prediction accuracy of macroscopic properties, such as density and viscosity with extremely low data and computational costs. Their approach effectively overcomes the limitations of purely data-driven methods, significantly enhances the reliability and generalization ability of  MLFF, and provides a scalable approach for physical embedding of MLFF. The article was published as an open access Research Article in CCS Chemistry, the flagship journal of the Chinese Chemical Society.