SiCN over Photonics Structures is a semiconductor manufacturing technology that improves the performance and reliability of photonic integrated circuits (PICs) through enhanced deposition and optimization of silicon carbon nitride (SiCN) and silicon nitride barrier layers. By selectively modifying these barrier materials over conductive structures, the invention reduces unwanted interactions within photonic devices while improving light-signal transmission. The approach is compatible with existing silicon-based fabrication processes, enabling adoption without major manufacturing changes. Potential applications include telecommunications, high-performance computing, aerospace, defense, automotive, and medical technologies that rely on advanced photonic systems. The patented technology is at TRL 6 and available for licensing.

Small bends in graphene’s two-dimensional, or one-atom-thick, structure can change the material’s electrical properties, according to a new study. Harnessing this property may allow scientists to one day power more responsive sensors and ultrathin electronic devices. 

Artificial intelligence (AI) and digitalization are driving rapidly growing demand for ever-faster and more powerful data connections. Researchers at Karlsruhe Institute of Technology (KIT) have developed a 3D printing process that enables tiny optical components used for data transmission to be connected with high precision and efficiency. The process enables the automated mass production of optical transmitters and receivers. For this innovation, the researchers were nominated for the German President’s Zukunftspreis on September 16, 2026.

For the development of the virtual laser laboratory simulator ‘femtoPro’, the physicists and physical chemists Professor Tobias Brixner and Dr Stefan Müller, together with computer scientist Professor Sebastian von Mammen from the University of Würzburg, have been awarded the 2026 Teaching Prize of the German Physical Society (Working Group on Physics Practical Classes).

Solar-driven water splitting offers a carbon-neutral path to green hydrogen, but its reliance on freshwater has long raised sustainability concerns. Seawater — covering 97% of Earth’s water — presents an abundant alternative, yet its chloride ions have traditionally been seen as a major obstacle, causing corrosive side reactions and sluggish kinetics. Now, a team led by researchers at Shanghai Jiao Tong University has turned this challenge into an opportunity. They developed a noble-metal-free photocatalyst — MnOₓ(OH)_y/GaN nanowires supported by a silicon wafer — that splits natural seawater efficiently under sunlight, achieving a solar-to-hydrogen efficiency of 6.15% without any sacrificial agents. The catalyst delivers a hydrogen production rate of 15.2 mol g⁻¹ h⁻¹ in natural seawater and operates stably for over 360 min. Surprisingly, the team discovered that chloride ions in seawater are not detrimental but actively beneficial: they reduce the Coulomb attraction energy between photogenerated electrons and holes, and simultaneously lower the energy barrier for water oxidation. Meanwhile, the hydroxyl groups inherent to the manganese oxyhydroxide cocatalyst undergo dynamic exchange with water molecules, further accelerating the formation of key reaction intermediates. Outdoor tests under concentrated natural sunlight confirmed the system’s practical feasibility.

Magnetic reconnection, a phenomenon in which magnetic fields in a plasma rapidly rearrange and release energy, is thought to drive explosive cosmic events such as solar flares. In a recent study, researchers from Japan used high-power lasers to recreate this process under controlled conditions and track its evolution. Their results showed that, despite substantially different plasma conditions, reconnection proceeded at a similar rate, suggesting that this rate is primarily governed by local physics in the reconnection region

Researchers developed a nanophotonic processor via monolithicly integrating symmetry-broken quasi-bound states in the continuum (BIC) metasurfaces with GaAs/AlGaAs multiple quantum wells (MQWs). By leveraging light-matter interaction, the leaky mode of quasi-BIC makes the vertical-field light absorption possible in MQW. A nonlinear photoresponse is driven by changing asymmetry parameter, while linear scenario by incident angle and bias voltage. Implementing multifunctional BIC-MQWs into an artificial neural network provides a new hardware paradigm for machine vision applications.