All-inorganic CsPbI₃ quantum dots (QDs) are regarded as promising candidates for advanced display materials due to their outstanding optoelectronic properties. However, conventional high-temperature thermal injection methods struggle with precise bandgap tuning, making it challenging to achieve pure red emission from CsPbI₃QDs. Now, in a study published in Science Bulletin, researchers from Zhejiang University of Technology have developed a thermally stable ethylammonium (EA⁺) doping strategy for CsPbI₃ QDs, achieving Rec.2020-standard pure-red perovskite light-emitting diodes (PeLEDs) with a high external quantum efficiency exceed 26%. The key innovation lies in an in situ acid–base equilibrium reaction that generates thermally stable ethylammonium oleate. This allows for the successful synthesis of EA⁺-doped CsPbI₃ QDs via high-temperature thermal injection, enabling precise emission tuning (630-650 nm) and exceptional spectral stability. The breakthrough opens new avenues for high-performance display technologies.

For the first time, Denmark is establishing production of microchip wafers at the leading standard. This makes Denmark a global player in chip production. The new “wafer factory,” which will be part of the University of Copenhagen, also gives a major advantage to Danish quantum researchers.

A study in National Science Review reports systematic observations of diazotroph abundance, community structure, and N₂ fixation rates in the western North Pacific. Using generalized additive models, the team characterized ecological niches of key cyanobacterial diazotrophs and quantified UCYN-B’s contribution to global N₂ fixation. The findings highlight UCYN-B’s pivotal role in marine N₂ fixation and provide new insights into ocean nitrogen cycle and productivity under climate change.

Inverse lithography technology (ILT) is driving transformative innovations in semiconductor patterning processes. This paper reviews the evolution of ILT, providing an analysis of the applications in semiconductor manufacturing. In recent years, artificial intelligence (AI) has introduced breakthroughs for ILT, such as convolutional neural networks, generative adversarial networks, and model-driven deep learning, demonstrating potential in large-scale integrated circuit design and fabrication. This paper discusses future directions for ILT, which is expected to provide insights into semiconductor industry development.