The research team from the Korea Research Institute of Chemical Technology (KRICT) led by Dr. Dowon Ahn, has made significant strides in addressing the key challenges of photoresponsive, visible light 3D printing.

Despite being a mature technology with existence for over several decades, silicon photonic modulators face scrutiny from industry and academic experts. In a recent editorial interview, experts emphasize the need to explore alternatives beyond the traditional platforms. The discussion centered on innovative modulator materials and configurations that could cater to emerging applications in data centers, artificial intelligence, quantum information processing, and LIDAR. Experts also outlined the challenges that lie ahead in this field.

Hydrogels created using carbon dioxide (CO₂) offer a safer alternative to those formed with acidic agents. While most research has focused on pre-gelation conditions affecting hydrogel properties, this study by researchers from Tokyo University of Science explores the impact of CO₂ release after gelation. The team prepared alginate-based hydrogels and found that faster CO₂ release decreases crosslinking, while slower release results in stiffer hydrogels. These findings could lead to improved hydrogels for medical applications.

The best global PhD in cementitious materials 2024 stays at the University of Malaga. The young scientist Shiva Shirani, postdoctoral researcher in materials science and X-ray imaging at the Faculty of Sciences of the UMA, has been awarded the Innovandi NanoCem PhD Prize.The best global PhD in cementitious materials 2024 stays at the University of Malaga. The young scientist Shiva Shirani, postdoctoral researcher in materials science and X-ray imaging at the Faculty of Sciences of the UMA, has been awarded the Innovandi NanoCem PhD Prize.

Multi-sequence knee magnetic resonance imaging (MRI) is an advanced non-invasive diagnostic method for knee pathology. However, MRI interpretation is highly time-consuming and heavily dependent on expertise. A research team from the School of Engineering, the Hong Kong University of Science and Technology (HKUST) has introduced a novel deep learning model which can assist with classifying 12 common types of knee abnormalities, enhancing both efficiency and accuracy.