POSTECH–SNU cut process temperature to 300°C, halving costs and advancing renewable energy storage.

POSTECH research team including Professors Keehoon Kim and Wan Kyun Chung develops sheet-type robot mimicking muscle protein movements.

Dr. Jung-Dae Kwon's research team at the Energy & Environmental Materials Research Division of the Korea Institute of Materials Science (KIMS, President Chul-Jin Choi) has successfully developed an amorphous silicon optoelectronic device with minimal defects, even using a low-temperature process at 90°C.

Identifying embryos with the highest likelihood of successful implantation is a critical component of the in vitro fertilization (IVF) process. Visual assessments are limited by the subjectivity of embryologists, making consistent evaluation of embryo health challenging with traditional methods. Recent advances in artificial intelligence (AI)—particularly in computer vision and deep learning—have enabled the automated analysis of embryo morphology images, reducing subjectivity and improving evaluation efficiency. Through an extensive literature search using keywords such as “embryo health assessment” and “artificial intelligence,” the present review focuses on AI-driven approaches for automated embryo evaluation. It examines AI techniques applied to embryo assessment across the early development, blastocyst, and full developmental stages. This review indicated the promising potential of AI technologies in enhancing the precision, consistency, and speed of embryo selection. AI models have been reported to outperform manual evaluations across several parameters, offering promising opportunities to improve success rates and operational efficiency in reproductive medicine. Additionally, this review discusses the current limitations of AI implementation in clinical settings and explores future research directions. Overall, the review provides insight into AI’s growing role in advancing embryo selection and highlights the path toward fully automated evaluation systems in assisted reproductive technology. 

Neuromorphic devices have shown great potential in simulating the function of biological neurons due to their efficient parallel information processing and low energy consumption. MXene-Ti₃C₂T_x, an emerging two-dimensional material, stands out as an ideal candidate for fabricating neuromorphic devices. Its exceptional electrical performance and robust mechanical properties make it an ideal choice for this purpose. This review aims to uncover the advantages and properties of MXene-Ti₃C₂T_x in neuromorphic devices and to promote its further development. Firstly, we categorize several core physical mechanisms present in MXene-Ti₃C₂T_x neuromorphic devices and summarize in detail the reasons for their formation. Then, this work systematically summarizes and classifies advanced techniques for the three main optimization pathways of MXene-Ti₃C₂T_x, such as doping engineering, interface engineering, and structural engineering. Significantly, this work highlights innovative applications of MXene-Ti₃C₂T_x neuromorphic devices in cutting-edge computing paradigms, particularly near-sensor computing and in-sensor computing. Finally, this review carefully compiles a table that integrates almost all research results involving MXene-Ti₃C₂T_x neuromorphic devices and discusses the challenges, development prospects, and feasibility of MXene-Ti₃C₂T_x-based neuromorphic devices in practical applications, aiming to lay a solid theoretical foundation and provide technical support for further exploration and application of MXene-Ti₃C₂T_x in the field of neuromorphic devices.