Structural batteries are used in industries such as eco-friendly, energy-based automobiles, mobility, and aerospace, and they must simultaneously meet the requirements of high energy density for energy storage and high load-bearing capacity. Conventional structural battery technology has struggled to enhance both functions concurrently. However, KAIST researchers have succeeded in developing foundational technology to address this issue. < Photo 1. (From left) Professor Seong Su Kim, PhD
2024-11-27A joint research team from Korea and the United States has developed next-generation, high-resolution image sensor technology with higher power efficiency and a smaller size compared to existing sensors. Notably, they have secured foundational technology for ultra-high-resolution shortwave infrared (SWIR) image sensors, an area currently dominated by Sony, paving the way for future market entry. KAIST (represented by President Kwang Hyung Lee) announced on the 20th of November that a research t
2024-11-22< (From left) Dr. Cindy Pricilia, Ph.D. Candidate Cheon Woo Moon, Distinguished Professor Sang Yup Lee > Currently, the world is suffering from environmental problems caused by plastic waste. The KAIST research team has succeeded in producing a microbial-based plastic that is biodegradable and can replace existing PET bottles, making it a hot topic. Our university announced on the 7th of November that the research team of Distinguished Professor Sang Yup Lee of the Department of Ch
2024-11-08- KAIST-Yonsei university researchers developed innovative dipole technology to maximize near-infrared photon harvesting efficiency - Overcoming the shortcoming of existing perovskite solar cells that cannot utilize approximately 52% of total solar energy - Development of next-generation solar cell technology with high efficiency and high stability that can absorb near-infrared light beyond the existing visible light range with a perovskite-dipole-organic semiconductor hybrid structure &l
2024-10-31- Professor Yong-Hoon Kim's team from the School of Electrical Engineering succeeded for the first time in accelerating quantum mechanical electronic structure calculations using a convolutional neural network (CNN) model - Presenting an AI learning principle of quantum mechanical 3D chemical bonding information, the work is expected to accelerate the computer-assisted designing of next-generation materials and devices The close relationship between AI and high-performance scientific computi
2024-10-30