Starquakes and the archaeology of stellar magnetism
Peer-Reviewed Publication
Updates every hour. Last Updated: 14-Apr-2026 03:15 ET (14-Apr-2026 07:15 GMT/UTC)
A research team led by Dr. Lee Young Jun of the RAMP Convergence Research Group at the Korea Institute of Science and Technology (KIST; President Oh Sang-rok), in collaboration with research teams led by Professor Yun Hongseok of Hanyang University and Professor Kang Junhee of Pusan National University, focused on lignin-a wood byproduct discarded in the timber industry-to overcome these limitations. The research team designed and developed a carbon-based catalyst capable of selectively generating hydrogen peroxide through electrochemical reactions using lignin, and demonstrated hydrogen peroxide production with a selectivity exceeding 95% under experimental conditions.A research team led by Dr. Lee Young Jun of the RAMP Convergence Research Group at the Korea Institute of Science and Technology (KIST; President Oh Sang-rok), in collaboration with research teams led by Professor Yun Hongseok of Hanyang University and Professor Kang Junhee of Pusan National University, focused on lignin-a wood byproduct discarded in the timber industry-to overcome these limitations. The research team designed and developed a carbon-based catalyst capable of selectively generating hydrogen peroxide through electrochemical reactions using lignin, and demonstrated hydrogen peroxide production with a selectivity exceeding 95% under experimental conditions.
Curious about how AI is reshaping 6G communication? A new Engineering study explores generative AI’s role in semantic communication, moving past traditional bit transmission to focus on semantic meaning. It tests an LLM-powered system with huge efficiency gains and spotlights real-world uses, while tackling key tech challenges for future 6G rollouts.
Materials research suffers from trial-and-error synthesis and fabrication, while the resulting numerical datasets fail to provide feedback for semantic recipe optimization. Therefore, the integration of a recipe language model (RLM) with robotic boxes enables the RLM and robotics continuously improve one another, as a physical AI for the materials intelligence.