A research team in Seoul, led by Dr. Chi-Young Jung from the Korea Institute of Energy Research (KIER), has introduced a groundbreaking approach to analyze the microstructure of carbon fibre paper, a vital material in hydrogen fuel cell stacks. By leveraging digital twin technology and AI, they have achieved a remarkable speed enhancement of 100 times compared to current methods. This innovation allows for the swift examination of carbon fibre paper, which is essential for water discharge and fuel supply in hydrogen fuel cells. Traditionally, carbon fibre paper examination required electron microscopes, but the new approach relies solely on X-ray tomography, offering a more efficient and real-time diagnostic capability. The team's AI-driven image learning model was trained with 5,000 images extracted from 200 carbon fibre paper samples, leading to a high accuracy rate of over 98% in predicting the 3D layout of crucial components like carbon fibers, binders, and coatings. Dr. Jung emphasized the significance of this advancement as it merges AI with virtual space utilization, shedding light on the structure-property link in energy materials. He envisions broader applications beyond hydrogen fuel cells, foreseeing its impact on secondary batteries and water electrolysis. The study's findings, published in the journal Applied Energy, highlight the practical utility of this technology in enhancing material analysis processes and its potential to revolutionize various energy-related sectors.