In Seoul, a team of researchers led by Dr Chi-Young Jung from the Hydrogen Research and Demonstration Center in the Korea Institute of Energy Research has introduced a groundbreaking method to analyze carbon fibre paper, a vital component in hydrogen fuel cells. Carbon fibre paper is crucial in hydrogen fuel cell stacks for facilitating water discharge and fuel supply. The team utilized digital twin technology and artificial intelligence (AI) to develop a system that analyzes the microstructure of carbon fibre paper at a speed 100 times faster than current methods. By leveraging X-ray diagnostics and an AI-based image learning model, the technology eliminates the need for an electron microscope, providing near real-time condition diagnosis capability. Through a study published in Applied Energy, the researchers detailed how they trained a machine learning algorithm with 5,000 images from 200 carbon fibre paper samples. This trained model achieved an impressive accuracy of over 98% in predicting the 3D distribution and arrangement of key components like carbon fibers, binders, and coatings. Dr Jung emphasized the significance of this study in enhancing analysis technology by combining AI with virtual space utilization, shedding light on the relationship between structure and properties of energy materials for practical applications.