X-SeeO2 · Real-time X-ray diffraction and microstructure imaging: accelerating the transformation of cementitious materials into CO2 sinks

X-SeeO2 delivers unique tools to the portfolio’s Challenge projects as transversal enabling technologies that will accelerate innovation for cement decarbonisation and smart carbonisation of concretes. Led by three renowned universities (Malaga, Bath and Manchester) and The European Synchrotron, X-SeeO2 leverages its X-ray specialised facilities and expertise in low-carbon cementitious materials, carbonation and materials imaging. The project offers access to advanced analytical techniques, including synchrotron and laboratory X-ray powder diffraction (PD) and micro-computed tomography (mCT), supporting the portfolio's research. It commits to collect and analyse over 600 synchrotron PD patterns, 140/70 synchrotron/lab mCT, respectively, supported by 51 person months for portfolio data analysis.These ambitious goals will be achieved by: (i) Designing/manufacturing a world-first in-situ high-pressure (HP) CO2 mineralisation cell for synchrotron mCT, and tailoring an existing in-situ HP cell for synchrotron PD; (ii) Creating robust workflows for PD and mCT data acquisition and analysis tailored to cementitious materials; and (iii) Validating workflows in two case studies. These studies also contribute to the other Challenge categories: “Novel supplementary cementitious materials (SMCs) produced by combining mechanochemical activation and CO2 mineralisation of alkaline industrial wastes”; and “high-throughput determination of autoclave accelerated CO2 curing kinetics of Portland cements (PC) and PC-SCMs blends”.The knowledge generated by X-SeeO2 will be transformative for the Challenge projects and for the advancement of cement materials science and engineering. The results will further enable the creation of two comprehensive atlases compiling and sharing the PD and mCT data and analytical results generated by the Portfolio. This legacy will have a lasting impact, supporting future predictive model development and minimising empirical testing.