Speaker
Description
The microstructure evolution of sodium geopolymers - comprising a reactive solid matrix and an evolving void network - is tracked in time using synchrotron 4D X-ray micro-computed tomography (µCT) to capture foaming from its earliest stages through growth, coalescence, and stabilization. Advanced segmentation is employed to overcome limited contrast and reconstruction artefacts that obscure the solid–pore interface, combining machine-learning and deep-learning models to generate accurate, time-consistent phase maps across full 3D volumes. From these segmented datasets, quantitative descriptors of foaming kinetics are extracted, including porosity evolution, bubble size distributions, growth laws, coalescence and rupture statistics, and connectivity, providing a quantitative basis to identify the physical mechanisms governing foam evolution. These time-resolved observations serve as the experimental foundation for developing a predictive, physics-based model of foaming in inorganic materials like sodium geopolymers that links formulation and processing parameters to foaming dynamics and stability limits. In the next stages, the model will be challenged through systematic formulation variations and sensitivity analyses, and ultimately coupled with complementary macroscopic measurements (e.g., rheology) to further constrain mechanisms and improve predictive capability, enabling rational design of stable mineral foams with application-driven microstructures.
| Country | France |
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