Even though gamma-alumina has been used as a catalyst support for more than 50 years, predicting its mass transfer properties remains particularly challenging due to the hierarchical organization and spatial heterogeneity of its pore network, with dimensions ranging from the nanometer to the millimeter scale. A new characterization strategy, combining diffusional and textural properties measurement, was implemented on alumina supports with similar specific surface area and total porosity, but different diffusional tortuosity factors ranging from 2 to 2.8. SEM images were analyzed using a deep learning AI algorithm specially developed for this study (Fig.1), yielding a 2D mapping of the support porosity. These results served as input for two-phase analytical models, which enabled to assess the impact of spatial heterogeneity on effective diffusivity. The multi-level hierarchical organization, characterized by N<sub>2</sub> scanning isotherms and spatially resolved SAXS, was found to greatly influence the severity of pore blocking phenomena directly impacting diffusional tortuosity. The proposed characterization methodology highlights the link between pore geometry and diffusion, and can set out guidelines for optimal design of catalysts. Furthermore, it can be applied to any mesoporous material and provide appropriate data for pore network modeling.

Multi-technique characterization of hierarchically organized gamma-alumina catalyst supports

ACS Spring Meeting 2021

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