Nanoporous materials used in industrial applications (e. g., catalysis and separations) draw their functionality from properties at the nanoscale (1-10 Å). When shaped into a technical form these solids reveal spatial variations in the same properties over much larger length scales (1 μm-1 cm). The multiscale characterization of these systems is impaired by the trade-off between sample size and image resolution that is bound to the use of most imaging techniques. We show here the application of X-ray computed tomography for the non-invasive spatial characterization of a zeolite/activated carbon adsorbent bed across three orders of magnitude in scale. Through the unique combination of gas adsorption isotherms measured locally and their interpretation by physisorption analysis, we determine three-dimensional maps of the specific surface area and micropore volume. We further use machine learning to identify and locate the materials within the packed bed. This novel ability to reveal the extent of heterogeneity in technical porous solids will enable a deeper understanding of their function in industrial reactors. Such developments are essential towards bridging the gap between material research and process design.
Keywords: X-ray computed tomography; adsorption; gas physisorption; machine learning; porous materials.
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