3D Mapping of Gas Physisorption for the Spatial Characterisation of Nanoporous Materials. Issue 4 (15th January 2019)

• Record Type: Journal Article
• Authors: Joss, Lisa; Pini, Ronny
• Is Part Of: Chemphyschem. Volume 20:Issue 4(2019)
• Page Start: 524
• Page End: 528
• Publication Date: 2019-01-15
• Subjects: adsorption -- gas physisorption -- machine learning -- porous materials -- X-ray computed tomography; Chemistry, Physical and theoretical -- Periodicals; 541.05
• DOI: 10.1002/cphc.201801148
• Languages: English
• ISSNs: 1439-4235

Abstract

Abstract: 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. Abstract : The digital adsorption workflow is deployed to produce a three‐dimensional characterization of a zeolite/activated carbon adsorption column in terms of textural properties (specific surface area and micropore volume) and operational metrics (cyclic capacity) resolved at millimeter resolution. A machine‐learning approach is applied to locate the two materials in the adsorber using a simple parameterization of the adsorption isotherm.