3D mapping of gas physisorption for the spatial characterisation of nanoporous materials

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Title: 3D mapping of gas physisorption for the spatial characterisation of nanoporous materials
Authors: Joss, L
Pini, R
Item Type: Journal Article
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.
Issue Date: 18-Feb-2019
Date of Acceptance: 13-Dec-2018
ISSN: 1439-4235
Publisher: Wiley
Start Page: 524
End Page: 528
Journal / Book Title: ChemPhysChem
Volume: 20
Issue: 4
Copyright Statement: © 2018 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim This is the accepted version of the following article: Joss, L. and Pini, R. (2018), 3D mapping of gas physisorption for the spatial characterisation of nanoporous materials. ChemPhysChem. Accepted Author Manuscript., which has been published in final form at
Sponsor/Funder: Qatar Shell Research and Technology Center QSTP LLC
Funder's Grant Number: 490000724
Keywords: Science & Technology
Physical Sciences
Chemistry, Physical
Physics, Atomic, Molecular & Chemical
gas physisorption
machine learning
porous materials
X-ray computed tomography
X-ray computed tomography
gas physisorption
machine learning
porous materials
Chemical Physics
0306 Physical Chemistry (incl. Structural)
0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics
0307 Theoretical and Computational Chemistry
Publication Status: Published
Online Publication Date: 2018-12-13
Appears in Collections:Faculty of Engineering
Chemical Engineering

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