Glowska, Aleksandra;
Jolimaitre, Elsa;
Hammoumi, Adam;
Moreaud, Maxime;
Sorbier, Loic;
de Faria Barros, Caroline;
Lefebvre, Veronique;
(2024)
SEM Image Processing Assisted by Deep Learning to Quantify Mesoporous γ-Alumina Spatial Heterogeneity and Its Predicted Impact on Mass Transfer.
The Journal of Physical Chemistry C
, 128
(20)
pp. 8395-8407.
10.1021/acs.jpcc.4c00323.
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Abstract
The pore network architecture of porous heterogeneous catalyst supports has a significant effect on the kinetics of mass transfer occurring within them. Therefore, characterizing and understanding structure-transport relationships is essential to guide new designs of heterogeneous catalysts with higher activity and selectivity and superior resistance to deactivation. This study combines classical characterization via N2 adsorption and desorption and mercury porosimetry with advanced scanning electron microscopy (SEM) imaging and processing approaches to quantify the spatial heterogeneity of γ-alumina (γ-Al2O3), a catalyst support of great industrial relevance. Based on this, a model is proposed for the spatial organization of γ-Al2O3, containing alumina inclusions of different porosities with respect to the alumina matrix. Using original, advanced SEM image analysis techniques, including deep learning semantic segmentation and porosity measurement under gray-level calibration, the inclusion volume fraction and interphase porosity difference were identified and quantified as the key parameters that served as input for effective tortuosity factor predictions using effective medium theory (EMT)-based models. For the studied aluminas, spatial porosity heterogeneity impact on the effective tortuosity factor was found to be negligible, yet it was proven to become significant for an inclusion content of at least 30% and an interphase porosity difference of over 20%. The proposed methodology based on machine-learning-supported image analysis, in conjunction with other analytical techniques, is a general platform that should have a broader impact on porous materials characterization.
| Type: | Article |
|---|---|
| Title: | SEM Image Processing Assisted by Deep Learning to Quantify Mesoporous γ-Alumina Spatial Heterogeneity and Its Predicted Impact on Mass Transfer |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1021/acs.jpcc.4c00323 |
| Publisher version: | http://dx.doi.org/10.1021/acs.jpcc.4c00323 |
| Language: | English |
| Additional information: | This publication is licensed under CC-BY 4.0 . License Summary* You are free to share(copy and redistribute) this article in any medium or format and to adapt(remix, transform, and build upon) the material for any purpose, even commercially within the parameters below: cc licence Creative Commons (CC): This is a Creative Commons license. ny licence Attribution (BY): Credit must be given to the creator. View full license *Disclaimer This summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. |
| Keywords: | Science & Technology, Physical Sciences, Technology, Chemistry, Physical, Nanoscience & Nanotechnology, Materials Science, Multidisciplinary, Chemistry, Science & Technology - Other Topics, Materials Science, TRANSPORT HETEROGENEITY, 2-COMPONENT MATERIAL, DIFFUSION, CONDUCTIVITY, PARTICLES, PELLETS |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10196942 |
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