eprintid: 10196942
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/19/69/42
datestamp: 2024-09-16 13:11:29
lastmod: 2024-09-16 13:11:29
status_changed: 2024-09-16 13:11:29
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Glowska, Aleksandra
creators_name: Jolimaitre, Elsa
creators_name: Hammoumi, Adam
creators_name: Moreaud, Maxime
creators_name: Sorbier, Loic
creators_name: de Faria Barros, Caroline
creators_name: Lefebvre, Veronique
creators_name: Coppens, Marc-Olivier
title: SEM Image Processing Assisted by Deep Learning to Quantify Mesoporous γ-Alumina Spatial Heterogeneity and Its Predicted Impact on Mass Transfer
ispublished: pub
divisions: UCL
divisions: B04
divisions: F43
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
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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.
date: 2024-05-13
date_type: published
publisher: AMER CHEMICAL SOC
official_url: http://dx.doi.org/10.1021/acs.jpcc.4c00323
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2277852
doi: 10.1021/acs.jpcc.4c00323
medium: Electronic-eCollection
lyricists_name: Coppens, Marc-Olivier
lyricists_id: MCOPP36
actors_name: Coppens, Marc-Olivier
actors_id: MCOPP36
actors_role: owner
funding_acknowledgements: EP/S03305X/1 [Research Councils UK]; [IFP Energies Nouvelles]
full_text_status: public
publication: The Journal of Physical Chemistry C
volume: 128
number: 20
pagerange: 8395-8407
pages: 13
event_location: United States
issn: 1932-7447
citation:        Glowska, Aleksandra;    Jolimaitre, Elsa;    Hammoumi, Adam;    Moreaud, Maxime;    Sorbier, Loic;    de Faria Barros, Caroline;    Lefebvre, Veronique;           Glowska, Aleksandra;  Jolimaitre, Elsa;  Hammoumi, Adam;  Moreaud, Maxime;  Sorbier, Loic;  de Faria Barros, Caroline;  Lefebvre, Veronique;  Coppens, Marc-Olivier;   - view fewer <#>    (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 <https://doi.org/10.1021/acs.jpcc.4c00323>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10196942/1/GlowskaJolimaitreHammoumiMoreaudSorbierBarrosLefebvreCoppens_JPCC24.pdf