SEM Image Processing Assisted by Deep Learning to Quantify Mesoporous γ-Alumina Spatial Heterogeneity and Its Predicted Impact on Mass Transfer-Reference-Cited by-同舟云学术

SEM Image Processing Assisted by Deep Learning to Quantify Mesoporous γ-Alumina Spatial Heterogeneity and Its Predicted Impact on Mass Transfer

Published:2024-05-13 Issue:20 Volume:128 Page:8395-8407
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Głowska Aleksandra¹²,Jolimaitre Elsa²^ORCID,Hammoumi Adam²^ORCID,Moreaud Maxime²^ORCID,Sorbier Loïc²^ORCID,de Faria Barros Caroline²,Lefebvre Veronique²,Coppens Marc-Olivier¹^ORCID

Affiliation:

1. Centre for Nature Inspired Engineering and Department of Chemical Engineering, University College London, London WC1E 7JE, United Kingdom

2. IFP Energies Nouvelles, Rond-point de l’échangeur de Solaize, BP 3, Solaize 69360, France

Funder

Research Councils UK

IFP Energies Nouvelles

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.4c00323

Reference45 articles.

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2. Kirchner, S. Approche Multi-échelle de l’agrégation dans le procédé de Précipitation de Boehmite; Institut National Polytechnique de Toulouse, 2015.

3. Higher-order structure formation of ultrafine boehmite particles in sols, gels, and dried materials

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