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KW  - Science & Technology
KW  -  Physical Sciences
KW  -  Technology
KW  -  Chemistry
KW  -  Physical
KW  -  Nanoscience & Nanotechnology
KW  -  Materials Science
KW  -  Multidisciplinary
KW  -  Chemistry
KW  -  Science & Technology - Other Topics
KW  -  Materials Science
KW  -  TRANSPORT HETEROGENEITY
KW  -  2-COMPONENT MATERIAL
KW  -  DIFFUSION
KW  -  CONDUCTIVITY
KW  -  PARTICLES
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AV  - public
SN  - 1932-7447
TI  - SEM Image Processing Assisted by Deep Learning to Quantify Mesoporous ?-Alumina Spatial Heterogeneity and Its Predicted Impact on Mass Transfer
EP  - 8407
IS  - 20
SP  - 8395
VL  - 128
PB  - AMER CHEMICAL SOC
A1  - Glowska, Aleksandra
A1  - Jolimaitre, Elsa
A1  - Hammoumi, Adam
A1  - Moreaud, Maxime
A1  - Sorbier, Loic
A1  - de Faria Barros, Caroline
A1  - Lefebvre, Veronique
A1  - Coppens, Marc-Olivier
JF  - The Journal of Physical Chemistry C
Y1  - 2024/05/13/
ID  - discovery10196942
UR  - http://dx.doi.org/10.1021/acs.jpcc.4c00323
N2  - 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 ?-alumina (?-Al2O3), a catalyst support of great industrial relevance. Based on this, a model is proposed for the spatial organization of ?-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.
ER  -