The application of automated image analysis to dense heterogeneities in partially sintered alumina

Dengi̇z, Orhan; McAfee, Richard; Nettleship, Ian; Smith, Alice E.

doi:10.1016/j.jeurceramsoc.2006.06.002

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…Since alumina inclusions are easily observable by SEM, their quantification can be performed via SEM image processing methods to evaluate their impact on the mentioned properties. Such quantification of alumina inclusions has been reported by Dengiz et al, where a quantitative analysis of dense heterogeneities in partially sintered alumina was performed to evaluate their effect on mechanical strength. In this study, automated SEM image analysis with an adaptive thresholding algorithm was applied for the quantification of inclusion area fraction and size distribution.…”

Section: Introductionmentioning

confidence: 87%

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

Głowska,

Jolimaitre,

Hammoumi

et al. 2024

J. Phys. Chem. C

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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.

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Section: Introductionmentioning

confidence: 87%

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

Głowska,

Jolimaitre,

Hammoumi

et al. 2024

J. Phys. Chem. C

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“…The exploration of microstructure behavior through image analysis allows the control and optimization of the final properties and performance of a material. Several studies have been performed with the use of image analysis in the quantification of the microstructure parameters to understand their effect on the properties of sintered materials and composites [39][40][41][42][43]. Shen et al [39] studied the evolution of local microstructure features to evaluate the densification and distortion behavior during initial stage liquid phase sintering at an effective sintering time using the image analysis technique.…”

Section: J Min Metall Sect B-metall 54 (2) B (2018) 169 -177mentioning

confidence: 99%

Microstructure characterization and quantitative analysis of copper alloy matrix composites reinforced with WC-xNi powders prepared by spontaneous infiltration

Daoud

Miroud

Yamanoğlu

2018

J min metall B Metall

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In this study, copper alloy matrix composites reinforced with tungsten carbide (WC) particles with the addition of different Ni contents (0, 3, 5, 7, and 10 wt.%) were prepared by the spontaneous infiltration process. Image analysis was used to quantify the microstructural parameters, such as the particle size and distribution, area fraction, binder mean free path, and pore size. The effect of Ni addition on the microstructure, density and hardness are discussed. The results show that a small addition of Ni improves the densification of the infiltrated composites. The highest density value of 11.84 g/cm 3 with a hardness of 327 HV was obtained for the infiltrated composite with the addition of 3 wt.% of Ni. The quantitative analysis results are in good agreement with the microstructure properties and hardness results.

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“…As a rule, the structure of nanopowders is agglomerated due to their high surface area and mostly elevated temperature‐assisted character of their synthesis. Thereby, consolidation of agglomerated nanopowders is often difficult because of the rapid densification of agglomerates and slow closure of large interagglomerated pores . This complicates the elimination of residual interagglomerate porosity during sintering or pressing and provokes rapid grain growth in agglomerates and the loss of unique properties of nanostructured materials.…”

Section: Introductionmentioning

confidence: 99%

“…Thereby, consolidation of agglomerated nanopowders is often difficult because of the rapid densification of agglomerates and slow closure of large interagglomerated pores. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] This complicates the elimination of residual interagglomerate porosity during sintering or pressing and provokes rapid grain growth in agglomerates and the loss of unique properties of nanostructured materials. At the same time, the specifics of agglomerated structures eventually offer the venues for a "self-therapy" of the above-mentioned processing challenges.…”

Section: Introductionmentioning

confidence: 99%

Homogenization of Biporous Agglomerated Powder Structures During High‐Temperature Consolidation

2015

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Specifics of agglomerate densification-important for hot pressing, conventional, and spark plasma sintering of powders-are analyzed based on the biporous material modeling framework. Numerical simulations are utilized to determine what technological parameters can be used to improve the postprocessing homogeneity of agglomerated powder-based materials. The modeling results indicate that the homogenization during sintering and hot pressing of agglomerated nano-and micrometer-size powders is significantly impacted by the viscosity of grain-boundary sliding and by the possible existence of high-intensity diffusion paths along the agglomerate boundaries.

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The application of automated image analysis to dense heterogeneities in partially sintered alumina

Cited by 5 publications

References 35 publications

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

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

Microstructure characterization and quantitative analysis of copper alloy matrix composites reinforced with WC-xNi powders prepared by spontaneous infiltration

Homogenization of Biporous Agglomerated Powder Structures During High‐Temperature Consolidation

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