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Local Porosity Measurement From Scanning Electron Microscopy Images in the Backscattered Electrons Mode

Published online by Cambridge University Press:  03 December 2020

Loïc Sorbier Open the ORCID record for Loïc Sorbier [Opens in a new window] ,
Hedwige Poncet ,
Vincent Lecocq ,
Guillaume Maillet ,
Marwa Moula  and
Vincent Le Corre Open the ORCID record for Vincent Le Corre [Opens in a new window]
Loïc Sorbier*
Affiliation:
IFP Energies nouvelles, Rond-point de l'échangeur de Solaize, BP 3, 69360Solaize, France
Hedwige Poncet
Affiliation:
IFP Energies nouvelles, Rond-point de l'échangeur de Solaize, BP 3, 69360Solaize, France
Vincent Lecocq
Affiliation:
IFP Energies nouvelles, Rond-point de l'échangeur de Solaize, BP 3, 69360Solaize, France
Guillaume Maillet
Affiliation:
IFP Energies nouvelles, Rond-point de l'échangeur de Solaize, BP 3, 69360Solaize, France
Marwa Moula
Affiliation:
IFP Energies nouvelles, Rond-point de l'échangeur de Solaize, BP 3, 69360Solaize, France
Vincent Le Corre
Affiliation:
IFP Energies nouvelles, Rond-point de l'échangeur de Solaize, BP 3, 69360Solaize, France
*
*Author for correspondence: Loïc Sorbier, E-mail: loic.sorbier@ifpen.fr
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Abstract

We propose a method to measure the local porosity of porous samples from scanning electron microscopy images in the backscattered electron mode. The porous samples are impregnated with a polymer resin and observed in polished cross sections. Image intensities are calibrated with intensities from pure resin and the bulk phase. The calibration model is justified with Monte Carlo simulations on perfectly homogeneous virtual samples. Uncertainties in measured porosity are given as a function of uncertainties on physical properties of the resin and the bulk phase and on measured signals. The methodology is applied to a series of heterogeneous alumina catalyst supports with varying porosities. A good agreement is found between the averaged local porosity by scanning electron microscopy and global porosity determined by mercury intrusion porosimetry. The use of local porosity statistics allowed the quantitative characterization of the porosity fluctuations of these supports that appeared to be linked with their preparation parameters.

Keywords

backscatteredMonte CarloporositySEM
Type
Materials Science Applications
Information
Microscopy and Microanalysis , Volume 27 , Issue 1 , February 2021 , pp. 20 - 27
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press on behalf of the Microscopy Society of America

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References

Armstrong, JT (1991). Quantitative elemental analysis of individual microparticles with electron beam instruments. In Electron Probe Quantitation, Heinrich, KFJ & Newbury, DE (Eds.), pp. 261–315. Boston, MA: Springer US.CrossRefGoogle Scholar
Ball, MD & McCartney, DG (1981). The measurement of atomic number and composition in an SEM using backscattered detectors. J Microsc 124, 57–68. doi:10.1111/j.1365-2818.1981.tb01305.xCrossRefGoogle Scholar
Castaing, R (1960). Electron probe microanalysis. Adv Electron Electron Phys 13, 317–386. doi:10.1016/S0065-2539(08)60212-7CrossRefGoogle Scholar
Donovan, JJ, Pingitore, NE & Westphal, A (2003). Compositional averaging of backscatter intensities in compounds. Microsc Microanal 9, 202–215. doi:10.1017/S1431927603030137CrossRefGoogle ScholarPubMed
Herrmann, R & Reimer, L (1984). Backscattering coefficient of multicomponent specimens. Scanning 6, 20–29. doi:10.1002/sca.4950060203CrossRefGoogle Scholar
Hoang, DG & Igarashi, S (2013). Determination of water-cement ratios of hardened cement pastes based on the estimation of under-pixel porosity in backscattered electron images. Int J Struct Eng 4, 4. doi:10.1504/IJSTRUCTE.2013.050760CrossRefGoogle Scholar
Howell, PG & Boyde, A (1998). Monte Carlo simulation of electron backscattering from compounds with low mean atomic number. Scanning 20, 45–49. doi:10.1002/sca.1998.4950200107CrossRefGoogle ScholarPubMed
Kolitcheff, S, Jolimaitre, E, Hugon, A, Verstraete, J, Carrette, P-L & Tayakout-Fayolle, M (2017). Tortuosity of mesoporous alumina catalyst supports: Influence of the pore network organization. Micropor Mesopor Mater 248, 91–98. doi:10.1016/j.micromeso.2017.04.010CrossRefGoogle Scholar
Lin, Q, Al-Khulaifi, Y, Blunt, MJ & Bijeljic, B (2016). Quantification of sub-resolution porosity in carbonate rocks by applying high-salinity contrast brine using X-ray microtomography differential imaging. Adv Water Resour 96, 306–322. doi:10.1016/j.advwatres.2016.08.002CrossRefGoogle Scholar
Moula, M, Meille, S, Le Corre, V & Chevalier, J (2020). Mechanical characterization of meso-porous alumina by micro- and nano-indentation. Mater Today Commun 25, 101315. doi:10.1016/j.mtcomm.2020.101315CrossRefGoogle Scholar
Prill, T & Schladitz, K (2013). Simulation of FIB-SEM images for analysis of porous microstructures. Scanning 35, 189–195. doi:10.1002/sca.21047CrossRefGoogle ScholarPubMed
Salvat, F, Fernández-Varea, JM & Sempau, J (2011). PENELOPE 2011: A code system for Monte Carlo simulation of electron and photon transport, Nuclear Energy Agency. doi:10.1787/ef77b746-en.Google Scholar
Sánchez, E, Torres Deluigi, M & Castellano, G (2012). Mean atomic number quantitative assessment in backscattered electron imaging. Microsc Microanal 18, 1355–1361. doi:10.1017/S1431927612013566CrossRefGoogle ScholarPubMed
Sempau, J, Acosta, E, Baro, J, Fernández-Varea, JM & Salvat, F (1997). An algorithm for Monte Carlo simulation of coupled electron-photon transport. Nucl Instrum Methods Phys Res B 132, 377–390. doi:10.1016/S0168-583X(97)00414-XCrossRefGoogle Scholar
Staub, D, Meille, S, Le Corre, V, Rouleau, L & Chevalier, J (2016). Identification of a damage criterion of a highly porous alumina ceramic. Acta Mater 107, 261–272. doi:10.1016/j.actamat.2016.01.071CrossRefGoogle Scholar
Taud, H, Martinez-Angeles, R, Parrot, JF & Hernandez-Escobedo, L (2005). Porosity estimation method by X-ray computed tomography. J Petrol Sci Eng 47, 209–217. doi:10.1016/j.petrol.2005.03.009CrossRefGoogle Scholar
Wu, D, Zhou, J & Li, Y (2007). Mechanical strength of solid catalysts: Recent developments and future prospects. AIChE J 53, 2618–2629. doi:10.1002/aic.11291CrossRefGoogle Scholar
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