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Sub-pixel Tomographic Methods for Characterizing the Solid Architecture of Foams

Published online by Cambridge University Press:  18 March 2022

Paula Cimavilla-Román Open the ORCID record for Paula Cimavilla-Román [Opens in a new window] ,
Saúl Pérez-Tamarit ,
Suset Barroso-Solares ,
Javier Pinto  and
Miguel Ángel Rodríguez-Pérez
Paula Cimavilla-Román*
Affiliation:
Cellular Materials Laboratory (CellMat), Condensed Matter Physics Department, University of Valladolid, Paseo Belen 7, Valladolid 47011, Spain
Saúl Pérez-Tamarit
Affiliation:
Cellular Materials Laboratory (CellMat), Condensed Matter Physics Department, University of Valladolid, Paseo Belen 7, Valladolid 47011, Spain
Suset Barroso-Solares
Affiliation:
Cellular Materials Laboratory (CellMat), Condensed Matter Physics Department, University of Valladolid, Paseo Belen 7, Valladolid 47011, Spain BioEcoUva, Research Institute on Bioeconomy, University of Valladolid, Valladolid, Spain
Javier Pinto
Affiliation:
Cellular Materials Laboratory (CellMat), Condensed Matter Physics Department, University of Valladolid, Paseo Belen 7, Valladolid 47011, Spain BioEcoUva, Research Institute on Bioeconomy, University of Valladolid, Valladolid, Spain
Miguel Ángel Rodríguez-Pérez
Affiliation:
Cellular Materials Laboratory (CellMat), Condensed Matter Physics Department, University of Valladolid, Paseo Belen 7, Valladolid 47011, Spain BioEcoUva, Research Institute on Bioeconomy, University of Valladolid, Valladolid, Spain
*
*Corresponding author: Paula Cimavilla-Román, E-mail: paulacimavilla@fmc.uva.es
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Abstract

Three independent analysis methods were developed to investigate the distribution of solid mass in foams analyzed by X-ray tomography with effective pixel sizes larger than the thickness of the solid network (sub-pixel conditions). Validation of the methods was achieved by a comparison with the results obtained employing high-resolution tomography for the same set of foams. The foams showed different solid mass distribution, which varied from being preferentially located on the edges, with a fraction of mass in the struts nearing 0.6, to materials in which the fraction of mass in the struts was low, under 0.15. In all cases, the accuracy of the proposed approaches was greater for materials with a higher fraction of mass in the struts. The method based on deconvolution of the attenuation probability density function yielded the closest results to the high-resolution characterizations. In contrast, analysis of the solid matrix thickness distribution after watershed segmentation, and binarization of high thickness regions (struts segmentation) required normalization through macroscopic measurements and revealed higher deviations with respect to the high-resolution results. However, segmentation-based methods allowed investigation of the heterogeneity of the fraction of mass in the struts along the sample.

Keywords

3D analysiscellular structurefoams characterizationfraction of mass in the strutsmicrotomographysub-pixel
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 28 , Issue 3 , June 2022 , pp. 689 - 700
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of the Microscopy Society of America

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References

Baur, M, Uhlmann, N, Pöschel, T & Schröter, M (2019). Correction of beam hardening in X-ray radiograms. Rev Sci Instrum 90, 025108.CrossRefGoogle ScholarPubMed
Bernardo, V, Laguna-Gutierrez, E, Lopez-Gil, A & Rodriguez-Perez, MA (2017). Highly anisotropic crosslinked HDPE foams with a controlled anisotropy ratio: Production and characterization of the cellular structure and mechanical properties. Mater Des 114, 83–91.CrossRefGoogle Scholar
Bernardo, V, Van Loock, F, Martin-de Leon, J, Fleck, NA & Rodriguez-Perez, MA (2019). Mechanical properties of PMMA-sepiolite nanocellular materials with a bimodal cellular structure. Macromol Mater Eng 304, 1–12.10.1002/mame.201900041CrossRefGoogle Scholar
Cantat, I, Cohen-Addad, S, Elias, F, Graner, F, Höehler, R, Pitois, O, Rouyer, F & Saint-Jalmes, A (2013). Chapter 2: Foams at equilibrium. In Foams: Structure and Dynamics, Fox, SJ (Ed.), 1st ed. Oxford: Oxford University Press.10.1093/acprof:oso/9780199662890.001.0001CrossRefGoogle Scholar
Caplan, J, Niethammer, M, Taylor, RM & Czymmek, KJ (2011). The power of correlative microscopy: Multi-modal, multi-scale, multi-dimensional. Curr Opin Struct Biol 21, 686–693.CrossRefGoogle ScholarPubMed
Dement'ev, AG & Tarakanov, OG (1970). Effect of cellular structure on the mechanical properties of plastic foams. Polym Mech 6, 519–525.CrossRefGoogle Scholar
Dierick, M, Masschaele, B & Van Hoorebeke, L (2004). Octopus, a fast and user-friendly tomographic reconstruction package developed in LabView®. Meas Sci Technol 15, 1366–1370.CrossRefGoogle Scholar
Dougherty, R & Kunzelmann, K-H (2007). Computing local thickness of 3D structures with ImageJ. Microsc Microanal 13, 1678–1679.CrossRefGoogle Scholar
Eaves, D (2004). Handbook of Polymer Foams. Shrewsbury: Rapra Technology Limited.Google Scholar
Estravís, S, Tirado-Mediavilla, J, Santiago-Calvo, M, Ruiz-Herrero, JL, Villafañe, F & Rodríguez-Pérez, MA (2016). Rigid polyurethane foams with infused nanoclays: Relationship between cellular structure and thermal conductivity. Eur Polym J 80, 1–15.CrossRefGoogle Scholar
Furat, O, Wang, M, Neumann, M, Petrich, L, Weber, M, Krill, CE & Schmidt, V (2019). Machine learning techniques for the segmentation of tomographic image data of functional materials. Front Mater 6, 145.10.3389/fmats.2019.00145CrossRefGoogle Scholar
García-Moreno, F (2016). Commercial applications of metal foams: Their properties and production. Materials 9, 85.CrossRefGoogle Scholar
Gibson, LJ & Ashby, MF (1997). Cellular Solids: Structure and Properties. Cambridge: Cambridge Solid State Science Series.CrossRefGoogle Scholar
Harbron, DR, Page, CJ & Scarrow, RK (2001). Methods of minimising density gradients in rigid polyurethane foams. J Cell Plast 37, 43–57.10.1106/7FW8-15L5-3N83-KQX3CrossRefGoogle Scholar
Hilyard, NC & Cunningham, A (1994). Low Density Cellular Plastics. 1st ed. London: Springer Science & Business Media.CrossRefGoogle Scholar
Hsieh, J (2003). Computed Tomography: Principles, Design, Artifacts, and Recent Advances, 3rd ed. Bellingham: Wiley.Google Scholar
Huang, G & Wang, P (2017). Effects of preparation conditions on properties of rigid polyurethane foam composites based on liquefied bagasse and jute fibre. Polym Test 60, 266–273.CrossRefGoogle Scholar
Klempner, D & Frisch, KC (2004). Chapter 3: Cellular structure and properties of foamed polymers. In Handbook of Polymeric Foams and Foam Technology, Klempner, D & Frisch, KC (Eds.), 2nd ed.Munich: Hanser.Google Scholar
König, J, Lopez-Gil, A, Cimavilla-Roman, P, Rodriguez-Perez, MA, Petersen, RR, Østergaard, MB, Iversen, N, Yue, Y & Spreitzer, M (2020). Synthesis and properties of open- and closed-porous foamed glass with a low density. Constr Build Mater 247, 118574.10.1016/j.conbuildmat.2020.118574CrossRefGoogle Scholar
Lauridsen, T, Willner, M, Bech, M, Pfeiffer, F & Feidenhans'l, R (2015). Detection of sub-pixel fractures in X-ray dark-field tomography. Appl Phys A: Mater Sci Process 121, 1243–1250.CrossRefGoogle Scholar
Laurson, P, Raudsepp, P, Kaldmäe, H, Kikas, A & Mäeorg, U (2020). The deconvolution of FTIR-ATR spectra to five Gaussians for detection of small changes in plant–water clusters. AIP Adv 10, 085214.CrossRefGoogle Scholar
Legland, D & Arganda-Carreras, I (2016). MorphoLibJ-User manual.Google Scholar
Legland, D, Arganda-Carreras, I & Andrey, P (2016). Morpholibj: Integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics 32, 3532–3534.Google ScholarPubMed
Leopold, LF, Leopold, N, Diehl, HA & Socaciu, C (2011). Quantification of carbohydrates in fruit juices using FTIR spectroscopy and multivariate analysis. Spectroscopy 26, 93–104.CrossRefGoogle Scholar
Liu, Y, Kiss, AM, Larsson, DH, Yang, F & Pianetta, P (2016). To get the most out of high resolution X-ray tomography: A review of the post-reconstruction analysis. Spectrochim Acta - Part B At Spectrosc 117, 29–41.CrossRefGoogle Scholar
Ma, Y, Hua, J, Liu, D, He, Y, Zhang, T, Chen, J, Yang, F, Ning, X, Yang, Z, Zhang, J, Pai, CH, Gu, Y & Lu, W (2020). Region-of-interest micro-focus computed tomography based on an all-optical inverse compton scattering source. Matter Radiat Extremes 5, 064401.CrossRefGoogle Scholar
Maaß, C, Knaup, M & Kachelrie, M (2011). New approaches to region of interest computed tomography. Med Phys 38, 2868–2878.10.1118/1.3583696CrossRefGoogle ScholarPubMed
Maire, É, Adrien, J & Petit, C (2014). Structural characterization of solid foams. C R Phys 15, 674–682.CrossRefGoogle Scholar
May, RA & Stevenson, KJ (2009). Software review of origin 8. J Am Chem Soc 131, 872.CrossRefGoogle Scholar
Mills, NJ (2007). Polymer Foams Handbook: Engineering and Biomechanics Applications and Design.Google Scholar
Minaee, S, Boykov, YY, Porikli, F, Plaza, AJ, Kehtarnavaz, N & Terzopoulos, D (2021). Image segmentation using deep learning: A survey. IEEE Trans Pattern Anal Mach Intell.CrossRefGoogle Scholar
Pardo-Alonso, S, Sorlórzano, E, Brabant, L, Vanderniepen, P, Dierick, M, Van Hoorebeke, L, Rodríguez-Peréz, MA, Solórzano, E, Brabant, L, Vanderniepen, P, Dierick, M, Van Hoorebeke, L & Rodríguez-Pérez, MA (2013). 3D analysis of the progressive modification of the cellular architecture in polyurethane nanocomposite foams vía X-ray microtomography. Eur Polym J 149, 999–1006.CrossRefGoogle Scholar
Pérez-Tamarit, S, Solórzano, E, Hilger, A, Manke, I & Rodríguez-Pérez, MA (2018). Multi-scale tomographic analysis of polymeric foams: A detailed study of the cellular structure. Eur Polym J 109, 169–178.CrossRefGoogle Scholar
Petit, C, Meille, S, Maire, É & Maire, E (2013). Cellular solids studied by X-ray tomography and finite element modeling - A review. J Mater Res 28, 2191–2201.CrossRefGoogle Scholar
Ramesh, NS & Lee, ST (2004). Chapter 3: Fundamentals of bubble nucleation and growth in polymers. In Polymeric Foams: Mechanisms and Materials, Lee, S-T & Ramesh, NS (Eds.), 1st ed. Boca Raton: CRC Press.Google Scholar
Roels, J, Vernaillen, F, Kremer, A, Gonçalves, A, Aelterman, J, Luong, HQ, Goossens, B, Philips, W, Lippens, S & Saeys, Y (2020). An interactive ImageJ plugin for semi-automated image denoising in electron microscopy. Nat Commun 11, 1–13.CrossRefGoogle ScholarPubMed
Ruan, C, Zhu, Y, Zhou, X, Abidi, N, Hu, Y & Catchmark, JM (2016). Effect of cellulose crystallinity on bacterial cellulose assembly. Cellulose 23, 3417–3427.10.1007/s10570-016-1065-0CrossRefGoogle Scholar
Russ, J C (2006). The Image Processing Handbook, 3rd ed. Boca Raton: CRC Press.10.1201/9780203881095CrossRefGoogle Scholar
Santiago-Calvo, M, Tirado-Mediavilla, J, Ruiz-Herrero, JL, Rodríguez-Pérez, MÁ & Villafañe, F (2018). The effects of functional nanofillers on the reaction kinetics, microstructure, thermal and mechanical properties of water blown rigid polyurethane foams. Polymer 150, 138–149.CrossRefGoogle Scholar
Solórzano, E, Pinto, J, Pardo, S, Garcia-Moreno, F & Rodriguez-Perez, MA (2013). Application of a microfocus X-ray imaging apparatus to the study of cellular polymers. Polym Test 32, 321–329.CrossRefGoogle Scholar
Stock, SR (2009). Micro Computed Tomography, Methodology and Applications. Boca Raton: CRC Press.Google Scholar
Verdejo, R (2003). Gas Loss and Durability of EVA Foams Used in Running Shoes. Birmingham: The University of Birmingham.Google Scholar
Wang, Y, Arns, JY, Rahman, SS & Arns, CH (2018). Three-dimensional porous structure reconstruction based on structural local similarity via sparse representation on micro-computed-tomography images. Phys Rev E 98, 1–16.CrossRefGoogle Scholar
Williams, MK, Weiser, ES, Fesmire, JE, Grimsley, BW, Smith, TM, Brenner, JR & Nelson, GL (2005). Effects of cell structure and density on the properties of high performance polyimide foams. Polym Adv Technol 16, 167–174.10.1002/pat.567CrossRefGoogle Scholar
Zhang, XD, Macosko, CW, Davis, HT, Nikolov, AD & Wasan, DT (1999). Role of silicone surfactant in flexible polyurethane foam. J Colloid Interface Sci 215, 270–279.CrossRefGoogle ScholarPubMed
Zhao, B, Wang, R, Li, Y, Ren, Y, Li, X, Guo, X, Zhang, R & Park, CB (2020). Dependence of electromagnetic interference shielding ability of conductive polymer composite foams with hydrophobic properties on cellular. J Mater Chem C 8, 7401–7410.CrossRefGoogle Scholar
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