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SimScience 2017: Simulation Science pp 145-158 | Cite as

On Microstructure-Property Relationships Derived by Virtual Materials Testing with an Emphasis on Effective Conductivity

  • Matthias Neumann
  • Orkun Furat
  • Dzmitry Hlushkou
  • Ulrich Tallarek
  • Lorenz Holzer
  • Volker Schmidt
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 889)

Abstract

Based on virtual materials testing, which combines image analysis, stochastic microstructure modeling and numerical simulations, quantitative relationships between microstructure characteristics and effective conductivity can be derived. The idea of virtual materials testing is to generate a large variety of stochastically simulated microstructures in short time. These virtual, but realistic microstructures are used as input for numerical transport simulations. Finally, a large data basis is available to study microstructure-property relationships quantitatively by classical regression analysis and tools from statistical learning. The microstructure-property relationships obtained for effective conductivity can also be applied to Fickian diffusion. For validation, we discuss an example of Fickian diffusion in porous silica monoliths on the basis of 3D image data.

References

  1. 1.
    DeQuilettes, D., Vorpahl, S.M., Stranks, S.D., Nagaoka, H., Eperon, G.E., Ziffer, M.E., Snaith, H.J., Ginger, D.S.: Impact of microstructure on local carrier lifetime in perovskite solar cells. Science 348, 683–686 (2015)CrossRefGoogle Scholar
  2. 2.
    Wilson, J.R., Cronin, J.S., Barnett, S.A., Harris, S.J.: Measurement of three-dimensional microstructure in a \({\rm{LiCoO}}_{2}\) positive electrode. J. Power Sour. 196(7), 3443–3447 (2011)CrossRefGoogle Scholar
  3. 3.
    Prakash, B.S., Kumar, S.S., Aruna, S.T.: Properties and development of Ni/YSZ as an anode material in solid oxide fuel cell: a review. Renew. Sustain. Energy Rev. 36, 149–179 (2014)CrossRefGoogle Scholar
  4. 4.
    Nischang, I.: Porous polymer monoliths: morphology, porous properties, polymer nanoscale gel structure and their impact on chromatographic performance. J. Chromatogr. A 1287, 39–58 (2013)CrossRefGoogle Scholar
  5. 5.
    Torquato, S.: Random Heterogeneous Materials: Microstructure and Macroscopic Properties. Springer, New York (2002)CrossRefGoogle Scholar
  6. 6.
    Hashin, Z.: The elastic moduli of heterogeneous materials. J. Appl. Mech. 29(1), 143–150 (1962)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Maire, E., Withers, P.J.: Quantitative X-ray tomography. Int. Mater. Rev. 59(1), 1–43 (2014)CrossRefGoogle Scholar
  8. 8.
    Holzer, L., Cantoni, M.: Review of FIB-tomography. In: Utke, I., Moshkalev, S., Russell, P., (eds.) Nanofabrication Using Focused Ion and Electron Beams: Principles and Applications, pp. 410–435. Oxford University Press, New York (2012)Google Scholar
  9. 9.
    Midgley, P.A., Dunin-Borkowski, R.E.: Electron tomography and holography in materials science. Nat. Mater. 8(4), 271 (2009)CrossRefGoogle Scholar
  10. 10.
    Chiu, S.N., Stoyan, D., Kendall, W.S., Mecke, J.: Stochastic Geometry and Its Applications, 3rd edn. Wiley, Chichester (2013)CrossRefGoogle Scholar
  11. 11.
    Matheron, G.: Random Sets and Integral Geometry. Wiley, New York (1975)zbMATHGoogle Scholar
  12. 12.
    Hlushkou, D., Hormann, K., Höltzel, A., Khirevich, S., Seidel-Morgenstern, A., Tallarek, U.: Comparison of first and second generation analytical silica monoliths by pore-scale simulations of eddy dispersion in the bulk region. J. Chromatogr. A 1303, 28–38 (2013)CrossRefGoogle Scholar
  13. 13.
    Doyle, M., Fuller, T.F., Newman, J.: Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. J. Electrochem. Soc. 140(6), 1526–1533 (1993)CrossRefGoogle Scholar
  14. 14.
    Holzer, L., Iwanschitz, B., Hocker, T., Keller, L., Pecho, O.M., Sartoris, G., Gasser, P., Münch, B.: Redox cycling of Ni-YSZ anodes for solid oxide fuel cells: influence of tortuosity, constriction and percolation factors on the effective transport properties. J. Power Sour. 242, 179–194 (2013)CrossRefGoogle Scholar
  15. 15.
    Shikazono, N., Kanno, D., Matsuzaki, K., Teshima, H., Sumino, S., Kasagi, N.: Numerical assessment of SOFC anode polarization based on three-dimensional model microstructure reconstructed from FIB-SEM images. J. Electrochem. Soc. 157(5), B665–B672 (2010)CrossRefGoogle Scholar
  16. 16.
    Tippmann, S., Walper, D., Balboa, L., Spier, B., Bessler, W.G.: Low-temperature charging of lithium-ion cells part I: electrochemical modeling and experimental investigation of degradation behavior. J. Power Sour. 252, 305–316 (2014)CrossRefGoogle Scholar
  17. 17.
    Gaiselmann, G., Neumann, M., Pecho, O.M., Hocker, T., Schmidt, V., Holzer, L.: Quantitative relationships between microstructure and effective transport properties based on virtual materials testing. AIChE J. 60(6), 1983–1999 (2014)CrossRefGoogle Scholar
  18. 18.
    Stenzel, O., Pecho, O.M., Holzer, L., Neumann, M., Schmidt, V.: Predicting effective conductivities based on geometric microstructure characteristics. AIChE J. 62, 1834–1843 (2016)CrossRefGoogle Scholar
  19. 19.
    Stenzel, O., Neumann, M., Pecho, O.M., Holzer, L., Schmidt, V.: Big data for microstructure-property relationships: a case study of predicting effective conductivities. AIChE J. 63(9), 4224–4232 (2017)CrossRefGoogle Scholar
  20. 20.
    Stoeckel, D., Kübel, C., Hormann, K., Höltzel, A., Smarsly, B.M., Tallarek, U.: Morphological analysis of disordered macroporous-mesoporous solids based on physical reconstruction by nanoscale tomography. Langmuir 30, 9022–9027 (2014)CrossRefGoogle Scholar
  21. 21.
    NM-SESES. http://nmtec.ch/nm-seses/. Accessed 2017
  22. 22.
    GeoDict (2017). www.geodict.com
  23. 23.
    Clennell, M.B.: Tortuosity: a guide through the maze. Geol. Soc. London Spec. Publ. 122, 299–344 (1997)CrossRefGoogle Scholar
  24. 24.
    Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, New York (2003)zbMATHGoogle Scholar
  25. 25.
    VSG - Visualization Sciences Group - Avizo Standard (2017). http://www.vsg3d.com/
  26. 26.
    Petersen, E.E.: Diffusion in a pore of varying cross section. AIChE J. 4(3), 343–345 (1958)CrossRefGoogle Scholar
  27. 27.
    Holzer, L., Iwanschitz, B., Hocker, T., Keller, L., Pecho, O.M., Sartoris, G., Gasser, P., Münch, B.: The influence of constrictivity on the effective transport properties of porous layers in electrolysis and fuel cells. J. Mater. Sci. 48, 2934–2952 (2013)CrossRefGoogle Scholar
  28. 28.
    Münch, B., Holzer, L.: Contradicting geometrical concepts in pore size analysis attained with electron microscopy and mercury intrusion. J. Am. Ceram. Soc. 91, 4059–4067 (2008)CrossRefGoogle Scholar
  29. 29.
    Møller, J., Waagepetersen, R.P.: Statistical Inference and Simulation for Spatial Point Processes. Chapman & Hall/CRC, Boca Raton (2004)zbMATHGoogle Scholar
  30. 30.
    Jaromczyk, J.W., Toussaint, G.T.: Relative neighborhood graphs and their relatives. Proc. IEEE 80, 1502–1517 (1992)CrossRefGoogle Scholar
  31. 31.
    Ross, S.: Simulation, 5th edn. Academic Press, New York (2013)zbMATHGoogle Scholar
  32. 32.
    Stenzel, O., Hassfeld, H., Thiedmann, R., Koster, L.J.A., Oosterhout, S.D., van Bavel, S.S., Wienk, M.M., Loos, J., Janssen, R.A.J., Schmidt, V.: Spatial modeling of the 3D morphology of hybrid polymer-ZnO solar cells, based on electron tomography data. Ann. Appl. Stat. 5, 1920–1947 (2011)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd edn. Springer, New York (2008)zbMATHGoogle Scholar
  34. 34.
    Enke, D., Gläser, R., Tallarek, U.: Sol-gel and porous glass-based silica monoliths with hierarchical pore structure for solid-liquid catalysis. Chemie Ingenieur Technik 88(11), 1561–1585 (2016)CrossRefGoogle Scholar
  35. 35.
    Liasneuski, H., Hlushkou, D., Khirevich, S., Höltzel, A., Tallarek, U., Torquato, S.: Impact of microstructure on the effective diffusivity in random packings of hard spheres. J. Appl. Phys. 116(3), 034904 (2014)CrossRefGoogle Scholar
  36. 36.
    Hlushkou, D., Liasneuski, H., Tallarek, U., Torquato, S.: Effective diffusion coefficients in random packings of polydisperse hard spheres from two-point and three-point correlation functions. J. Appl. Phys. 118(12), 124901 (2015)CrossRefGoogle Scholar
  37. 37.
    Daneyko, A., Hlushkou, D., Baranau, V., Khirevich, S., Seidel-Morgenstern, A., Tallarek, U.: Computational investigation of longitudinal diffusion, eddy dispersion, and trans-particle mass transfer in bulk, random packings of core-shell particles with varied shell thickness and shell diffusion coefficient. J. Chromatogr. A 1407, 139–156 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Matthias Neumann
    • 1
  • Orkun Furat
    • 1
  • Dzmitry Hlushkou
    • 2
  • Ulrich Tallarek
    • 2
  • Lorenz Holzer
    • 3
  • Volker Schmidt
    • 1
  1. 1.Institute of StochasticsUlm UniversityUlmGermany
  2. 2.Department of ChemistryPhilipps-Universität MarburgMarburgGermany
  3. 3.Institute of Computational PhysicsZHAWWinterthurSwitzerland

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