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Multiscale Computer Simulation of Tensile Failure in Polymer-Coated Silica Aerogels

Published online by Cambridge University Press:  01 February 2011

Brian Good
Brian Good*
Affiliation:
brian.s.good@grc.nasa.gov, NASA Glenn Research Center, Materials and Structures Division, Cleveland, Ohio, United States
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Abstract

The low thermal conductivities of silica aerogels have made them of interest to the aerospace community for applications such as cryotank insulation. Recent advances in the application of conformal polymer coatings to these gels have made them significantly stronger, and potentially useful as lightweight materials for impact absorption as well. In this work, we perform multiscale computer simulations to investigate the tensile strength and failure behavior of silica and polymer-coated silica aerogels. The gels' nanostructure is simulated via a Diffusion Limited Cluster Aggregation (DLCA) procedure. The procedure produces fractal aggregates that exhibit fractal dimensions similar to those observed in real aerogels. The largest distinct feature of the clusters is the so-called secondary particle, typically tens of nm in diameter, which is composed of primary particles of amorphous silica an order of magnitude smaller. The secondary particles are connected by amorphous silica bridges that are typically smaller in diameter than the particles they connect. We investigate tensile failure via the application of a uniaxial tensile strain to the DLCA clusters. In computing the energetics of tensile strain, the detailed structure of the secondary particles is ignored, and the interaction among secondary particles is described by Morse pair potentials, representing the strain energetics of the silica gel and the polymer coating, parameterized such that the potential ranges are much smaller than the secondary particle size. The Morse parameters are obtained by separate atomistic simulation of models of the interparticle bridges and polymer coatings, with the tensile behavior of these bridges modeled via molecular statics. We consider the energetics of tensile strain and tensile failure, and compare qualitative features of low-and high-density gel failure.

Keywords

aerogelsimulationstress/strain relationship
Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 1130: Symposium W – Computational Materials Design via Multiscale Modeling , 2008 , 1130-W06-14
Copyright
Copyright © Materials Research Society 2009

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References

REFERENCES

1. Husing, N. and Schubert, U., Angew. Chem. Int. Ed. 37, 22 (1998).Google Scholar
2. Pierre, A. C. and Pajonk, G. M., Chem. Rev. 2002, 102, 4243 (2002).Google Scholar
3. Rousset, J. L., Boukenter, A., Champagnon, B., Dumas, J., Duval, E., Quinson, J, F and Serughetti, J., J. Phys.: Condens. Matter 2, 8445 (1990).Google Scholar
4. Meador, M. A. B., Fabrizio, E. F., Ilhan, F., Dass, A., Zhang, G., Vassilaras, P., Johnston, J. C. and Leventis, N., Chem. Mater. 17, 1085 (2005).Google Scholar
5. Meakin, P., Phys. Rev. Lett. 51, 1119 (1983).Google Scholar
6. Mazurin, O. V., Streltsina, M. V. and Shvaiko-Shvaikovskaya, T. P., 1983 Handbook of Glass Data, Part A: Silica Glass and Binary Silicate Glasses, Elsevier, Amsterdam.Google Scholar
7. Woignier, : Woignier, T. and Phalippou, J., J. Non-Cryst. Solids 93 17 (1987).Google Scholar
8. Schaeffer, D. W., Martin, J. E. and Keefer, K. D., Phys. Rev. Lett. 56, 2199 (1986).Google Scholar
9. Freltoft, T., Kjems, J. K. and Sinha, S. K., Phys. Rev. B 33, 269 (1986).Google Scholar
10. Vacher, R., Woignier, T, Pelous, J. and Courtens, E., Phys. Rev. B 37, 6500 (1988).Google Scholar
11. Hasmy, A., Anglaret, E., Foret, M., Pelous, J. and Julien, R., Phys. Rev. B 50, 1305 (1994).Google Scholar
12. Eden, M. in Proc. of the Fourth Berkeley Symp. Of Mathematical Statistics and Probability, Vol. IV, Neyman, F., ed., University of California, Berkeley, 1961.Google Scholar
13. Bensimon, D., Sharaiman, B. and Liang, S., Phys. Lett. 102A, 238 (1984).Google Scholar
14. Witten, T. A. and Sander, L. M., Phys. Rev. Lett. 47, 1400 (1981).Google Scholar
15. Weitz, D. A. and Oliveria, M., Phys. Rev. Lett. 52, 1433 (1984).Google Scholar
16. Ma, H.-S., Jullien, R. and Scherer, G. W., Phys. Rev. E 65, 041403 (2002).Google Scholar
17. Rose, J. H., Ferrante, J. and Smith, J. R., Phys. Rev. Lett. 47, 675 (1981).Google Scholar
18. Meador, M.A.B., Vivod, S. L., McCorkle, L, Quade, D., Sullivan, R. M., Ghosn, L. J., Clark, N. and Capadona, L. A., J. Mater. Chem. 18, 1852 (2008).Google Scholar