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Computer Simulation of Compressive Failure in Silica Aerogels

Published online by Cambridge University Press:  01 February 2011

Brian S. Good*
Affiliation:
brian.s.good@grc.nasa.gov, NASA Glenn Research Center, Materials and Structures Division, 21000 Brookpark Road, Cleveland, OH, 44026, United States
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Abstract

Historically, the low thermal conductivity of silica aerogels has made them of interest to the aerospace community for applications such as cryotank insulation. However, recent advances in the application of conformal polymer coatings to these gels have made them significantly stronger, and potentially useful as lightweight structural materials. In this work, we perform multiscale computer simulations to investigate the compressive strength and failure behavior of silica aerogels.

The gels' nanostructure is simulated via the diffusion limited cluster aggregation (DLCA) process, which produces fractal aggregates that are structurally similar to experimentally observed gels. 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 compressive failure via the application of a uniaxial compressive strain to the DLCA clusters. In computing the energetics of the compression, the detailed structure of the secondary particles is ignored, and the interaction among secondary particles is described by a Morse pair potential parameterized such that the potential range is much smaller than the secondary particle size; an angular potential contribution is included in some of the simulations as well. The Morse and angular parameters are obtained by atomistic simulation of models of the interparticle bridges, with the compressive and bending behavior of these bridges modeled via molecular statics. We consider the energetics of compression and compressive failure, and compare qualitative features of low-and high-density gel failure.

Type
Research Article
Copyright
Copyright © Materials Research Society 2008

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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., J, , Quinson, 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. Meador, M.A.B., Vivod, S., McCorkle, L., Quade, D., Sullivan, R. M, Ghosn, L. J, Clark, N. and Capadona, L., submitted to Chemistry of Materials.Google Scholar
6. Good, B., Mater. Res. Soc. Symp. Proc Vol. 885E, 0885-A09-35.1Google Scholar
7. Good, B., Mater. Res. Soc. Symp. Proc Vol. 978, 0978-GG05-03.Google Scholar
8. Meakin, P., Phys. Rev. Lett. 51, 1119 (1983).Google Scholar
9. Woignier, : Woignier, T. and Phalippou, J., J. Non-Cryst. Solids 93 17 (1987).Google Scholar
10. 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
11. Schaeffer, D. W, Martin, J. E and Keefer, K. D, Phys. Rev. Lett. 56, 2199 (1986).Google Scholar
12. Freltoft, T., Kjems, J. K and Sinha, S. K, Phys. Rev. B 33, 269 (1986).Google Scholar
13. Vacher, R., Woignier, T, Pelous, J. and Courtens, E., Phys. Rev. B 37, 6500 (1988).Google Scholar
14. Hasmy, A., Anglaret, E., Foret, M., Pelous, J. and Julien, R., Phys. Rev. B 50, 1305 (1994).Google Scholar
15. 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
16. Bensimon, D., Sharaiman, B. and Liang, S., Phys. Lett. 102A, 238 (1984).Google Scholar
17. Witten, T. A and Sander, L. M, Phys. Rev. Lett. 47, 1400 (1981).Google Scholar
18. Weitz, D. A and Oliveria, M., Phys. Rev. Lett. 52, 1433 (1984).Google Scholar
19. Rose, J. H, Ferrante, J. and Smith, J. R., Phys. Rev. Lett. 47, 675 (1981).Google Scholar