Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-27T12:33:51.115Z Has data issue: false hasContentIssue false
  • English
  • Français

Neural net formulations for organically modified, hydrophobic silica aerogel

Published online by Cambridge University Press:  31 January 2011

David Noever ,
Laurent Sibille ,
Raymond Cronise ,
Subbiah Baskaran  and
Arlon Hunt
David Noever
Affiliation:
Biophysics Branch, Mail Code ES-76, George C. Marshall Space Flight Center, National Aeronautics and Space Administration, Huntsville, Alabama 35812
Laurent Sibille
Affiliation:
Universities Space Research Association, Huntsville, Alabama 35806
Raymond Cronise
Affiliation:
Biophysics Branch, Mail Code ES-76, George C. Marshall Space Flight Center, National Aeronautics and Space Administration, Huntsville, Alabama 35812
Subbiah Baskaran
Affiliation:
Institut fuer Molekulare Biotechnologie, e.V., Beutenbergerstr. 11, DO-7745, Jena, Germany
Arlon Hunt
Affiliation:
Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720
Get access

Abstract

Organic modification of aerogel chemical formulations is known to transfer desirable hydrophobicity to lightweight solids. However, the effects of chemical modification on other material constants such as elasticity, compliance, and sound dampening present a difficult optimization problem. Here a statistical treatment of a 9-variable optimization is accomplished with multiple regression and an artificial neural network (ANN). The ANN shows 95% prediction success for the entire data set of elasticity, compared to a multidimensional linear regression which shows a maximum correlation coefficient, R = 0.782. In this case, using the Number of Categories Criterion for the standard multiple regression, traditional statistical methods can distinguish fewer than 1.83 categories (high and low elasticity) and cannot group or cluster the data to give more refined partitions. A nonlinear surface requires at least three categories (high, low, and medium elasticities) to define its curvature. To predict best and worst gellation conditions, organic modification is most consistent with changed elasticity for sterically large groups and high hydroxyl concentrations per unit surface area. The isocontours for best silica and hydroxyl concentration have a complex saddle, the geometrical structure of which would elude a simple experimental design based on usual gradient descent methods for finding optimum.

Type
Articles
Information
Journal of Materials Research , Volume 12 , Issue 7 , July 1997 , pp. 1837 - 1843
Copyright
Copyright © Materials Research Society 1997

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1.Tewari, P. H., Hunt, A. J., and Lofftus, K. D., Aerogels, edited by Fricke, J. (Springer-Verlag, Berlin, 1986), p. 31.CrossRefGoogle Scholar
2.Rao, A. V., Pajonk, G. M., Parvathy, N. N., and Elaloui, E., in Sol-Gel Processing and Applications, edited by Attia, Y. A. (Plenum Press, New York, 1994), pp. 237–245.CrossRefGoogle Scholar
3.Klett, U., Heinrich, T., Emmerling, A., and Fricke, J., in Sol-Gel Processing and Applications, edited by Attia, Y. A. (Plenum Press, New York, 1994), pp. 295–302.CrossRefGoogle Scholar
4.Schwertfeger, F., Emmerling, A., Gross, J., Schubert, U., and Fricke, J., in Sol-Gel Processing and Applications, edited by Attia, Y. A. (Plenum Press, New York, 1994), pp. 343–349.CrossRefGoogle Scholar
5.Montgomery, D. C., Design and Analysis of Experiments (John Wiley and Sons, New York, 1991).Google Scholar
6.Neurocomputing, Foundations of Research, edited by Anderson, J. A. and Rosenfeld, E. (MIT Press, Cambridge, 1988).CrossRefGoogle Scholar
7.Wittner, B. and Denker, J., “Strategies for Teaching Layered Networks Classification Tasks,” IEEE Conf. Neural Info Processing, (Piscataway: IEEE, 1987).Google Scholar
8.Pawlicz, A. V. and Peters, R. H., Environ. Sci. Tech. 27, 2801–2806 (1993).CrossRefGoogle Scholar
9.Baskaran, S. and Noever, D., “Excursions Sets and a Modified Genetic Algorithms: Intelligent Slicing of the Hypercube,” 1992 Lectures in Complex Systems, edited by Nadel, L. and Stein, D., SFI Studies in the Sciences of Complexity, Lect. Vol. V (Addison-Wesley, Reading, MA, 1992).Google Scholar