Glass transition temperature variation, cross-linking and structure in network glasses
Disordered Systems and Neural Networks
2009-10-31 v1
Abstract
We give general topological rules which very accurately predict the chemical trends in glass transition temperature variation as a function of cross-linking. In multicomponent glasses, these chemical trends permit to distinguish homogeneous compositions (random network) from inhomogeneous ones (local phase separation). The stochastic origin of the Gibbs-Di Marzio equation is predicted at low connectivity and the analytical expression of its parameter emerges naturally from the calculation.
Cite
@article{arxiv.cond-mat/9906190,
title = {Glass transition temperature variation, cross-linking and structure in network glasses},
author = {Matthieu Micoulaut and Gerardo G. Naumis},
journal= {arXiv preprint arXiv:cond-mat/9906190},
year = {2009}
}
Comments
5 pages, Revtex, accepted for publication in Europhysics Letters, 2 figures not included, available at http://www.gcr.jussieu.fr/matthieu.htm#Research