2012
DOI: 10.1016/j.jnoncrysol.2012.02.007
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Predicting glass transition and crystallization temperatures of silicate bioglasses using mixture designs

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Cited by 18 publications
(6 citation statements)
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“…In the literature, DOM has been used to great effect to predict the impact of compositional changes on glass properties, including viscosity-temperature behavior, coefficient of thermal expansion, and T g . 71,72…”
Section: Discussionmentioning
confidence: 99%
“…In the literature, DOM has been used to great effect to predict the impact of compositional changes on glass properties, including viscosity-temperature behavior, coefficient of thermal expansion, and T g . 71,72…”
Section: Discussionmentioning
confidence: 99%
“…The target of the present simulations was to identify correlations between specific structural features and experimental solubility of YAS glasses, and to incorporate these features in a predictive model. Multivariate approaches are frequently needed to reveal and interpret hidden correlations in large data sets . For a small data set like the present one, however, the same goal can be reached through a simpler direct approach.…”
Section: Discussionmentioning
confidence: 99%
“…Breed et al [8] have applied nonlinear regression analyses to historical data to construct predictive models for T g and the coefficient of thermal expansion for Zn and Sr containing bioactive glasses. More recently, Duée et al [14] however, have proposed a different approach for T g and crystallization temperature (T c ) prediction by applying experimental design methodology using a mixture design. The mixture designs elaborated in this study focused on a predefined domain belonging to the SiO 2 -CaO-Na 2 O-P 2 O 5 -CaF 2 system.…”
Section: Introductionmentioning
confidence: 99%