title: Regression model for predicting selected thermal properties of next-generation bioactive glasses.
abstract: The compositional palette traditionally used to develop bioactive glasses has grown in recent times to include therapeutic inorganic species such as zinc and strontium. Historical regression models used for predicting the properties of bioactive glasses as a function of composition have not evolved to consider this expanded compositional space. In this work, nonlinear regression analysis was applied to historical data to construct predictive models for the glass transition temperature and the coefficient of thermal expansion of next-generation bioactive glasses. The new regression models also provide some degree of improvement over existing models in predicting the properties of traditional bioactive glasses.
authors.affiliation: Alfred University, Inamori School of Engineering, Alfred, NY 14802, USA.
authors.forename: S M
authors.initials: SM
authors.lastname: Breed
authors.forename: M M
authors.initials: MM
authors.lastname: Hall
mesh_terms: D001672:Biocompatible Materials; D005898:Glass; D008962:Models, Theoretical; D012044:Regression Analysis; D013696:Temperature
chemical_list: D001672:Biocompatible Materials