Tailoring sodium silicophosphate glasses containing SiO6-octahedra through structural rules and topological principles

Anhydrous Silicophosphoric Acid Glass: Thermal Properties and Proton Conductivity

Anhydrous silicophosphoric acid glass with an approximate composition of H₅ Si₂ P₉ O₂₉ was synthesized and its thermal and proton-conducting properties were characterized. Despite exhibiting a glass transition at 192 °C, the supercooled liquid could be handled as a solid up to 280 °C owing to its high viscosity. The glass and its melt exhibited proton conduction with a proton transport number of ∼1. Although covalent O-H bonds were weakened by relatively strong hydrogen bonding, the proton conductivity (4×10^-4  S cm^-1 at 276 °C) was considerably lower than that of phosphoric acid. The high viscosity of the melt was due to the tight cross-linking of phosphate ion chains by six-fold-coordinated Si atoms. The low proton conductivity was attributed to the trapping of positively charged proton carriers around anionic SiO₆ units (expressed as (SiO_6/2 )^2- ) to compensate for the negative charges.

Predicting the onset temperature (Tg) of GexSe1-x glass transition: a feature selection based two-stage support vector regression method

Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the onset temperature (T_g) of Ge_xSe_1-x glass transition remains an open challenge. In this paper, a predictive model for the T_g in Ge_xSe_1-x glass system is presented by a machine learning method named feature selection based two-stage support vector regression (FSTS-SVR). Firstly, Pearson correlation coefficient (PCC) is used to select features highly correlated with T_g from the candidate features of Ge_xSe_1-x glass system. Secondly, in order to simulate the two-stage characteristic of T_g which is caused by structural variation with a turning point at x = 0.33 via the structural analysis, SVR is utilized to build predictive models for two stages separately and then the two achieved models are synthesized using a minimum error based model for T_g prediction. Compared with the topological and other methods based on SVR, the FSTS-SVR gives the highest predictive accuracy with the root mean square error (RMSE) and mean absolute percentage error (MAPE) of 10.64 K and 2.38%, respectively. This method is also expected to be more efficient for the prediction of T_g of other glass systems with the multi-stage characteristic.