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Omata T, Sharma A, Suzuki I, Ishiyama T, Kohara S, Ohara K, Ono M, Ren Y, Zagarzusem K, Fujioka M, Zhao G, Nishii J. Anhydrous Silicophosphoric Acid Glass: Thermal Properties and Proton Conductivity. Chemphyschem 2021; 23:e202100840. [PMID: 34862847 DOI: 10.1002/cphc.202100840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Indexed: 11/10/2022]
Abstract
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 (SiO6/2 )2- ) to compensate for the negative charges.
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Affiliation(s)
- Takahisa Omata
- Institute of Multidisciplinary Research for Advanced Materials (IMRAM), Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan
| | - Aman Sharma
- Institute of Multidisciplinary Research for Advanced Materials (IMRAM), Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan
| | - Issei Suzuki
- Institute of Multidisciplinary Research for Advanced Materials (IMRAM), Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan
| | - Tomohiro Ishiyama
- Research Institute for Energy Conservation, National Institute of Advanced Industrial Science and Technology (AIST), AIST Central 5, Higashi 1-1-1, Tsukuba, Ibaraki, 305-8565, Japan
| | - Shinji Kohara
- Research Center for Advanced Measurement and Characterization, National Institute for Materials Science (NIMS), Sengen, Tsukuba, Ibaraki, 305-0047, Japan
| | - Koji Ohara
- Diffraction and Scattering Division, Japan Synchrotron Radiation Research Institute (JASRI), Sayo-gun, Hyogo, 679-5198, Japan
| | - Madoka Ono
- Research Institute for Electronic Science, Hokkaido University, Kita 21 Nishi 10, Kita-ku, Sapporo, 001-0021, Japan
| | - Yang Ren
- School of Materials Sciences and Technology, Xi'an University of Technology, Xi'an, 710048, P. R. China
| | - Khurelbaatar Zagarzusem
- Research Institute for Electronic Science, Hokkaido University, Kita 21 Nishi 10, Kita-ku, Sapporo, 001-0021, Japan
| | - Masaya Fujioka
- Research Institute for Electronic Science, Hokkaido University, Kita 21 Nishi 10, Kita-ku, Sapporo, 001-0021, Japan
| | - Gaoyang Zhao
- School of Materials Sciences and Technology, Xi'an University of Technology, Xi'an, 710048, P. R. China
| | - Junji Nishii
- Research Institute for Electronic Science, Hokkaido University, Kita 21 Nishi 10, Kita-ku, Sapporo, 001-0021, Japan
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Predicting the onset temperature (T g) of Ge xSe 1-x glass transition: a feature selection based two-stage support vector regression method. Sci Bull (Beijing) 2019; 64:1195-1203. [PMID: 36659690 DOI: 10.1016/j.scib.2019.06.026] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 06/18/2019] [Accepted: 06/26/2019] [Indexed: 01/21/2023]
Abstract
Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the onset temperature (Tg) of GexSe1-x glass transition remains an open challenge. In this paper, a predictive model for the Tg in GexSe1-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 Tg from the candidate features of GexSe1-x glass system. Secondly, in order to simulate the two-stage characteristic of Tg 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 Tg 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 Tg of other glass systems with the multi-stage characteristic.
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Hermansen C, Guo X, Youngman RE, Mauro JC, Smedskjaer MM, Yue Y. Structure-topology-property correlations of sodium phosphosilicate glasses. J Chem Phys 2015; 143:064510. [DOI: 10.1063/1.4928330] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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