Parsimonious viscosity–composition relationships for high-temperature multicomponent glass melts

Ferkl, Pavel; Hrma, Pavel R.; Kruger, Albert A.

doi:10.1080/21870764.2021.2012903

Cited by 16 publications

(8 citation statements)

References 59 publications

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“… 12,48 The similarity in the trends is unsurprising as SiO 2 , Al 2 O 3 and ZrO 2 act as network formers that strengthen the glass structure, 49 whereas the alkali oxides create nonbridging oxygens, mostly around silica and boron, that weaken the glass structure 19,44 . Alkaline earth oxides and Fe 2 O 3 have a relatively minor effect on T g and viscosity 12,48 . The main difference is observed in the effect of B 2 O 3 , whose addition leads to an increase in T g but a decrease in viscosity at comparable temperatures above T g .…”

Section: Modelmentioning

confidence: 97%

“…19,44 Alkaline earth oxides and Fe 2 O 3 have a relatively minor effect on 𝑇 g and viscosity. 12,48 The main difference is observed in the effect of B 2 O 3 , whose addition leads to an increase in 𝑇 g but a decrease in viscosity at comparable temperatures above 𝑇 g . The relationship between the glass structure, viscosity, and 𝑇 g is theoretically described by the constraint model, 50 which considers broken and intact bonds between atoms in the glass network to derive temperature-dependence of configuration entropy.…”

Section: Modelmentioning

confidence: 98%

“…The trends agree with melt viscosity models, which generally show that the viscosity (or more precisely the activation energy of the Arrhenius equation, 𝐵 in Figure 4B) is decreased most by Li 2 O and Na 2 O and increased most by Al 2 O 3 and SiO 2. 12,48 The similarity in the trends is unsurprising as SiO 2 , Al 2 O 3 and ZrO 2 act as network formers that strengthen the glass structure, 49 whereas the alkali oxides create nonbridging oxygens, mostly around silica and boron, that weaken the glass structure. 19,44 Alkaline earth oxides and Fe 2 O 3 have a relatively minor effect on 𝑇 g and viscosity.…”

Section: Modelmentioning

confidence: 99%

“…Response trace plot for (A) T g based on model M4 and (B) activation energy of Arrhenius equation for viscosity 48 . Reference composition was taken as a mean composition of combined datasets.…”

Section: Modelmentioning

confidence: 99%

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Glass transition temperature of low‐activity waste nuclear glasses

George

Ferkl

Marcial

et al. 2023

Int J of Appl Glass Sci

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The glass transition temperature (𝑇 g ) is a parameter used in many glass melt viscosity models as it denotes a temperature around which liquid-glass transition occurs. In this work, 𝑇 g values were measured for a series of low-activity waste (LAW) glasses using differential scanning calorimetry. These data were combined with 𝑇 g data of other waste glasses available from literature. The combined dataset, consisting of 137 data points, was used for the development of several models to estimate 𝑇 g from glass composition. When testing the number of influential components and different supervised learning methods, we demonstrated that using more than 10 components or using non-linear methods brought marginal improvement to the model accuracy. The best model predicts 𝑇 g of both LAW and high-level waste glasses with reasonable accuracy.

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Section: Modelmentioning

confidence: 97%

Section: Modelmentioning

confidence: 98%

Section: Modelmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

See 2 more Smart Citations

Glass transition temperature of low‐activity waste nuclear glasses

George

Ferkl

Marcial

et al. 2023

Int J of Appl Glass Sci

Self Cite

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“…For temperature-dependent properties such as viscosity and electrical conductivity, temperature and composition dependence can be addressed using expressions with predefined functional forms for temperature and composition-dependent parameters. For example, Vogel-Fulcher-Tammann (VFT), [32][33][34][35][36][37][38] Arrhenius, [39][40][41][42][43] Avramov-Milchev, 44,45 Gibbs-Adams, [46][47][48] and Mauro-Yue-Ellison-Gupta-Allan (MYEGA) 49 models have been used to represent the temperature effects on viscosity, whereas PQMs have been used to represent the composition effects on the temperature independent parameters. 30,47,48,50,51 Example property models are summarized in Table 2.…”

Section: Model Formsmentioning

confidence: 99%

Glass formulation and composition optimization with property models: A review

Vienna

2023

J Am Ceram Soc.

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Glass is a versatile material with a remarkable history and many practical applications. It plays a critical role in our everyday lives, the advancement of science, and the development of many technologies. The Edisonian type trial‐and‐error method was commonly used for conventional design of glass compositions, which was time‐consuming and costly. With the urgent need to develop new glass compositions for technology applications rapidly, it has become necessary to develop precise property models with predictive powers using large databases and efficient formulation approaches. This paper reviews the design of glass compositions using these analytical and numerical models of composition–structure–property relations of glasses, some based on large databases and machine learning approaches. Aspects of data collection, model fitting, feature extraction, model evaluation, and uncertainty quantification will be covered. Furthermore, advances in the glass optimization framework and available tools are summarized with examples. The outlook and perspective for further glass property model development and formulation approaches are discussed.

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