Understanding the Vogel–Fulcher–Tammann law in terms of the bond strength–coordination number fluctuation model

Ikeda, Masahiro; Aniya, Masaru

doi:10.1016/j.jnoncrysol.2013.04.034

Cited by 54 publications

(53 citation statements)

References 49 publications

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“…For strong liquids (i.e., those that easily form glass, like SiO2), it takes a large value (typically ≥ 100), while for fragile liquids, it takes a small value ( < 10). 48 Thus, the value of showed that our crude oil sample was not a strong liquid. In addition, the value of 0 showed that the glass transition temperature was far below 288 K. As mentioned above, the calculated density also showed that the glass transition temperature was outside the range of 288-403 K. Therefore, the calculated viscosity showed the consistency with the calculated density in terms of glass transition temperature.…”

Section: Calculation Of Viscositymentioning

confidence: 94%

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Development of Digital Oil for Heavy Crude Oil: Molecular Model and Molecular Dynamics Simulations

et al. 2017

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We constructed a molecular model (digital oil model) for heavy crude oil based on analytical data and our newly developed method. Crude oil was separated into four fractions: saturates, aromatics, resins, and asphlatenes (SARA). Although it is classified as a heavy crude oil, the asphaltenes turned out to be at very low weight concentration (~0.4 wt. %), and were ignored in our study. The digital oil was constructed as a mixture of representative molecules of four fractions: saturates, aromatics, resins, and lost components (which resulted from our SARA analysis). Representative molecules were generated by quantitative molecular representation (QMR), a technique that provides a set of molecules consistent with analytical data, such as elemental composition, average molecular mass, and the proportions of structural types of hydrogen and carbon atoms, as revealed by 1 H and 13 C nuclear magnetic resonance. To enable the QMR method to be applicable to saturates, we made two developments: the first was the generation of non-aromatic molecules by a new algorithm that can generate a more branched structure by separating the chain bonding into main and subsidiary processes; the second was that the molecular mass distribution of the model could be fitted to that obtained from experiments. To validate the digital oil thus obtained, we first confirmed the validity of the model for each fraction in terms of plots of double-bond equivalent as a function of carbon number. We then calculated its density and viscosity by molecular dynamics simulations. The calculated density was in good agreement with experimental data for crude oil. The calculated viscosity was higher than experimental values; however, the error appeared systematic, being a factor of ~1.5 higher than that of experiments. Moreover, the calculated viscosity as a function of temperature was well described by the Vogel-Fulcher-Tammann equation. Digital oil will be a powerful tool to analyze both macroscopic properties and microscopic phenomena of crude oil under any thermodynamic conditions.

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Section: Calculation Of Viscositymentioning

confidence: 94%

“…The VFT equation can be applied for wide ranges of temperature, types of chemical compounds, and fields of research. 47,48 Thus, we used the VFT equation for correlation in this study. Figure 12 shows a comparison of calculated viscosity with the VFT equation.…”

Section: Calculation Of Viscositymentioning

confidence: 99%

Development of Digital Oil for Heavy Crude Oil: Molecular Model and Molecular Dynamics Simulations

et al. 2017

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“…[ 18,29–32 ] Now this model has been found to be very effective in the study of the structural relaxation. For instance, it was shown [ 19 ] that the BSCNF model provides a theoretical background to the Vogel–Fulcher–Tammann (VFT) viscosity equation, which is a well‐known expression given as η = η 0 exp[ B VFT /( T – T 0 )]. Specifically, in light of the BSCNF model, the empirical parameters of the VFT equation, B VFT and T 0 , correspond to the mean binding energy and the degree of the fluctuations, respectively.…”

Section: Ac Phenomena In the Structural Relaxation And The Electricalmentioning

confidence: 99%

“…In some of the recent studies, the physical meaning of T A is thought to be crucial for clarifying the origin of the fragility. [ 16 ] Meanwhile, the Bond Strength–Coordination Number Fluctuation (BSCNF) model [ 17–19 ] proposed by the authors predicts the existence of a characteristic temperature T x whose numerical value is close to T A . This suggests that the cooperativity onset occurs at such temperature.…”

Section: Introductionmentioning

confidence: 99%

Arrhenius Crossover Phenomena and Ionic Conductivity in Ionic Glass‐Forming Liquids

Aniya

Ikeda

2020

Physica Status Solidi (b)

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Herein, the relation between the Arrhenius crossover (AC) behaviors in the structural relaxation and the electrical conductivity relaxation is explored in terms of the Bond Strength–Coordination Number Fluctuation (BSCNF) model developed by the authors. For the ionic glass‐forming liquids and solution examined, Ca2K3(NO3)7, Ca2Rb3(NO3)7, ZBLAN20 and (LiCl)0.16(H2O)0.84, the electric conductivity at high temperature resembles the behavior of the high‐temperature structural relaxation. This mutual relationship suggests a similar origin of the AC behaviors for both, structural and electrical relaxation. The difference between them is reflected in the different temperatures that demarcate the AC behaviors. The implication of this difference to the occurrence of the high ionic conduction via the decoupling mechanism is discussed in terms of the BSCNF model and the fractional Stokes–Einstein law.

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“…From another view point, the behavior of forming Bi metallic colloids in the glass host during heat treatment can be considered as the process that Bi ions agglomerate together through thermal diffusion, in which melt viscosity plays an important role. However, according to the theory of the Vogel-Fulcher-Tammann equation and other reports [21][22][23] , it can infer that the viscosity of SBAR glass melts increase in the order of Li + -Na + -K + . This means that there are some other critical mechanisms or factors determining the transformation of Bi-NIR-emitting centers in the glass matrix.…”

Section: Influence Of Alkali Metal Ions On Thermal Stability Of Bi-acmentioning

confidence: 88%

Influence of alkali metal ions on thermal stability of Bi-activated NIR-emitting alkali-aluminoborosilicate glasses

Wan¹,

Song²,

Li³

et al. 2014

Chin. Opt. Lett.

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We study the influence of alkali oxides on the near-infrared (NIR)-emitting thermal stability of Bi-doped R 2 O-SiO 2-B 2 O 3-Al 2 O 3 (R = Li, Na, K) glasses below T g. Results show that undergoing heat treatment, remarkable luminescence quenching occurs for the glasses containing Na 2 O and K 2 O due to the formation of Bi metallic colloids, whereas the glass with Li 2 O shows much better thermal stability. These changes can be understood by the tendency of modifier cations with lower mobility and higher tightness network to restrain the transport of Bi-related NIR-emitting centers. The results provide a scientific reference for composition design of Bi-doped optical fiber.

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Understanding the Vogel–Fulcher–Tammann law in terms of the bond strength–coordination number fluctuation model

Cited by 54 publications

References 49 publications

Development of Digital Oil for Heavy Crude Oil: Molecular Model and Molecular Dynamics Simulations

Development of Digital Oil for Heavy Crude Oil: Molecular Model and Molecular Dynamics Simulations

Arrhenius Crossover Phenomena and Ionic Conductivity in Ionic Glass‐Forming Liquids

Influence of alkali metal ions on thermal stability of Bi-activated NIR-emitting alkali-aluminoborosilicate glasses

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