Viscosity Arrhenius parameters correlation: extension from pure to binary fluid mixtures

Kacem, Rami Haj; Ouerfelli, N.; Herráez, J.V.

doi:10.1080/00319104.2015.1048248

Cited by 27 publications

(9 citation statements)

References 25 publications

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“…The reported activation energy of pure water is about 15 kJ/mol [23], which is close to the value obtained in current work for the buffer ( Table 1). The temperature dependence of the viscosity of the buffer-glycerol mixtures measured by steady share stress technique under the same conditions as the samples with biopolymer is also characterized by E Ã a that well corresponds to the value, expected from data published earlier [18].…”

supporting

confidence: 74%

Contrasting relationship between macro- and microviscosity of the gelatin- and starch-based suspensions and gels

2016

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The problem of correlation between rheological properties in macroand micro-scales of media with biopolymers of polypeptide (gelatin) and polysaccharide (starch) nature is investigated. The viscosity of the biopolymer solutions with concentrations 0.5-5 wt% was estimated by standard rotational rheometry technique and with fluorescent molecular rotor at 15-50°C. Opposite trends were observed for relationship between microviscosity g m and macroviscosity g for two biopolymers: g m \ \ g for gelatin and g m [ [ g for starch solutions. The temperature dependence of g m followed the monoexponential decay law in all samples over the whole temperature range indicating insensitivity of microviscosity to gel mesh melting under heating. The dissimilarity of macro-and micro-rheological properties of gelatin and starch-containing media is discussed in terms of difference in architecture of the gels.

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confidence: 74%

Contrasting relationship between macro- and microviscosity of the gelatin- and starch-based suspensions and gels

2016

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“…Industrial lubricants are introduced in machinery (e.g., engines and hydraulic systems) primarily to act as barrier films to reduce frictional forces between moving surfaces in contact and hence the wear of the components. Instead of measuring the viscosity of lubricants, we can resort to theoretical modeling; numerous schemes have been established to calculate the viscosity of liquid mixtures. − One family of methods assumes a strong correlation between the properties of a molecule and its structure, hence aptly named quantitative structure–property relationship (QSPR). − A multivariate analysis is performed to express each property as a function of its molecular information, including its chemical constituents and functionalities. The model then allows the prediction of properties of new compounds with a similar structure.…”

Section: Introductionmentioning

confidence: 99%

Viscosity Prediction of Lubricants by a General Feed-Forward Neural Network

Loh

Lee

Tee

et al. 2020

J. Chem. Inf. Model.

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Modern industrial lubricants are often blended with an assortment of chemical additives to improve the performance of the base stock. Machine learning-based predictive models allow fast and veracious derivation of material properties and facilitate novel and innovative material designs. In this study, we outline the design and training process of a general feed-forward artificial neural network that accurately predicts the dynamic viscosity of oil-based lubricant formulations. The network hyperparameters are systematically optimized by Bayesian optimization, and strongly correlated/collinear features are trimmed from the model. By harnessing domain knowledge in the selection of features, the quantitative structure–property relationship model is built with a relatively simple feature set and is versatile in predicting the dynamic viscosity of lubricant oils with and without enhancement by viscosity modifiers (VMs). Moreover, partial dependency, local-interpretable model-agnostic explanations, and Shapley values consistently show that the eccentricity index, Crippen MR, and Petitjean number are important predictors of viscosity. All in all, the neural model is reasonably accurate in predicting the dynamic viscosity of lubricant solvents and VM-enhanced lubricants with an R 2 of 0.980 and 0.963, respectively.

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“…It can be seen that lnη linearly decreases with the increase of T. The variation following the Arrhenius' exponential rule is typical for conventional polymer melts or solutions. The Arrhenius relation, or more general the Vogel- Fulcher-Tammann equation, has been observed for different liquids from ionic compounds to polymers (Eiden et al 2011;Haj-Kacem et al 2015). However, to our knowledge, no such relation for isotropic PPTA/H 2 SO 4 solution has been observed and reported yet.…”

Section: Temperature Effectmentioning

confidence: 61%

Steady shear viscosity and oscillatory complex viscosity of poly(p-phenylene terephthalamide) solutions in sulfuric acid

2016

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This study investigated the steady shear viscosity η and the oscillatory complex viscosity η * of poly(p-phenylene terephthalamide) solutions in sulfuric acid (PPTA/H 2 SO 4 ) by rheometric measurements. The influences of steady shear rate, concentration, and temperature were investigated in detail. The results indicated that in the plateau regime (η independent of γ : regime), η shows a non-monotonic variation with the concentration increase, which reaches the maximum value when the solution changes from the isotropic phase into the nematic-isotropic biphasic system. At the low shear rate, the corresponding critical concentration C * η is close to the critical concentration C * , at which the birefringence emerges in solutions. In the plateau regime, the dependence of η on temperature follows different rules when the system is in different phases. In the isotropic phase, η decreases with the increasing temperature and obeys the Arrhenius exponential rule. Unlike the isotropic solution, η increases with the temperature in the nematic-isotropic biphasic region and decreases linearly with the temperature in the nematic phase region. The η * measured at different angular frequencies (ω) was compared with η measured at different γ : . For the isotropic solutions, the |η * |~ω curve coincides with the η~γ : variation in a wide shear rate range for the plateau regime, while in the nematic phase region, |η * | is always smaller than η. For the biphasic systems, the consistency can be seen only in a narrow shear rate range and |η * | deviates from η with the increasing shear rate.

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Viscosity Arrhenius parameters correlation: extension from pure to binary fluid mixtures

Cited by 27 publications

References 25 publications

Contrasting relationship between macro- and microviscosity of the gelatin- and starch-based suspensions and gels

Contrasting relationship between macro- and microviscosity of the gelatin- and starch-based suspensions and gels

Viscosity Prediction of Lubricants by a General Feed-Forward Neural Network

Steady shear viscosity and oscillatory complex viscosity of poly(p-phenylene terephthalamide) solutions in sulfuric acid

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