Shear flow behavior and dynamic viscosity of few-layer graphene nanofluids based on propylene glycol-water mixture

Hamze, Samah; Cabaleiro, David; Maré, Thierry; Vigolo, Brigitte; Estellé, Patrice

doi:10.1016/j.molliq.2020.113875

Cited by 20 publications

(12 citation statements)

References 45 publications

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“…Decreases in viscosity with temperature are commonly observed in most nanofluids, including graphene nanofluids, and over much greater concentration and temperature ranges than the ones presented in this study. [25][26][27]30,[32][33][34]43 However, it is often noted that these decreases largely depend on the properties of the base fluid and not on the presence of the nanoparticles themselves. 25,44,45 When a fluid is heated, higher energy is supplied to the fluid molecules.…”

Section: Viscosity Of O-gnf Nanofluidsmentioning

confidence: 99%

“…where 0 is the relative viscosity, A and T 0 are fitting parameters, and T is the temperature in kelvin. 43 The values of the fitting parameters were calculated but will not be provided here because overfitting may limit their physical significance. However, it can be mentioned that the values of the fitting parameters are constant for each concentration.…”

Section: Dynamic Viscosity Of Methane Hydrate and O-gnf Systems 321 T...mentioning

confidence: 99%

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Non-Einsteinian Viscosity Behavior in Plasma-Functionalized Graphene Nanoflake Nanofluids and Their Effect on the Dynamic Viscosity of Methane Hydrate Systems

McElligott

Guerra

et al. 2022

ACS Appl. Energy Mater.

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The dynamic viscosities of nanofluids containing oxygen-functionalized graphene nanoflakes (O-GNFs) were measured for concentrations ranging from 0.1 to 10 ppm under pressures from 0 to 30 MPag and temperatures from 0 to 10 °C. Water’s viscosity dependence on temperature was not affected by the presence of O-GNFs, though the effective viscosity of the solution was reduced (termed non-Einsteinian viscosity) against common expectations. Hydrogen bond strength may have been reduced at the hydrophobic part of the O-GNF surface, whereas density fluctuations were enhanced. Therefore, larger sites of free volume may have formed, and weaker intermolecular interactions could allow for less-restricted diffusion into those sites, reducing the effective viscosity. The internal friction that would otherwise raise the solution viscosity could be overcome by these surface effects. Water’s viscosity dependence on pressure was also not affected by O-GNFs, except at 10 ppm, where the shuttle effect may have increased the presence of hydrophobic methane bubbles in the solution. Under high pressures, the relative viscosity of the system remained non-Einsteinian at all temperatures except 2 °C. This may have been because the density anomaly of water was shifted to a colder temperature as the hydrogen bonding network was weaker. The phase transition from liquid to hydrate was identical to that of pure water, indicating that the presence of different stages of growth was not affected by the presence of O-GNFs. However, the times to reach a maximum viscosity were faster in O-GNF systems compared to pure water. This said, the hydrate formation limitations inherent to the measurement system were not overcome by the addition of O-GNFs. The times to application-relevant viscosity values were maximized in the 1 ppm system at 49.75% (200 mPa·s) and 31.93% (500 mPa·s) faster than the baseline. Therefore, the presence of O-GNFs allowed for shorter times to desired viscosities at lower driving forces than the baseline, improving the viability of the hydrate technologies to which they can be added.

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Section: Viscosity Of O-gnf Nanofluidsmentioning

confidence: 99%

Section: Dynamic Viscosity Of Methane Hydrate and O-gnf Systems 321 T...mentioning

confidence: 99%

Non-Einsteinian Viscosity Behavior in Plasma-Functionalized Graphene Nanoflake Nanofluids and Their Effect on the Dynamic Viscosity of Methane Hydrate Systems

McElligott

Guerra

et al. 2022

ACS Appl. Energy Mater.

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“…Temperature-dependent viscosity can be modeled using the Vogel-Fulcher-Tammann (VFT) model, widely used in the literature [50,51,71]. The model is expressed in Eq.…”

Section: Impact Of Temperaturementioning

confidence: 99%

Rheological behavior of stabilized diamond-graphene nanoplatelets hybrid nanosuspensions in mineral oil

Ilyas

Ridha

Sardar

et al. 2021

Journal of Molecular Liquids

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Recent advancements in thermo-fluid technology assisted with highly thermal conductive nanomaterials have shown assuring outcomes. It is also proven that thermal conductivity alone cannot define the overall heat transfer characteristics, and the viscous properties are equally significant towards thermal management. Therefore, this research involves investigating the rheological behavior of hybrid nanosuspensions containing high thermally conductive diamond and graphene nanoplatelets (1:1). These nanomaterials are dispersed in mineral oil using a two-step technique. Hybrid nanofluids' stability is achieved using a non-ionic stabilizer Span85, exhibiting no sedimentation for a minimum of five months. Nanomaterial characterizations are performed to study morphology, purity, and chemical analysis. The flow behavior of hybrid nanosuspensions is investigated at varying nanomaterial mass concentrations (0-2 %), temperatures (298.15-338.15 K), and shear rates (1-2000 s -1 ). Hybrid nanofluids exhibit shear-thinning behavior, which is also correlated with the Ostwald-de-Waele model. The temperature-viscosity relationship is well predicted using the Vogel-Fulcher-Tammann model. Hybrid nanofluids show a maximum enhancement of 35% viscosity at 2% concentration. A generalized twovariable correlation is used to express viscosity as a function of temperature and nanofluid concentration with an excellent agreement. Three different machine learning methods, i.e., Artificial Neural Network (ANN), Gradient Boosting Machine (GBM), and Random Forest (RF) algorithms are also introduced to predict the viscosity of hybrid nanofluids based on the three input parameters (temperature, concentration, and shear rate). The parity plots conclude that all algorithms can predict big-data viscous behavior with high precision.

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“…20,24,25 The use of surfactants can lead to stable dispersions but it decreases the overall thermal conductivity of the fluid. 26,27 The originality of this contribution lies in the use of surface-functionalized GNPs, [28][29][30][31][32][33][34] displaying a multilayer structure with less than 20 graphene layers stacked together, instead of using surfactants or full exfoliation of graphene oxide. Retaining the graphitic core after surface oxidation was found to maintain a high thermal conductivity and enhance the efficiency of the heat transfer fluid.…”

Section: Introductionmentioning

confidence: 99%

“…To obtain a stable dispersion of graphene structures in the base fluid (EG/H 2 O), without losing their good thermal property, the chemical modification of the hydrophobic surface of graphene becomes mandatory 20,24,25 . The use of surfactants can lead to stable dispersions but it decreases the overall thermal conductivity of the fluid 26,27 …”

Section: Introductionmentioning

confidence: 99%

Functionalized few‐layered graphene nanoplatelets for superior thermal management in heat transfer nanofluids

Wilhelm

Ludwig

Fischer

et al. 2021

Int J Applied Ceramic Tech

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The superior thermal conductivity and lightweight of graphene flakes make them materials of choice for advanced heat transfer applications, especially for transport of electricity from sustainable power stations such as concentrating solar power plants. In view of the excellent thermal conductivity of graphene or graphene-like nanomaterials (3000-5000 W m −1 K −1 ), their dispersion into conventional host fluids such as water (0.613 W m −1 K −1 ) or ethylene glycol (0.25 W m −1 K −1 ) can significantly improve fluid heat transfer characteristics.The two-dimensional structure and high surface area as well as the cost-efficient carbon-based material make graphene nanoplatelets (GNPs) suitable for largescale applications in colloidal thermal conductive fluids. For an efficient dispersion of GNPs in base fluids, intrinsically hydrophobic GNPs were acid treated to obtain highly concentrated (4 wt.%) graphene-based nanofluids. Investigations on various GNP sizes and reaction parameters showed significant influences on the resulting thermal conductivity values of the nanofluid. After 14 h measurements in a dormant system, the most efficient nanofluid reached a thermal conductivity of 0.586 W m −1 K −1 (the base fluid of 0.391 W m −1 K −1 ) and a low viscosity of 6.39 cP resulting in an overall efficiency improvement of 77%, when compared to the base fluid without particles.

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Shear flow behavior and dynamic viscosity of few-layer graphene nanofluids based on propylene glycol-water mixture

Cited by 20 publications

References 45 publications

Non-Einsteinian Viscosity Behavior in Plasma-Functionalized Graphene Nanoflake Nanofluids and Their Effect on the Dynamic Viscosity of Methane Hydrate Systems

Non-Einsteinian Viscosity Behavior in Plasma-Functionalized Graphene Nanoflake Nanofluids and Their Effect on the Dynamic Viscosity of Methane Hydrate Systems

Rheological behavior of stabilized diamond-graphene nanoplatelets hybrid nanosuspensions in mineral oil

Functionalized few‐layered graphene nanoplatelets for superior thermal management in heat transfer nanofluids

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