ar ticle

Copyright © 2018 by American Scientific Publishers

Journal of Nanofluids Vol. 7, pp. 1–8, 2018 (www.aspbs.com/jon)

All rights reserved. Printed in the United States of America

Dynamic Viscosity and Surface Tension of Stable Graphene Oxide and Reduced Graphene Oxide Aqueous Nanofluids D. Cabaleiro1, 2 , P. Estellé2, ∗ , H. Navas3 , A. Desforges3 , and B. Vigolo3 1

Dpto. Física Aplicada, Facultade de Ciencias, Universidade de Vigo, E-36310 Vigo, Spain Univ Rennes, LGCGM, EA3913, F-35000 Rennes, France 3 Institut Jean Lamour, CNRS-Universitè de Lorraine, BP70239, F-54506 Vandœuvre-lès-Nancy, France 2

KEYWORDS: Graphene Oxide, Reduced Graphene Oxide, Nanofluid, Water, Surface Tension, Dynamic Viscosity.

1. INTRODUCTION Considering the rapid growth in energy consumption experienced worldwide in the last century, the enhancement of thermal performance has become one of the main issues in the energy field.1 2 With this aim, the improvement of the physical properties of heat transfer fluids (HTFs) conventionally used in thermal facilities has focused increasing attention. The remarkable advances recently made in nanotechnology have played an important role in this task. Thus, nanofluids, new thermal fluids engineered by dispersing and stably suspending nanometer-size solid particles in common base fluids, have emerged as potential candidates to replace HTFs traditionally used in a wide range of applications.3 4 Among the different types of nanoadditives utilized in literature, carbon nanostructures commonly exhibit thermal conductivities higher than those of metal oxides or metallic nanoparticles and consequently the first kind of ∗

Author to whom correspondence should be addressed. Email: [email protected] Received: 18 January 2018 Accepted: 13 February 2018

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nanostructures are expected to be better thermal enhancers in nanofluid design.5 In particular, graphene exhibits the highest characteristics within the thermal conductivity spectrum of carbon-based nanomaterials.5 6 Unfortunately, it is well-known that ideal pristine graphene (G) is hydrophobic and thus it tends to agglomerate in the presence of common solvents and particularly in water (W), the most widely used thermal medium.7 8 Conversely, covalently functionalized graphene oxide (GO) contains hydroxyl and epoxy groups, which balance bonding interactions in aqueous dispersions and render the material hydrophilic and even water soluble.9 10 Nevertheless, the oxidation process partially destroys the sp2 conjugated carbon structure of graphene and thus GO exhibits thermal conductivities considerably lower than those of pristine graphene. A controlled reduction of GO can restore to some extent the graphene structure with a moderate decrease in hydrophilicity. The possibility of reaching a compromise between the advantages of G and GO confers to reduced graphene oxide (rGO) a great potential in the preparation of dispersions with improved thermal properties and long-term stabilities.11 doi:10.1166/jon.2018.1539

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This experimental work studies the effect of graphene oxide chemical reduction on the dynamic viscosity and surface tension of water-based nanofluids. Graphene oxide (GO) nanopowder was produced from commercial synthetic graphite through a derived Hummers’ method and reduced graphene oxides (rGOs) were chemically reduced from GO by using various concentrations of sodium borohydride. Three different aqueous nanofluid sets were designed using GO and rGOs at nanoparticle volume concentrations ranging from 0.0005 to 0.1%. Shear flow behavior of nanofluids were obtained with a rotational rheometer at temperatures of 20.0 and 30.0 C and surface tension of nanofluids was studied at 20.0 C with a drop shape analyzer based on the pendant drop method. rGO nanofluids at 0.1% exhibit lower apparent viscosities and weaker shear-thinning behaviors compared to the corresponding GO nanofluids. For lower concentrations, a Newtonian behavior of nanofluids is reported. Relative viscosity enhancement of nanofluids with nanoparticle content is also modelled by Maron-Pierce’s equation. Surface tension is decreased by 3% with increasing nanoadditive loading and without influence of chemical treatment. Such behavior of the prepared graphene-based nanofluids is interesting for the envisaged applications often involving circulating fluids.

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Dynamic Viscosity and Surface Tension of Stable Graphene Oxide and Reduced Graphene Oxide Aqueous Nanofluids

Over the last two decades, many studies have evidenced the promising thermal conductivities of these nanostructured materials and their good heat transfer performances.12 13 However, efficient design and subsequent control of thermal facilities also require an accurate characterization of thermal or physical properties necessary to define the flow and dynamic wetting behavior of nanofluids. Dynamic viscosity, , is wellknown to condition these two characteristics, as well as to directly affect pressure drop and subsequent pumping power.14 Surface tension (ST), , does not only influence the surface wettability, but also controls the formation and growth of bubbles. Thus, this last physical property plays a main role in boiling and condensation processes, especially in two-phase heat transfer flows, heat pipes and thermosiphons.15–18 Besides, flow and dynamic wetting performance are especially relevant in the case of microfluidics, an engineering field that is attracting great attention due to the necessity of downsizing heat transfer equipment in many applications. Since surface tension and/or viscous forces are predominant in microfluidics, when compared with inertial or gravitational forces,19 accurate knowledge of surface tension and dynamic viscosity is particularly important in this kind of systems. Thus, in the case of twophase droplet-based micro-flows, droplet size at a fixed shear condition is the result between competing viscous forces tending to draw the fluid along the microchannel and capillary forces tending to minimize interfacial surface by creating droplets.20 21 Available literature shows that nanofluids can exhibit Newtonian or non-Newtonian rheological behaviors depending on design parameters such as nanoparticle size, shape or concentration.22 In the case of (reduced) graphene (oxide), several works have studied the influence of nanoparticle loading on the viscosity and flow behavior of nanofluids based on water11 23–30 or glycol-water mixtures.6 31–33 Tesfai et al.23 and Kamatchi et al.11 carried out flow curve tests for water-based nanofluids prepared at nanoadditive concentrations between 0.05 and 0.5 g/L of GO and between 0.01 and 0.3 g/L of rGO. Both studies reported non-Newtonian shear thinning behaviors at shear rates lower than 200 s−1 in the case of Tesfai et al.23 and lower than 60 s−1 in the case of Kamatchi et al.;11 the phenomenon being more pronounced as nanoadditive concentration increases. Mehrali et al.24 25 analyzed the rheological behavior of graphene aqueous dispersions prepared using nanoadditives with three different specific surface areas (300, 500 and 750 m2 /g) and found a pseudo plastic behavior at low shear rates for some nanofluids. Esfahani et al.30 studied the shear rate dependence of dynamic viscosity for aqueous dispersions of GO and reported maximum increases in this property of up to 130% for the highest mass concentration, 0.5 wt.%. Shear thinning nonNewtonian behavior was found for high concentrations at shear rates below 20 s−1 . Dhar et al.26 analyzed the 2

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dynamic viscosity of water-based nanofluids loaded with rGO concentrations up to 0.5% in volume and obtained increase in this property ranging from 13 to 18% in the temperature interval from 25 to 70 C. Agromayor et al.28 found viscosity increases up to 80% regarding the base fluid for acid-functionalized graphene aqueous nanofluids. Amiri et al.27 studied the effect of covalent and noncovalent graphene functionalizations on the viscosity of water-based nanofluids. They found that dispersions of graphene non-covalent functionalized using sodium dodecyl benzene sulfonate exhibited higher dynamic viscosities than acid-treated graphene nanofluids. Thus, while dynamic viscosity enhancements at shear rates of 300 s−1 reach 136% for the 0.1 wt.% loading of non-covalent graphene, the maximum increase is 29% for the same concentration of covalent functionalized graphene. As regards to surface tension of graphene-based nanofluids, only few works were found in literature. Zheng et al.34 35 studied the influence of nanoparticle size, in the range from 14 to 80 nm, nanoadditive mass concentration, from 0.0006 to 0.1%, and temperature, from 20.0 to 30.0 C, on surface tension of graphene oxide nanofluids. ST was reported to rise as the nanoparticle content increases, up to a maximum enhancement of 3% at the highest temperature, and decrease as both the particle size and temperature increase. Kamatchi et al.11 studied rGO/water nanofluids prepared at different nanoparticle loadings (0.01, 0.1 and 0.3 g/l) and reported increases in this property with rGO concentration up to 3%. Authors attributed this trend to the increase in surface energy due to displacement and accumulation of rGO nanoflakes at liquid-gas interface.11 Ahammed et al.36 experimentally investigated graphene/water nanofluids stabilized with SDBS and observed that surface tension decreases with nanoadditive concentration, with maximum reductions reaching up to 13.8%. The present study aims to investigate the effect that nanoparticle loading and graphene functionalization has on viscosity and surface tension of graphene aqueous nanofluids. To this end, three different nanofluid sets based on one GO and two different rGO powders at six nanoparticle volume concentrations (0.0005, 0.001, 0.005, 0.01, 0.05, and 0.1%) were experimentally studied.

2. MATERIALS AND METHODS 2.1. Materials SFG6 synthetic graphite was provided by Timcal Inc. (Illinois, USA). Sodium nitrate (NaNO3 ) purchased from Alfa Aesar (Karlsruhe, Germany), potassium permanganate (KMnO4 ) from VWR Chemicals (Fontenay-sousBois, France), sulfuric acid (H2 SO4 ), hydrogen peroxide (H2 O2 ), hydrochloric acid (HCl), and sodium borohydride (NaBH4 ) from Sigma Aldrich (Saint-Quentin-Fallavier; France) were used for the chemical treatments of the nanopowder. Ethylene glycol and toluene utilized in the J. Nanofluids, 7, 1–8, 2018

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checking of surface tension measurements were also supplied by Sigma Aldrich. All reagents were of analytical grade and used as received without further purification. Deionized water was produced using a Milli-Q Plus system from Millipore Corporation (Breford, USA), with a resistivity of 18.2 M · cm at 25 C.

Fig. 1.

2.3. Nanofluid Preparation and Stability Three nanofluid sets were prepared as dispersions of each of the three synthetized nanopowders in deionized water. As the chemical reduction used to produce the rGO is never complete, the oxygen-containing groups incorporated during the covalent functionalization of GO are also present at the rGO platelets surface.38 The existence of these functional groups improves affinity between nanoparticles and water molecules easing dispersion of both GO and rGO without any surfactant assistance. Hence, the predetermined amounts of nanomaterials

Typical SEM (a), TEM (b–d) images of rGO_0.2 and inset image is the corresponding FFT diffraction pattern of (d).

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2.2. Nanopowder Synthesis and Characterization GO was prepared from SFG6 graphite following a modified Hummers’ method.37 Details regarding the used conditions are given elsewhere.38 To obtain the reduced graphene oxide, GO was chemically treated using different amounts of a NaBH4 -water solution at 2 vol.% of NaBH4 . Specifically, volume concentrations of 0.1% and 0.2% of this NaBH4 -water mixture were selected to prepare the two rGO nanopowders studied in this work, which will be denoted hereafter as rGO_0.1 and rGO_0.2, respectively. Themorphology of the carbon nanomaterials was analyzed with Transmission Electron Microscopy (TEM) and Scanning Electron Microscopy (SEM). SEM was performed with a XL30 S-FEG microscope (Philips, Netherlands) by using an ultra-high resolution mode. For TEM observations, a JEOL ARM 200F cold FEG

apparatus (JEOL, Japan) operating at a voltage of 80 kV was used. Due to the gentle reduction agent used to produce rGO, no differences in morphology were observed by electronic microscopy between GO and rGO. Electron microscopy images of rGO_0.2 are shown in Figure 1. They show a typical thin platelet shape (Figs. (a) and (b)) of thickness of 2–10 graphene layers (Fig. (c)) and side length of up to several micrometers. The Fast Fourier Transform (FFT) diffraction pattern images of the basal plane of rGO typically show the 6 ring spots expected for graphene of good crystallinity (Fig. 1(d)).


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Fig. 2. Photographs of GO (a and b), rGO_0.1 (c and d) and rGO_0.2 (e and f) nanofluids 5 days after their preparation for 2 different nanoparticle volume concentrations: 0.1% (a, c and e) and 0.005% (b, d and f).

and base fluid necessary to obtain the desired nanofluid concentrations were simply mixed and sonicated for only a few minutes using a low power ultrasonic bath model Transsonic TI-H-S from Fisher Bioblock Scientific (Rungis Complexe, France). The temporal stability of prepared dispersions was followed by visual inspection of the solutions over time. As an example, Figure 2 shows photographs of the GO and rGO based nanofluids 5 days after preparation. Aggregation and precipitation of GO rapidly occur (Figs. 2(a and b)), while dispersion stability is preserved over a long period for rGO based nanofluids (Figs. 2(c–f)). No evidence of any sedimentation was observed several months after the preparation of rGO samples. In agreement with these observations, zeta potential of the rGO based nanofluids was studied with a Zetasizer Nano ZS from Malvern Instruments Ltd. (Malvern, United Kingdom) following the procedure indicated in Ref. [33] and measured to be around −40 mV (@25 C). Zeta potential measurements were repeated several weeks after the first tests without observing any appreciable difference between initial values and replicas. This confirms the more pronounced stability visually observed of nanofluids produced with rGO. 2.4. Nanofluid Physical Characterization Viscosity measurements were carried out at temperatures of 20.0 and 30.0 C with a Malvern Kinexus Pro stress-controlled rheometer from Malvern Instruments Ltd. (Malvern, United Kingdom) working with a cone-plate geometry appropriate for studying low-viscosity colloidal dispersions. Diameter and angle of the cone were 60 mm and 1 , respectively, while the gap between cone-and-plate was 0.03 mm. A Peltier temperature control device with a precision of ±0.1 C and placed below the lower surface was used for temperature control. This system was also combined with thermal clovers to ensure constant temperature within the sample gap during experiments. An amount of 1 cm3 , considered optimal for this geometry, was transferred to the lower plate and a stabilization time of 5 min was waited before experiments. Tests were performed in steady-state regime at shear stresses logarithmically increasing from 0.01 to 2.0 Pa with at least 7 points per decimal, necessary to cover the range of shear rates 4

between 10 and 1000 s−1 . The estimated uncertainty of the dynamic viscosities measured with this device is less than 4% within the studied shear rate range. Additional information about this experimental device and the measuring procedure can be found in Halelfadl et al.39 Experiments were performed at least in three replicates for each nanofluid without difference in flow curves obtained. Surface tension at the sample fluid/air interface was measured at 200 ± 05 C with a Drop Shape Analyzer DSA-30 from KRÜSS GmBH (Hamburg, Germany) based on the pendant drop technique. This instrument records and digitally analyzes the shape of sample drops formed at the end of a vertical syringe just in the moment when the drop snaps from the apex of the needle. Surface tension is obtained from a drop shape analysis through a balance of internal and external forces acting on the drop and based on the Young-Laplace equation. In this study a 15-gauge needle with an outer diameter of 1.83 mm was utilized to produce drops with a volume of around 30 m. Reported ST values were obtained as the average of at least 10 measurements. Drop shape analyses were also replicated with no observable deviation between measurements. Studies were carried out taking special care to capture the image of the pendant drop as soon as it was formed in order to limit possible perturbations due to air currents and ambient humidity. The experimental uncertainty of measurements performed with this device was estimated to be less than 1%.3

3. RESULTS AND DISCUSSION 3.1. Dynamic Viscosity The shear rate dependence, from 10 to 1000 s−1 , of dynamic viscosity was studied for the base fluid and the three nanofluid sets at 20.0 and 30.0 C. A comparison between the results obtained for distilled water, the base fluid used to prepare the nanofluids, and previous literature values40 shows deviations lower than 1.5%, which is well within the uncertainty of the device reported by the manufacturer. Flow curves obtained at 20.0 C for the three nanofluids sets are presented in Figure 3. As it can be observed, nanofluids prepared at nanoparticle concentrations lower than or equal to 0.01 vol.% exhibit a Newtonian behaviour within the studied shear rate range. J. Nanofluids, 7, 1–8, 2018

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(a) 100 1.4 1.3

η(mPa s)

η(mPa · s)

(b) 10

1

1.2 1.1 1.0 0.9 10

0.1 10

100

1000

1000

. γ (1/s)

(d) 2.0

1.8

1.8

1.6

1.6

η(mPa · s)

(c) 2.0

η(mPa · s)

100

. γ (1/s)

1.4 1.2

1.4 1.2 1.0

1.0

100

1000

. γ (1/s)

0.8 10

100

1000

. γ (1/s)

Fig. 3. Flow viscosity curves of (a, b) GO/water, (c) rGO_01/water and (d) rGO_02/water nanofluid sets at 20.0 C and different nanoparticle volume concentrations: (×) base fluid, () 0.0005, () 0.001, () 0.005, (♦) 0.01 and (+) 0.05 and ( ) 0.1 vol.%.

Conversely, higher nanoadditive loadings lead to shearthinning non-Newtonian dependence of dynamic viscosity. Differences in viscosity between the three nanofluids sets are within the experimental uncertainty when comparing the Newtonian nanofluids and the 0.05 vol.% concentrations. However, viscosities of 0.1 vol.% GO-based nanofluid are drastically higher than those of the two rGObased nanofluids prepared at 0.1 vol.% volume concentration. This indicates that reduction of GO mainly leads to improvements in this transport property for high content in nanoparticles. Similar trends were obtained at the 30.0 C temperature with a reduction in viscosity due to the increase in temperature. As for the non-Newtonian nanofluids, maximum increases reach 100–130% in the case of GO nanofluids, while they are about 70–80% for the rGO sets. For the 0.1 vol.% concentrations, larger increases are observed at 30.0 C than at 20.0 C. The shear-thinning behavior was Table I. Power-law parameters obtained from the flow curve experiments of the highest nanoparticle concentration, 0.1 vol.%, of the thee nanofluid sets. GO/water Parameter k (mPa · sn ) n

(rGO_01)/water

(rGO_02)/water

20.0 C

30.0 C

20.0 C

30.0 C

20.0 C

30.0 C

919 037

652 040

2.24 0.95

1.83 0.96

2.15 0.95

1.76 0.95


described by using the Ostwald-de Waele (Power Law) viscosity model, as described by Eq. (1). nf = k · ˙n−1

(1)

where the flow consistency factor (k) and the flow behaviour index (n) are used as fitting parameters. Good adjustments were obtained for the six non-Newtonian nanofluids, with percentage deviations in dynamic viscosity around 3%. As an example, Power-Law parameters obtained for the highest volume concentration, 0.1 vol.%, are reported in Table I. Comparing the three nanofluids sets, it can be observed that while flow behaviour index is really close to 1 for the rGO dispersions, it drops to about 0.4 for the GO nanofluid. This evidences that the addition of GO leads to a stronger shear thinning behaviour than rGOs at high content. Finally, the relative viscosity values at high shear of the nanofluids are plotted in Figure 4. A comparison of Figures 4(a) and (b) shows first that, while nanofluids viscosity depends on temperature, relative viscosity is not significantly influenced by temperature. Then, in the range from 0.0005 to 0.1 vol.%, it is observed that increases in relative viscosity reach around 70% and 130% for rGO and GO nanofluids, respectively. Among the different equations available in the literature to describe how the addition of non-interacting and stationary hard particles influences the dynamic viscosity of infinitely dilute suspensions, Maron and Pierce (MP) model41 has proven to 5

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0.8 10


(a) 2.30

(b)

2.50 2.30

2.10

2.10

1.90

1.90

ηnf /ηbf

1.70

ηnf /ηbf

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1.70 1.50

1.30 1.30 1.10

1.10

0.90 0.70 0.001

0.90 0.01

0.1

0.70 0.001

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0.1

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Fig. 4. Viscosity ratios, nf /bf , obtained from dynamic viscosities at shear rates around 900 s−1 versus nanoparticle volume concentration, vol.%, at (a) 20.0 C and (b) 30.0 C. (×) GO/water, () rGO_01/water, and () rGO_02/water nanofluid sets. (——), (- - - -) Results provided by MP equation. Error bars indicate expanded uncertainty of the ratio calculated based on a 95% confidence (k = 2).

be effective in the description of material dispersions with different aspect ratios such as fibres39 42 or flakes.6 43 44 As presented below in Eq. (2), this model has the same functional form as Krieger-Dougherty relationship but MP does not require the knowledge of intrinsic viscosity where nanoparticle shape is considered and requires an accurate evaluation. −2 nf = 1− (2) bf m where is the nanoparticle volume fraction, while m is the maximum packing volume fraction, which is here used as fitting parameter.45 In this case, a good correlation with an absolute average deviation (AAD%) of around 1.9% was obtained by using maximum packing volume concentrations around 0.42 vol.% and 0.32 vol.% for rGO and

GO nanofluids respectively. This confirms the ability of chemical reduction to improve the dispersion of graphene platelets in water for high loading without significant influence of reduction rate considered. Finally, the goodness of this equation to describe our experimental data was also shown in Figure 4. 3.2. Surface Tension In order to check the calibration of the experimental device and the followed measurement procedure, ST was first studied at room temperature for distilled water, ethylene glycol, and toluene, respectively. A good agreement between data previously reported in literature and our experimental results was found for the three fluids, with an AAD% lower than 1.3%. As an example, Figure 5 presents (b1)

2 mm

(a)

σ: 72.74 mN/m B: 0.603 Volume: 31.0 μl

2 mm

(c1)

σ : 72.89 mN/m B: 0.603 Volume: 30.5 μl

2 mm

σ: 73.13 mN/m B: 0.603 Volume: 30.6 μl 2 mm

σ: 71.09 mN/m B: 0.605 Volume: 30.3 μl

(b2)

2 mm

(c2)

σ: 71.19 mN/m B: 0.602 Volume: 29.5 μl

Fig. 5. Pendant drop images of (a) water, (b) GO/water and (c) rGO_02/water sets at 0.005 % (b1 and c1 ) and 0.1% (b2 and c2 ) concentrations.

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(a) 74

(b)

–102 · (σnf – σbf)/σbf

73

σ (mN/m)

4

Water GO/Water (rGO_0.1)/Water (rGO_0.2)/Water

72

71

3

GO/Water (rGO_0.1)/Water (rGO_0.2)/Water

2

1

0

70 0

0.02

0.04

0.06

0.08

0.1

0.005

vol.%

0.05

0.1

vol.%

Fig. 6. Effect of nanoparticle volume concentration, vol.%, and graphene reduction treatment on (a) surface tension, , and (b) surface tension percentage modification of the nanofluid (nf) in relation to the base fluid (bf) at room temperature.

4. CONCLUSIONS The studied rGO-based nanofluids show long-term stabilities without any evidence of sedimentation several months after their preparation. Dynamic viscosity and surface tension of stable reduced graphene nanofluids were experimentally studied and compared with GO nanofluids at same concentration. A Newtonian behavior was observed in the studied shear rate range for graphene volume concentrations lower or equal to 0.01%, while nanofluids with higher concentrations exhibit a shear-thinning behavior. Higher dynamic viscosities and stronger shear-thinning were observed for the 0.1 vol.% nanofluid of the GO set in comparison to rGO samples prepared at the same concentration, while differences in this transport property are within the experimental uncertainty when comparing the three nanofluid sets at the other concentrations. This indicates that chemical reduction of GO leads to improvements in this transport property for high concentrations. In addition, it was shown that relative viscosity enhancement of nanofluids can be well modelled by MaronPierce’s equation, maximum packing fraction being lower J. Nanofluids, 7, 1–8, 2018

with chemically reduced nanofluids compared to simple GO nanofluids. As for surface tension, this property was reported to decrease as graphene loading increases, with maximum diminutions of about 3% for the highest volume concentrations and without a clear effect of the graphene functionalization process. The experimental determination of other properties such as thermal conductivity and/or isobaric heat capacity would complete the thermophysical characterization of these novel materials and would allow analyzing their potential as heat transfer fluids.

ABBREVIATIONS AAD%: absolute average deviation; bf: base fluid; G: graphene; GO: graphene oxide; HTF: heat transfer fluid; k: flow consistency factor (mPa · sn); n: flow behavior index; nf: nanofluid; rGO: reduced graphene oxide; SEM: scanning electron microscopy; ST: surface tension (mN/m); T : temperature ( C); TEM: transmission electron microscopy; vol.%: percentage volume concentration; W: water; : ˙ shear rate (1/s); : dynamic viscosity (mPa · s); : shear stress (Pa); : surface tension (N/m); : volume concentration; , m : Maron-Pierce’s (MP) fitting parameters. Conflicts of Interest The authors declare no conflict of interest. Acknowledgments: P. Estellé acknowledges the European Union through the European Regional Development Fund (ERDF), the Ministry of Higher Education and Research, the French region of Brittany and Rennes Métropole for the financial support related to surface tension device used in this study. D. Cabaleiro is recipient of a postdoctoral fellowship from Xunta de Galicia (Spain) and acknowledges EU COST for the STMS grant ref. COST-STSM-CA15119-34906, and ENE2017-86425-C21-R supported by the Ministry of Economy and Competitiveness (Spain) and the ERDF Program. Authors also want to thank J. P. Vallejo and L. Lugo for Zeta Potential measurements. This work was presented at the first 7

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different captures of drops obtained for the water used as base fluid and two concentrations of the GO and rGO based nanofluid sets. It must be mention that no macro aggregates were detected during the measurements. The influence of the nanoparticle loading on the surface tension is plotted for the three nanofluid sets in Figure 3. It can be observed as ST decreases as graphene loading increases. Maximum reductions reach 3% for the highest volume concentrations, 0.1%, without any clear effect of the chemical reduction. Diminutions in ST can be attributed to an increase in the intermolecular spacing at the liquid-air interface which reduces the forces of attraction between the water molecules inside the bulk liquid. Reductions in this physical property can lead to increases in Bond and Webber numbers. This situation could be generally positive in order to increase the boiling heat transfer coefficient but, in turn, it could deteriorate the transference of convective heat with external flows.


European Symposium on Nanofluids ESNf-1 held at the University of Lisbon, Portugal during October 8–10, 2017.

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