ethylene glycol based nanofluids

ethylene glycol based nanofluids

International Journal of Heat and Mass Transfer 150 (2020) 118981 Contents lists available at ScienceDirect International Journal of Heat and Mass T...

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International Journal of Heat and Mass Transfer 150 (2020) 118981

Contents lists available at ScienceDirect

International Journal of Heat and Mass Transfer journal homepage: www.elsevier.com/locate/ijhmt

Experimental investigation on stability, thermal conductivity and rheological properties of rGO/ethylene glycol based nanofluids Syed Nadeem Abbas Shah a,b, Syed Shahabuddin c,⇑, Mohd Faizul Mohd Sabri a,⇑, Mohd Faiz Mohd Salleh d, Mohamad Azlin Ali a, Nasir Hayat b, Nor Azwadi Che Sidik e, Mahendran Samykano f, R. Saidur g a

Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia Department of Mechanical Engineering (Main Campus Lahore), University of Engineering and Technology Lahore, 54890, Pakistan Department of Science, School of Technology, Pandit Deendayal Petroleum University, Knowledge Corridor, Raisan Village, Gandhinagar, 382007 Gujarat, India d Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia e Malaysia – Japan International Institute of Technology (MJIIT), University Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra (Jalan Semarak), 54100 Kuala Lumpur, Malaysia f Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia g Research Centre for Nano-Materials and Energy Technology (RCNMET), School of Science and Technology, Sunway University, 47500 Selangor Darul Ehsan, Malaysia b c

a r t i c l e

i n f o

Article history: Received 24 May 2019 Received in revised form 6 October 2019 Accepted 31 October 2019

Keywords: Nanofluids Colloidal stability Reduced graphene oxide Rheology Thermal conductivity

a b s t r a c t The present study reports stability, thermal conductivity and rheological properties of reduced graphene oxide (rGO)/ethylene glycol (EG) based nanofluids at three volume concentrations (0.02%, 0.04%, 0.05%). The properties of the prepared nanofluids were comprehensively characterised and analysed by employing different techniques such as field emission scanning electron microscopy (FESEM), particle size analyser, Zetasizer, UV–vis spectrometry, rheometer, thermal conductivity meter and a pH measurement system. The effect of various surfactants such as SDS, SDBS and CTAB at four volume concentrations (0.05%, 0.5%, 1%, 2%) on the thermo-physical properties of the nanofluid were investigated. The experimental analysis revealed that the concentration of rGO and surfactants has greatly influenced stability, particle size distribution, dynamic viscosity and the thermal conductivity of the nanofluids. In addition, the viscoelastic rheological analysis shows considerable yield stress due to the presence of the rGO which was subsequently improved by surfactant, whereas non-Newtonian flow prevails at a shear rate below 10 s1 followed by Newtonian behaviour afterward. Besides, temperature sweep measurements within temperature range (25–70 °C) indicates that the viscosity decrease with temperature and improvement persists. Consequently, the results suggested that the surfactants improve the zeta potential but the average particle size of colloids also increases due to agglomeration. The optimum rGO/EG nanofluid (0.02% rGO with 1% SDBS) was selected based on maximum thermal conductivity enhancement (11.3%), high relative concentration (~83% after four days) and the reduction in viscosity (~14.4% less than ethylene glycol). However, anomalous reduction in dynamic viscosity up to 22% and an increase in thermal conductivity up to 11.3% propose the use of surfactant base nanofluids in potential engineering applications. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction Heat transfer research has tangible influence on the economy in various sectors of life. It has been reported that 10% efficiency improvement in all relevant sectors (of heat transfer) can add about 110 billion US$/annum in USA GDP [1]. Therefore, energy efficiency is considered to be one of the important areas of research which accounts for GDP growth. With the passage of time, the ⇑ Corresponding authors. E-mail addresses: [email protected] (S. Shahabuddin), [email protected]. my (M.F.M. Sabri). https://doi.org/10.1016/j.ijheatmasstransfer.2019.118981 0017-9310/Ó 2019 Elsevier Ltd. All rights reserved.

growing need of energy has attracted researcher’s attention for efficiency enhancement of thermal systems. A new class of engineered nanofluids has been introduced by Choi and co-workers with improved thermal transport properties as compared to the conventional heat transfer medium such as water, ethylene glycol and oil [2,3]. Early researchers have focused on metal particles (Ag, Cu, Fe, Au, etc.) dispersion in base fluids to investigate the synergistic improvement in thermal performance of nanofluids. Further, following the synergistic thermal performance enhancement of nanofluids, researchers have employed various particles in base fluid such as metal oxides, carbon-based material like carbon nanotubes (SWCNTs, MWCNTs), graphene, conducting polymers etc.

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These nanofluids have shown promising optical, electrical and thermal properties [4–8]. In all studies, an increasing trend in thermal conductivity with nanoparticles loading has been observed. Apart from thermal conductivity enhancement, with particle(s) loading, dynamic viscosity has also been also increased, as reported in many studies [6,8–10]. With the development of graphene, a single atom thick 2D material with honey comb structure having high thermal, electrical and mechanical properties, a new horizon in nanofluids research has been initiated [11]. However, due to the hydrophobic nature of graphene, quick sedimentation occurs, limiting its usage as a nano additive in nanofluids. Also, the high surface area of graphene sheets allow quick agglomeration due to strong Vander Waals interactions and p-p interaction between the planar basal planes causing the graphene nano sheets to restack [12,13]. It has been reported that the intrinsic thermal conductivity of single layer graphene is between 4800 and 5300 W/mK at room temperature which is highest as compared to the other nanomaterials [14]. Graphene is usually transformed into graphene oxide by adding oxygen functionalities on its surface via the modified Hummers method [15]. However, the reduction of the graphene oxide (GO) into reduced graphene oxide (rGO) results in elimination of some oxygen containing functionalities leading to instability of rGO due to agglomeration in acidic solution (pH < 5) [15]. The colloidal stability of rGO has been correlated to pH of the dispersion and for all the stable suspensions, the pH appears far away from the isoelectric point (IEP). The value of IEP for rGO ranges from 2.5 to 4.7 as reported in the previous literatures [15,16]. The hydrophobic behaviour may also be increased due to the higher concentrations of rGO which can increase the contact angle [10,15]. Therefore, the stability is the long-standing challenge for researchers to commercialize the nanofluids. However, Kannan et al. have reported 10 days stability without the use of surfactants for water based rGO nanofluid with a minor increase in thermal conductivity (1.52% at 0.002 wt% of rGO) [6]. In another study, maximum 10% enhancement in thermal conductivity was reported at 0.3 g/l concentration for rGO-water nanofluids [10]. They also observe a maximum increase in dynamic viscosity at about 12.32% at the concentration of 0.3 g/l. Selvam et al reported ~21% and ~16% improvement in thermal conductivity of ethylene glycol (EG) and water-based graphene nanoplatelets (GnP) nanofluids, respectively, with a particle loading of 0.5 vol% using Sodium deoxycholate (0.75 vol%) as surfactant. They have presented 15 days stability of the prepared nanofluids. Though intrinsic graphene has very high thermal conductivity, its potential does not reflect to maximum in nanofluids due to interfacial thermal resistance between base fluid and nanoparticles [17]. Amiri et al. had shown maximum ~2% increase in dynamic viscosity at a temperature range of 25–65 °C for crumpled nitrogen doped graphene (CN DG) with 0.001–0.01% weight concentration [18]. Sarsam et al. have reported the thermal conductivity enhancement of ~8.36% along with ~7.4% increase in viscosity for distilled water based (1–1) SDBS-GnP nanofluids [19]. Ghozatloo et al. has reported that the thermal conductivity of CNT/water nanofluids treated with SDS showed the highest augmentation of 24.9% as compared to the base fluid [20]. In a previous investigation, an interesting behaviour of graphene quantum dots (GQD) was observed. Indeed, it showed an enhancement in thermal conductivity (9% at 2 wt% for distilled water). This small increment in thermal conductivity for higher concentration is limited due to more phonon scattering at the boundaries of GQDs as compared to graphene. The research also showed a considerable reduction in viscosity using GQD as compared to glycerol [21]. Esfahani et al. have reported the influence of particle size distribution on the dynamic viscosity and thermal conductivity enhancement of aqua based graphene oxide (GO) nanofluids. Due to strong intermolecular forces of attraction (Van

Dar Waals), electrostatic forces (surface charge) and intermolecular interaction between water and nanoparticle functional groups (p-p stacking), hydrogen bond and water-molecules collision and aggregation formation is expected. Due to this reason, when concentration of GO was changed to 0.01–0.5 wt%, the aggregate size increased from 600 nm to 2800 nm, while the stability of nanofluids increased with increasing concentration of GO. The rheological behaviour was interesting as up to 0.05 wt% of GO concentration, the dynamic viscosity was almost same as that of the base fluid (DIW), whereas it changes rapidly for 0.1 wt% (38% enhancement) and 0.5 wt% (130% enhancement) at 25 °C and 100 s1 shear rate. This was due to the formation of aggregates at the higher concentrations which experience more shear resistance against the base fluid, consequently large force is required to displace them. The thermal conductivity enhancement at a lower temperature (25 °C) for 0.01 wt% and 0.5 wt% was found to be 8.7% and 19.9% respectively, which is attributed to the intrinsic properties of GO like high aspect ratio and stiffness. On the contrary, at elevated temperature (60 °C), the increment behaviour was different, with 8.8% and 55.2%, enhancement in thermal conductivity at lower (0.01 wt%) and higher concentration (0.5 wt%) respectively. This phenomenon was due to the reduction in viscosity of base fluid and enhanced Brownian motion at an elevated temperature that augments the convection currents [22]. Ranjbarzadeh et al. investigated the effect of various parameters such as particle loading, shear rate, time of constant shear and temperature on the water based GO/SiO2 nanofluids. They reported the maximum increment in dynamic viscosity of about 128% for 1 vol% at 20 °C. They also reported that the relative viscosity was affected by temperature and concentration significantly. A maximum 345% increase in relative viscosity was observed for 1 vol% at 60 °C, and an increasing trend with temperature was maintained. This indicates that in heat transfer systems, dynamic viscosity is a key parameter in terms of performance and pumping power considerations [23]. Wang et al. reported the reduction in the kinematic viscosity of graphene/oil based nanfluids. A maximum about 12% reduction in kinematic viscosity was observed along with about 25% enhancement in thermal conductivity. However, they highlighted an important aspect of concentration optimization as higher concentration may give rise to the viscosity [24]. Abdullah et al. reported the influence of MWCNT with AOH functional group on the thermal conductivity and dynamic viscosity of the water and EG based nanofluids. A maximum enhancement in thermal conductivity was found to be 8.86% and 5.37% for water (0.8 wt% MWCNT) and EG (0.2 wt% of MWCNT) respectively. They suggested that in case of EG, at elevated temperature the Van Waal forces reduced and hydrogen bonds break, thereby reducing the viscosity. Such findings confirmed that the use of PVP surfactant has more value in EG as compared to DIW [25]. Myekhlai et al. reported the synthesis of a composite material comprising graphene-silver nanoparticles (GN-Ag NPs) and demonstrated that the thermal conductivity had been enhanced as compared to pure GN and Ag NPs in water based nanofluids. The maximum average augmentation in thermal conductivity was observed to be 6.59% for GN-Ag NPs at 0.6 wt% (0.2 wt% GN and 0.4 wt% Ag). This increase in the thermal conductivity of composite based nanofluids was due to the better dispersion as a result of Ag NPs integration with GN and improvement in the specific surface area [26]. The chemical and plasma functionalization have been adopted by researchers to modify stability, but it is an expensive and time taking approach which can affect the economic feasibility of nanofluids. Therefore, surfactants are widely used to improve the surface charge of the colloids for better stability due to their economic feasibilities and ease of processing the nanofluids [27]. The chemical structure of surfactants has a hydrophilic head and hydrophobic tail which enable the dispersion of nano materials

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in base fluid for a longer time. However, the optimum concentration of base materials and surfactants is a crucial factor for improvement in the stability and thermo-physical properties of nanofluids [11,27–30]. Further, the critical micelles concentration (CMC) of surfactants plays a vital role in stability [7]. If the concentration exceeds the CMC, surfactant particles agglomerate with each other, resulting in quick sedimentation, consequently poor stability [31]. The higher concentration of surfactants also causes foaming action which can affect the thermo-physical properties of nanofluids. The rheology of colloidal suspensions is also affected by the addition of surfactants, as surfactants may enhance the viscous nature of colloidal suspensions. However, the concentration of the surfactant close to CMC cannot further effect the rheology even at the low concentration of nanoparticles [32]. This indicates that for improving the rheology of solutions with surfactants, there must be an appropriate combination of surfactants and nanoparticles. In addition, the interaction between surfactant and nanoparticles, as well as inter-particle interaction is an important parameter effecting the viscoelastic behaviour of colloids. Recently, Zhou et al. reported that TiO2 nanoparticles and SDS surfactant reduces the viscosity individually as well as in combination [33]. They ascribed this reduction to lubricating effect of the nanoparticles and analogous micelle formed by the nanoparticles with ionic surfactant adsorption. Different surfactants have been reported in literature to enhance the stability of nanofluids such as sodium dodecyl benzene sulfate (SDBS), 4-(1,1,3,3)-Tetramethylbutyl) phenyl-polyethylene glycol (Triton X-100), sodium dodecyl sulphate (SDS), Hexadecyltrimethylammonium Bromide (CTAB) and polyvinylpyrrolidone (PVP) [7,27,29,31]. Surfactants such as SDS and PVP have been reported to decrease the thermal conductivity ratio at higher concentration of surfactants [29]. Rashidi et al. reported that the xanthan gum (XG) surfactant decreases the thermal conductivity of CNT/water at an elevated temperature while the cationic gemini surfactants show enhancement in thermal conductivity for surfactant coated Ag/water nanofluids [5]. On the other hand, Gum Arabic surfactant has been reported to have 25.7% increment in thermal conductivity of CNT/EG-water nanofluids as compared to CTAB and PVP [34]. Also, the authors show that samples with higher values of zeta potential are not necessarily to yield higher thermal conductivity as Gum Arabic induce very less value (6.09 mV) as compared to CTAB (23.4 mV) and PVP (9.37 mV). A comprehensive literature review revealed that there is a very little comprehensive study on the effect of cationic and anionic surfactants on stability, thermal conductivity and rheology of rGO based nanofluids. Assuming the constant Nusselt number for a flowing fluid in any conduit, heat transfer coefficient is proportional to the thermal conductivity of fluid whereas increased viscosity put additional pumping power on the system. Therefore, considering the need of heat transfer fluids, enhanced thermal conductivity give rise to heat transfer characteristics, at the same time any increase in viscosity leads to enhanced pumping power. Therefore, it is necessary to find a solution which may keep balance in these two parameters. It is suggested by researchers that the chemically functionalized graphene may precipitate if it is not stabilized using certain stabilizing agents [35]. Such precipitation might develop irreversible restacking of the graphene sheets. In the present work, a comprehensive experimental study on the effect of anionic and cationic surfactants on the stability, thermal conductivity and rheological behaviour of rGO/EG based nanofluids have been carried out. This study was performed keeping in view the diversity of findings by various research groups related to thermal conductivity and dynamic viscosity of nanofluids. The enhancement in thermal conductivity of nanofluids is obvious with certain percentage in all previous research work. However, there is no sufficient data on the

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dynamic viscosity to authenticate the results of various studies. The present research get motivation from few to further explore the potential of nanomaterials with self-lubrication effect and surfactant interfacial tension lowering capability in nanofluids which can lower the viscosity. The idea is to reduce the rGO/EG nanofluids viscosity along with improvement in thermal conductivity and stability by employing various ionic surfactants. Previous research on nanofluids uses surfactants for colloidal stability only. However, surfactants potential in lowering the viscosity has not been explored in detail. Also, an increase in viscosity have been reported in nanofluids as compared to base fluids, which might put an additional pumping load on the flow system. The present work contributes to improve zeta potential, reducing viscosity and augmenting thermal conductivity of rGO/EG nanofluids with surfactants. Being a 2D material, rGO with sheet like morphology has better self-lubrication characteristics as well as high intrinsic thermal conductivity. On the other hand, interfacial tension lowering capability of surfactants helps in reducing viscosity. Such properties of rGO and surfactants have been exploited in nanofluids to reduce viscosity along with improvement in thermal conductivity and stability. In present work, the viscosity reduction also shows synergistic effect with optimum concentration of surfactant and rGO. The rGO/EG nanofluids were prepared by varying volume concentration over a wide range along with different surfactants. The stability and particle size distribution of the prepared nanofluid were recorded at a temperature of 25 °C. The thermal conductivity of stable nanofluids has been investigated experimentally at 25 °C, 50 °C, and 70 °C while viscoelastic and flow behaviour were explored experimentally at 25 °C along with the temperature dependence of dynamic viscosity for a temperature range (25–70 °C). Rheology data has also been compared and correlated with the appropriate models. In addition, to correlate the stability of prepared nanofluids, pH of all samples has been measured. The present work was carried out with an objective to prepare stable rGO dispersions with improved thermo-physical properties by employing readily available surfactants (SDS, SDBS, and CTAB). 2. Experimental section 2.1. Materials All the reagents in the experimental investigations were used without any purification. The specifications of nanoparticles and surfactants/base fluid detail are shown in Tables 1 and 2 respectively. 2.2. Sample(s) preparation The samples were prepared using the two-step method, widely used for nanofluids preparation due to ease of processing. Firstly, the volume of ethylene glycol (EG) as base fluid was measured and kept constant at 50 ml. Subsequently, the rGO flakes (0.02 vol% w.r.t to EG) were added along with varying the volume concentrations of all three surfactants from 0.05% to 0.5%, 1% and 2%. The minimum volume concentration of 0.05% was taken as a start point because the foaming is the obvious limitation of surfactant. However, the concentrations were varied (0.5%, 1%, 2%) in order to optimize the prepared nanofluids with improved stability, thermal conductivity and rheology. The previous studies suggests that the rigorous concentration optimization is desirable for better performance [36]. In the present work, the volume fraction of the powder(s) in liquid was calculated from the weight of the dry powder(s) and the total volume of the mixture [37]. In order to homogenise the solution, the samples were subjected to magnetic

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Table 1 rGO specifications. Nanoparticles

Purity (%)

Density (g/ cm3)

Average Thickness (nm)

Average Lateral size (mm)

Specific Surface area (m2/g)

Number of layers (–)

Source

RGO grade AO-2

99.9

2.3

8

5

15

20–30

Graphene supermarket (Graphene Laboratoires Inc. NY, USA)

Table 2 Surfactants and base fluid detail. Material

Name

Chemical Formula

Molecular Weight (g/mol)

Density (g/cm3)

Type (–)

Source

Surfactants

SDS* SDBS* CTAB* EG*

NaC12H25SO4 C18H29NaO3S C19H42BrN (CH2OH)2

288.38 348.48 364.45 62.07

1.01 1.06 0.5 1.11

Anionic Anionic Cationic organic

Fisher scientific UK

Base fluid

Sigma Aldrich

*SDS: Sodium dodecyl sulphate. SDBS: Sodium dodecylbenzenesulfonate. CTAB: Cetyltrimethylammonium bromide. EG: Ethylene Glycol.

stirring for 15 min at 40 °C and 125 rpm followed by ultrasonication for 90 min at 45 kHz frequency with temperature not exceeding 50 °C. Same procedure was repeated for other volume concentrations (0.04% and 0.05%) of rGO. 2.3. Characterization techniques 2.3.1. RGO morphology The surface morphology of the rGO flakes was studied using JEOL JSM-7600F FESEM operated at 10 kV. 2.3.2. Stability evaluation 2.3.2.1. Zeta potential and UV–vis spectrometry measurement. The stability analysis of rGO nanofluids has been carried out using particle analyser (litesizer 500, Anton Paar) by estimating zeta potential. Zeta potential is the widely used technique for estimating the stability of the colloidal suspensions. It works on the principal of electrophoretic light scattering (ISO 13099-2:2012) which is used to determine electrophoretic mobility (m, electrophoretic velocity per electric field strength) of dispersed particles in solutions. The electrophoretic mobility assists to estimate the surface charge and zeta potential using theoretical models, well described in (ISO 13099-1:2012). The litesizer500 was calibrated using the standard solution of polymer latex particles with known zeta potential value of 42 mV as shown in Fig. S4 (Supplementary information). The 10% accuracy of instrument was given by manufacturer for the zeta potential measurements. The error in measured and standard value of zeta potential was calculated to be 9.5%, which is slightly below the accuracy. While the relative standard deviation less than 1% was calculated for zeta potential distribution which shows good precision. In order to quantify the stability of optimized nanofluids, UV–vis spectrum was recorded using Lambda 750 UV–vis-NIR photo-spectrometer (Perkin Elmer). Before recording UV–vis spectrum, the nanofluids were diluted at a ratio of 1:6 using ethylene glycol. 2.3.2.2. Particle size measurement. The litesizer-500 is also capable of measuring particle size. The particle size estimation is based on dynamic light scattering technique (ISO 22412:2017). To estimate the particle size, the diluted supernatant liquid was used as the sample. Supernatant liquid was prepared from parent sample using table top centrifuge (Anton Paar) at 5000 rpm for 3 min. The particle size calibration was also carried out with standard latex particles of known mean diameter 221 ± 6 nm. The measured

results showed ~6% error with less than 2% precision as shown in Fig. S5 (Supplementary information). 2.3.2.3. pH measurement. To correlate stability with IEP, pH of all samples was measured using pH 700 (Eutech instruments, thermo fisher scientific). The calibration of the apparatus was done using buffer solution of known pH of 4, 7 and 10. 2.3.2.4. Sedimentation analysis. The sedimentation analysis was carried out by capturing digital images of samples over a period of time. The detail of samples considered for sedimentation analysis is shown in Table 3. 2.3.3. Rheological measurements Rheological properties of the prepared rGO nanofluids were investigated using the MCR series rheometer (Model 302SN82171186, Anton Paar Asia Pacific Laboratory, Kuala Lumpur, Malaysia). A double gap measuring systems (DG 26.7-SN39066) having concentric cylinders with 1 mm gap has been used in rheological measurements [37,38]. The volume of sample required for such measuring system is approximately 3 ml. The rheometer performance was verified using quick air check before and after measurements, which was found within system uncertainty as shown in Fig. S9 (Supplementary information). The torque was found within the uncertainty limits of the device. Moreover, the rheometer performance was verified with distilled water (DW) at 10 °C, 25 °C, 50 °C and 70 °C and compared with the literature data Table 3 Samples nomenclature for sedimentation analysis. Sample(s)

RGO (vol%)

SDS (vol%)

GC1 GC2 GC3 1 2 3 4 13 14 15 16 25 26 27 28

0.05 0.04 0.02 0.05 0.05 0.05 0.05 0.04 0.04 0.04 0.04 0.02 0.02 0.02 0.02

– – – 0.05 0.5 1 2 0.05 0.5 1 2 0.05 0.5 1 2

Analysis Intervals     

As Prepared 9 Hours Later 24 Hours Later 51 Hours Later 76 Hours Later

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[39]. The maximum error was found to be ~5% in a temperature range of 10–70 °C when compared with reference values as shown in Table S9 (Supplementary information). However, maximum relative standard deviation was ~3.8% in a shear rate of 10–1000 s1. While the performance verification of MCR302 has already been recognized by the other research groups using the DG measuring cell. Sarsam et al. and Yu et el. showed the maximum 2.9% and 2.77% error in measured viscosity of distilled water respectively for rotational measurements [19,37]. The experimental data was recorded and analysed using the Rheo-compass software equipped with the rheometer. Also, before measurements, each sample was kept at constant temperature to avoid unwanted effect of shear history. The measurements were carried out at constant temperature of 25 °C (maintained with Peltier device, C-PTD200 SN82169343) for shear flow behaviour and possible viscoelastic regime. The rotational test (control shear rate, CSR) was performed at 25 °C, for a range of shear rates (0.01–1000 s1) to evaluate the dynamic viscosity and flow behaviour of nanofluids with logarithmic ramp. The temperature dependence dynamic viscosity was investigated at a constant shear rate (10 s1 and pre-shear for 180 s) and temperature between 25 °C and 70 °C with a temperature ramp of 2 °C/min. The viscoelastic behaviour was analysed using oscillation tests (strain sweep test also known as direct strain oscillation (DSO) and frequency sweep test). The strain sweep test at constant frequency (1 Hz) with variable range of strain/deformation (0.01–1000%) and variable shear stress was performed to determine the linear viscoelastic region (LVER), flow point, true yield point, G0 (storage modulus) and G00 (loss modulus). The frequency sweep test within LVER strain and constant shear stress was also performed to check the stability of colloidal suspension for a range of frequency (0.1–100 Hz). Finally, 3ITT test following the oscillation-rotation-oscillation (O-R-O) sequence was performed to predict the thixotropic and recovery time of the viscoelastic structure of the nanofluid. The first oscillation interval in 3ITT test determine the viscoelastic parameters at low value of strain in 60 s following the structure deformation, the second rotation interval break the viscoelastic structure and material flow at constant shear rate of 100 s1 for 10 s and the last oscillation interval measure the viscoelastic parameters until storage modulus becomes equal to loss modulus to predict the recovery time of the structure in 500 s. 2.3.4. Thermal conductivity measurements The thermal conductivity of the prepared rGO nanofluids was measured using KD2 pro thermal property analyser (Decagon devices Inc., Pullman Washington, USA) as per ASTM D5334 and IEEE 442-1981 [30,40]. The KS-1 sensor probe (60 mm length and 1.3 mm dia) of thermal property analyzer has an accuracy of ±5% within range of 0.2–2 W/mK [15,28,31]. However, the thermal conductivity measurements for identical samples by 30 research organizations worldwide showed accuracy within ±10% [41]. Before measurements, the sensor was calibrated using the standard glycerine solution, distilled water and ethylene glycol (EG) as shown in Table S10 (Supplementary information). The calibration results were compared with literature data and found in great agreement with the measurements within instrumental accuracy. The glycerine standard sample thermal conductivity was measured which was compared with meter group Inc. USA, certificate of quality insurance, for glycerine value is 0.282 W/ mK as shown in Fig. S6 (supplementary information) [30]. The thermal conductivity was compared with reference values for water and EG and error was computed [39,42]. The maximum error was found to be 4.8% with relative standard deviation of 3.6%. The measured values of thermal conductivity for glycerine, DW and EG were found within instrumental accuracy of ±5% over a period of six days at 25 °C as shown in Fig. S7 (supplementary information).

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Moreover, the thermal conductivity of ethylene glycol at 25 °C was compared with literature and found within ±0.5% deviation [43]. In order to evaluate the statistical significance of the thermal conductivity data, t-Test has been performed and the results are presented in the Tables S2–S4 (Supplementary information). Further, the uncertainty analysis was carried out following the Kline & McClintock approach [38,44]. Making sure the reliability of the calibration measurements, samples measurements were then carried out. All measurements were taken at three different temperatures (25 °C, 50 °C and 70 °C). The constant temperature of samples was maintained using the temperature-controlled water bath (Daihan Scientific) with an accuracy of ±0.5 °C. The samples were kept inside water bath for 20 min to achieve equilibrium temperature. After thermal equilibrium, 10 measurements were taken within 3 h at each temperature and concentration and an average of consecutive values were presented in the analysis [30,44,45]. Notwithstanding about 8% of the data was observed to be outlier hence discarded during measurements. 3. Results and discussion The morphological analysis of the rGO is presented in Fig. 1. As evident from the obtained result, the FESEM image shows the clear crumpled rGO sheets with stacked formation. The crumples in rGO, mainly at the edge can be ascribed to structural mutilation of the sp2 carbon arrangement, which happens during oxidation, solution processing and drying processes in synthesis. However, the disorder arrangement of stacked sheets confirms the layered 2D structure of rGO. The morphology of rGO is in good agreement with the results reported in literature [6,15]. 3.1. Stability investigation The zeta potential is a physical parameter used for the estimation of the electrical potential of the solid particle(s). It actually measures the potential difference between the base fluid and solid-charged particles, which is the tribute to repulsive forces between charged solid particles in the fluid. The stable colloids possess the high value of surface charge which provides sufficient repulsive forces among the particles to keep them stable within the base fluid network [15]. A zeta potential value of less than ±30 mV is considered to be unstable while above ±30 mV is assumed to be a stable dispersion [46]. On the other hand, the particle size distribution has been analysed to determine the polydispersity index (PDI) of the solution and the hydrodynamic diameter of the particle(s). The particle size measurement gives information about the

Fig. 1. FESEM Image of rGO.

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agglomeration and adsorption of surfactant on solid particle(s) surfaces in suspension, which can be corelated to colloidal stability. The colloidal stability is governed by the DLVO theory which is an archetypal to comprehend the balance between van der Waals and electrostatics forces which determines the colloidal stability. The robust van der Waals forces on the nanoparticle surface tries to attract the other co-particles, which causes aggregation. [47].

3.1.1. Without surfactant Fig. 2(a) provides the zeta potential distribution of rGO without using any surfactants for various volume concentrations. The results indicate that the optimum concentration of rGO was found to be 0.04 vol% as compared to 0.02 and 0.05 vol% having the mean zeta potential values of 30.7 mV, 6.5 mV and 12 mV, respectively. The results of zeta potential are in good agreement with visual analysis of samples as shown in Fig. 3. Digital image of samples in Fig. 3 clearly depict that the 0.04 vol% sample is more stable as compared to the 0.02 and 0.05 vol% of rGO over a period of time. Fig. 2(b) presented the particle size distribution of various formulation of the rGO based nanofluid. As obvious from the figure, the mean diameter of the particle first decreases for 0.02 vol% (1276 nm) to 0.04 vol% (820 nm) of the rGO nanofluid and then increased at the concentration of 0.05 vol% (1598 nm). This might be due to the agglomeration tendency of rGO flakes within the base fluid network at the higher volume ratio. However, at optimum

concentration (0.04 vol%), the average particle size reduces because of better stability conditions as evident by the zeta potential in Fig. 2(a). On the other hand, the polydispersity index (PDI) further endorse that 0.04 vol% of rGO is an optimum concentration since the PDI value was found to be 23.3%. The PDI value indicates the broadening of size distribution where a PDI value greater than 30% represents the poly-disperse colloidal suspensions while less than 30% is the indication of mono-disperse suspension.

3.1.2. With surfactant Furthermore, the surfactants were added to rGO based nanofluid at different concentrations and the stability of the resulted nanofluid was then estimated. As the stability of colloidal solutions is directly related to electro-kinetic properties, therefore, the stability of the suspension also depends on the pH value [48]. A high stable suspension has pH value far away from the isoelectric point (IEP). In total 36 samples were prepared for three volume concentrations of rGO flakes using SDS, SDBS and CTAB. Fig. 4(a–b) indicates the influence of SDS on pH and zeta potential. Predominantly, at all vol% of rGO, the pH value increase with the increase in the surfactant amount. However, a minor decrease in pH value observed when the surfactant amount varies from 0.5 to 1 vol% for 0.05 vol% of rGO flakes. This might happen due to agglomeration and sedimentation of rGO as the zeta potential value also decrease within this region. However, at 0.02 and

Fig. 2. (a) Zeta Potential distribution without Surfactant, (b) Particle size Distribution without Surfactant, (c) Average Particle size with Surfactant and (d) Polydispersity index with Surfactant.

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Fig. 3. Visual Sedimentation Analysis of rGO nanofluids.

0.04 vol% of rGO, there is an exponential continuous increment in the pH while the zeta potential value increase when the surfactant amount varies from 0.05 to 0.5 vol%, afterwards zeta potential shows oscillating behaviour. This trend suggests that the optimum concentration of SDS surfactant was found to be 0.5 vol% w.r.t. to rGO. Fig. 4(c–d) shows the pH and the zeta potential value for SDBS. Likewise, SDS, the SBDS stabilised nanofluids depicts the increment in the pH value with concentration while zeta potential shows oscillating behaviour. On the other hand, the addition of CTAB provides a lower value of pH as shown in Fig. 4(e). This is due to the presence of positively charged hydrophilic head in the structure of CTAB. Therefore, as the concentration of the CTAB is increased, the amount of positively charged head groups also increase proportionally, consequently, the colloidal suspension becomes acidic. Thus, the inversion in the values of zeta potential is also observed from negative to positive as the concentration increases as presented in Fig. 4(f). Collectively, an increasing trend in zeta potential is recorded for the rGO nanofluids upon the addition of CTAB in the formulation. The stability results suggested that zeta potential is not strongly correlated with pH for SDS and SDBS, while for CTAB it does. Moreover, the value pH is far away from the IEP of rGO for all surfactants as reported by Sadri et al. [6]. The obtained results are also in good agreement with the results reported by Mechiri et al. [32], as for the rGO, zeta potential value is high in the basic range of pH for SDS and SDBS while the zeta potential value is small in the acidic range of pH for CTAB. This is due to fact that the surface charge density of rGO increase at basic range of pH. Such surface charges are developed on the surface of rGO due to ionization and chemical nature of different functional groups [12].

In addition, surfactants have shown a high mean value of zeta potential, the maximum value of zeta potential with corresponding surfactant concentration is shown in Table 4. These high values of zeta potential are apparent indication of anomalous surface charge density to provide enough repulsive forces for stable dispersion of rGO. However, for 0.05 vol% rGO, SDBS showed high zeta potential as compared to SDS, which is in good agreement with previous study [11]. The difference in zeta potential may be attributed to different chemical properties of both anionic surfactants and the presence of small functional groups (AOH and SO3A) which causes better dispersion [29]. The addition of surfactants strongly affects the particle size due to adsorption of surfactants on the surface of rGO. On one side, the adsorption of surfactants also causes steric hindrance which provides enough electrostatic repulsive force for better dispersion. On the other side, particle size increase from few nano meters to microns as indicated in Fig. 2(c). However, the particle size distribution remain slightly unchanged as the PDI remains close to 30% as shown in Fig. 2(d). The increase in size of the colloids due to surfactants adsorption may reduce the long-term stability as largest size undergoes flocculation very quickly. 3.2. Thermal conductivity analysis Uncertainty analysis of all tested rGO nanofluids reveals that the standard deviation from mean is less than ±5% at 25 °C and 50 °C respectively, whereas less than 10% at 70 °C with 95% confidence interval. The summary of the mean thermal conductivity of all tested samples based on uncertainty analysis is presented in supplementary data (Table S1). On the other hand, statistical

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Fig. 4. Relationship between pH and Zeta Potential at different Volume % of rGO and Surfactants (a–b) With SDS, (c–d) With SDBS and (e–f) With CTAB.

Table 4 Maximum value of zeta potential for rGO nanofluids. RGO (Vol%)

0.02 0.04 0.05

Surfactant Concentration (vol%)

Maximum Zeta Potential (mV)

SDS

SDBS

CTAB

SDS

SDBS

CTAB

No Surfactant

0.5 1 0.5

0.5 0.05 0.05

2 2 2

66.8 52.2 60.6

62.3 67.6 73.2

57.8 49.2 57.2

6.5 30.7 12

analysis is carried out in order to check the significance of the thermal conductivity data, the results are highlighted in supplementary data (Tables S2–S4). While the detailed uncertainty values on each nanofluid are shown in supplementary data (Tables S5–S7). The t-Test shows data significance at 25 °C and

50 °C while not significant at 70 °C. This could be due to the micro-convection induced at the 70 °C which might produce randomness in the measurements. In addition, the uncertainty was computed for all tested samples as shown in Tables S5–S7 (Supplementary information). The measurement error in thermal

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conductivity is found fairly below 5% at 25 °C and 50 °C while below 10% at 70 °C. This might be due to the limitation of measuring instrument as the chances of micro-convection increased at a high temperature which could not be eliminated. Fig. 5 represents the thermal conductivity ratio (knf/kbf) of three selected concentrations (0.02 vol%, 0.04 vol%, 0.05 vol%) of rGO with surfactants (SDS, SDBS, CTAB) at 25 °C, 50 °C and 70 °C. The knf represents the thermal conductivity of nanofluids while kbf is for base fluid (ethylene glycol). 3.2.1. Effect of surfactant The thermal conductivity ratio increased for all surfactants at first and then decreases following the concentration with oscillating response. These results are in good agreement with the results reported by Xia et al. [29]. The same concentration of surfactants interacts differently with various volume concentrations of rGO at different temperatures. Consequently, the thermal conductivity ratio varies accordingly. At 25 °C, the thermal conductivity ratio increases with the increase in the SDS volume concentration from 0.05% to 2% for 0.02% volume concentration rGO. However, for 0.04% and 0.05% volume concentration rGO, linear trend followed till 1% and 0.5% volume concentration of SDS respectively, then drops down as shown in Fig. 5(a). At 50 °C, for all rGO volume concentrations, the thermal conductivity ratio shows oscillating response with SDS as shown in Fig. 5(b). Similar phenomenon was observed at 70 °C for all rGO volume concentrations except for 0.05% volume concentration as depicted in Fig. 5(c). Fig. 5(d– f) and (g–i) highlights the thermal conductivity ratio of rGO

9

nanofluids with SDBS and CTAB respectively. Like SDS, no consistent trend is observed in thermal conductivity ratio for SDBS and CTAB. The insufficient surfactant amount causes less electrostatic repulsion due to partial adsorption onto surface of rGO, whereas, at higher surfactant amounts, the micelles formation occurs causing quick flocculation. Both circumstances reduces the thermal transport capabilities of nanofluids [29]. Therefore, an optimum volume fraction of both surfactants and nanomaterial is necessary to attain desired properties of nanofluids. However, it is evident that the thermal conductivity enhancement is significant up to 50 °C at all rGO and surfactants volume concentrations. Nevertheless, at elevated temperature of 70 °C, all formulations of nanofluids shows reduced transport capability except for few samples. The possible reason behind the reduced thermal conductivity at 70 °C may be attributed to the foam formation due to inherent characteristics of surfactants [44]. Also, the viscosity of base fluid reduces significantly (Fig. 10) at elevated temperature which further causes the quick sedimentation of rGO as shown in Fig. 6, since the viscosity of base fluid/force buoyancy also aid to increase the stability [49]. As the temperature increases, the volume of the base fluid increases whereas density, viscosity and buoyancy force decreases. Therefore, the buoyancy of the base fluid cannot overcome the gravity force against which suspended particles (clusters) are moving downward with certain terminal velocity. Consequently, sedimentation occurs quickly leading to unstable colloidal suspension. The optimal concentration of surfactants obtained in present work for maximum enhancement in mean thermal conductivity at 25 °C, 50 °C and 70 °C for all rGO volume fraction

Fig. 5. Thermal Conductivity Ratio at Different Temperatures (a–c) With SDS, (d–f) With SDBS and (g–i) with CTAB.

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Fig. 6. Foaming and sedimentation after measurement at 70 °C.

are summarised in Table 5 and compared with control samples (without surfactant). The mean thermal conductivity increases for all volume fractions of rGO based nanofluid without using any surfactant. The increment is observed till 50 °C, but at 70 °C it show anomalous decrease as compared to the base fluid as shown in Fig. 7(d). This may be due to the decrease in viscosity at elevated temperature. Without using any surfactant, maximum 7.4% increase in thermal conductivity is observed for 0.05 volume concentration of rGO based nanofluid at 50 °C. This increment may be attributed to the intrinsic properties of graphene such as high thermal conductivity and high aspect ratio as well as the decrease in viscosity which may give rise to Brownian motion at elevated temperatures [22]. On the other hand, using surfactants, thermal conductivity improve significantly and maximum increment appear around 11.3% at 0.02% volume concentration of rGO with 1% volume concentration of SDBS at 50 °C. Interestingly, this anomalous enhancement in thermal conductivity with surfactant occur at minimum rGO volume concentration of 0.02%. SDS shows maximum mean thermal conductivity enhancement at very low volume concentration of surfactant (0.05%) while SDBS at high volume concentration (1%). This behaviour with SDS and SDBS is described by Seong et al. using graphene [11]. Consequently, all surfactants have improved the thermal transport behaviour of rGO nanofluids at optimal combinations of rGO and surfactants. It is well known fact that the addition of surfactant may affect the thermal conductivity and viscosity of the base fluid [44,50]. Therefore to understand surfactants influence on mean thermal conductivity of base fluid, all surfactants at different volume fractions were tested at 25 °C, 50 °C and 70 °C without the addition of any rGO. Thermal conductivity of base fluid with surfactants slightly increase (~1%) at 25 °C

Fig. 7(a), whereas it reduces at 50 °C Fig. 7(b), and 70 °C Fig. 7(c). Selvam et al. [51] have reported 2% enhancement in thermal conductivity of base fluid with addition of 0.75 vol% of sodium deoxycholate (SDC) surfactant whereas the adverse effect of surfactant on ethylene glycol reduced thermal conductivity is also reported by Yu et al. [52]. They showed 2% reduction in thermal conductivity of ethylene glycol with 5 wt% PVP surfactant. Therefore, it can be concluded that at elevated temperatures, surfactants affect sufficiently on the thermal properties of the base fluids. Such influence can easily be observed in thermal transport behaviour of nanofluids in the form of reduction in thermal conductivity ratio, specifically at 70 °C. In order to validate the thermal conductivity results, a repeatability study was performed over six days with respect to rGO concentration (0.02, 0.04, 0.05%) and optimized nanofluids (0.02 vol% rGO + 1 vol% SDBS). For rGO concentrations the temperature is varied from 25 °C to 70 °C while for optimized samples study is carried out at 50 °C as shown in Fig. 8. The repeatability study reflects sufficient reproducibility of data within instrument designed accuracy. The reproducibility of thermal conductivity results for the tested samples found well within the instrument designed accuracy at 25 °C and 50 °C while the results deviates at 70 °C more than the designed accuracy of the instrument which is in good agreement to findings of Buongiorno et al. [5]. As the heat transfer fluid undergoes alternate heating and cooling cycles, the stability evaluation was performed for the optimized nanofluids based on the thermal conductivity. In order to mimic the stability of NFs under thermal cycles, zeta potential has been measured for three samples selected based on the highest thermal conductivity as shown in Fig. S5.1 (supplementary information). It can be seen the NFs shows apparent high zeta potential value over entire tested temperature range (25 °C, 50 °C and 70 °C) for SDBS. However, only the SDBS stabilised NFs passed threshold value (>±30 mV). Therefore, it could be postulated that these NFs will remain stable at tested temperatures in heat transfer applications with substantial improvement in thermal conductivity. Moreover, the maximum thermal conductivity was achieved at 50 °C. Three samples were selected based on the maximum thermal conductivity enhancement for further quantitative analysis of stability by employing UV–vis spectrometer as well as temporal measurements of zeta potential. The temporal study of zeta potential with all concentration of nanoparticles with optimized surfactant, based on stability and thermal conductivity improvement, have been carried out as shown in Table S8 (supplementary information). The optimized concentrations show sufficient repulsive forces over tested period of four days. However, the SDBS stabilised optimized nanofluids have shown enhanced performance over CTAB as CTAB hardly crosses the threshold zeta potential

Table 5 Optimal Surfactant Concentrations Corresponding to Maximum Thermal Conductivity Enhancement at different temperatures. RGO Vol%

Optimal Surfactant Concentration (vol%)

Maximum Thermal Conductivity Enhancement (%)

SDS

SDBS

CTAB

SDS

SDBS

CTAB

No Surfactant

T = 25 °C 0.02 0.04 0.05

2 1 0.5

1 0.05 1

0.05 1 2

3.5 3.9 2.3

3.3 2.8 4.2

2.7 2.1 4.6

2.4 2.6 1.6

T = 50 °C 0.02 0.04 0.05

0.05 1 0.05

1 0.5 1

0.5 1 0.5

6.1 3 7.4

11.3 7.4 8.3

3.4 5.8 7.4

3.3 3.3 7.4

T = 70 °C 0.02 0.04 0.05

1 0.5 1

2 0.5 0.05

0.05 2 1

3.5 0.6 0.1

4.5 4.5 2.6

3.2 2.8 3.8

3.8 2.3 3.2

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Fig. 7. Effect of surfactants on thermal conductivity of base fluid (a) with SDS, (b) with SDBS, (c) with CTAB and (d) thermal conductivity of base fluid (EG) and rGO without surfactant at different temperatures.

(±30 mV) over tested period. Thus, the zeta potential has shown sufficient repulsive forces in the presence of surfactant. However, the coagulation in the colloidal suspension is the obvious phenomenon, therefore, flocculation might occur leading to sedimentation of large colloids. In order to estimate the relative concentration of tested nanofluids, UV–vis spectroscopy has been employed to record light spectra over a period of four days. Based on the recorded spectra, the relative concentration has been estimated [17–19]. The UV–vis spectrum of nanofluids is presented in Fig. 9(a–c) whereas the corresponding relative concentration is given in Fig. 9d. The spectrum peak for the tested nanofluids was obtained in a wavelength range of 279–306 nm. Based on the quantitative results obtained from UV–vis analysis, the relative concentration was found to be ~83% (0.02 vol% rGO + 1 vol% SDBS), ~54.81% (0.04 vol% rGO + 1 vol% CTAB) and ~55.4% (0.05 vol% rGO + 1 vol% SDBS) after four days of preparation, respectively. The current results suggest that the 0.02 vol% of rGO with 1 vol% SDBS is the optimum nanofluids with maximum relative concentration among tested nanofluids after four days. Sarsam et al. reported ~82% relative concentration with (1–1) SDBS-GNPs water based nanofluids after 60 days [19]. Selvam et al. showed ~21% sedimentation (79% relative concentration after 15 days) and ~10% sedimentation (90% relative concentration after 15 days) for water and ethylene glycol based 0.01 vol% GNP nanofluids respectively using 0.75 vol% sodium deoxycholate (DOC) as a surfactant [17]. Amiri et al. depicted ~26% sedimentation (74% relative concentration after 30 days) for crumpled nitrogen doped graphene (CNDG) in a water and ethylene glycol mixture [18]. The variation in current and previous findings regarding the relative concentration over time could be ascribed to materials used and preparation technique. However, the current research also high-

lights the substantial improvement in thermal conductivity, stability as well as reduction in viscosity as compared to the base fluid. 3.2.2. Enhancement mechanisms The thermal conductivity enhancement primarily depends on the interconnected percolation networks, synergic effect of nanoparticles and the inherent properties of the base fluid [53– 56]. The percolation network form clusters which contributes to heat conduction. However, the Van Der Waals forces of attraction often causes the particles to aggregate. The small aggregate remains suspended inside the base fluid and establish a connecting network between various small size suspended clusters, provided the space among them is small. These interconnected clusters form a thermal short circuitry which reduces the thermal resistance leading to the better heat conduction [57]. The nanoparticles concentration, shape and size influence transport properties due to phonon conduction which is ascribed to the high aspect ratio backbone. This phenomenon suggested that the long chain fibre particles can enhance thermal conductivity effectively due to less dead ends. The rheological properties and intrinsic thermal conductivity of the base fluid are responsible for the effective enhancement of thermal conductivity of the nanofluids. In addition, liquid layering and Brownian motion have been considered as the driving force for possible enhancement in the thermal conductivity. The base fluid molecules interact with nanoparticles at the interface to form an interfacial layer, which act as a liaison between the nanoparticle and bulk fluid, thereby giving rise to thermal conductivity, as it acts like solid phase structure which improves heat conduction [58]. The Brownian motion depends on the size of particles/clusters. The smaller size particles/clusters have sufficient movement inside base fluid which create micro

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Fig. 8. Repeatability study of thermal conductivity w.r.t concentration of rGO and optimized sample with SDBS.

convection. Under such circumstances, thermal conductivity enhancement could be attributed to thermal diffusion of particles/ clusters [59]. Zhu et al. witnessed a comparable clustering behaviour with aqueous-Fe3O4 nanofluids, representing a nonlinear thermal conductivity augmentation with shriller increase at relatively lower particle concentration [60]. They ascribed the decrease in the augmentation after a certain volume concentration as at higher concentration relatively more dense and compact clusters are formed. They indicate that at low volume concentrations, lightly packed clusters are formed which leads to an increase in the number of heat transfer paths, while additional increase in concentration just surges the density of existing paths rather than creating new. However, a recent study have shown that clustering may also cause overestimation of thermal conductivity of nanofluids using hot wire technique, as large cluster may start settling down during measurements leading to strong convection error around the sensor needle [61]. Therefore, besides having so many mechanisms of thermal conductivity enhancement, the unusual behaviour of nanofluids cannot be ignored since there are no universal rules which govern the behaviour of nanofluid. 3.3. Rheological analysis Ethylene glycol is considered to be Newtonian fluid. The addition of nanoparticles may change the viscous behaviour of ethylene glycol to elastic [62]. This phenomenon may cause additional pumping power in flow systems by using nanofluids as compared to base fluid. Therefore, oscillation and rotational tests were performed to investigate the possible viscoelastic regime and shear

flow behaviour of rGO nanofluids for SDS surfactant as a comprehensive model. 3.3.1. Oscillation test results Firstly, in order to analyse the detailed rheology of the nanofluid, the strain sweep test was performed. Fig. 10(a–b) shows the effect of rGO concentration on the elastic (storage modulus, G0 ) and viscous (loss modulus, G00 ) behaviour of control (without surfactant) nanofluids. As obvious from the obtained results, it was observed that the elastic behaviour becomes dominant at higher concentration of rGO. At a low value of deformation, 0.05 vol% rGO shows a clear hall-marked plateau above 0.02 and 0.04 vol% which are called linear viscoelastic (LVER) domains. In such regions, nanofluids behaves like gels or semi-solid structured fluids without flow. When, both G0 and G00 start decreasing, it indicates the breakdown of the structured network of nanofluids. The crossover point of G0 and G00 is called flow point of the nanofluid. The flow point stress corresponding to 0.02, 0.04 and 0.05 vol% rGO was found to be 0.1813, 0.0795 and 3.388 mPa respectively. As evident by higher LVER limiting stress, the flow point stress was found to be maximum at 0.05 vol% of rGO but reduces from 0.02 to 0.04 vol% rGO, as presented by the Fig. 10(a). To understand this reduction phenomenon, frequency sweep test was performed as shown in the Fig. 10(c–d) for various concentrations of rGO. Ignoring the odd data points in the analysis, G0 and G00 appears to be less and high respectively at 0.04 vol% rGO. The crossover (G0 = G00 ) frequencies are 1.7886, 0.34356 and 1.4537 Hz corresponding to 0.02, 0.04 and 0.05 vol% rGO, respectively. These values are consistent with the strain sweep test results. This behaviour is often referred to the stability of structured fluids.

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Fig. 9. UV–vis spectrum of rGO/EG surfactant contained nanofluids and corresponding relative concentration.

Fig. 10. Storage (G0 ) and loss (G00 ) moduli without surfactant as a function of, (a–b) Deformation and (c–d) Frequency.

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Probably, at 0.04% rGO, the particles are well mixed at lower frequency as compared to the 0.02 and 0.05 volume concentration. Therefore, it shows the less flow point stress at 0.04 vol% rGO. However, structured nanofluids rheology needs in depth investigation due to complicated sample network and behaviour. Another verification test (3ITT) was also performed to establish the structured network of nanofluids. The recovery time of samples was recorded as 1.0887 s, 1.0703 s, and 1.0677 s for 0.02, 0.04 and 0.05 vol% rGO respectively (see supplementary data Fig. S1(a–c)). According to the 3ITT assays, the recovery time was found to be less, which confirms the samples regain viscoelastic domain quickly after deformation is removed, with no significant thixotropy. Consequently, the results indicate that the viscoelastic behaviour of nanofluids is concentration dependent. However, stable samples play a vital role to record accurate trends. Table 6 demonstrates the detailed analysis of the SDS concentration effect on flow behaviour of nanofluids at 25 °C based on the oscillation test. SDS addition have shown that at small concentration crossover stress increases due to cluster formation as the surfactant adsorbed onto the rGO surface which may offer resistance to structure breakdown. This effect holds well for 0.02 and 0.04 vol% rGO but at 0.05 vol% it shows the reduction, which may be attributed to quick sedimentation due to large agglomerates. A similar effect has also been observed for the rest of the SDS concentrations with some variations. The other reason for such behaviours may be the sensitivity of measuring system, as quick sedimentation allows the system to identify the liquid part of the samples.

3.3.2. Rotational test results 3.3.2.1. Shear flow behaviour without SDS. In addition to the oscillation study, rotational investigation was also performed since the concentration of nanoparticles and surfactants may change the shear flow behaviour of nanofluids, along with temperature dependent viscosity. The shear flow analysis was performed for SDS and control samples. The experimental uncertainty associated with the viscosity measurements of SDS containing nanofluids is presented in Table S11 (Supplementary information). Fig. 11 shows the shear stress plotted against the dynamic viscosity which is an obvious indication for modification in shear flow behaviour based on rGO and SDS concentration. At low shear rates (0.01–1 s1), all samples show Non-Newtonian behaviour which turns to Newtonian at high shear rates, specifically in a shear rate range of 10–1000 s1. The viscosity at low shear rates appear to be higher due to the presence of rGO flakes. This may be due to the reason that the flakes oppose the fluid movement, as the flakes may orient themselves in opposite to the spindle rotation. The Newtonian viscosity of ethylene glycol at 25 °C was calculated to be 15.46 mPas at a shear rate of 10–1000 s1 using the regression model. When rGO is added in base fluid, Newtonian viscosity show considerable reduction due to self-lubrication characteristics of rGO. This reduction in viscosity was in good agreement with findings of Wang et al. [24]. They observed a decrease in viscosity of thermal oil base nanofluids using graphene and inferred that this reduction cannot be considered linear with concentration.

In addition, Liñeira et al. have shown a considerable reduction in friction coefficient of nanolubricants with the addition of rGO [63]. Esfahani et al. reported that the addition of graphene oxide in DI water did not alter the viscosity up to 0.05 wt% of the nanoparticles [22]. Therefore, current results are consistent with literature data. The reduction in viscosity was noted about 6.9% to 8.8% for 0.02 and 0.04 vol% rGO respectively Fig. 11(a). This shows that the nanofluids possesses better liquidity due to combine effect of base fluid and nanoparticles [64]. Such reduction in viscosity is desirable for flow thermal systems for optimum performance [65]. However, at 0.05 vol% rGO, the viscosity show some increment of about 4.7%, as the higher concentration offer more resistance to flow [66]. These results show the concentration dependency of viscosity, as some optimum concentrations of rGO play an essential role in reducing the interfacial tension between base fluid and rGO network. At higher concentrations, the aggregate size becomes large which offer more resistance to flow of nanofluid in the spindle rotation direction. These results are in good agreement with the oscillation measurements. Taking note on low shear rate/frequency, thinning/thickening behaviour is obvious due to the addition of nanoparticles in base fluids. Unlike polymeric liquids, nanoparticles show non-Newtonian behaviour at low shear/frequency due to strong interaction among particles (with large surface area to volume ratio) leading to percolation which can remain up to a certain length scale of shear rate and frequency [67]. 3.3.2.2. Shear flow behaviour with SDS. Further, the addition of SDS surfactant has shown improvement in the apparent viscosity in combination with rGO. The reduction in viscosity goes up to 21.9% at 0.5 vol% SDS with 0.02 vol% rGO as shown in Fig. 11(b). This improvement persists even at higher volume concentrations of rGO. A 12.2% reduction in viscosity was observed at 1 vol% SDS with 0.04 vol% rGO while 10.6% at 2 vol% SDS with 0.05 vol% rGO as depicted in Fig. 11(c) and (d). This effect of surfactant may be attributed to its reduction ability for interfacial resistance. By doing so, the interaction between the surface adsorbed rGO and base fluid improves, which assist to reduce viscosity. Fig. 13(a) gives a clear picture of the viscosity reduction in terms of relative viscosity over a range of shear rates. 3.3.2.3. Temperature sweep test results. In addition to shear flow behaviour, temperature sweep viscosity of all tested samples reduces with temperature. This is due to the minimization of intermolecular forces between the base fluid network layers as shown in Fig. 12. The viscosity of the base fluid was reduced to 56.4% and 73.9% at 50 °C and 70 °C respectively. However, the addition of nanoparticles further improves this reduction to 58.7%, 58.9% and 75.2%, 75.7% at 0.02 and 0.04 vol% rGO, respectively, due to self-lubrication ability [68]. Interestingly, the addition of 0.05 vol % rGO showed increment about 50.6% and 61.3% at 50 °C and 70 °C, respectively, as compared to base fluid with the overall decreasing trend with temperature. The tested samples have shown similar behaviour as reported in literature for CuO based nanofluids where the viscosity reduces to 40% at 65 °C. This

Table 6 Storage (G0 ) and loss (G00 ) moduli as a function of Deformation at 25 °C using SDS for all concentrations of rGO. RGO vol%

0.02 0.04 0.05

Flow Point Stress (mPa) at G0 = G00 SDS Vol% 0.05

0.5

1

2

3.443 10.08 0.4146

0.00399 2.744 0.1033

0.000885 4.63 2.5

0.43 No crossover (see supplementary data Fig. S2) 0.4151

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15

Fig. 11. Shear flow behaviour at 25 °C temperature.

reduction is inferred as a result of improved Brownian motion at elevated temperature as the high random movement of the particles results in low intermolecular forces among them and base fluid, which lowers the viscosity at high temperature [54]. The viscosity measurement results of nanofluids with the addition of SDS surfactant at 25 °C, 50 °C and 70 °C in terms of % increase/decrease are summarised in Table 7. Here, it is worth mentioning that a critical temperate limit where the viscosity inverts and start increasing was observed in the nanofluid, as shown in Fig. 13 which represents the relative viscosity plotted against temperature and shear rate for 0.02 vol% rGO without and with SDS. Fig. 13(b) indicates that the critical temperature after which the decrease in viscosity inverts and start increasing. Though, again it showed decrement around 70 °C within targeted temperature range. Such behaviour of nanofluids viscosity with temperature is obvious as reported by Yu et al. [37]. They attributed such behaviour to the fact that, at the same temperature, the viscosity of the base fluid network might not decrease sufficiently as compared to agglomeration networks viscous hindrance. They propose, such behaviour might be avoided using high shear rates at elevated temperatures. Similar viscosity behaviour with other measured samples has also been observed. In order to perform repeatability study for dynamic viscosity, the optimized nanofluid was selected with 0.02 vol% of rGO and 1 vol% of SDBS. Moreover, to verify the repeatability of present data with SDS surfactant, four samples were selected including control sample with 0.02 vol% rGO and base fluid. In total, six samples have been taken for repeatability over a period of four days. The repeatability study results for SDS contained nanofluids are presented in Fig. S11. The maximum relative standard deviation in repeatability was noted to be ~5.6%. Therefore, the present viscosity data is

acceptable within the given range of uncertainty. However, the 0.02 vol% rGO with 1 vol% SDBS has shown maximum enhancement in thermal conductivity along with maximum relative concentration. Therefore, the dynamic viscosity study was performed for four days duration as shown in Fig. 14. The maximum relative standard deviation was computed to be ~4% for nanofluid and ~2.3% for ethylene glycol. The overall viscosity reduction data for optimized nanofluid concentration is presented in the Table 8. The maximum reduction in viscosity is achieved to be ~13.4%, ~14.4% and ~15.8% at 25 °C, 50 °C and 70 °C respectively. The data appear to be repeatable and viscosity remain less than the base fluid over four days of testing. 3.3.2.4. Shear flow model analysis. Apart from the experimental investigation, the power law behaviour was also analysed for the tested samples, which is the indication for the viscoelastic regime. Researchers have employed Herschel-Bulkley (H-B) model to find the possible yielding [62]. Therefore, the obtained data is fitted using the Herschel-Bulkley (H-B) model between 10 and 1000 s1 as described by Eq. (1). (Supplementary data Fig. S3).

s ¼ so þ K ccc_ n

ð1Þ

where s is the shear stress, so is yield stress, K and n are consistency and flow index respectively. In the absence of yield stress, model converges to simple power law while for n = 1 simplified to Newton’s law. H-B model fitting parameters are presented in Table 8 for all variations of SDS and control samples. It is observed from Table 8, the yield stress is low at all measured rGO concentrations without SDS. However, it varies considerably by adding different concentration of SDS, which confirms the strong structured network of the nanofluids.

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Fig. 12. Temperature dependent viscosity from 25 °C to 70 °C.

Fig. 13. Relative viscosity variation (a) with shear rate and (b) with temperature.

3.3.2.5. Empirical and classical viscosity models. In addition to viscoelastic model fitting, few empirical and classical viscosity models are also compared with experimental data. The Eqs. (2),(3),(4) and (5) represent the Einstein [69], Batchelor [70], Wang et al. [3] and Zhu et al. [54] viscosity model respectively.

ls ¼ lbf ð1 þ 2:5£Þ

ð2Þ

ls ¼ lbf ð1 þ 2:5£ þ 6:2£2 Þ

ð3Þ

ls ¼ lbf ð1 þ 7:3£ þ 123£2 Þ

ð4Þ

ls ¼ lbf ð1 þ 117£ þ 269:8£2 þ 299:6£3 Þ

ð5Þ

where ms is colloidal suspension viscosity, mbf is base fluid viscosity and / is the particle concentration. As all models are derived from the Einstein, so they assume spherical shape of particles. Due to complexity of the nanofluids structures, no model perfectly validate the experimental data due to many factors [54]. All models predict

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Table 7 Comparison of Viscosity data for enhancement/reduction (%) at 25 °C, 50 °C and 70 °C using constant shear rate of 10 s1. ‘‘+” sign indicate enhancement and ‘‘–” sign indicate reduction in viscosity as compared to the base fluid. RGO 0.02 vol% 25 °C

SDS (vol%)

RGO 0.04 vol% 50 °C

70 °C

RGO 0.05 vol%

25 °C

50 °C

70 °C

25 °C

50 °C

70 °C

8.8 9.4 5.5 12.2 0.7

5.7 5.7 2.6 7.6 1.8

6.7 0.7 4.6 8.5 1.3

4.7 10.5 8.8 3.2 10.6

13.3 6.6 3.0 0.2 6.4

48.6 5.8 6.0 0.9 3.6

Viscosity Enhancement/Reduction (%) 6.9 11.3 21.9 15.9 11.3

EG-Day 1 EG-Day 2 EG-Day 3 EG-Day 4

Viscosity (mPa·s)

18

5.3 9.0 13.3 11.7 7.6

4.7 1.4 15.4 6.9 6.2

BF Relative Standard deviation=2.279%

Experimental Einstein Batchelor Wang et al. Zhu et al.

100

Viscosity (m.Pas)

0 0.05 0.5 1 2

15

80 60 40 20

NFs Relative Standard deviation=4.043%

12

0.02 vol% rGO + 1 vol% SDBS-Day 1 0.02 vol% rGO + 1 vol% SDBS-Day 2 0.02 vol% rGO + 1 vol% SDBS-Day 3 0.02 vol% rGO + 1 vol% SDBS-Day 4

10

100

0 0.00

0.01

0.02

0.03

0.04

0.05

RGO (Vol%) 1000

Fig. 15. Viscosity data comparison with classical models and empirical relations.

Shear Rate (1/s) Fig. 14. Optimized nanofluid dynamic viscosity study over four days.

higher value of the viscosity with respect to concentration of nanoparticles as shown in Fig. 15. It might be due to fact that, the Einstein model remains valid for particles concentration but not for particle intrinsic properties, as in our case lubricity effect is observed up to certain concentration. Similarly, all other models are derived from the Einstein model. Specifically, Einstein model assume spherical particles with no surface charge and interaction. Therefore, no general model exist which holds good for all type of nanofluids. Current results show that the various parameters, such as particle size, shape, temperature, collation, dispersion state of the nanoparticles, interface interactions between nanoparticles and base fluids, and the dispersion quality have substantial effect on the viscosity change. 3.3.2.6. Temperature sweep model analysis. To demonstrate the effect of temperature on the viscosity of the fluid, Arrhenioustype equation has been used [71,72].

geff ¼ g1;T :eRT Ea

ð6Þ

where geff is the experimental viscosity at any given temperature, g1,T is viscosity at infinite temperature, Ea is the activation energy which correspond to inter layer friction in fluid and the R is the

universal gas constant. The activation energy and infinite viscosity shows the behaviour of fluid flow. Therefore, the experimental data is flitted using Eq. (6) and corresponding values of activated energy and infinite viscosity are recorded as shown in Table 9 for nanofluids, whereas Table 10 depicts the base fluid fitting parameters for both Arrhenious and Herschel-Bulkley models. Addition of nanoparticles show the reduction in activation energy from 0.2708 to 0.2623 and 0.2588 for 0.02 and 0.04 vol% rGO respectively. This phenomenon suggests the lubricity behaviour of nanoparticles which is in good agreement with rheological results. Further, the addition of SDS in 0.02 and 0.04 vol% rGO reduced the activation energy, which is attributed to behaviour of surfactant as observed in rheological analysis. In addition, it is worth mentioning that the viscosity of the nanofluids became more sensitive to temperature especially at 0.02 and 0.04 vol% rGO as compared to base fluid, whereas at 0.05 vol% it showed less sensitive behaviour. Further, the coefficients of determination (R2) is an indication of quality of fit, the fitting tendencies show good agreement with the experimental data, both for H-B and Arrhenius models (see Table 11). It is a well known fact that, the performance of any heat transfer fluid such as coolant not only depends on the improved thermal conductivity but also on the viscosity [73]. It was demonstrated that, the viscosity enhancement can deteriorate the system efficiency as 5–53% reduction in efficiency was shown using nanofluids as compared to base fluid. Therefore, considering the

Table 8 Viscosity data on optimized nanofluid. RGO 0.02 vol% Day 1 SDBS (vol%)

25 °C

Day 2 50 °C

70 °C

Day3

Day 4

25 °C

50 °C

70 °C

25 °C

50 °C

70 °C

25 °C

50 °C

70 °C

10.1

11

12.2

9.3

8.7

6.8

12.9

11

12.3

Viscosity Enhancement/Reduction (%) 1

13.4

14.4

15.8

18

S.N.A. Shah et al. / International Journal of Heat and Mass Transfer 150 (2020) 118981

Table 9 Herschel-Bulkley rheological parameters obtained from flow curves in shear regime at 25 °C. RGO 0.02 vol%

RGO 0.04 vol%

SDS (vol%)

s0 (Pa)

K (Pa s )

n (–)

R

0 0.05 0.5 1 2

0.000670045 0.000528801 0.000919009 0.00153 0.02689

0.01488 0.01396 0.01202 0.01292 0.02104

0.99621 0.99887 0.99754 1.00067 0.92995

0.99999 1 0.99998 0.99999 0.99615

n

2

RGO 0.05 vol%

s0 (Pa)

K (Pa s )

n (–)

R

0.00069685 0.00891 0.00264 0.0012 0.0000635

0.01462 0.01644 0.01492 0.01385 0.01598

0.99611 0.98983 0.99621 0.99643 0.99739

0.99999 0.99998 1 0.99999 0.99999

n

2

s0 (Pa)

K (Pa sn)

n (–)

R2

0.00891 0.0027 0.00139 0.00412 0.00419

0.01644 0.01397 0.01422 0.01503 0.01396

0.98983 0.99871 0.99847 0.99844 0.99894

0.99998 0.99999 0.99999 0.99998 0.99998

Table 10 Arrhenius equation fitting parameters for SDS contained nanofluids at 25 °C. RGO 0.02 vol% SDS (vol%)

Ƞ1 (mPas)

RGO 0.04 vol% Ea (Jmol1)

R2

RGO 0.05 vol%

Ƞ1 (mPas)

Ea (Jmol1)

R2

Ƞ1 (mPas)

Ea (Jmol1)

R2

31.2889 30.96 32.086 28.04 35.13

0.2588 0.2536 0.2546 0.2397 0.2645

0.9975 0.9943 0.9923 0.9870 0.9964

30.88 30.30 29.60 33.37 30.04

0.2193 0.2546 0.2436 0.2598 0.2523

0.9704 0.9969 0.9927 0.9969 0.9962

Arrhenius equation fitting parameters 0 0.05 0.5 1 2

32.28 30.35 21.58 27.95 30.11

0.2623 0.2584 0.2013 0.2503 0.2555

0.9965 0.9953 0.9532 0.9940 0.9966

Table 11 Base fluid fitting parameters at 25 °C. Arrhenius Parameters

Herschel-Bulkley Parameters

Ƞ1 (mPas)

Ea (Jmol1)

R2

s0 (Pa)

K (Pa sn)

n (–)

R2

35.95

0.2708

0.9958

0.00675

0.01535

0.99964

0.99999

requirements of heat transfer fluid, this work will give a new direction in lowering the viscosity along with improved thermal conductivity using surfactant. The results of stability, thermal conductivity and the dynamic viscosity clearly indicates, 0.02% rGO with 1% SDBS is the optimum nanofluid with significant zeta potential and relative concentration over a period of four days. In addition, the optimized nanofluid shows substantial enhancement in thermal conductivity and reduction in dynamic viscosity as compared to base fluid.

4.

5.

6. 4. Conclusions The present work has demonstrated a comprehensive experimental characterization of stability, thermal conductivity and the rheology of rGO nanofluids with varying volume concentrations (0.02%, 0.04%, 0.05%) with and without surfactants (SDS, SDBS and CTAB). Only one type of surfactant (SDS) is considered to perform detailed rheological analysis in combination with rGO by employing volume concentrations of 0.05%, 0.5%, 1% and 2%. The work is concluded as follows: 1. Addition of surfactant has improved the dispersion stability in terms of surface charge (high zeta potential), thermal conductivity and the viscosity. 2. In contrast to high zeta potential by adding surfactants, large cluster sizes were observed in particle size distribution which can act as a possible phenomenon to enhance thermal conductivity as well as quick sedimentation under gravity. 3. However, there are optimum combinations of surfactant and nanoparticles which can alter these properties as per the requirement. For example, the present results revealed that without using any surfactant, 0.04 vol% rGO showed maximum stability, thermal conductivity and reduction in viscosity as compared to 0.02 and 0.05 vol% rGO. These properties are tuned

7.

8.

9.

with 1 vol% of SDS as zeta potential improved from 30.7 mV to 52.2 mV, thermal conductivity improves from 2.6% to 3.9% and the viscosity reduction improves from 8.8% to 12.2%. Viscoelastic analysis showed considerable yielding phenomena which show improvement at particular combinations of surfactant and rGO. No significant thixotropy is observed within 10–1000 s1 shear rate range for all tested samples which clearly indicates the Newtonian characteristics. The temperature sweep test indicated that the viscosity start increasing after critical temperature point which may deteriorate the thermal system performance at elevated temperature. The detailed rheology of one of the rGO nanofluids containing SDS as a model has been investigated experimentally. Therefore, the SDBS and CTAB effect remains to be explored and inclusion of surfactants should be an imperative topic to conduct extensive experiments to evaluate their effect on rheology of nanofluids. The results shows that the maximum enhancement in thermal conductivity (11.3%), reduction in viscosity (~14.4%) and the maximum relative concentration (~83% over four days) makes 0.02 vol% rGO with 1 vol% SDBS as best nanofluids combination at 50 °C. Finally, the present study highlights the potential of surfactant based nanofluids in thermal applications as anomalous reduction in viscosity (22%) along with thermal conductivity augmentation (11.3%) have been achieved at particular combinations of surfactant and rGO.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

S.N.A. Shah et al. / International Journal of Heat and Mass Transfer 150 (2020) 118981

Acknowledgements The authors acknowledge the financial support from University of Engineering and Technology Lahore, Pakistan under faculty development program and University of Malaya. Moreover, the authors would like to thanks Anton Paar Malaysia Sdn Bhd and Mr. Nigel Foong (Application Specialist, Anton Paar Malaysia Sdn Bhd) for assistance in rheological measurements using MCR302 rheometer.

[20]

[21]

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[23]

Appendix A. Supplementary material [24]

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijheatmasstransfer.2019.118981.

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