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Thermodynamics, Transport, and Fluid Mechanics

Reduced graphene oxide-Fe3O4 nanocomposite based nanofluids: Study on ultrasonic assisted synthesis, thermal conductivity, rheology and convective heat transfer Divya Barai, Bharat Apparao Bhanvase, and Virendra Saharan Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b05733 • Publication Date (Web): 16 Apr 2019 Downloaded from http://pubs.acs.org on April 16, 2019

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Industrial & Engineering Chemistry Research

Reduced graphene oxide-Fe3O4 nanocomposite based nanofluids: Study on ultrasonic assisted synthesis, thermal conductivity, rheology and convective heat transfer Divya P. Barai1, Bharat A. Bhanvase1,*, Virendra K. Saharan2 1

Department of Chemical Engineering, Laxminarayan Institute of Technology, Rashtrasant

Tukadoji Maharaj Nagpur University, Nagpur 440033, MS, India 2

Department of Chemical Engineering, Malviya National Institute of Technology, Jaipur,

Rajasthan, India 302017

* Corresponding author: E-mail address: [email protected] (B. A. Bhanvase)

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Abstract The present work deals with the preparation of reduced graphene oxide-Fe3O4 (rGO-Fe3O4) nanocomposite and its nanofluid by ultrasound assisted method for convective heat transfer study. Formation of rGO-Fe3O4 nanocomposite with uniform distribution of smaller sized (1020 nm) Fe3O4 nanoparticles on graphene nanosheets was confirmed from UV/Vis, TEM, Raman, XRD and XPS analysis. Thermal conductivity of prepared rGO-Fe3O4 nanocomposite based nanofluids with the aid of ultrasound showed an 83.44% enhancement for 0.2 vol.% concentration of rGO-Fe3O4 nanocomposite at 40℃. Rheological study revealed nonNewtonian behavior of the nanofluids. Various viscosity models were used to predict the behavior of rGO-Fe3O4 nanofluids. The estimated heat transfer coefficient with the use of 0.02 vol.% rGO-Fe3O4 nanofluid at the exit of the test section was 4289.5 W/m2K for the Reynolds number equal to 7510 ± 5. A new correlation for the estimation of Nusselt number has been proposed for the rGO-Fe3O4 nanofluid which very well fits the experimental data. ---------------------------------------------------------------------------------------------------------------Keywords: rGO-Fe3O4 nanocomposite based nanofluid; XPS; Thermal conductivity; Heat Transfer Coefficient; Rheological parameters

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1. Introduction Heat transfer improvement has become a major area of interest in order to conserve the energy. Use of conventional heat transfer fluids in industries such as water, ethylene glycol, oil etc. does not give satisfactory heat transfer rate. Increasing the thermal conductivity of the base fluids used in industries can prove to be an effective way of enhancement in heat transfer. Dispersions of solid particles in base fluids are found to have a higher thermal conductivity than the base fluid1. Macro- or micron-sized particles have been dispersed in base fluids but due to many disadvantages such as clogging, settling, abrasion of surfaces and increase in pressure drop, these dispersions are found to be uneconomical at large scale. Choi and Eastman2 have named fluids containing nano-sized solid particles as ‘nanofluid’ which have superior thermal properties due to better stability. Many researchers have investigated the heat transfer properties of nanofluids containing different metal oxide nanoparticles3-8. Fe3O4 is found to be an interesting material to be used in nanofluid applications as it has good thermal and magnetic properties and also a low cost and environmentally safe9-12. The main disadvantage is that the Fe3O4 nanoparticles tend to agglomerate. This can be avoided if these particles are loaded onto a surface like graphene thus avoiding contact of individual particles and therefore their agglomeration. So, a nanocomposite of magnetite i.e. Fe3O4 with some other material can be formed. Nanocomposites are the particles composed of two or more materials amongst which one is in nanoscale. Since Geim and Novoslev13 discovered graphene, there had been many studies reported on the applications of graphene due to its high thermal conductivity14. Graphene is a 2-D carbon structure that has sp2-hybridization. The sheets of graphene have strong out-of-plane bonds and therefore graphene tends to agglomerate by sheet stacking. Graphene oxide is an oxidized form of graphene which has comparatively lower thermal conductivity than graphene. It is 3



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commonly synthesized using the Hummers method as it is the fastest and simplest method15. Due to the repulsive forces exerted by the oxygen functionalities present on the surface of graphene oxide, it has lower chances of stacking and thus agglomerating. There are studies on thermal conductivity enhancement of nanofluids containing nanocomposites of graphene and metal oxides16-18. Many researchers have synthesized nanocomposite of graphene and magnetite for various applications like in dye removal19-21, lithium ion batteries22-24, as an adsorbent material25, in magnetic resonance imaging26, electromagnetic interference shielding27,

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, supercapacitors29, desalination30, targeted drug delivery31 and as H2O2

biosensors32. Further, it has been established that the use of ultrasonic irradiation with frequency more than 20 kHz in the preparation of nanocomposite leads to substantial reduction of the particle size of nanomaterials with significant reduction in the agglomeration. This is attributed to the generation of intense environment (10000 K and 1000 atm) due to collapse of cavities generated in the presence of high intensity ultrasound33-35. This phenomenon occurs locally for milliseconds. Further, due to the constant stirring action, this heat gets dissipated in the reaction medium and show negligible change in the temperature, mainly in the case of larger volume ultrasound bath. This intense environment creates physical and chemical changes in the reaction medium. The collapse of cavities leads to formation of shock waves, micro-mixing, turbulence, shearing action etc. during the preparation of targeted nanocomposite which leads to formation of uniformly and finely dispersed rGO-Fe3O4 nanocomposite particles36-38. The various applications of the graphene-Fe3O4 nanocomposite material have influenced researchers to try and apply it in the field of heat transfer. Askari et al.39 has studied the thermophysical properties of kerosene-based nanofluid containing Fe3O4 decorated graphene nanoparticles and found a maximum enhancement in thermal conductivity of 31% at 50℃ for 4


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nanofluid with particle concentration of 1%. They have used oleic acid in order to stabilize the nanoparticles and reported that the nanofluids are stable in kerosene for more than five months. A 66% enhancement in the heat transfer rate for the graphene-Fe3O4 nanofluid at a Reynolds number of 4553 was reported. They have claimed that the viscosity increment shown by prepared nanofluids was negligible at lower concentration which was satisfactory for its industrial applications. Mehrali et al.40 and Sadeghinezhad et al.41 also prepared stable graphene/Fe3O4 based ferro-nanofluid using tannic acid as reductant and stabilizer to analyse its heat transfer and entropy generation during flow in presence of magnetic field. A thermal conductivity enhancement of 11% was found for the 0.5 wt.% nanofluid at 40℃. Also, they found the enhancement of local heat transfer coefficient of 4% which was less than the thermal conductivity enhancement. Askari et al.42 synthesized water-based nanofluids using Fe3O4/graphene nanohybrid and investigated its thermal conductivity. They found an enhancement of 32% in thermal conductivity at 40℃ for nanofluid having 1 wt.% nanohybrid concentration. They carried out the convective heat transfer studies for the nanofluid in a straight tube heat exchanger and found a 14.5% enhancement in the convective heat transfer coefficient for 0.1 wt.% graphene-Fe3O4 at Reynolds number of 4248 compared to that given by water. The viscosity of the Fe3O4/graphene nanofluid was found to be 1.15 mPa.s at 20℃ for concentration of 0.5 wt.% which was negligible as far as its industrial applications were concerned. Two different theoretical models were used to correlate the viscosity data of the nanofluids. The study of rheology becomes important as far as the flow behaviour of the fluids is concerned. In the available literature, there are very few studies on rheological behaviour of nanofluids. Mahbubul et al.43 has studied how the yield stress is related to the sonication time utilized for preparation of the Al2O3 nanofluid and observed a rapid decrease in the yield stress 5



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at the beginning of ultrasonication and a gradual decrease for further ultrasonication period. Phuoc et al.44 stated that nanoparticles in the nanofluids exist as flocs which are structures of inter-linked network formed due to the forces of attraction between the nanoparticles. The presence of such flocs resists the flow thus giving rise to a certain amount of yield stress requirement even at lower concentrations of the nanofluids. The comparison of the viscosity obtained from the experimental data to the viscosity calculated from different rheology models along with the values of different rheological parameters are rarely studied for nanofluids. Hojjat et al.45 studied the rheological characteristics of different nanofluids and noted that the two fitting parameters of the power law, namely, the consistency index and the power law index are a function of concentration and temperature of the nanofluid. They also found that the viscosity predicted by the Einstein model and the Brinkman model agrees well with that obtained experimentally at lower concentrations of Al2O3, CuO and TiO2 nanofluids. There have been studies reporting the convective heat transfer properties of the grapheneFe3O4 nanofluid, but for a successful application of graphene-Fe3O4 nanocomposite in nanofluid, use of proper synthesis method is of great importance as the properties of the nanocomposite shall depend on the way it has been synthesized. Here, we report for the first time, the heat transfer study of rGO-Fe3O4 nanocomposite synthesized by ultrasonic assisted process. Nanofluids of different concentrations ranging from 0.01 to 0.2 vol. % were prepared using water as a base fluid without the use of surfactant and thermal conductivity was investigated at different temperatures ranging from 25 to 40℃. Also, a correlation for thermal conductivity and volume fraction of the nanofluid was determined using the data at particular temperatures. Rheological study was also done for different concentrations of the nanofluid. Three different viscosity models were used to fit the experimental viscosity data. Also, the relative viscosity was compared to that predicted by the Einstein model. Convective heat 6


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transfer studies were done considering the 0.01 and 0.02 vol.% rGO-Fe3O4 nanofluid along with a pressure drop study comparing water and 0.01 vol.% nanofluid. For this purpose, a labscale straight tube heat exchanger was used. 2. Experimental 2.1 Materials The materials required for the synthesis of rGO-Fe3O4 nanocomposite include graphite powder (98%), KMnO4 (99%), NaNO3, FeCl2.xH2O (98%), H2O2 solution (30%) were purchased from Loba Chemie Pvt Ltd, India. Anhydrous FeCl3 was purchased from Sisco Research Laboratories Pvt. Ltd, India. H2SO4 (98%) was purchased from S D Fine-Chem Limited, India. Hydrochloric acid (37%) and ammonia solution (25%) were purchased from Merck Specialities Pvt. Ltd., India. All the chemicals were used as received. All the solutions were prepared in deionized water (DI water). 2.2 Ultrasound assisted synthesis of graphene oxide Graphene oxide was synthesized using the Hummers’ Method in a bath sonicator (Dakshin Ultrasonics, India) which has a fixed frequency of 30 KHz and power of 500 W. 46 ml of H2SO4 was added to a mixture of 1 g of graphite powder and 1 g of NaNO3 in an ice bath. This mixture was ultrasonicated for 10 minutes. After sonication, 5 g KMnO4 was added slowly to the above mixture and it was again ultrasonicated for 30 minutes. Ice bath was still maintained for controlling the reaction temperature. Further, 100 ml DI water was added drop by drop to the solution and it was ultrasonicated again for 5 minutes. After completion of ultrasonication, 8 ml H2O2 was added drop by drop to the resulting mixture and it was then filtered. After complete filtration, it was washed with 150 ml of 10 wt.% HCl solution and 100 ml DI water.

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The obtained suspension was filtered and dried in an oven at 60℃ for 2 hrs. Fig. 1 depicts the graphene oxide synthesis steps in a graphical format. 2.3 Ultrasound assisted synthesis of rGO-Fe3O4 nanocomposite The rGO-Fe3O4 nanocomposite was synthesized using ultrasonication (bath type sonicator, Dakshin Ultrasonics, India) with a frequency of 30 KHz and power of 500 W so as to decorate the graphene sheet uniformly with the Fe3O4 nanoparticles. In order to achieve this, graphene oxide solution (1 mg/ml) was prepared by adding 30 mg of the prepared GO to 30 ml DI water and sonicated for 20 minutes. Then, a solution of 99 mg FeCl2.xH2O and 270 mg FeCl3 in 30 ml DI water was prepared and sonicated for 5 minutes. Both the solutions were then mixed and again sonicated for 5 minutes. Further, ammonia solution (25%) was added drop by drop to this mixture under continuous stirring and a pH between 10 and 11 was maintained. A black coloured precipitate appeared in the solution. This solution was then heated at 85℃ for 15 minutes so as to remove excess amount of the ammonia from the reaction mixture and then ultrasonicated for 30 minutes at room temperature to avoid agglomeration and to ensure the preparation of finely dispersed rGO-Fe3O4 nanocomposite with smaller sized Fe3O4 nanoparticles. The obtained suspension was filtered, washed thrice with DI water and then dried in oven at 60℃ for 2 hrs. Fig. 2 shows graphical illustration of synthesis steps involved in the preparation of rGO-Fe3O4 nanocomposite. This product was then characterized using UV-Vis, TEM, Raman, XRD and XPS techniques. 2.4 Characterization The UV-visible spectra of GO and rGO-Fe3O4 nanocomposite prepared by ultrasound assisted method were obtained on UV-Vis spectrophotometer (LABINDIA Analytical UV3200 model). TEM image of the rGO-Fe3O4 nanocomposite was obtained from a Transmission Electron 8


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Microscope (Tecnai G2 20, FEI Company). Raman spectrum of the rGO-Fe3O4 nanocomposite was obtained on STR-500 Confocal Micro Raman Spectrometer. XRD pattern of the rGOFe3O4 nanocomposite was recorded on a Rigaku Mini-Flex X-ray diffractometer. The X-ray photoelectron spectroscopy (XPS) of the rGO-Fe3O4 nanocomposite was done using an Omicron ESCA (Electron Spectroscope for Chemical Analysis), Germany. 2.5 rGO-Fe3O4 nanocomposite based nanofluid preparation In order to investigate the thermal conductivity and convective heat transfer properties of the rGO-Fe3O4 nanocomposite, water-based nanofluids of different concentrations as 0.01, 0.05, 0.07, 0.1 and 0.2 vol.% were prepared by dispersing measured amount of rGO-Fe3O4 nanocomposite in distilled water. Dispersion was carried out using ultrasonication. 2.6 Thermal conductivity measurement of rGO-Fe3O4 nanocomposite based nanofluid The thermal conductivity is the major property which certifies the use of the prepared nanofluid for heat transfer enhancement. Thermal conductivity of the rGO-Fe3O4 nanocomposite based nanofluids was measured at different temperatures using the KD2 Pro thermal property analyser (Decagon Devices, Inc., USA) with its KS-1 sensor inserted vertically in the nanofluid. Nanofluids were prepared at different concentrations and ultrasonicated just before the thermal conductivity measurement. The thermal conductivity probe needle immersed in the nanofluid was maintained at a particular temperature for 2 minutes. The thermal conductivity analyser utilises transient hot wire method for measurement of thermal conductivity and the instrument directly displays the value after 2 minutes. Thus, measurement of thermal conductivity at nanofluid concentrations of 0.01, 0.05, 0.07, 0.1 and 0.2 volume % each at temperatures of 25℃, 30℃, 35℃ and 40℃ were obtained. The enhancement in thermal conductivity was calculated by using the Equation (1), where 𝑘𝑘𝑛𝑛𝑛𝑛 and 9



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𝑘𝑘𝑏𝑏𝑏𝑏 are thermal conductivities of nanofluid and basefluid, respectively. The value of thermal conductivity of water at various temperatures has been referred from available data46. 𝑘𝑘𝑛𝑛𝑛𝑛 −𝑘𝑘𝑏𝑏𝑏𝑏

% Enhancement in thermal conductivity = �

𝑘𝑘𝑏𝑏𝑏𝑏

� × 100

(1)

2.7 Rheology study of rGO-Fe3O4 nanocomposite based nanofluid The addition of nanoparticles has its effect on the rheological properties of the nanofluid. This can be studied by a test on Rheometer where viscosity of the nanofluid at different shear rate reveals its behaviour against applied forces. Viscosity measurements were conducted on RM200 Touch Rheometer by Lamy Rheology Instruments, France. This Rheometer was accompanied by a CP-1 (cone and plate) temperature controller utilizing Peltier effect. Nanofluids of required concentrations were prepared and ultrasonicated just before the viscosity measurement. A small amount of sample about 0.6 ml was poured onto the test plate. Ramp adjustments were made in the Rheometer software so as to obtain results at a particular range of shear rate applied for a given time. Thus the shear rate vs. shear stress data of the nanofluids prepared having concentrations as 0.01, 0.05, 0.07 and 0.1 volume % each at a temperature of 25℃ were used to compare with different viscosity models. The models considered for the comparison were Bingham model, Power law model and the Casson model. Bingham model47 is as given in Equation (2), where τ is the shear stress, τ0 is the Bingham

yield stress and n is known as the Bingham plastic viscosity. It assumes a linear relationship of shear stress and shear rate having the yield stress as a threshold stress. du

(2)

τ = τ0 + n dr

10


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Power law model is as given in Equation (3), where k is the consistency index and m is the flow behaviour index. It is used to describe shear thinning as well as shear thickening fluids. It can be simplified to obtain Equation (4). du m

(3)

τ = k � dr �

du

(4)

ln τ = lnk + mln � dr � The Casson model is as given in Equation (5), where

du dr

is the shear rate, τ0 is the Casson yield

stress and kc is the Casson plastic viscosity. It is a modified form of Bingham model and considers that the suspended nanoparticles form flocs of rod-like structures and break down which leads to increase in the shear rate48. It can be simplified to get Equation (6).

τ

1� 2

1 τ �2

= τ0 =

1� 2

1 τ0 �2

du

1� 2

+ �k c dr �

(5)

+

(6)

1 1� du �2 2 k c � dr �

The very first model to calculate the viscosity of a nanofluid was developed by Einstein49 which is given in Equation (7). It relates the viscosity of a nanofluid to its basefluid viscosity and volume fraction of nanoparticles. This model assumes spherical particles dispersed in a fluid at volume fractions of less than 0.02 and is the simplest of all the other models. μnf μbf

(7)

= 1 + 2.5φ

Later, Brinkman50 modified the Einstein’s model and developed another equation which is given in Equation (8) to make it applicable for moderate particle concentrations of up to 4 vol.%. 11



μnf μbf

1

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(8)

= (1−φ)2.5

Both these conventional models along with two specific models reposted in Equation (9) and (10) for water-based rGO-Fe3O4 nanofluids developed by Askari et al.42 have been used to predict the relative viscosity of the 0.01 vol.% and 0.1 vol.% rGO-Fe3O4 nanocomposite based nanofluid. μnf

= 1 − 0.1φ + 15φ2

μnf

= (1−1.6φ)0.75

μbf μbf

(9)

1

(10)

2.8 Experimental set-up for the convective heat transfer study using rGO-Fe3O4 nanocomposite based nanofluids The study of heat transfer enhancement offered by the prepared rGO-Fe3O4 nanofluid were carried out using a lab scale straight tube heat exchanger setup as shown in Fig. 3 under constant heat flux condition. The experimental setup consists of a test section for which a copper tube was fabricated with 1 m length and 1-inch inner diameter. This tube was covered with a heating coil which was connected to a dimmerstat. Five thermocouples were mounted on test section for measuring the wall temperatures at equal distances along the length of the tube. Also U-tube manometer was connected to the inlet and outlet of the test section in order to measure the pressure drop. The prepared nanofluid was stored in a nanofluid reservoir of the system. A centrifugal pump was used to circulate the nanofluid from reservoir to the test section. The outlet nanofluid from the test section was cooled and was brought back to its original temperature. Inlet temperature of the nanofluid was measured using a mercury thermometer and for measuring the outlet temperature of the nanofluid a thermocouple was used. 12


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2.9 Convective heat transfer and pressure drop study of rGO-Fe3O4 nanocomposite based nanofluid Experimental runs were carried out to study the effect of different concentrations of rGO-Fe3O4 nanocomposite in distilled water (0.01 and 0.02 vol.%) at particular flowrate of the nanofluid on heat transfer coefficient and Nusselt number. Also, the effect of wide range of Reynolds numbers (940 to 7510) on the heat transfer coefficient and Nusselt number was investigated for a particular volume % of rGO-Fe3O4 nanocomposite in the nanofluid. For the purpose of calculating the heat transfer coefficient and Nusselt number, various formulae were used along with determination of physical properties of nanofluids. Equation (11) was used to estimate the density of the nanofluid, which is given as follows. (11)

ρnf = (1 − φ)ρbf + φρp

where ρnf is the density of nanofluids, φ is the nanocomposites volume fraction, ρbf is the

density of the base fluid and ρp is the density of the nanocomposites. Also, the specific heat capacity was determined using Equation (12).

(12)

Cpnf = (1 − φ)Cpbf + φCpp

where Cp𝑛𝑛𝑛𝑛 is the specific heat of nanofluids, Cp𝑏𝑏𝑏𝑏 is the specific heat of the base fluid and

Cp𝑝𝑝 is the specific heat of the nanocomposites. Further, the viscosity of the nanofluids was

determined using the Equation (13) which is a simplified from the Einstein’s formula as given in Equation (7). (13)

μnf = μbf (1 + 2.5φ)

where μnf is the viscosity of nanofluid and μbf is the viscosity of the base fluid. 13



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The convective heat transfer coefficient ‘h(x)’ at a distance ‘x’ from the inlet was calculated with the use of Equation (14), qs s (x)−Tb (x)

(14)

h(x) = T

where 𝑞𝑞𝑠𝑠 is the heat flux applied to the fluid, Ts (x) is the wall temperature measured at a distance ‘x’ from the inlet and Tb (x) is the bulk temperature of the fluid measured at a distance

‘x’ from the inlet. The value of Tb (x) was calculated for each value of ‘x’ using the Equation (15).

q .P

(15)

s Tb (x) = Tb,i + �ṁ.Cp �x

where 𝑇𝑇𝑏𝑏,𝑖𝑖 is the fluid bulk temperature at the inlet, P is the perimeter of the copper tube, x is the axial distance, ṁ is the mass flow rate of the fluid and Cp is the specific heat capacity of the fluid. The heat flux was estimated with the use of Equation (16), qs =

ṁCp(Tb.o −Tb.i )

(16)

A

where Tb.o is the outlet fluid bulk temperature, Tb.i is the inlet fluid bulk temperature and A is

the inner surface area of the copper tube. The Nusselt number (Nu) was calculated from Equation (17) as follows. Nu(x) =

h(x)Di

(17)

kf

where D𝑖𝑖 is the inner diameter of copper tube and k is the thermal conductivity of the fluid.

The attached U-tube manometer was used to determine the difference in the level of water and thus the pressure drop was calculated using the Equation (18). Pressure drop study for 0.01 volume % rGO-Fe3O4 nanofluid was done and was compared with that of water. 14


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(18)

ΔP = Δhρg

where Δh is the difference in height of the fluid in manometer, 𝜌𝜌 is the density of the fluid and g is the acceleration due to gravity. The friction factor was determined with the available pressure drop data using the Darcy-Weisbach equation51 as given in Equation (19). 2D ΔP

f = ρv2𝑖𝑖

(19)

L

where D𝑖𝑖 is the inner diameter of the copper tube, v is the velocity of the fluid flowing through the copper tube and L is the length of copper tube. 3. Results and Discussion 3.1 Characterization of ultrasonically prepared rGO-Fe3O4 nanocomposite The UV/Vis, TEM, Raman, XRD and XPS analysis of rGO-Fe3O4 nanocomposite prepared with the help of ultrasound was carried to study the successful formation of the said nanocomposite. The UV-visible absorption spectrum of rGO-Fe3O4 nanocomposite was recorded and depicted in Fig. 4. In the case of graphene oxide, a peak at 229 nm is due to the presence of π→π* transition of C-C bonds and the shoulder at 300 nm is attributed to the n→π* transition of C=O bonds52,

53

. Both of these observations confirm the formation of

graphene oxide with the use of ultrasound assisted method. Further, a redshift of the absorption peak of graphene oxide to 250 nm can be seen in the absorption spectrum of rGO-Fe3O4 nanocomposite54, which is due to the restoration of C=C bonds in the graphene sheets. Another peak at 332 nm confirms attachment of Fe3O4 to reduced graphene oxide. The characteristics peak at 332 nm indicates the interaction of Fe3O4 nanoparticles with reduced graphene oxide which arises from the formation of Fe-O-C functionalities during the formation of rGO-Fe3O4 nanocomposite in presence of ultrasonication. This is also attributed to the presence of oxygen 15



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functionalities over the graphene oxide sheets that act as nucleation sites for the growth of Fe3O4 nanoparticles on graphene oxide nanosheet and that facilitates the formation of rGOFe3O4 nanocomposite55. The morphology of ultrasonically prepared rGO-Fe3O4 nanocomposite was studied with the help of TEM analysis. The obtained TEM images of rGO-Fe3O4 nanocomposite are shown in Fig. 5. It can be seen that a significant amount of smaller sized Fe3O4 nanoparticles cover the complete sheet of reduced graphene oxide. Nearly spherical nature of Fe3O4 nanoparticles loaded on rGO was observed and the observed particle size of Fe3O4 nanoparticles is around 10-20 nm with very less amount of agglomeration. This confirms the uniform and fine loading of the Fe3O4 nanoparticles on rGO sheet. This is attributed to the physical effects (intense mixing, intense shearing action, turbulence etc.) of the ultrasound which causes the complete exfoliation of rGO sheets and reduction in the particle size of Fe3O4 nanoparticles loaded on rGO sheet23,

56-58

. Further, loading of Fe3O4 nanoparticles on both sides of rGO sheets is

possible due to presence of hydroxyl and carboxylic functional groups onto the both sides of the graphene oxide. In the Raman spectrum of rGO-Fe3O4 nanocomposite depicted in Fig. 6(a), two intense peaks at 1345 cm-1 and 1600 cm-1 were observed which are attributed to the D band and G band, respectively of carbon with ID/IG ratio equal to 0.8859. The D band appears as a signature of the defects, disordered carbon and oxygen-containing functional groups on surface of graphene and the G band being the indication of in-plane stretching of ordered sp2 carbon atoms. The 2D band at 2676 cm-1 is also seen. A small characteristic A1g mode of Fe3O4 was observed at 685 cm-1 that confirms the successful formation of rGO-Fe3O4 nanocomposite which is reported to be one of the highest frequency modes of Fe3O4 in available literature60.

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The structural properties of ultrasonically prepared rGO-Fe3O4 nanocomposite were studied with the help of XRD analysis. Fig. 6(b) depicts XRD pattern of rGO-Fe3O4 nanocomposite. The characteristics diffraction patterns of Fe3O4 nanoparticles loaded on rGO sheets at 30.5, 35.9, 43.4, 52.9, 57.6o and 63.3° corresponding to the planes at (2 2 0), (3 1 1), (4 0 0), (4 2 2), (5 1 1) and (4 4 0) respectively, were observed that confirms the deposition of Fe3O4 nanoparticles on rGO sheets. This is according to the standard data of Fe3O4 (JCPDS card No. 19-0629). Similar type of results and its discussion is reported for rGO-Fe3O4 nanocomposite in the literature59, 61. Further, Fig. 7 shows the XPS survey scan (Fig. 7(a)) and other narrow scans spectrum of Fe2p, C1s, and O1s of rGO-Fe3O4 nanocomposite in order to get in depth analysis of the element composition and confirmation of formation of rGO–Fe3O4 nanocomposite. It confirms the existence of carbon, oxygen and iron relating to the Fe3O4 nanoparticles and graphene sheet. The formation of Fe3O4 nanoparticles is confirmed by the narrow scan of Fe2p spectrum (Fig. 7(b)) in which the peaks at 710.8 and 724.2 eV represent Fe2p3/2 and Fe2p1/2 that designates the formation of Fe3O4 (which is a mixed oxide of Fe(II) and Fe(III))62. Fig. 7(c) depicts the narrow scan of C1s spectrum of rGO-Fe3O4 nanocomposite. The peaks at 284.3, 286.3, 287.9 and 288.5 eV represent the existence of four C-C/C=C, C-O, C=O and O-C=O groups, respectively40. Further, the peak intensity for the oxygen containing functionalities present in rGO i.e. C-O, C=O and O-C=O groups is comparatively less which is an indication of reduction of graphene oxide during the formation of rGO–Fe3O4 nanocomposite61. The O1s spectrum of rGO–Fe3O4 nanocomposite (Fig. 7(d)) shows peaks at 530.7, 531.4 and 532.9 eV representing the bonds of Fe-O, C=O and C-O, respectively62. The peak at 530.7 eV is due to the anionic oxygen in Fe3O4 nanoparticles loaded on rGO sheets. Further, the characteristics peaks at 531.4 eV and 532.9 eV are attributed to the carbonyl oxygen in C=O and oxygen in C17



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O. The intensity of the peak due to carbonyl oxygen in C=O at 531.4 eV is much less than intensity of the anionic oxygen in Fe3O4 at 530.7 eV which is attributed to the reduction of graphene oxide to rGO. Overall, the XPS analysis confirms successful formation of rGO– Fe3O4 nanocomposite using ultrasound assisted method. 3.2 Thermal conductivity study of rGO–Fe3O4 nanocomposite based nanofluids Thermal conductivity of a nanofluid is affected by many factors amongst which temperature and the concentration of the nanofluid are widely studied. The plot for thermal conductivity of different concentrations of rGO–Fe3O4 nanocomposite based nanofluids and reference data for deionized water as a function of temperature is given in Fig. 8(a). It can be clearly seen that for a given volume fraction of nanofluid the thermal conductivity increases with an increase in the temperature. This is attributed to enhancement in the Brownian motion of the nanoparticles present in the nanofluid with an increase in the temperature, thus giving rise to intensified heat dissipation caused by them. With an increase in temperature, the surface energy of the nanoparticles decreases, thus decreasing their agglomeration and also reduction in the viscosity of the nanofluid resulting in intensified Brownian motion of the nanoparticles. Percentage thermal conductivity increased from 9.21% at 25℃ to 83.44% at 40℃ for 0.2 vol.% nanofluid.

It can be also seen that an increase in the concentration of rGO–Fe3O4 nanocomposite in the base fluid gives a higher value of thermal conductivity. In nanofluids, the nanoparticles in the form of solids dispersed in liquids are in motion and can be called as “heat boats” that transport the heat energy and also as “stirrers” that induce the process of convection so as to increase the thermal conductivity of the nanoﬂuids by augmenting the motion of molecules and thus their collisions63. Further, the specific heat capacity of the nanofluids with 0.2 volume % of rGOFe3O4 nanocomposite particles in basefluid was found to be decreased by 0.25% when compared with the specific heat capacity of water i.e. basefluid. It is in well agreement with the 18


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reported literature64, 65. It has been claimed that the interaction present in the interface of the solid nanoparticles and the basefluid plays a major role in the decrease in the specific heat capacity of the nanofluids64. An increase in the number of nanoparticles in the base fluid increases these types of collisional effects and so the transfer of heat through conduction. Thus, at 40℃, the values of percentage thermal conductivity enhancement given for the concentrations of 0.01, 0.05, 0.07, 0.1 and 0.2 vol.% rGO–Fe3O4 nanocomposite based nanofluids were 41.35, 57.23, 66.76, 77.72 and 83.44%, respectively. Further, Fig. 8(b) depicts the plot for thermal conductivity vs. volume fraction of the nanofluids. Here, the thermal conductivity of the nanofluids is clearly found to be increasing with the volume fraction of the rGO–Fe3O4 nanocomposite at different temperatures. Following are the possible key factors that take part in enhancement of thermal conductivity of the rGO–Fe3O4 nanocomposite based nanofluids as far as the concentration of rGO–Fe3O4 nanocomposite particles in the nanofluid and temperature of the nanofluid is concerned. 1. Loading of rGO–Fe3O4 nanocomposite particle is a key factor which has influence on the thermal transport in the nanofluids and thereby enhancing the thermal conductivity with increased concentration of rGO–Fe3O4 nanocomposite particle in basefluid. 2. There are different types of forces acting on a single rGO–Fe3O4 nanocomposite particle in the nanofluid on a micro-level which are induced by various factors like those related with the basefluid or the surrounding particles which are affected by temperature of the nanofluid66. 3. The rGO–Fe3O4 nanocomposite particles carry the heat along with them and transport the heat energy throughout the fluid thus dissipating a substantial amount of thermal energy with increased concentration of rGO–Fe3O4 nanocomposite particle. 19



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4. Increased concentration of rGO–Fe3O4 nanocomposite particles induce the process of convection in the nanofluid due to their stirring effect which causes increase in the thermal conductivity. 5. Greater number of rGO–Fe3O4 nanocomposite particles present in the nanofluid increases the possibility of collisions between them resulting into immediate conduction of heat. Also a well-arranged liquid layer at rGO–Fe3O4 nanocomposite particle interfaces and ‘tunneling’ of heat-carrying phonons from one particle to another increases with an increase in the number of particles in the nanofluids are responsible for enhancement in the thermal conductivity. 6. Higher temperature leads to excitement of the rGO–Fe3O4 nanocomposite particles thus enhances their Brownian motion which leads to higher chances of their collisions a thereby increase in the thermal conductivity of nanofluid. 7. High temperature also lowers down the surface energy of the rGO–Fe3O4 nanocomposite particles when these are present in basefluid which enable them to detach from each other and thus reduction in agglomeration is achieved in turn enhances the thermal conductivity. 8. An increase in temperature also helps in lowering the viscosity of the basefluid thus enhancing the flow properties of the rGO–Fe3O4 nanocomposite based nanofluid, which ultimately increases the Brownian motion of the rGO–Fe3O4 nanocomposite particles and thus their chanced of collision and that enhances thermal conductivity of nanofluid. It is also clear that there is no significant increase in thermal conductivity of the nanofluid when rGO–Fe3O4 nanocomposite volume fraction was increase from 0.001 to 0.002 even at higher temperatures. For example, thermal conductivity measured at 40℃ for 0.001 volume fraction of nanofluid was 1.119 W/mK and that for 0.002 volume fraction nanofluid was 1.155 W/mK which proves that increasing the concentration of the nanofluid do not bring about large 20


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changes in the thermal properties of the nanofluid although the temperature of the nanofluid is high. This shows the dominating effect of volume fraction of rGO–Fe3O4 nanocomposite on the nanofluid thermal conductivity over the temperature of the nanofluid. The enhancement in the thermal conductivity with respect to temperature is significant at higher rGO–Fe3O4 nanocomposite volume fractions compared to lower volume fraction. This is attributed to increase in the number of suspended rGO–Fe3O4 nanocomposite particles in the formed nanofluid that boosts the ratio of surface to volume and collisions between rGO–Fe3O4 nanocomposite particles. Also, because of the presence of large number of rGO–Fe3O4 nanocomposite particles in the nanofluid, the temperature has significant effect on the motion of the suspended particles67 and thereby enhancing the thermal conductivity at higher volume fraction significantly with temperature compared to that of lower volume fraction. Further, due to presence of larger number of rGO–Fe3O4 nanocomposite particles at higher volume fraction, significant enhancement in the Brownian motion is observed with respect to temperature, which in turn responsible for higher enhancement in the thermal conductivity. Also at higher volume fraction of rGO–Fe3O4 nanocomposite particles, the motion of these particles in formed nanofluid in more appreciable, which is responsible for the conduction of more heat and therefore the thermal conductivity is significantly higher with respect to temperature at higher volume fraction of rGO–Fe3O4 nanocomposite particles in the nanofluid. It is also observed that the thermal conductivity enhancement of 0.2 vol.% nanofluid is nearly 14.6 times greater than that of 0.01 vol.% nanofluid at 25℃ whereas it is just twice at 40℃ which again depicts the higher concentration-dependence of thermal conductivity than temperature. The reason for this is the excessively increased interaction between the rGO– Fe3O4 nanocomposite particles due to increased concentration. This kind of interaction leads to micro-aggregation of the nanoparticles which cannot be disturbed even by higher temperatures. 21



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The data for thermal conductivity of rGO–Fe3O4 nanocomposite based nanofluids for different concentration at a particular temperature has been used to determine a relation between the thermal conductivity and volume fraction of nanofluid by polynomial regression. A second order polynomial equation was obtained for each temperature as a relationship between thermal conductivity and volume fraction of the rGO–Fe3O4 nanocomposite based nanofluid as given in Equation (20), where k nf is the thermal conductivity of the nanofluid, φ is the volume fraction of the nanofluid and a0 , a1 and a2 are constants.. k nf = a0 + a1 φ + a2 φ2

(20)

Here, the constant a0 can be replaced by thermal conductivity of the basefluid (k bf ). Thus, modifying the equation and rewriting it by holding k bf as a common term, we obtain Equation (21).

a

a

k nf = k bf �1 + k 1 φ + k 2 φ2 � bf

(21)

bf

The constants a1 and a2 exhibit the extent to which the thermal conductivity of the nanofluids

vary with the first and second power of the volume fraction of the nanoparticles in the nanofluid, respectively, while a0 is the intercept. As can be seen from Table 1, the values of the constants increase profoundly in equations derived for higher temperatures, which is due to

larger enhancement in values of thermal conductivity at higher temperatures. Also, the developed empirical model was considered to be function of volume fraction of rGO–Fe3O4 nanocomposite particles in nanofluid. The coefficients estimated in the present work are depicted in Table 1. It has been observed that the value of thermal conductivity appears to be close to that of water for the temperatures 25, 30 and 35 oC. However, this shows overestimate for the temperature equal to 40oC. Also with the substitution of φ = 0 in the correlation, the

correlation predict the thermal conductivity of water reasonably for the temperatures equal to

22


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25, 30 and 35 oC. However for higher temperature i.e. 40oC, it shows over estimate of the thermal conductivity value at 𝜑𝜑 = 0. Therefore, it can be concluded that the developed correlation predicts the thermal conductivity reasonably for lower temperature range. 3.3 Rheological study of rGO-Fe3O4 nanocomposite based nanofluids The variation in viscosity of the nanofluid flowing in a condition of an applied force can be determined from the viscosity vs. shear rate data. The viscosity of nanofluid is generally affected by the factors like temperature and volume % of the rGO–Fe3O4 nanocomposite in the nanofluid. Fig. 9 shows the viscosity vs. shear rate plots for various concentrations (0.01 to 0.2 vol.%) of the rGO–Fe3O4 nanocomposite based nanofluid at different temperatures (18 to 45oC). The viscosity of rGO–Fe3O4 nanocomposite based nanofluids decreased exponentially as a function of shear rate at lower shear rate value in all the cases. This indicates the shear thinning i.e. pseudoplastic behaviour of the nanofluid. Thus, it can be concluded that the nanofluids exhibit non-Newtonian shear thinning behaviour. Nanofluid is a structured fluid and hence bears a great impact of shear stress acting upon it. At lower shear rates, when the spindle starts rotating, the structure of the nanofluid starts changing. The rGO–Fe3O4 nanocomposite present in the fluid itself gets aligned in the direction of the shear stress applied. This makes the nanofluid to exhibit less resistance to flow which is very high at the start due to nonaligned molecules present in the sample on the plate. This reduction in the resistance finally leads to reduction in viscosity (which is measured as the resistance to flow). At very high shear rates, the molecules have had completely aligned themselves thus attaining maximum shear ordering due to complete breaking down of the molecules. This breaking down of molecules takes place due to weakening of the particle-particle interactions due to the shearing force. This decreases the friction between fluid layers and thus the viscosity. Further increase in the shear rate won’t have any effect on the viscosity as can be seen in the Fig. 9 where the viscosity becomes 23



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almost constant. In case of 0.07 vol.% and 0.1 vol.% rGO–Fe3O4 nanocomposite concentration in nanofluid, the viscosity was observed to be increasing from 0.9 cP and 1.05 cP to 1.735 cP and 2.09 cP, respectively with decrease in the temperature from 45℃ to 18oC at a shear rate of 4000 s-1. This is attributed to the weakening of the inter-particle and inter-molecular adhesive forces present in the spaces between the particles with an increase in temperature. This leads to intensified Brownian motion of the particles within the nanofluid thus enhancing the flow of the fluid and decreasing the viscosity68, 69. For lower volume % of nanofluid, i.e. for 0.01 vol.% and 0.05 vol.%, this is not true (Fig. 9(a) and 9(b)), respectively. It is possibly due to the very less amount of rGO–Fe3O4 nanocomposite in nanofluid producing non-uniform or improperly sonicated dispersions interfering in the measurements. Also, it is the same case for 0.2 vol.% rGO–Fe3O4 nanocomposite based nanofluid in Fig. 9(e). The reason for the same can also be attributed to the evaporation of the base fluid i.e. distilled water from the test plate at higher temperatures thus increasing the measured value of viscosity as a little evaporation can affect the entire composition of this small amount of nanofluid at such higher concentrations. Further, Fig. 10 depicts viscosity as a function of shear rate at 25℃ for base fluid i.e. distilled water and rGO–Fe3O4 nanocomposite based nanofluids at different concentrations. The viscosity at a particular shear rate is seen to be higher for higher concentration of rGO–Fe3O4 nanocomposite in the nanofluid. It is observed that the shear thinning property is more prominent for higher concentrations of the nanofluid. This shows that, higher the concentration of rGO–Fe3O4 nanocomposite in the nanofluid, more is the interaction with the base fluid, thus making more contribution in changing the properties. The low concentration of rGO–Fe3O4 nanocomposite in nanofluid ultimately have higher amount of base fluid i.e. water and thus greater role of the base fluid, which leads to the appearance of their respective shear thinning curves nearer to the curve of water. Further, it has been observed that the viscosity of rGO– 24


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Fe3O4 nanocomposite based nanofluids is slightly higher than that of water after 2000 s-1 and negligible change in the viscosity of nanofluid was observed at higher shear rates. The obtained viscosity vs. shear rate data was used for the prediction of behaviours using various available models like Bingham, power law and Casson model. Fig. 11 depicts the comparison of the experimentally measured viscosity with the viscosity predicted by these three models for rGO–Fe3O4 nanocomposite based nanofluids at 25℃. It is clear that the viscosity of the 0.01 vol.% as well as the 0.05 vol.% rGO–Fe3O4 nanocomposite based nanofluid at 25℃ can be predicted more accurately by the Bingham model. However, for the case of 0.07 vol.% rGO–Fe3O4 nanocomposite based nanofluid, the behaviour can be significantly predicted by the power law model followed by the other two models which also give values close to the experimental values. Casson model can better predict the viscosity of 0.1 vol.% rGO–Fe3O4 nanocomposite based nanofluid at 25℃. Table 2 provides the values of the rheology parameters found in order to calculate the viscosity using the different models. It can be observed from Fig. 12(a) that the Bingham yield stress for the nanofluid increases from 2216.56 mPa to 4015.25 mPa with an increase in the concentration from 0.01 vol.% to 0.1 vol.% of the rGO–Fe3O4 nanocomposite in the nanofluid. Higher the particles concentration, greater is the resistance it offers to flow leading to a larger yield stress. Variation of the two parameters of the power law model i.e. the consistency index and the flow behaviour index with respect to concentration of the nanofluid is reported in Fig. 12(b). An increment in the consistency index from 409.46 to 1952.46 and the reduction in the flow behaviour index from 0.276 to 0.106 was observed with an increase in concentration from 0.01 vol.% to 0.1 vol.% of the rGO–Fe3O4 nanocomposite in the nanofluid. This is attributed to the increased interaction of the nanoparticles in the nanofluid with an increase in their concentration. Fig. 12(c) reports the yield stress calculated with the use of Casson model, which increased from 1467.08 mPa to 25



3638.3 mPa with an increase in the concentration of the nanofluid from 0.01 vol.% to 0.1 vol.%. These characteristics are consistent with those previously investigated45,70. Further, Fig. 13 presents the relative viscosity of 0.01 vol.% and 0.1 vol.% rGO–Fe3O4 nanocomposite based nanofluids as a function of shear rate at 25℃ along with the relative viscosity predicted by the Einstein model49, Brinkman model50 and other two models developed by Askari et al.42 for comparison. It is observed that the experimental data can be satisfactorily predicted by all the model equations for lower volume fraction i.e. for 0.01 vol.% rGO–Fe3O4 nanocomposite based nanofluid than the 0.1 vol.% rGO–Fe3O4 nanocomposite based nanofluid. A clear shear thinning region of the curve stretching from the shear rate of 500 s-1 to 2000 s-1 can be seen in graph. All the models thus become more relevant at higher shear rates and the values predicted by them show good agreement with the experimental values. The error analysis and estimation of standard deviation for these reported models has been carried out for the prediction of the viscosity data of the rGO–Fe3O4 nanocomposite based nanofluid. It has been observed that the variation in standard deviation is marginal for these models. However, the Einstein model presents a least value of standard deviation of 0.7311 and 1.5228 for 0.01 vol.% and 0.1 vol.% of rGO–Fe3O4 nanocomposite based nanofluid, respectively compared to other models. This suggests that the Einstein model provides a better fit and that it well predicts the viscosity data as compared to other reported models. 3.4 Convective heat transfer study of rGO–Fe3O4 nanocomposite based nanofluids 3.4.1

Effect of volume % of rGO–Fe3O4 nanocomposite based nanofluid

Fig. 14 shows variation of heat transfer coefficient with respect to axial position for the flow of the nanofluids with different volume % (0.01 and 0.02 vol.%) of rGO–Fe3O4 nanocomposite at Reynolds number of 940, 1880, 3750, 5630 and 7510 ± 5. It can be seen that the heat transfer coefficient decreases along with the axial distance and it has a higher value for higher 26


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concentration of the nanofluid. This is because of the higher thermal conductivity possessed by nanofluid of higher concentration of rGO–Fe3O4 nanocomposite. A large number of rGO– Fe3O4 nanocomposite particles present in the nanofluid bring about more chaotic movements in the flowing fluid, which disturbs the thermal boundary layer formation and in turn offers enhancement in the heat transfer. The thickness of the thermal boundary layer is mainly a function of Reynolds number and Prandtl number. This shows that various nanofluid properties have significant effect on the thickness of the thermal boundary layer. Further, the viscosity, density, heat capacity and thermal conductivity of the nanofluid are considered to be important properties that play a vital role in the determination of the thermal boundary layer produced in a nanofluid flow. However, the increment in viscosity of the rGO-Fe3O4 nanocomposite based nanofluid is negligible for almost all of the concentrations. In fact, the rGO-Fe3O4 nanocomposite particles present in the nanofluid provide convection currents due to their Brownian motion which causes disturbance in the thermal boundary layer and thus an enhancement in the heat transfer coefficient. The higher the amount of rGO-Fe3O4 nanocomposite particles, more pronounced is the effect. At the entrance of the test section, heat transfer coefficient has a higher value which is attributed to the entrance effect. After a certain axial distance travelled by the nanofluid in the test section, there is decrease in the heat transfer coefficient marginally. This happens due to the development of thermal boundary layer, which offers resistance to heat flow. Further, an increased number of nanocomposite particles in the nanofluid of higher concentration is responsible for the disturbance of thermal boundary layer formation, which enhances the heat transfer coefficient exhibited by the rGO–Fe3O4 nanocomposite based nanofluid. At the exit, heat transfer coefficient for water at Reynolds number of 7510 ± 5 is found to be 1980.5 W/m2K and that for 0.01 vol.% and 0.02 vol.% rGO–Fe3O4 nanocomposite based nanofluid is found to be 3444.1 W/m2K and 4289.5 W/m2K, respectively. The addition of rGO–Fe3O4 nanocomposite in the nanofluid leads to an increase 27



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in the effective thermal conductivity (k) of the nanofluid. This reduces the thickness of the boundary layer (δ). The heat transfer coefficient being the ratio of thermal conductivity and thickness of boundary layer (h = k/δ), increases with an increase in the thermal conductivity and decrease in the boundary layer thickness. The Brownian motion of the rGO–Fe3O4 nanocomposite also plays significant role in enhancement of heat transfer coefficient. It is the natural motion of the nanoparticles in the nanofluid which increases the thermal transport. Further, Fig. 15 depicts variation of Nusselt number with respect to axial position for the flow of the nanofluids with different volume % of rGO–Fe3O4 nanocomposite for the range of Reynolds number from 940 to 7510 ± 5. The Nusselt number shows similar trend with change in the axial distance as shown by the heat transfer coefficient. The Nusselt number for 0.01 vol.% and 0.02 vol.% rGO–Fe3O4 nanocomposite based nanofluid at the exit for Reynolds number equal to 7510 ± 5 is found to be 129.9 and 147.9, respectively. 3.4.2

Effect of Reynolds number of rGO–Fe3O4 nanocomposite based nanofluid

Fig. 16(a) illustrates the variation of heat transfer coefficient along the axial distance for 0.01 vol.% and 0.02 vol.% rGO–Fe3O4 nanocomposite based nanofluid at various Reynolds numbers. It can be seen that the value of heat transfer coefficient increases with an increase in the Reynolds number of the rGO–Fe3O4 nanocomposite based nanofluid. The increase in Reynolds number results in an increase in the turbulence in the fluid and increase in turbulence increases the chaotic movements of the nanoparticles which ultimately increases the heat transfer coefficients. The increase in Reynolds number also produces eddies in the flow that reduces the thickness of the boundary layer and this reduction in the boundary layer thickness also results in the increase in the heat transfer coefficient. At the entrance, the Reynolds number affects the heat transfer coefficient along with the entrance effect as discussed before. At the exit of the test section, the heat transfer coefficient for 0.01 vol.% rGO–Fe3O4 28


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nanocomposite based nanofluid flowing at Reynolds number of 940 ± 5 is 653.3 W/m2K whereas it is 3444.1 W/m2K at Reynolds number equal to 7510 ± 5 for the same concentration of nanofluid. Further, the heat transfer coefficient at the exit for 0.02 vol.% rGO–Fe3O4 nanocomposite based nanofluid flowing at Reynolds number of 940 ± 5 is 1043.3 W/m2K whereas for the same nanofluid, it is 4289.5 W/m2K at Reynolds number of 7510 ± 5. Further, Fig. 16(b) illustrates the variation of Nusselt number along the axial distance for 0.01 vol.% and 0.02 vol.% rGO–Fe3O4 nanocomposite based nanofluid at various Reynolds numbers. The Nusselt number for 0.01 vol.% rGO–Fe3O4 nanocomposite based nanofluid at the exit for Reynolds number of 940 ± 5 is 22.4 and that for Reynolds number of 7510 ± 5, it is 129.9 for the same nanofluid concentration. And, it is found to be 32.7 and 147.9 for 0.02 vol.% rGO– Fe3O4 nanocomposite based nanofluid at Reynolds number of 940 ± 5 and 7510 ± 5, respectively at the exit of test section. Fig. 16(c) shows the variation in heat transfer coefficient and Nusselt number calculated at exit of the test section with Reynolds number for different volume % of rGO–Fe3O4 nanocomposite based nanofluid. The Nusselt number data of the rGO-Fe3O4 nanofluids was used for curve-fitting using multiple regression analysis and a new correlation was found. Thus, the obtained correlation as given in Equation (22) could relate the Nusselt number (Nu) with Reynolds number (Re), Prandtl number (Pr) and volume fraction (φ) of the rGO-Fe3O4 nanocomposites in the nanofluid. Nu = 5.474Re0.675 Pr1.216φ0.524

(R2 = 0.98)

(22)

It can be seen from Fig. 17, that the obtained correlation for Nu is in good agreement with the experimental values of Nu for 0.01 and 0.02 vol.% rGO-Fe3O4 nanocomposite based nanofluid. However, it is clear that the conventional correlations like Dittus-Boelter71, Xuan-Li72 and Gnielinski73 correlation are unable to predict the experimental data. In fact, these conventional 29



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correlations under predict the Nu data to a larger extent. In these correlations, the dimensionless numbers, namely Nusselt number (Nu), Reynolds number (Re), Prandtl number (Pr) and Peclet number (Pe) are used which are defined in the nomenclature of the manuscript. One of the classical correlations for determining Nusselt number is the Dittus-Boelter correlation71. Applying this equation for determining Nusselt number in nanofluid heat transfer would definitely produce a gap between the experimental and the predicted results. This is because the Dittus-Boelter equation does not take into account the effect of nanoparticle concentration and shape of the nanoparticles in the nanofluid. So, the functional factors of the rGO-Fe3O4 nanocomposite particles such as their surface to volume ratio, movement and diffusional effects are simply neglected during the Nusselt number prediction. On the other hand, the Xuan-Li correlation72 which is specifically developed for predicting Nusselt number in nanofluid flow, also under predicts the experimental data for the rGO-Fe3O4 nanocomposite based nanofluid. This is possibly because of the model being valid for metallic nanoparticles dispersed in basefluids which are mostly spherical in shape. The Gnielinski correlation73, like the Dittus-Boelter, also does not take into account the concentration and shape of nanoparticles dispersed in the nanofluid and thus under predicts the Nusselt number. The behavior of nanoparticles in the nanofluid depends upon their nature. The rGO-Fe3O4 nanocomposite particles are a combination of spherical Fe3O4 nanoparticles dispersed over two-dimensional nanosheets of reduced graphene oxide. This makes the rGO-Fe3O4 nanocomposite based nanofluid to have quite different heat transfer behavior and there is a requirement of the correlation that can well predict their heat transfer behavior. As found in this study, the conventional correlations do not serve the purpose and so there arose a need to develop a new correlation for predicting the heat transfer behavior of the rGO-Fe3O4 nanocomposite based nanofluid in terms of Nusselt number. 30


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3.5 Pressure drop study of rGO–Fe3O4 nanocomposite based nanofluids The comparison between pressure drop exhibited by water and 0.01 vol.% rGO–Fe3O4 nanocomposite based nanofluid as a function of Reynolds number is depicted in Fig. 18(a). The pressure drop increases with the Reynolds number as expected. For low Reynolds number, the pressure drop is less because the entrance length of the flow is small. This entrance length is more at higher Reynolds number thus increasing the pressure drop74. Pressure drop for 0.01 vol.% rGO–Fe3O4 nanocomposite based nanofluid for Reynolds number of 1880 ± 5 is 343.4 N/m2. The higher pressure drop possessed by the nanofluid is due to its higher viscosity compared to pure water. But, even at maximum Reynolds number of 7510 ± 5, the value of pressure drop due to the nanofluid is 1304.7 N/m2 which is only 10.83% more than that of water. This is because of the negligible increase in viscosity, which ultimately suggests the acceptable use of nanofluid for heat transfer applications. Also, the trend of friction factor of water and 0.01 vol.% rGO–Fe3O4 nanocomposite based nanofluid as a function of Reynolds number is depicted in the Fig. 18(b). As expected, the friction factor decreases with an increase in Reynolds number. The friction factor was calculated using Darcy-Weisbach51, Blasius75 and Petukhov equation76 as given in Equation (19), (23) and (24), respectively. The friction factor calculated by these equations at different Reynolds numbers for 0.01 vol.% rGO-Fe3O4 nanocomposite based nanofluid are depicted in Fig. 19(a). It can be noticed that there is a large difference between the friction factor values obtained using Darcy-Weisbach equation and those calculated using Blasius and Petukhov equations. f = 0.3164 × Re−0.25

(23)

f = (0.79 × lnRe − 1.64)−2

(24)

31



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Further, the Reynolds77 and Chilton-Colburn analogies78 as given in Equation (25) and (26) were applied for the prediction of Nusselt number using friction factor calculated by DarcyWeisbach, Blasius and Petukhov equation, resulting plots of which are depicted in Fig. 19(b). Fig. 19(c) and Fig. 19(d), respectively. 𝑓𝑓

(25)

𝑓𝑓

(26)

Nu = 2 Re Nu = 2 RePr 1⁄3

It is clear that the Reynolds analogy can closely predict the Nusselt number which is calculated with the use of friction factor by both, Blasius and Petukhov equations. However, the Reynolds as well as the Chilton-Colburn analogy seems to over predict the Nusselt number calculated using friction factor estimated by the Darcy–Weisbach equation. The standard deviation for calculated Nusselt number using Reynolds analogy was found to be 2634.34, 8.82 and 5.07 for the case of the fraction factor calculated with Darcy-Weisbach, Blasius and Petukhov equation, respectively. This confirmed the better applicability of Petukhov equation which predicts the friction factor closely and then the Nusselt number calculated by Reynolds analogy compared to other cases. The standard deviation for Chilton-Colburn analogy was found to be quite higher. It has been already found by Azmi et al.79 that the Blasius and Petukhov equations provide a better prediction of the friction factor of nanofluids flow under turbulent conditions as compared to Darcy–Weisbach equation. This may be because of the validity of Darcy– Weisbach equation being limited to simple flow systems and not the complex flow behaviours like those of a nanofluid. While the Blasius and Petukhov equations consider the physical properties of the nanofluid for the determination of the friction factor. Also, the difference between the friction factor of water and 0.01 vol.% rGO–Fe3O4 nanocomposite based nanofluid is greater in the laminar flow regime and it becomes almost 32


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negligible at turbulent conditions. The increase in Reynolds number causes an increase in the turbulence in the rGO–Fe3O4 nanocomposite based nanofluid, this reduces the friction factor and increases the chaotic movements of the added rGO–Fe3O4 nanocomposite particles. This phenomenon is considered responsible for ultimate increase in the heat transfer coefficients. The increase in Reynolds number is also responsible for the generation of the eddies in the flow which in turn causes the mixing of several fluid layers and also in the presence of rGO– Fe3O4 nanocomposite particles in nanofluid reduces the thickness of the boundary layer. These explained parameters are then responsible for the increase in the heat transfer coefficient. At low Reynolds number, the friction factor is higher because of the resistance caused by the slip between the fluid layers becomes more pronounced in case of nanofluid due to the presence of nanoparticles between the moving layers. This kind of slip between the layers becomes infrequent at higher Reynolds number thus decreasing the friction factor of the flow. At laminar flow conditions, there is a steep decline in the friction factor which becomes gradual at turbulent conditions depicting that after a certain Reynolds number, there is not much decrease in the friction between the layers. One of the reasons for the increased friction factor of the nanofluid is due to the presence of rGO–Fe3O4 nanocomposite in the fluid which increases the viscosity of the nanofluid. A very negligible difference in the friction factor of water and that of 0.01 vol.% rGO–Fe3O4 nanocomposite based nanofluid reveals that there would be no significant increment in pumping power required. This presents the most important advantage of the using rGO–Fe3O4 nanocomposite based nanofluid against water for heat transfer augmentation as it offers very small increase in pressure drop compared to water. 4. Conclusions In the present study, successful preparation of rGO–Fe3O4 nanocomposite was carried out in the presence of ultrasonic irradiation. Successful preparation of rGO–Fe3O4 nanocomposite 33



Page 34 of 73

was confirmed from UV/Vis, TEM, Raman, XRD and XPS analysis. The nearly spherical morphology of ultrasonically prepared rGO-Fe3O4 nanocomposite with particle size of Fe3O4 nanoparticles around 10-20 nm loaded on rGO sheets with very less amount of agglomeration was observed from TEM analysis. The reasons behind this fine dispersion of nano-sized Fe3O4 particles on rGO sheets are the physical effects (intense mixing, intense shearing action, turbulence etc.) of the ultrasound which causes the complete exfoliation of rGO sheets and reduction in the particle size of Fe3O4 nanoparticles loaded on rGO sheet. XRD, XPS and Raman analysis confirms the formation of rGO–Fe3O4 nanocomposite. Further XPS analysis confirmed the reduction of graphene oxide to rGO during formation of rGO–Fe3O4 nanocomposite with ultrasound assisted method. This finely dispersed rGO–Fe3O4 nanocomposite based nanofluid shows a significant enhancement in thermal conductivity (83.44%) for 0.2 vol.% rGO–Fe3O4 nanocomposite based nanofluid at 40℃. Rheological investigation of rGO–Fe3O4 nanocomposite based nanofluid reveals a shear thinning NonNewtonian behaviour which is also confirmed from the prediction of the viscosity data by selected models. Increased thermal conductivity, reduction in viscosity, decreased thickness of boundary layer enhances convective heat transfer coefficient, which is found to be increased from 3444.1 at 0.01 volume % to 4289.5 W/m2K at 0.02 volume % of rGO–Fe3O4 nanocomposite in water based nanofluid at the exit of test section for Reynolds number equal to 7510 ± 5. Heat transfer coefficient also increased from 1043.3 W/m2K to 4289.5 W/m2K with an increase in the Reynolds number from 940 to 7510 ± 5 for 0.02 vol.% rGO–Fe3O4 nanocomposite based nanofluid. A new correlation for Nusselt number has been put forward which predicts the experimental data very well because the conventional correlations seem to under predict the data as they do not consider the functional factors (concentration, shape, surface to volume ratio, movement and diffusional effects) of the rGO-Fe3O4 nanocomposite particles in the nanofluid. Finally, the pressure drop studies reveal that the rGO–Fe3O4 34


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nanocomposite based nanofluid has marginal effect on the pressure drop and also on friction factor which eliminates any extra pumping cost requirements. Also, it has been found that the Petukhov equation predicts the friction factor closely and thus the Nusselt number as calculated by Reynolds analogy fits better with experimental Nusselt number values compared to other cases. Acknowledgment This work was supported by the Science & Engineering Research Board (SERB), Department of Science and Technology (Government of India) [Start Up Research Grant (Young Scientists), Sanction order no. YSS/2014/000889, 2015]. Nomenclature A = Inner surface area of the copper tube (m2) a0, a1 & a2 = Constants in the correlation for thermal conductivity Cp = Specific heat capacity of the fluid (J/kgK) Cpbf = Specific heat capacity of base fluid (J/kgK) Cpnf = Specific heat capacity of nanofluid (J/kgK) Cpp = Specific heat capacity of nanoparticles (J/kgK) Di = Inner diameter of the copper tube (m) du/dr = Shear rate (s-1) f = Friction factor (-) 35



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g = Acceleration due to gravity (m/s2) havg = Average heat transfer coefficient (W/m2K) h(x) = Heat transfer coefficient at a distance 'x' from the inlet (W/m2K) k = Consistency index (Pa.sm) kbf = Thermal conductivity of base fluid (W/mK) kc = Casson plastic viscosity (Pa.s) kf = Thermal conductivity of the fluid (W/mK) knf = Thermal conductivity of nanofluid (W/mK) L = Length of copper tube (m) m = flow behaviour index (-) ṁ = Mass flow rate of the fluid (kg/s) n = Bingham plastic viscosity (Pa.s) Nu = Nusselt number =

havg Di knf

Nu(x) = Nusselt number at a distance 'x' from the inlet P = Perimeter of the copper tube (m) Pe = Peclet number = Re × Pr Pr = Prandtl number =

Cpnf µnf knf

36


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qs = Heat flux applied to the fluid (W/m2) Re = Reynolds number =

Di vρnf µnf

R2 = Coefficient of determination Tb(x) = Bulk fluid temperature at a distance 'x' from the inlet (℃) Tb,i = Inlet fluid bulk temperature (℃) Tb,o = Outlet fluid bulk temperature (℃) Ts(x) = Wall temperature at a distance 'x' from the inlet (℃) v = Velocity of the fluid (m/s) x = Axial distance (m) Δh = Height difference of fluid in the manometer (m) ΔP = Pressure drop (N/m2) μbf = Viscosity of base fluid (Pa.s) μnf = Viscosity of nanofluid (Pa.s) ρ = Density of fluid (kg/m3) ρbf = Density of base fluid (kg/m3) ρnf = Density of nanofluid (kg/m3) ρp = Density of nanoparticles (kg/m3) 37



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τ = Shear stress (N/m2) τ0 = Yield stress (N/m2) φ = Volume fraction of nanoparticles in nanofluid (-)

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List of Figures and Tables: Fig. 1.

An illustration of synthesis of graphene oxide by Hummers’ method in the presence of ultrasound

Fig. 2.

An illustration of synthesis of rGO-Fe3O4 nanocomposite prepared by ultrasound assisted method

Fig. 3.

Experimental set-up for the study of heat transfer characteristics using rGO-Fe3O4 nanocomposite based nanofluid

Fig. 4.

UV-visible absorption spectra of (a) graphene oxide and (b) rGO-Fe3O4 nanocomposite prepared by ultrasound assisted method

Fig. 5.

TEM images of rGO-Fe3O4 nanocomposite prepared by ultrasound assisted method

Fig. 6.

(a) Raman spectrum and (b) XRD pattern of rGO-Fe3O4 nanocomposite prepared by ultrasound assisted method

Fig. 7.

(a) XPS survey scan of ultrasonically prepared rGO-Fe3O4 nanocomposite; narrow scan of (b) Fe2p spectrum, (c) C1s spectrum and (d) O1s spectrum of rGO-Fe3O4 nanocomposite

Fig. 8.

Variation in thermal conductivity with respect to (a) temperature for different concentrations of rGO-Fe3O4 nanocomposite based nanofluids and (b) volume fraction of rGO-Fe3O4 nanocomposite in nanofluids

Fig. 9.

Effect of shear rate on viscosity of (a) 0.01 vol.%, (b) 0.05 vol.%, (c) 0.07 vol.%, (d) 0.1 vol.% and (e) 0.2 vol.% rGO-Fe3O4 nanocomposite based nanofluids at different temperatures. (Insets shows the same plot at magnified scale)

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Fig. 10. The effect of shear rate on viscosity of rGO-Fe3O4 nanofluids at different concentration of the nanofluids at 25℃. (inset showing same plot at magnified scale) Fig. 11. Comparison of Bingham model, Power law model and Casson model for different volume % rGO-Fe3O4 nanocomposite based nanofluid at 25℃ Fig. 12. Rheological parameters as a function of rGO-Fe3O4 nanofluid concentration (a) Bingham yield stress as a function of concentration of rGO-Fe3O4 nanocomposite based nanofluid (b) Consistency and flow behaviour indices as a function of concentration of rGO-Fe3O4 nanocomposite based nanofluid (c) Casson yield stress as a function of concentration of rGO-Fe3O4 nanocomposite based nanofluid Fig. 13. Relative viscosity of rGO-Fe3O4 nanocomposite based nanofluids as a function of shear rate at 25℃ Fig. 14. Variation of heat transfer coefficient along the axial distance for different volume % of rGO-Fe3O4 nanocomposite based nanofluid at various Reynolds numbers Fig. 15. Variation of Nusselt number along the axial distance for different volume % of rGO-Fe3O4 nanocomposite based nanofluid at various Reynolds numbers Fig. 16. Variation of (a) heat transfer coefficient and (b) Nusselt number along the axial distance for rGO-Fe3O4 nanocomposite based nanofluids at various Reynolds numbers. (c) Variation of heat transfer coefficient and Nusselt number as a function of Reynolds number for water and different volume % of rGO-Fe3O4 nanocomposite based nanofluid at the outlet of the test section

50


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Fig. 17. Nusselt number comparison between experimental and models as a function of Reynolds number Fig. 18. Variation of (a) pressure drop and (b) friction factor as a function of Reynolds number for water and 0.01 vol.% rGo-Fe3O4 nanocomposite based nanofluid Fig. 19. (a) The calculated friction factor using various equations and comparison of experimental Nusselt number with that calculated using Reynolds and ChiltonColburn analogies using friction factor estimated by (b) Darcy–Weisbach equation, (c) Blasius equation and (d) Petukhov equation for 0.01 vol.% rGO-Fe3O4 nanocomposite based nanofluid. Table 1. Regression parameters of empirical model developed for thermal conductivity vs. volume fraction data Table 2. Rheological parameters of different viscosity models used to predict the behaviour of rGO-Fe3O4 nanocomposite based nanofluids

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Fig. 1. An illustration of synthesis of graphene oxide by Hummers’ method in the presence of ultrasound

52


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Fig. 2. An illustration of synthesis of rGO-Fe3O4 nanocomposite prepared by ultrasound assisted method

53



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Fig. 3. Experimental set-up for the study of heat transfer characteristics using rGO-Fe3O4 nanocomposite based nanofluid

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Fig. 4. UV-visible absorption spectra of (a) graphene oxide and (b) rGO-Fe3O4 nanocomposite prepared by ultrasound assisted method

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Fig. 5. TEM images of rGO-Fe3O4 nanocomposite prepared by ultrasound assisted method

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3500

(a)

3000

D-band G-band

Intensity (a.u.)

2500 2000 1500 1000 500

500

1000

1500

2000 Raman shift (cm-1)

2500

3000

1000 (b) 800

PSD

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


600 400 200

30

40

50

2θ (Degree)

60

70

Fig. 6. (a) Raman spectrum and (b) XRD pattern of rGO-Fe3O4 nanocomposite prepared by ultrasound assisted method.

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Fig. 7. (a) XPS survey scan of ultrasonically prepared rGO-Fe3O4 nanocomposite; narrow scan of (b) Fe2p spectrum, (c) C1s spectrum and (d) O1s spectrum of rGO-Fe3O4 nanocomposite

58


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Fig. 8. Variation in thermal conductivity with respect to (a) temperature for different concentrations of rGO-Fe3O4 nanocomposite based nanofluids and (b) volume fraction of rGO-Fe3O4 nanocomposite in nanofluids 59



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Fig. 9. Effect of shear rate on viscosity of (a) 0.01 vol.%, (b) 0.05 vol.%, (c) 0.07 vol.%, (d) 0.1 vol.% and (e) 0.2 vol.% rGO-Fe3O4 nanocomposite based nanofluids at different temperatures. (Insets shows the same plot at magnified scale)

60


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Fig. 10. The effect of shear rate on viscosity of rGO-Fe3O4 nanofluids at different concentration of the nanofluids at 25℃. (inset showing same plot at magnified scale)

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Fig. 11. Comparison of Bingham model, Power law model and Casson model for different volume % rGO-Fe3O4 nanocomposite based nanofluid at 25℃ 62


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Fig. 12. Rheological parameters as a function of rGO-Fe3O4 nanofluid concentration (a) Bingham yield stress as a function of concentration of rGO-Fe3O4 nanocomposite based nanofluid (b) Consistency and flow behaviour indices as a function of concentration of rGO-

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Fe3O4 nanocomposite based nanofluid (c) Casson yield stress as a function of concentration of rGO-Fe3O4 nanocomposite based nanofluid

Fig. 13. Relative viscosity of rGO-Fe3O4 nanocomposite based nanofluids as a function of shear rate at 25℃

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Fig. 14. Variation of heat transfer coefficient along the axial distance for different volume % of rGO-Fe3O4 nanocomposite based nanofluid at various Reynolds numbers

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Fig. 15. Variation of Nusselt number along the axial distance for different volume % of rGOFe3O4 nanocomposite based nanofluid at various Reynolds numbers

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Fig. 16. Variation of (a) heat transfer coefficient and (b) Nusselt number along the axial distance for rGO-Fe3O4 nanocomposite based nanofluids at various Reynolds numbers. (c) Variation of heat transfer coefficient and Nusselt number as a function of Reynolds number for water and different volume % of rGO-Fe3O4 nanocomposite based nanofluid at the outlet of the test section

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Fig. 17. Nusselt number comparison between experimental and models as a function of Reynolds number 68


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Fig. 18. Variation of (a) pressure drop and (b) friction factor as a function of Reynolds number for water and 0.01 vol.% rGO-Fe3O4 nanocomposite based nanofluid

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Fig. 19. (a) The calculated friction factor using various equations and comparison of experimental Nusselt number with that calculated using Reynolds and Chilton-Colburn analogies using friction factor estimated by (b) Darcy–Weisbach equation, (c) Blasius equation and (d) Petukhov equation for 0.01 vol.% rGO-Fe3O4 nanocomposite based nanofluid.

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Table 1. Regression parameters of empirical model developed for thermal conductivity vs. volume fraction data Temperature (℃)

a0

a1

a2

R2

25

0.60444

48.7380

-9752.5315

0.967

30

0.61198

72.7597

-9357.2128

0.945

35

0.64523

254.4432

-76130.2043

0.957

40

0.72296

619.0482

-202795.1591

0.888

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Table 2. Rheological parameters of different viscosity models used to predict the behaviour of rGO-Fe3O4 nanocomposite based nanofluids Nanofluid

Bingham model

Power law model

Casson model

concentration (vol.%)

𝝉𝝉𝟎𝟎

n

k

m

𝝉𝝉𝟎𝟎

kc

0.01

2216.56

0.421

409.46

0.276

1467.08

0.156

0.05

2986.68

0.293

854.37

0.195

2346.50

0.070

0.07

4140.52

0.143

1554.26

0.137

3604.74

0.020

0.1

4015.25

0.151

1952.46

0.106

3638.30

0.016

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Graphical Abstract

Fig. For Table of Contents Only

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Reduced graphene oxide-Fe3O4 nanocomposite ... - ACS Publications

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