NANOFLUID

FIELD OF INVENTION

The present invention relates to a method of producing functionalized graphene nanofluid. Particularly, the present invention relates to a method of producing functionalized graphene nanofluid that uses water as base fluid for dispersion of the functionalized graphene while omitting the use of surfactant, stabilizing agent or dispersing agent.

BACKGROUND OF THE INVENTION

Due to rapid development in industry and transportation, followed by fast increment in energy consumption, the saving of energy is a vital issue; and further, managing high thermal load has become critical. Heat transfer is a significant key in many sectors such as air conditioning, power generation, chemical process plant and microelectronics for heating and cooling. It is favourable to improve the heat transfer efficiency of systems employed in these sectors; so this development can make it possible to minimize the size of heat transfer equipment and reduce the operating costs of the corresponding processes. Thus, several efforts have been taken to increase the heat transfer properties in the heat transfer equipment. One important factor in heat transfer is the thermal conductivity of the working liquids.

Hence, recently with growth of using these coolants, the thermal properties have become more critical. Fluids containing nanoparticles or nanofluids are known for improving heat transfer in various applications. Although nanofluids (colloidal suspension of nanoparticles) have more advantages than conventional heat transfer liquids, they are not widely utilized in thermal equipment due to difficulties in preparation of stable nanofluids. So, several techniques such as adding surfactants, functionalization and pH control are typically used to improve the stability of the nanofluids. It is noteworthy to say that whenever the pH value is far away from the isoelectric point (point of zero charge), the nanofluids will be stabilized. However, the addition of surfactants can have damaging effect on thermo -physical and transport properties of the nanofluids as they enhance viscosity and reduce thermal conductivity of the nanofluids.

It has been demonstrated that the thermal conductivity of the nanofluids depends on various factors such as temperature, nanoparticle concentration, particle size and Brownian motion of nanoparticles as well as preparation method thereof.

Among nanoparticles, graphene, a single atomic layer of sp ² hybridized carbon in hexagon lattice, exhibits remarkable properties such as mechanical strength, high charge mobility, large specific surface area, good tensile stress tolerance, excellent transparency and high aspect ratio as well as high thermal and electrical conductivity. These unique physical properties of graphene lead to promote its application in industry especially biomaterial, transistors, semiconductors, sensors and thermal energy systems.

Many methods have been developed to synthesize graphene including one-step (direct conversion of graphite to graphene) and two-step (graphite is turned to graphite oxide (GO) then products are converted to graphene by reduction process) approaches, such as solar exfoliation technique, mechanical exfoliation of graphite, chemical vapor deposition (CVD), arc discharge process, unzipping of CNT as well as chemical and thermal reduction of GO. Lately, radio frequency catalytic chemical vapor deposition (RF-cCVD) has been carried out as large-scale preparation and fast emerging technique to produce layered graphene. Nevertheless, it is still desirable to develop alternative facile method for synthesizing graphene that is low cost and environmentally friendly.

GO, a middle product in two-step route of synthesizing graphene, exhibits feeble electrical, thermal and optical properties resulted from its loss of systematically arranged conjugated structure during oxidation process. Consequently, GO is repaired by removing bonded hydroxyl, carboxyl and epoxy groups by employing a reducing agent which catalyzes electron transfer, electrochemical, chemical and thermal reduction. These processes restore sp ² double bond between carbon atoms conjugated structure. The reduced GO (RGO) product tends to precipitate and aggregate due to p- p stacking. Therefore, another challenge arises on the subject about improving solubility of the RGO in solvents which plays an important role for further material processing.

Among various methods of preparing RGO, chemical reduction is recognized as a facile technique for preparing graphene in bulk quantities. For instance, hydrazine, sodium borohydride, hydroxyl amine and hydroquinone are typically used. Nevertheless, these chemicals are either toxic or corrosive. Consequently, green and environmentally friendly reducing agents including phenols, metals, alkaline solutions, alcohols, sugars, vitamin C and glycine have been proposed to replace the toxic chemicals aforementioned. However, there are some disadvantages of using these alternative green reducing agents. For example, low reducing ability of tea polyphenol and methanol results in poor deoxygenation of graphene oxide. In addition, reduction of GO using metals causes impurities in the RGO.

To date, the methods of producing nanofluids require either addition of a dispersing agent in the base fluid or employing corrosive and hazardous acids for functionalization purpose.

The present invention provides a green nanofluid preparation method by using water as base fluid with functionalized graphene dispersed therein, without the use of dispersing agent, surfactant, stabilizing agent or toxic acids in the graphene synthesis and functionalization process.

SUMMARY OF INVENTION

The main aspect of the present invention is to provide a method of preparing functionalized graphene nanofluid that involves facile preparation step, and is environmentally friendly.

Another aspect of the present invention is to provide a method of preparing functionalized graphene nanofluid that is able to produce stable dispersion of graphene nanoparticles in water in substantial absence of surfactant, stabilizing agent, dispersing agent or any hazardous materials.

At least one of the preceding aspects is met, in whole or in part, by the present invention, in which the embodiment of the present invention describes a method of producing a functionalized graphene nanofluid comprising the steps of preparing exfoliated GO by treating graphite with acid in the presence of potassium permanganate; reacting the exfoliated GO with saffron in a reaction medium comprising ammonium solution to obtain functionalized graphene; ultrasonicating the functionalized graphene; and dispersing the ultrasonicated functionalized graphene in distilled water to produce the functionalized graphene nanofluid.

According to a preferred embodiment, the saffron is preheated at a temperature of 75- 85 °C until it achieves reddish color prior to reacting with the exfoliated GO.

In another preferred embodiment of the present invention, the acid is concentrated sulfuric acid (H2SO4), concentrated phosphoric acid (H3PO4) or a mixture thereof. More preferably, the acid is a mixture of concentrated thSC and concentrated H3PO4 at a ratio of 4:1.

Preferably, the reaction medium has a pH value of 10-10.5.

Advantageously, the step of ultrasonication is performed at a temperature of 85-90 °C.

Yet another aspect of the present invention is to provide a functionalized graphene nanofluid produced from the method aforementioned. In one embodiment, the functionalized graphene nano fluid aforementioned is suitable for use as coolant in a heat transfer system.

The present preferred embodiment of the invention consists of novel features and a combination of parts hereinafter fully described and illustrated in the accompanying drawings and particularly pointed out in the appended claims; it being understood that various changes in the details may be effected by those skilled in the arts but without departing from the scope of the invention or sacrificing any of the advantages of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of facilitating an understanding of the invention, there is illustrated in the accompanying drawing the preferred embodiments from an inspection of which when considered in connection with the following description, the invention, its construction and operation and many of its advantages would be readily understood and appreciated.

Figure 1 shows schematic diagram of preparation and oxidation of graphite. Figure 2 shows schematic diagram of chemical composition of saffron.

Figure 3 shows procedure for preparing the saffron-reduced graphene oxide (SrGO).

Figure 4 shows schematic diagram of reduction reaction of GO by saffron.

Figure 5 shows (a) schematic diagram and (b) image of the experimental set-up for the measurement of convective heat transfer.

Figure 6 shows (a) Fourier transform infrared (FTIR) spectra of GO and SrGO, (B) TGA curves of GO and SrGO.

Figure 7 shows (a) Raman spectra and (b) wide-scan Ols and Cls X-ray photoelectron spectroscopy (XPS) spectra for GO and SrGO.

Figure 8 shows Cls XPS spectra for (a) GO and (b) SrGO, Ols XPS spectra for (c) GO and (d) SrGO, and Nls XPS spectra for (e) SrGO,

Figure 9 shows (a) transmission electron microscopy (TEM) images of SrGO and (b) zeta potential values of the SrGO nanofluid as a function of pH.

Figure 10 shows (a) UV-vis spectroscopy analyses of water-based SrGO nanofluids at different concentrations and wavelengths, (b) absorption values of SrGO dispersed in distilled water at different concentrations and (c) colloidal stability of SrGO dispersed in distilled water.

Figure 11 shows thermal conductivity variation of water-based SrGO nano fluids and distilled water as a function of temperature and weight concentration. Figure 12 shows dynamic viscosity of water-based SrGO nanofluids and distilled water as a function of temperature and weight concentration at a shear rate of 150 s ¹ .

Figure 13 shows dynamic viscosity of water-based SrGO nanofluids compared with those calculated by theoretical models as function of concentration and temperature at a shear rate of 150 s ¹ .

Figure 14 shows plots of the measured values of dynamic viscosity versus shear rate for SrGO aqueous nanofluids at various weight concentrations and temperatures (a) distilled water, (b) 0.025 wt%, (c) 0.075 wt% and (d) 0.1 wt% and temperatures.

Figure 15 shows variation of specific heat capacity of water-based SrGO nano fluids at different temperatures and particle concentrations.

Figure 16 shows (a) average Nusselt number of the water-based SrGO nanofluids and distilled water at various Reynolds number and (b) average heat transfer coefficient of the water-based SrGO nanofluid and distilled water at various Reynolds number.

Figure 17 shows variation of (a) pressure drop and (b) friction factor of water-based SrGO nano fluids as a function of Reynolds number for various concentrations.

Figure 18 shows relative pumping power of the water-based SrGO nano fluids and distilled water.

Figure 19 shows performance index variation of the water-based SrGO nano fluids versus Reynolds number at different concentrations.

Figure 20 shows (a) variation of local Nusselt number versus the conduit axial distance for distilled water, (b) comparison of measured Nusselt number of distilled water with the empirical correlations, and (c) comparison of the measured friction factors for distilled water with the empirical correlations.

DETAILED DESCRIPTION OF THE INVENTION

Hereinafter, the invention shall be described according to the preferred embodiments of the present invention and by referring to the accompanying description and drawings. However, it is to be understood that limiting the description to the preferred embodiments of the invention and to the drawings is merely to facilitate discussion of the present invention and it is envisioned that those skilled in the art may devise various modifications without departing from the scope of the appended claim.

The present invention relates to a method of producing a functionalized graphene nanofluid comprising the steps of preparing exfoliated GO by treating graphite with acid in the presence of potassium permanganate; reacting the exfoliated GO with saffron in a reaction medium comprising ammonium solution to obtain functionalized graphene; ultrasonicating the functionalized graphene; and dispersing the ultrasonicated functionalized graphene in distilled water to produce the functionalized graphene nanofluid.

According to the preferred embodiment, graphene is exfoliated from graphite flakes and subsequently subjected to treatment with acid for oxidation process to produce GO. It is preferable that the acid is concentrated H ₂ SO ₄ , concentrated H ₃ PO ₄ or a mixture thereof. More preferably, the acid is a mixture of concentrated H ₂ SO ₄ , concentrated H ₃ PO ₄ at a ratio of 4:1. As a result of the oxidation process, the mixture comprising the graphene and acid will change from dark purplish green to dark brown.

To terminate the oxidation process, it is preferable that sufficient amount of ice cubes made of deionized water is added to the mixture aforementioned, followed by addition of hydrogen peroxide (H2O2) solution thereinto under stirring condition. Emergence of bright yellow color of the mixture indicates a high oxidation level of the graphene.

Preferably, the GO is washed with hydrochloric acid (HC1) solution and repeatedly centrifuged with deionized water until a pH of about 4-5 is achieved. In one embodiment, thickening of the GO occurs during the centrifugation process with the deionized water to form an exfoliated GO gel.

Next, the exfoliated GO is preferably functionalized and reduced by employing saffron in the reaction medium comprising ammonium solution. Preferably, the ammonium solution is added dropwise in to the reaction medium in order to adjust pH of the medium at about 10-10.5 to enhance the stability of the exfoliated GO. In addition, it is preferable that the saffron used in the present invention has a composition comprising carbohydrate, protein, minerals, fats and vitamins. Preferably, the saffron is preheated at a temperature of 75-85 °C until it achieves reddish color prior to reacting with the exfoliated GO.

After the functionalization and reduction reaction are completed, the functionalized graphene is then preferably subjected to an ultrasonication process which is performed at a temperature of 85-90 °C. Thereafter, the ultrasonicated functionalized graphene is centrifuged and washed repeatedly until a neutral pH of the functionalized graphene is achieved. To form the functionalized graphene nanofluid of the present invention, the functionalized graphene is dispersed in water in a concentration of 0.025-0.1 wt%.

According to the present invention, the functionalized graphene dispersion in the water does not form agglomerates. It is found that the stable dispersion of the functionalized graphene in the water can be attributed to disruption of p-p stacking between the functionalized graphenes by the saffron components including the vitamins, proteins and fats, in which the components form hydrogen bonds with the residual oxygen and nitrogen (amine group) functionalities on the functionalized graphene surface.

In accordance with a preferred embodiment, a functionalized graphene nanofluid is produced using the method of the present invention. The functionalized graphene nanofluid of the present invention is able to exhibits satisfactory level of thermo physical, rheological and heat transfer properties. In one embodiment, the functionalized graphene nanofluid aforementioned is used as a coolant in a heat transfer system.

EXAMPLES

The present invention can be further understood through consideration of the following non-limiting Example.

Example 1

Nanofluid Preparation

There are three stages involved in the preparation method: (1) preparation of the GO; (2) preparation of saffron extract; and (3) reduction and functionalization of the GO. In the first step, the GO was prepared by simplified Hummer’s method. Figure 1 presents the preparation procedure of the GO. Oxidation of graphite was initially carried out by mixing highly concentrated H2S04and H3PO4 with the ratio of 320:80 mL in a 2 L beaker, which was followed by addition of graphite flakes and 18 g KMhq ₄ · The one-pot mixture was left for stirring for 3 days to allow the oxidation of graphite. The color of the mixture changed from dark purplish green to dark brown. Then, for the termination of the oxidation process, sufficient amount of distilled water iced cubes were added to the mixture, after that 27 ml H2O2 solution (concentration 30%) was slowly poured into the solution under stirring mode. Exothermic condition was controlled by the ice cubes. When the color of the mixture changed to bright yellow, it was the indication of high oxidation level of graphite. The GO formed was washed three times with 1 M of HC1 aqueous solution and repeatedly centrifuged with deionized water at 11,500 rpm until a pH of 4-5 was achieved. The washing process was carried out using simple decantation of supernatant via a centrifugation technique with a centrifugation force of 10,000 g. During the washing process with the deionized water, the GO experienced exfoliation, which resulted in the thickening of the graphene solution, forming a GO gel.

GO consists of layered GO sheets that contain many oxygen functional groups including hydroxyl, epoxide, carbonyl, and carboxyl groups. These functional groups significantly alter van der Waals interactions between layers and endow GO with strong hydrophilicity. Based on models proposed in our study, the GO contain oxygen functionalities on the basal planes and edges of sheets. The presence of these functional groups destroys the planar sp ² carbons of graphite and converts them to sp ³ carbons. Therefore, the p-p electronic configuration of the graphite is destroyed in the GO. Our study proposed an eco-friendly method for covalent functionalization and reduction of GO by employing saffron, in order to improve its electrical and thermal conductivity besides its stability in polar solvents. Saffron used in our study comprises 65.37/100 g carbohydrate, 11.43/100 g protein, 2.54/100 g minerals, 5.85/100 g fat and 0.08/100 g vitamins. Figure 2 shows component list of saffron reported by National Nutrient Database.

Synthetic routes used to prepare saffron extract solution, and the reduction and functionalized precursors material are presented in Figures 3 (a) and (b) respectively. Firstly, 0.8 g of saffron was added into 100 ml of distilled water that was preheated at 80 °C. The solution was then stirred continuously for 30 min in heating condition at 80 °C in order to achieve reddish coloured solution. Lastly, extract solution was filtered using a 45 pm polytetrafluoroethylene (PTFE) membrane in a vacuum filtration system. In the second step, 20 ml of GO aqueous solution was transferred to a beaker containing 100 ml saffron extract solution, which was followed by the addition of few drops of ammonium solution (25%) to adjust pH to 10.5 in order to enhance the stability of the GO. The reaction mixture was ultra- sonicated for 40 min and then heated to 90 °C under reflux for 6 h (colour change from brown to black). The resultant suspension was centrifuged at 14,000 rpm and the suspension was washed with a significant amount of distilled water repeatedly until a neutral pH is obtained for the suspension. The stable saffron-reduced GO was collected after cooling at room temperature and the concentrations of the nano fluids were maintained at 0.025, 0.075 and 0.1 wt%. Figure 3 depicts the proposed reaction of GO with the ammonia and different components of the saffron. As shown in Figure 3, oxygen- containing functional groups are removed during reduction process, and thiamine of the saffron covalently functionalized the GO using nucleophilic ring opening of epoxide functional groups by amine group in the presence of ammonia. Also, GO interacts with the ammonia and the rest of the saffron components at high temperature, and produced amine group over the GO. The reduced GO may also have some residual oxygen and amine functionalities, such as the carboxylic groups and thiamine as shown in Figure 4. Thus, the saffron components including vitamins, proteins and fats, might form hydrogen bonds with the residual oxygen and nitrogen (amine group) functionalities on the reduced GO surfaces. Such interactions can disrupt the p-p stacking between the reduced GO sheets, and further prevent the formation of agglomerates.

Example 2

Characterization

The removal of the oxygen-containing groups from GO is clearly reflected in the FTIR spectra of GO and SrGO samples. Figure 6 (a) depicts the FTIR spectra of GO and SrGO samples. The GO shows couple of peaks in the range of 3424 cm ¹ which can be attributed to the O-H stretching vibrations, whereas the peaks at 1731, 1626, and 1383 cm ¹ are assigned to the C=0 stretching, sp ² -hybridized C=C group and O-H bending respectively. Meanwhile, the peaks at 1062 cm ¹ can be attributed to the C-0 vibration of the epoxy or alkoxy groups in the GO respectively. The presence of new symmetric and asymmetric sharp peaks vibrations of C-H bonds are observed within a wavenumber range of 2850-3000 cm ¹ for both the GO and SrGO. Other outstanding differences are observed in the appearance of the two peaks at 1562 and 1162 cm ¹ attributed to N-H bending and C-N stretching respectively, which appears in the spectra of the reduced GO samples. These observations confirm that most of the oxygen functionalities have been removed from the GOs. Although the reduction of the functional groups in the SrGO is obvious, the remaining variety of the functional groups in the final product maintains the stability of the SrGO in the water.

TGA characterization was also performed to assess the thermal stability of the materials. TGA plots of GO and SrGO are shown in Figure 6 (b). There were three distinctive steps of weight losses for both the GO and SrGO within the temperature range of 0-800 °C. Both the samples showed a little mass loss at around 100 °C, which is mostly attributed to the removal of the trapped water molecules and epoxy oxygen functional groups. A second weight loss was observed to occur at a temperature between 200-500 °C, which can be attributed to the removal of phenolic groups and decomposition of sp ³ hybridized carbon atoms located at the defect site of the GO. This result indicates the high degree of oxidation of the graphite after chemical exfoliation. However, for SrGO, there is less amount of weight loss below 525 °C, demonstrating the effective reduction and removal of oxygen functional groups. As compared to GO, after the functionalization and reduction with saffron, the thermal stability decreased and a large mass loss occurred after 600°C. This can be attributed to the decomposition of nitrogen-containing functional groups in the SrGO. The last weight loss occurred after 550 °C, which was caused by the degradation of the graphitic structures in air. Raman spectroscopy is one of the most widely used techniques to characterize the structural and electronic properties of graphene including disorder and defective structures as well as defect density. This technique is able to identify graphene from graphite and few layers of graphene. Figure 7 (a) shows Raman spectra of GO before and after reduction and functionalization with saffron. The Raman spectra shows significant structural changes occurred during the chemical processing from GO to SrGO. Both the GO and SrGO have a couple of Raman-active bands in the spectra, with D band at 1350 cm ¹ corresponding to defects or edge areas and G band at 1598 cm ¹ related to the vibration of sp ² hybridized carbon. The Raman spectra shows a reduction in the D/G ratio, from 1.313 for GO to 1.261 for SrGO, indicating the increased defects or edge areas by reduction of GO. These defects might be due to the smaller size of graphene sheets as well as the remaining functionalities. For SrGO, two peaks at 1484 cm ¹ were attributed to C=N stretching vibration of quinonoid ring. The stretching band assigned to C-N appeared at 1331 and 1210 cm ¹ . A band at 1016 cm ¹ was attributed to N-H bending in-plane of the benzenoid ring. The peaks at 1162 cm ¹ were attributed to C-H bending vibration of the benzenoid ring. All evidences supported that the functional group have been grafted onto the surface of reduced GO. Generally, a peak for the 2D band of the monolayer graphene sheets is observed at 2676 cm ¹ whereas this peak is broadened and shifted to higher wavenumber in case of multi-layer graphene. In this investigation, 2D bands were observed at 2696 and 2709 cm ¹ for GO and reduced GO respectively. This indicates that both GO and reduced GO have multilayer structure. It is also clear that after reduction of GO, the 2D band has shifted towards higher value, which suggested about stacking of graphene layers. GO has different types of functional groups which may prevent stacking of graphene layers, but after the reduction process, a few graphene layers are stacking and formed multilayer reduced GO due to the decrease of such functional groups.

To further illustrate the formation of graphene, XPS was performed to characterize the removal of the oxygen groups. Figure 7(B) shows the XPS spectra of graphite oxide and graphene reduced saffron. As shown in the wide-scan XPS spectra of GOs and SrGOs (Figure 7 (B)), each peak was fitted to the binding energy of standard carbon, 284.6 eV. The Cls, Ols and Nls peaks centered at about 284.6 ev, 532.8 eV and 400 eV respectively. The GO sample includes high amount of oxygenated functional groups, and the O atomic percent is obviously higher than that of the SrGO, as the oxygen-to-carbon (O/C) ratio drops from 0.74 (GO) to 0.218 (SrGO). Four different peaks centered at 284.6, 286.78, 287.708, and 288.905 eV are observed, corresponding to C-C/C=C in aromatic rings, C-O, C (epoxy and alkoxy), C=0 and 0-C=0 groups in GO respectively (Figure 8). The peak intensities of these components (C-0 and C=0) in saffron reduced GO are considerably smaller than those in GO, indicating considerable deoxygenation during the reduction process. It seems that the degree of reduction derived from the C/O ratio is not as high as expected. The Cls spectra of SrGO deconvo luted into C=C at 284.6, C-O/C-N at 285.917, C=0 at 286.799, 0-C=0/N-C=0 at 288.146 and p-p at 289.178 which confirms the reduction and functionalization of SrGO. The results of the XPS analysis are summarized in Table 1.

Table 1

Table 1 shows XPS analysis results for GO and SrGO.

This may occur because some components of the saffron such as protein and vitamin were bound to the reduced GO sheets and thus the high oxygen and nitrogen content of saffron (proteins and vitamins) contributed to the low O/C ratio of SrGO. In the high resolution spectrum of Nls, the peaks at 398.938 and 400.362 eV correspond to the N-C and N-H respectively (Figure 8(E)). The higher peak intensities at 531.482 and 532.572 eV in the 01s spectrum confirms the presence of large fractions of oxygen atoms in the form of C=0 and C O functional groups in the GO (Figure 8(C and D)). The Ols peaks intensities deceased significantly after reduction with saffron.

Example 3

Morphology of Nano fluid

Figure 9(A) illustrates the TEM images of SrGO nanosheets. It can be seen from the figure that the reduced GO mostly consists of individual planar structure wrapped together to display a crumpling morphology. Further, as shown in Figure 9(A), a close-up observation on the surface and edge structures of the reduced GO sheet reveals an elaborated roughness based on color intensity variation, which consists of a combination of dark and gray spots. Dark areas indicate the thick stacking nanostructure of several GO and/or graphene layers with some amount of oxygen functional groups. The higher transparency areas indicate much thinner films of a few layers graphene oxide and/or reduced GO resulting from stacking nanostructure exfoliation. Significantly larger surface area of high transparency of delaminated graphene layers (of about one to few layer thickness) is shown by the reduced GO sample contrary to GO sample, indicating layer delamination due to reduction. The light gray domain represents the defect free crystalline graphene layer having identical atomic configuration as to graphite. The wrinkles in SrGO, particularly at the edge which can be associated with the structural damage of the sp ² carbon arrangement, which occur during oxidation process, solution processing and drying.

Example 4

Stability Investigation with Zeta Potential

Zeta potential measurement is one of the common characterization technique for determining the level of stability of colloids by providing a measure of the magnitude and sign of the effective surface charge associated with the double layer around the colloid particles. Highly dispersed nanofluids could be attributed to the suspensions with high surface charge density so that it can produce considerable electrostatic repulsive forces. The nanofluids that possess a zeta potential value stronger than about +30 mV or more negative than 30 mV are considered as a stable suspension due to interparticle electrostatic repulsion. Close to the isoelectric point (point of zero charge), the particles could no longer be repelled strongly, and therefore start to aggregate and sediment over time. Figure 9(B) shows the measured zeta potentials values as a function of pH for the SrGO nanofluids. The results show that SrGO sheets are highly negatively charged in the range of -33.4 mV to -52 mV for the pH range of 6.45 to 10.73. Therefore, the SrGO in the investigated pH values has strong electrostatic repulsion force which avoids the SrGO sheets to aggregate by noncovalent interactions such as p-p interactions or H-bonding. On the other hand, zeta potential results of SrGO dispersion shows less negative values than that of the GO for the same pH range which was attributed to reduction and partial elimination of the oxygen functionalities at the surface of GO. In acidic solution (PH < 6.45), the material tends to agglomerate and undergo intermolecular dehydration catalyzed by H ⁺ , leading to the coupling of SrGO via ether linkages. Figure 9(B) suggests that the SrGO aqueous suspensions is stable at pH more than 6 (the zeta potential around 33 mV) and never exceed the isoelectric point for the range of pH of interest. The SrGO nanofluids became more stable by adding alkali to the aqueous suspension, which leads to generation of additional negative charge in nanoparticles. Generally, the pH plays a critical role in dispersion of the nanofluids SrGO, and the SrGO nanofluid of the present invention is stable in even slightly acidic conditions.

UV-vis spectroscopy analysis is a common procedure used for investigating stability of nanoparticles aqueous suspensions. According to the Beer-Lamb ert’s law, there is a direct connection between the absorbance of a solution and the concentration of the absorbing species such as particles in the solution. Following this law, the absorption spectrum of the prepared nanofluids exhibited a maximum peak at around 265 nm corresponding to p— p transition of conjugation system in the polyaromatic structures.

The UV-vis spectroscopy analysis for the distilled water-based SrGO nano fluids with different weight fractions were evaluated and the photometric analysis of the UV-vis spectroscopy was employed to pursue the variation of relative weight fraction of the samples versus times (day). The UV-vis spectrum for the water-based SrGO nanofluids with different weight concentrations are presented in Figure 10 (a). It shows that the peak values of absorbance for all the samples were located in the wavelength of 264 nm that is due to the presence of SrGO. Also it can be seen that the absorbance of SrGO reduces from 0.1 to 0.025 wt.% that illustrates the enhancing amount of dispersed SrGO which will increase the value of absorbance. As shown in Figure 10(b), there is a good linear relationship between the concentration of SrGO and the absorbance, which conforms to the Beer's law and proves that SrGO sheets were dispersed well in the base fluid.

UV-vis spectroscopy is a common technique employed to study the dispersibility of nanofluids as a function of the sedimentation time. Figure 10(c) indicates the stability of SrGO-water nanofluids with respect to the number of days after preparation. It is seen that the relative concentration of the nanofluids decreases by enhancing in the number of days after sample preparation. However, the relative concentration of the nanofluids is almost constant after Day 42 for all the samples. The highest sedimentation magnitude is found to be 7.2, 9.07 and 11.2% for the sample containing 0.025, 0.075 and 0.1 wt% of SrGOs, respectively. This confirms the stability of our SrGO-water nanofluids.

Example 5

Thermal Conductivity and Viscosity

The effective thermo-physical properties of the water-based SrGO nanofluids as well as distilled water were experimentally measured for various ranges of temperature and weight concentration and the results are represented in Figure 11. The Figure 11 demonstrates the effective thermal conductivity as one of the most principal thermo - physical properties, for the deionized water and SrGO nanofluids at weight concentrations of 0.025, 0.075 and 0.1% and the temperature ranging from 20 to 45 °C. It can be shown that the thermal conductivity of deionized water is in good agreement with the NIST database with a maximum error of 1%. The Figure 11 clearly displays the measured thermal conductivity of the SrGO nanofluids which is much higher than that of the base fluid and it increases by loading nanoparticles. The reason for this discrepancy is associated with the liquid/particle interfacial nano -layers (nano-layer at the interface between nanoparticle and fluid), Brownian motion and specific surface area (SSA) of the nanoparticles; and consequently the higher thermal conductivity of the SrGO nanoparticles compared to that of the deionized water.

The graphene sheets with higher SSA loaded in base fluid leads to higher thermal conductivity of the corresponding nanofluid. Furthermore, some fundamental parameters such as temperature, thermal conductivities of the basefluid and nanoparticles, weight concentration, shape or geometry of nanostructures play significant roles in determining the thermal conductivity of the nano fluid. Also, it can be obviously seen in Figure 11 that the measured thermal conductivity of the nanofluids as well as distilled water enhanced by increasing fluid temperature, which is an expected phenomenon. This shows the temperature plays a significant role in enhancing the thermal conductivity of aqueous suspensions that it contributes to the increased Brownian motion of the SrGO nanoparticles dispersed in the base liquid. The thermal conductivity of the SrGO nanofluids increased up to 8.95, 20.72 and 28.88% respectively, for the weight concentration of 0.025, 0.075 and 0.1% at temperature of 45^C.

Figure 12 demonstrates the results of dynamic viscosity measurement for the distilled water and SrGO nano fluids at the shear rate of 150 s ¹ , weight concentrations of 0.025, 0.05 and 0.1% and the temperature ranging from 20 to 45 °C. It can be observed that the viscosity plots of the SrGO nanofluids resemble the viscosity plot for distilled water, whereby there is an insignificant increase in the viscosity of SrGO nanofluids compared to that of the distilled water with an increase in SrGO weight concentration, as shown in Figure 12. The reason behind this phenomenon can be attributed to the low weight concentrations considered for the nanofluids in addition to the successfully implementation of the covalent functionalization which leads to the well dispersing of the SrGO nanoparticles into the base fluid. It can be seen from the Figure 12 that the viscosity decreases when the temperature is increased, which is in tandem to the water behaviour, and this trend is caused by the weakening of the intermolecular adhesion forces.

The experimentally measured values of viscosity for the water-based SrGO suspension were compared with those calculated with the empirical correlations of Batchelor (Khanafer & Vafai, 2011) and Einstein (J.-H. Lee et al., 2008) using equations (1) and (2), respectively, and the results are depicted in the Figure 13. This indicates that the values calculated from experimental data are in good agreement with those determined from empirical correlations for water-based SrGO nanofluid with a maximum error of about 6%.

Batchelor mode, ^ = 1 2.5 _< s % 6.5 _< s ² (1)

bf

Einstein model, = 1+2.5 <b (2)

¾ f

Where, is the viscosity of the nanofluid, ¾ _f is the viscosity of the base fluid and f is the volume fraction of the nanoparticles.

It is crucial to keep down the increment in viscosity of the heat transfer working fluids by using an appropriate synthesis method and considering low weight concentrations for the nanofluids, particularly in heat transfer systems, where the overall positive impact in heat transfer is being undermined by pumping fluid penalty occurs with an increase in viscosity. Hence, in order to maximize the heat transfer performance of closed-loop systems in which nanofluids are used as the working fluids, it is imperative to maintain the resultant colloidal mixtures in Newtonian behavior since this will reduce the pumping power compared to non-Newtonian fluids.

The Figure 14 represented the dynamic viscosity of nano -coolants as a function of shear rate at different temperatures. As shown in Figures 14 (a-d), the water-based SrGO nanofluids synthesized in this research exhibit Newtonian behavior, whereby the viscosity of the nanofluids remains constant with an increase in the shear rate. From a thermal transport perspective, this nano material is deemed suitable as an additive to enhance heat transfer performance.

Example 6

Experimental System

The overall experimental set-up for convective heat transfer includes the horizontal pipe as a test section, a reservoir tank, a pump, a data acquisition system, a cooling unit, a heated test section, and measuring instruments including a differential pressure transmitter (DPT), a flow meter and thermocouples. The experimental set-up is shown in Figure 5 (a-b). The aqueous suspensions were pumped using Araki EX-70 R magnet pump from a 10 L stainless steel jacketed tank at a flow rate of 0-14 L/min. The pressure loss and flow rate were measured using a Foxboro™ differential pressure transmitter and SE 32 and an inline paddle wheel flow transmitter with display (Biirkert Contromatic Corp., USA) respectively. The pump flow was regulated using Hoffman Muller inverter. A straight seamless tube (stainless steel) with a 12 ± 0.1 mm outer diameter, 10 mm inner diameter and length of 1400 mm, was used as the test section. An ultra-high-temperature flexible tape heater (with a 12.5 mm width and 3.6 m length dimension) was carefully wrapped surrounding the test section at a maximum power of 940 W to prepare the final heated section of 1.2 m. The heater was linked to a QPS VT2-1 variable voltage transformer (Success Electronics & Transformer Manufacturer Sdn. Bhd., Malaysia) and, voltage and current were measured to set the desired heating power. Five K-type thermocouples were inserted into stainless still thermocouple sleeves, which were installed on the upper surface of the test section employing high temperature epoxy glue. The axial distances of the thermocouples from test tube inlet are 20, 40, 60, 80, and 100 cm as it can be seen in Figure 5. The bulk temperatures of the flow were measured using two platinum resistance temperature detectors (Pt-100 RTDs) which were placed inside the pipe at the outlet and inlet of the test section. The maximum error for thermocouples was ± 0.2°C. The thermocouples were connected to a GF220 10-channel midi logger (Graphtec Corporation, Japan) in order to monitor and record the temperature data. The test section was wrapped with thick fibreglass wool in order to reduce heat loss to the surroundings, as well, as achieving steady state temperatures at the inlet and outlet of the test section.

Example 7

Data Processing

The experimental values were processed to assess the heat transfer performance and hydrodynamic behaviour of a novel eco-friendly synthesized graphene coolant in a closed conduit system. By considering the conduction in the tube wall and convection heat transfer with the fluid at the test section, a calibration is essential to measure the temperatures at the internal surface of the tube accordingly, the Wilson plot technique (Fernandez- Seara, Uhia, Sieres, & Campo, 2007) was used which acts by equating the resistance between various sections of the heat transfer direction and measuring the internal surface temperatures of the horizontal pipe via mathematical manipulation. To study the effect of the SrGO aqueous suspensions on thermal properties of DI water, the significant parameters, namely convective heat transfer coefficient (h), Nusselt number (Nu) and pressure drop (DP) are required to be evaluated. The convective heat transfer coefficient was evaluated from the experimental values including the measured bulk, surface, inlet and outlet temperatures via the Newton’s cooling law, equation (3).

Where, T _K „ T _b and q represents the wall temperature, bulk temperature and heat flux, respectively. T _b is defined as

_% where T _a and T _;. are the outlet and inlet temperatures respectively of the flow. The heat flux can be calculated using equation

(4):

Q

$ = A (4)

Where, Q is the input power (VI) provided by the power supply and A is the internal heated surface area of the tube. Note that A= DL.

The input power (VI) of 600 W was used in this experimental study. The Nusselt number is expressed by equation (5):

h X D

Nu =

K (5)

Where, K, h and D are, respectively, the thermal conductivity, the heat transfer coefficient and the tube inner diameter, respectively. The Reynolds number (Re) was evaluated using the equation (6):

Where, i?;.. p and m are the velocity, density and the dynamic viscosity respectively of the working fluid.

For single-phase fluids, the empirical correlations for Nusselt number were suggested by Gnielinsky (Volker Gnielinski, 1975), Petukhov (Petukhov, 1970) and Dittuse Boelter (Duangthongsuk & Wongwises, 2008), as presented by equations (7), (8) and (9) respectively. Here, Re is the Reynolds number, Pr is the Prandtl number and f is the friction factor. Equation (7) is used in the range of 3xi0 ³ < Re < 5xl0 ⁶ and 0.5< Pr < 2000.

Equation (8) is applicable for the range of 3000 < Re < 5xl0 ⁶ and 0.5< Pr < 2000.

Nu = 0 ,Q23Re ^&s Fr ^QA (9)

Equation (9) is used in the range of Re > 10 ⁴ and 0.6 < Pr < 200.

The friction factor (f), in equations (7) and (8) is evaluated by Petukhov (Petukhov, 1970) equation (10).

/ = (0.79 Ln Re - 1.64} ^-2 (I _Q )

The equation (10) is valid for the range of 2300 < Re < 5 X i0 ^b .

The friction factor (f) of the distilled water and SrGOs aqueous suspensions was determined from the pressure drop across the test section measured from experiments using the following equation (11):

Where, DR and v are the pressure drop and flow velocity, respectively.

The empirical correlations suggested by Petukhov (Petukhov, 1970) and Balsius (Blasius, 1907) for the friction factor of the base fluid is evaluated by the equations (10) and (12), respectively.

f = 0.3164 Re ^® - ^'S (12)

Valid for the range of Reynolds numbers, 3000< Re < 10 ⁼ .

Viscosity of the nanofluids could be evaluated by different models such as Batchelor (equation 1), Einstein (equation 2) etc.

Example 8

Validation Test for Distilled Water

In order to validate the reliability and accuracy of the used closed conduit heat transfer system, a series of experiments are performed using distilled water at constant heat flux boundary conditions prior to running the experiments using SrGO-water nanofluids. The local Nu is determined using equation (5) based on the acquired experimental data. The local Nu is plotted versus the axial distance of the horizontal stainless steel tube for various Re, as shown in Figure 20 (a). The fully developed thermal entry length for turbulent flows is given by x > 10 D. It is seen from Figure 20 (a) that the local Nu is practically invariant for the fully developed thermal boundary layer region at various points along the closed conduit. This can be explained by considering the constant heat flux boundary condition and fully developed thermal boundary layer. In general, the temperature gradient is invariant along the axial distance, which results in a relatively constant local heat transfer coefficient and consequently, constant local Nu. Besides, the average Nu is plotted as (Figure 20 (b)) and compared with the values determined from empirical correlations proposed by Gnielinski (Volker Gnielinski, 1975), Petukhov (Petukhov, 1970) and Dittus-Boelter (Duangthongsuk & Wongwises, 2008). These empirical correlations are given by Equations (7), (8) and (9), and are applicable for turbulent flow. It is apparent from Figure 20 (b) that the Nu increases with an increase in Re. More importantly, the Nu values determined from experiments show good agreement with those calculated from empirical correlations. The average error between the experimental values and the values determined from Gnielinski’s (Volker Gnielinski, 1975), Petukhov’s (Petukhov, 1970) and Dittus-Boelter’s (Duangthongsuk & Wongwises, 2008) empirical correlations are 7.11, 2.01 and 7.96% respectively. Hence, it can be deduced that the experimental results conform well to those from empirical correlations for the range of Re investigated in this study, indicating the reliability of this experimental set-up for heat transfer measurements. Hence, the present experimental set-up can be employed to assess the heat transfer properties of the SrGO-water nanofluids. In order to determine the pressure drop in the test section, the friction factor for the DI water is calculated using equation (11) and compared with those determined from Petukhov’s (Petukhov, 1970) and Blasius’s (Blasius, 1907) empirical correlations given by equations (10) and (12) respectively. The friction factor results are presented in Figure 20 (c). Present results are indeed encouraging since the maximum difference in the friction factor between experiments and those obtained from Petukhov’s (Petukhov, 1970) and Blasius’s (Blasius, 1907) empirical correlations are equations 10 and 12, respectively. The maximum difference is small (less than 5%), which indicates that the current experimental set-up is reliable to measure the pressure drop over the range of Re investigated in this research.

Example 9

Uncertainty Analysis of the Test Results

The uncertainty analysis of the measured data along with that of the relevant parameters obtained from the data reduction process is presented in Table 2 and is estimated based on the error propagation method. Table 2

Table 2 shows specifications and errors of the measuring instruments and sensors used in the present experiment.

Example 10

Convective Heat Transfer Coefficient of the Nano fluids

In order to validate the reliability and accuracy of the present closed conduit heat transfer system, a series of experiments were performed using distilled water at constant heat flux boundary conditions prior to running the experiments using SrGO- water nanofluids. The local Nu is determined using equation (5) based on the current experimental data. The local Nu is plotted versus the axial distance of the horizontal stainless steel tube for various Re, as shown in Figure 20 (a). The fully developed thermal entry length for turbulent flows is given by x > 10 D. It is seen from Figure 20 (a) that the local Nu is practically invariant for the fully developed thermal boundary layer region at various points along the closed conduit. This can be explained by considering the constant heat flux boundary condition and fully developed thermal boundary layer. In general, the temperature gradient is invariant along the axial distance, which results in a relatively constant local heat transfer coefficient and consequently, constant local Nu. Besides, the average Nu is plotted as (Figure 20 (b)) and compared with the values determined from empirical correlations proposed by Gnielinski (Volker Gnielinski, 1975), Petukhov (Petukhov, 1970) and Dittus-Boelter (Duangthongsuk & Wongwises, 2008). These empirical correlations are given by Equations (7), (8) and (9), and are applicable for turbulent flow. It is apparent from Figure 20 (b) that the Nu increases with an increase in Re. More importantly, the Nu values determined from experiments show good agreement with those calculated from empirical correlations. The average errors between the experimental values and the values determined from Gnielinski’ s (Volker Gnielinski, 1975), Petukhov’s (Petukhov, 1970) and Dittus-Boelter’s (Duangthongsuk & Wongwises, 2008) empirical correlations are 7.11, 2.01 and 7.96% respectively. Hence, it can be deduced that the experimental results conform well to those from empirical correlations for the range of Re investigated in this study, indicating the reliability the present experimental set-up for heat transfer measurements. Hence, the current experimental set-up can be employed to assess the heat transfer properties of the SrGO-water nanofluids. In order to determine the pressure drop in the test section, the friction factor for the distilled water is calculated using equation (11) and compared with those determined from Petukhov’s (Petukhov, 1970) and Blasius’s (Blasius, 1907) empirical correlations given by Equations (10) and (12), respectively. The friction factor results are presented in Figure 20 (c). The present results are indeed encouraging since the maximum difference in the friction factor between experiments and those obtained from Petukhov’s (Petukhov, 1970) and Blasius’s (Blasius, 1907) empirical correlations as presented by equations 10 and 12 respectively. The maximum difference is small (less than 5%), which indicates that the present experimental set-up is reliable to measure the pressure drop over the range of Re investigated in this research.

To study the convective heat transfer coefficient of water-based SrGO nanofluids, a series of experiments have been performed at the Reynolds number range of 6370+5 to 15925+5, input power of 600 W and inlet temperature of 30 °C under turbulent boundary condition. Three SrGO weight concentrations of 0.025, 0.075 and 0.1 % are considered in this research. The convective heat transfer coefficient of water-based SrGO nano fluids and distilled water were evaluated using equation (1) and the corresponding results are demonstrated in Figure 16 (a) as a function of the Reynolds number. It can be seen clearly from Figure 16 (a) that there is an increment in the convective heat transfer coefficient when the Reynolds number is elevated for the SrGO water-based SrGO nanofluids and distilled water. Furthermore, the SrGO weight concentration has a noticeable effect on convective heat transfer coefficient enhancement of the water-based SrGO nanofluids, which can be assigned to the thin thermal boundary layer and the enhanced thermal conductivity for the water-based SrGO nanofluids as well as the lower thermal resistance between the nanofluids and inner wall surface of the test section at higher Reynolds number. Carbon allotropes such as carbon nano tubes and graphene nanoplatelets tend to reduce the thickness of thermal boundary layer. Moreover, other parameters such as the specific surface area and Brownian motion of the SrGO nanoparticles in base fluid play a key role in the convective heat transfer coefficient. The convective heat transfer coefficient of the SrGO-water nanofluids rises by approximately 12.95, 31.59 and 41.79% for a weight concentration of 0.025, 0.075 and 0.1 wt% respectively.

The ratio of convective to conductive heat transfer of water-based SrGO nanofluids is determined by inserting the corresponding average convective heat transfer coefficient values into Equation (5) and the results are illustrated in Figure 16 (b) with respect to the nanoparticle weight concentration and Reynolds numbers. It can be observed from Figure 16 (b) that there is a clear increment in the average Nusselt number with an increase in the Reynolds number and SrGO weight concentration. The greater average Nusselt number for the water-based SrGO nanofluids is attributed to the enhanced thermal conductivity as well as the Brownian motion and micro -convection of SrGO nanoparticles dispersed into the base fluid. A maximum average Nusselt number enhancement of 6.17, 15.2 and 19.5% for the weight concentrations of 0.05, 0.075 and 0.1 wt% respectively at Reynolds number of 15925 ± 5 were achieved. Example 11

Pressure Drop of the Nanofluid

The pressure drops across the experimental test section examined for various weight concentrations and Reynolds number of water-based SrGO nanofluids and the results are depicted in Figure 17 (a). The corresponding friction factor values are evaluated using equation (11) and the results are illustrated in Figure 17 (b). It can be observed clearly that pressure drop and friction factor increases slightly with an increase in weight concentration compared to that of distilled water. The pressure drops for the SrGO nanofluids at low concentration of 0.025 wt% is quite close to that for the distilled water, which is assigned to the low weight concentration of SrGO. The enhancement in friction factor and pressure drop is due to the minor increment in the dynamic viscosity of nanofluids. It should be mentioned that for a constant Reynolds number, an increase in viscosity requires a small increase in fluid velocity. Accordingly, the increase in the velocity of working fluid can be considered as the main reason for increase in friction factor and pressure drop of the nanofluids in convective heat transfer systems. The maximum increase in the friction factor of nano fluids for weight concentration of 0.025, 0.075 and 0.1% are about 1.17, 2.48 and 3.21% respectively.

The power consumption of a heat transfer set-up is a significant parameter in terms of economy and energy saving in various thermal applications. The pumping power for turbulent flow regime can be measured using the following equation (15):

W = 0.158

, ^ , _^

Where, m is the mass flow rate. Using p =— and t? = - for a fixed Re = and substituting m into the equation (15), the relative pumping power constant Reynolds number is given by equation (16).

Where, ML _f and are the pumping power of the base fluid and SrGO nano fluids respectively. The relative pumping power of the SrGO nanofluids is calculated employing the equation (16) and the results are presented in Figure 18 for various weight concentrations. This figure shows that the pumping power required for the nanofluids with various SrGOs nanoparticles loading is quite close to that of distilled water.

To assess the economic performance of SrGO nanofluid as a suitable alternative candidate for use in heat transfer equipment such as car radiators, heat exchangers and solar collectors, the performance index parameter (e) is evaluated. The performance index is described as the ratio of the enhancement in convective heat transfer coefficient (desired efficiency) to the enhancement in pressure drop (unpleasant efficiency) of nanofluid relative to the base fluid. The performance index is presented by equation (17):

Here, ¾is the ratio of the convective heat transfer enhancement, and is the ratio of pressure drop.

In this part, the thermal and economic performance of the nanofluids in heat transfer equipment is evaluated according to the performance index which is defined as the ratio of the enhancement in convective heat transfer coefficient to the enhancement in pressure drop of nanofluid relative to the base fluid. The addition of high thermal conductive nanoparticles improves the convective heat transfer, however, the pressure drops across the test section also elevates which is undesirable. Evidently, when the performance index is greater than 1, it implies that the technique is more in the favour of heat transfer enhancement rather than in the favour of pressure drop increasing. Therefore, the heat transfer methods with performance indexes greater than 1 would be feasible choices in practical applications. The performance index for the water- based SrGO nano fluids is determined using equation (17) and the data is demonstrated in Figure 19 at various Reynolds numbers and weight concentrations. It could be clearly seen from the Figure 19 that the values of performance index for all of the samples including SrGO is greater than 1 and it rises with an increase in Reynolds number and weight concentration. Among the tested SrGO nanofluids, the sample with 0.1 % wt. nano fluid shows the highest performance index ratio.

Although the invention has been described and illustrated in detail, it is to be understood that the same is by the way of illustration and example, and is not to be taken by way of limitation. The scope of the present invention is to be limited only by the terms of the appended claims.