Doping Effect on the Thermal Conductivity of Metal Oxide Nanofluids

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Doping Effect on the Thermal Conductivity of MetalOxide Nanofluids: Insight and Mechanistic Investigation Anjani P. Nagvenkar, Ilana Perelshtein, and Aharon Gedanken J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.7b10020 • Publication Date (Web): 09 Nov 2017 Downloaded from http://pubs.acs.org on November 12, 2017

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The Journal of Physical Chemistry

Doping Effect on the Thermal Conductivity of Metal-Oxide Nanofluids: Insight and Mechanistic Investigation Anjani P. Nagvenkar†, Ilana Perelshtein† and Aharon Gedanken†* †

Department of Chemistry and Institute for Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel *Corresponding author: [email protected]

1 ACS Paragon Plus Environment


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Abstract Nanofluids which are dispersions of nanoparticles in liquids, are known to exhibit anomalous heat transfer properties compared to conventional base liquids. Numerous mechanisms were proposed to explain the unusual enhancement in thermal conductivity of nanofluids, including Brownian motion, interfacial thermal resistance, and conduction due to particle aggregation. In the present study, the individual contributions of the various mechanisms are detailed. Nanofluids of pristine metal oxides (ZnO and CuO) and of Zn2+ doped CuO in water as base fluid were sonochemically prepared, without a surfactant, using a probe sonicator. Varying the specific heat capacity (Cp) of the synthesized nanomaterials was exploited to understand the interfacial resistance (Kapitza resistance) in the base liquid, which influences the thermal flow between the particle and the liquid molecules wrapping over the particle surface (the nanolayer). The thermal conductivity was evaluated at two different concentration ranges. The enhancement at low concentrations is attributed to Brownian motion and thermophoresis, whereas the rise in the heat transfer at the higher concentration range was ascribed to the conduction mechanism that results from particle aggregation.

Introduction The energy efficiency requirements of today are focused on the enhancement of heat transfer phenomena for practical applications.1 A high thermal efficiency of heat transfer processes can be achieved by raising the thermal conductivity of the working fluid.2 Nanofluids are a class of colloids which are dispersions of solid nanoparticles in base fluid with higher thermal conductivity, thus exhibiting anomalous heat conducting properties compared to conventional


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fluids (water, ethylene glycol, etc.).3,4 Among metal and metal oxide nanofluids, which possess higher thermal conductivity than other nanfluids,5–7 CuO nanofluids have gained significant attention due to their implicit heat transfer ability.8 A large increase in the thermal conductivity of ethylene glycol (EG) based Cu and CuO nanofluids has been consistently reported over the years.9 The two important findings to date which pioneered the use of Cu-based nanofluids as efficient heat transfer fluids, are by Lee et al. and Eastman et al.10,11 the former study observed a 20% thermal conductivity enhancement with a CuO-EG nanofluid at a volume fraction of 0.04%, whereas the latter study found a 40% thermal conductivity enhancement using a Cu-EG nanofluid at a volume fraction of 0.3 %). The water-based CuO nanofluids are fairly explored for their heat transport properties owing to the lower stability of the nanoparticles in the base fluid without the surfactant.12 The mechanism for the enhancement of thermal conductivity is a highly debated research topic over a decade; with various models proposed which can better fit the experimental data. The various mechanisms proposed for the thermal conductivity of the nanofluids include layering of the liquid molecules at the particle-liquid interface (thermal boundary resistance), Brownian motion of the nanoparticles in the fluid, aggregation, nanoparticle size effects, and ballistic phonon motion.

13,14

The enhancement of the thermal conductivity

is supported by two

established models, that of Maxwell and that of Hamiltonian, a mechanism that provides a very good fit for the vast majority of reported remains elusive.15,16 Although Yu et al. modified the Maxwell model (without the nanolayer) by accounting the role of interfacial layers for the enhancement of thermal conductivity, the report lacked the justification for the behavior of the particles with the nanolayer at higher concentration as the model assumed very low volume fraction where there is no overlap of the equivalent particles with the nanolayer.



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In the current work, we attempt to manifest the controversial role of the interfacial layer in the mechanism and to reaffirm the effect of particle aggregation on the enhancement of thermal energy transfer, by providing experimental evidence for the thermal conductivity of pure and doped metal oxides. For nanofluids of pure metal oxides (ZnO, CuO) and their doped counterparts, the thermal conductivities were measured and compared, and the variation in the thermal conductivity was used to elucidate the mechanism behind the unusually high thermal transport properties of nanofluids by detailing the nanolayer contribution. In addition, the heat transfer behavior of these nanofluids was formulated at very low volume fractions without the use of a surfactant, explicating the different mechanisms of thermal conductivity enhancement at two different concentration ranges.

Experimental: Synthesis of the nanomaterials: Copper acetate monohydrate (Cu(CH3COO)2·H2O) and, zinc acetate dihydrate (Zn(CH3COO)2·2H2O), were purchased from Sigma-Aldrich, Israel. Pristine ZnO and CuO were synthesized in water by dissolving known amounts of the precursor salt in deionized water and applying sonochemical irradiation with a high-intensity ultrasonic Ti-horn (20 kHz, 750W, 45 W/cm2). Upon attaining ∼60 °C, an aqueous solution of ammonium hydroxide (28−30%) was injected into the reaction cell to obtain a pH of ∼8-9. The change in the suspension color from colorless to white and tinted-blue to black-brown indicated the formation of ZnO and CuO respectively. The reaction mixture was irradiated for another 30 min on a chilled ice-bath to maintain the temperature of the reaction to 25 °C. The procedure is repeated for preparing the three nanomaterials by doping the CuO with varying molar ratios of Cu2+ and Zn2+ 1:1, 2:1 and


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3:1. Since the dopant is not formed in pure water,17 the volume ratio of the solvent during synthesis was maintained as 9:1 ethanol to water. The obtained product was filtered, washed with ethanol and deionized water, and dried in vacuum.

Thermal conductivity measurement of the nanofluid: Nanofluids with different volume fractions of nanoparticles were prepared by considering the density of the nanoparticles that were synthesized. A known amount of nanoparticles was weighed (for appropriate volume fraction) and dispersed in water (the base fluid) using ultrasound irradiation, by inserting the probe (Ti-horn @ 20 kHz, 100 W cm-2) into the solution and irradiating for 3 min. Thermal conductivity measurements were conducted using a Decagon Devices KD2 Pro Thermal Property Analyzer. The device was calibrated against glycerol as a standard with known thermal conductivity. The single hot-wire probe was immersed in 30 mL of the sample, in a cylindrical glass tube with a diameter of 2.5 cm. During the measurements, the samples were placed in a constant temperature water bath. For each data point, at least 10 measurements were recorded within an hour to ensure thermal equilibrium of the sample. During the measurements the care was taken not to avoid rapid drift in the temperature. Characterization methods and instrumentation: The Crystallographic peaks were identified through X-ray diffraction (XRD) technique using Bruker D8 Advance X-107 ray diffractometer with Cu Kα (λ = 1.5418 Å) as the source. Dynamic light-scattering (DLS) measurements were carried out on a Nano ZS Malvern Zeta sizer instrument. High-resolution (HR) TEM images were obtained for morphological microstructure characterization using a JEOL JEM-2100 model operated at an accelerated



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voltage of 200 kV. Inductively coupled plasma (ICP) technique was used to determine the ratio of Cu/Zn in the doped nanomaterials performed on ICP-OES spectrometer (Ultima 2, Jobin Yvon Horiba). Differential scanning calorimetry (DSC) measurements were carried out on DSC1 analyzer (Mettler Toledo, USA) over the temperature range of 30− 800 °C at a 5 °C/min heating rate under argon. Thermal conductivity data of the nanofluids were recorded using KD2 Pro Thermal Properties Analyzer with KS-1 sensor probe manufactured by Decagon Devices, Inc. USA.

Results and Discussion In the current study CuO and ZnO were synthesized in their pristine form following a procedure that was published elsewhere17.. In brief, the aqueous solutions of the metal acetates were sonicated at basic pH of ≈ 8. The doping of Zn2+ in CuO was achieved by initially varying molar ratios of zinc ions in the precursor containing the Cu+2 ions. Of the three molar ratios, 1:1, 2:1, and 3:1 between Cu:Zn only the 3:1 lead to the formation of one perfect dopant, with a formula of Cu0.89Zn0.11O called herein “the dopant”. The two lower ratios formed defected lattices with an excess of non-intercalated zinc ions forming ZnO in parallel with the dopant. The XRD data indicates a clear distinction between the three nanomaterials. Pristine ZnO and CuO showed typical XRD patterns, however, gradual incorporation of zinc ions in the lattice caused impurity defects responsible for the shifts in the XRD diffraction peaks. In the case of a 1:1 molar ratio, the redundant amount of zinc ions contributes to the formation of ZnO, leading to a co-existence of two different crystalline phases. These two distinct phases of ZnO and CuO are visible in the XRD pattern (Fig. 1). The reduced amount of zinc, by 17% in the case of the 2:1 molar ratio, led


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to the formation of the doped CuO, with small excess of Zn2+ ion impurities forming ZnO, which cannot be detected by XRD below an impurity content of 2%, which is the detection limit of the XRD instrument. A perfect dopant was obtained with the metal ion precursor ratio of 3Cu2+:1Zn2+ indicated by an apparent shift in 2Ө angle compared with pristine CuO. A singlephase structure with nominal composition of Cu0.89Zn0.11O (11% of Cu ions replaced by Zn) was confirmed by ICP, which determined the ratio of Cu/Zn in the solid as 8:1. Thus further thermal conductivity studies were mainly focused on Cu0.89Zn0.11O. The nanofluids were prepared by a two-step method, dispersing the required volume of nanoparticles prepared in the first stage in deionized water, followed by the application of ultrasonic waves to the solution to obtain a stable colloidal suspension. The use of a surface capping agent for stabilizing the solution against aggregation, was avoided as it might influence the thermal conductivity of the base liquid (water) and thus mask the real enhancement due to the nanoparticles. In the present research we first explored the influence of doping on the thermal conductivity of a CuO nanofluid. The effective thermal conductivity enhancement in the volume fraction range of 0.01 - 0.05, is plotted in Fig. 2. Cu0.89Zn0.11O shows a substantial increase in thermal conductivity at 333 K compared to its undoped counterparts (namely ZnO and CuO). For Cu0.89Zn0.11O, an appreciable thermal conductivity enhancement is noticeable at low temperatures (293-313 K), indicating a significant effect of lattice doping on the overall thermal property of the nanofluid. However, at increased temperatures a steady rise in heat conduction is observed. Considering thermal conductivity as a heat-dependent intrinsic transport property, this moderately increased enhancement at higher temperatures could be tentatively ascribed to the fact that the system is approaching the steady state of heat conduction. In other words, while for the dopant only small changes in thermal conductivity were detected over the 303-333 K



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temperature range, for other nanofluids a dramatic change in thermal conductivity occurs between 303-313 K and from 313-323 K. A small gain in heat by the equilibrated system at low temperature results in higher thermal conductance due to the large temperature gradient, whereas a slight increment in the heat at higher temperatures induces a small effect. Thus for all nanofluids, the thermal conductivity increases very slightly above 323 K, and for a given nanofluid very similar thermal conductivity levels are observed at higher temperatures. Several groups reported on the mechanism of thermal conductivity, taking into account the role of the particle-liquid interface.18–25 They proposed that the interfacial liquid nanolayer surrounding the solid nanoparticle plays a key role in determining the thermal conductivity of the nanofluid ( ).16 The presence of an interface, or a nanolayer, causes a discontinuity in the exchange of energy between the two phases across the nanolayer. This interfacial thermal resistance, also known as the Kapitza resistance is a crucial parameter, which influences the thermal flow between the particle and the liquid molecules wrapping over the particle surface; calculated by20,26 taking the ratio of the discontinuity or drop in the temperature profile ∆T, and the heat flux, q.

= Conceiving that the thermal boundary resistance Rk is a function of the temperature gradient between the two phases (solid particle and the base liquid) due to the different thermal conductivities, the current study emphasizes the role of the nanolayer as a thermal bridge between the nanoparticle and the base liquid. In order to achieve this, the heat exchange between the solid particle and the liquid surface needs to be altered, which can influence the Kapitza


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resistance and in turn affect the overall thermal conductivity. The thermal energy transfer for a solid is determined by its specific heat capacity, Cp. For the three zinc-doped nanomaterials (with different Cu/Zn molar ratios), the Cp values were determined from DSC measurements (Fig. 3). It was found that the Cp increases with Zn2+ precursor ratio in the CuO, and is the lowest for the dopant which has an impeccable doped crystal lattice structure (Table 1). The high specific heat capacity implies a higher tendency of the material to draw heat into the system (the nanoparticle) from the surroundings, resulting in a temperature drop at the nanolayer, which corresponds to the increase in the Kapitza resistance.20 Thus the Cp of the nanoparticle raises the thermal barrier between the solid surface and the adjacent liquid layer.24 Although the nanolayer plays just a partial role in the overall thermal conductivity enhancement and is not solely responsible,22,27 the present reported enhancement in the thermal conductivity of the nanofluids (Cu0.89Zn0.11O > 2Cu:1Zn > 1Cu:1Zn > CuO) could be a result of the decreasing Cp of the nanomaterial (Cu0.89Zn0.11O < 2Cu:1Zn < 1Cu:1Zn < CuO) (Fig.4). These findings affirm the fact that the Kapitza resistance in the interfacial layer negates the positive influence of the layer as a conductive medium, which facilitates the thermal transport between the two phases. A theoretical basis to this hypothesis probing the heat exchange between the particle and the liquid has been established by Vladkov et al.15,25 In support of the above findings, we measured the cooling curves for the nanofluids (Fig. 5). The Cu0.89Zn0.11O nanofluid demonstrated improved cooling performance

over

the

other

nanofluids.

The

better

cooling

performance

implies

an improved heat transfer. The studies has proved that the addition of nanoparticles with low heat capacity result in decrease in the heat capacity of the nanofluid.28 So also, increasing the volume fraction of the nanoparticles decreases the heat capacity of the nanofluid.29 Comparing



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these reported facts and the present results, the efficient cooling rates can thus be ascribed to enhanced heat transfer and decreased heat capacity of the nanoparticles. In a second step, we examined the thermal conductivity over a range of concentrations from 0.001-0.05% volume fraction of nanoparticles in the nanofluid. At lower volume fractions the thermal conductivity of the nanofluids displays an unusual trend demonstrating a peak effect (Fig. 6). With increasing volume fractions, the effective thermal conductivity increases substantially, attains a maximum at an optimal concentration, and then decreases. With a further increase in the concentration of the nanoparticles (>0.006 % volume fraction) the thermal conductivity rises steadily. Although there are countless reports on the thermal conductivity mechanism of nanofluids, the studies have not focused in detail on very low volume fractions of nanoparticles. Herein, we propose mechanisms for the thermal conductivity at two different ranges of concentrations. In the low concentration region of ~0.001-0.006, the enhancement in heat transfer is due to collective Brownian motion and the thermophoretic effect. The higher range of volume fractions, > 0.006 %, shows a steady rise in conductivity attributed to the conductive transport at increased volume fractions. The following explanation is provided for the observed measurements in the low concentration region: the Brownian motion is associated with random motion of the particles in the fluid, whereas thermophoresis relates to the movement of the particles in response to the temperature gradient. Buongiorno24 developed a model speculating a significant contribution of Brownian motion and thermophoresis (the Soret effect) to the observed heat conduction. The Soret effect becomes pronounced with the introduction of a minimal volume of nanoparticles in the base fluid. Upon application of heat, the nanoparticles at the boundary between the heat source and the nanofluid undergo vigorous agitation ensuing effective Brownian motion. The sharp variation in temperature near the heat source generates a


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high temperature gradient with respect to the bulk nanofluid. This results in local turbulent flow regime, along with the temperature gradient, leading to a thermal drift of the nanoparticles to cooler regions by thermophoresis. The role of Brownian motion in heat transfer has been an issue of ongoing debate with no certain conclusions drawn to date.30–33 However, we claim that Brownian motion combined with thermophoresis effect are significant at very low concentrations of nanofluids.34–36 Kwak and Kim also reported a similar phenomenon at lower concentrations of the CuO nanofluid, supporting the fact that at the high dilution limit the observed effect in thermal conductivity is very high due to free movement and rotation of the particles.37 As seen in Fig. 6, upon attaining a certain critical concentration, the thermal conductivity drops with a further increase in volume fraction. In view of the above explanation, with a further increase in concentration above 0.0035 %, the movement of the nanoparticles is restricted resulting in little impact of the Brownian motion and thermophoretic effect on the overall thermal conductivity enhancement; higher particle volume fractions result in smaller diffusion coefficients weakening the Brownian motion.38 The possibility that the heat transfer rate could be enhanced due to conduction is eliminated because of the continued drop in thermal conductivity. This drop supports the fact that at small volume fractions the conduction component is trivial and thus an optimum concentration of particles is necessary for the effect to be observed. 39,40 Prasher et al. proposed a thermal conductivity model, which is based purely on conductive heat transfer between the nanoparticles. According to this model and the other reports with an increase in volume fraction of the nanoparticles, the rise in thermal conductivity due to the effect of Brownian motion becomes insignificant.40,41 The observed enhancement in thermal conductivity is supported by considering the effects of particle size and aggregation.42,43 The influence of particle size on the thermal properties of the nanofluids is still debated, as the results



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reported in the literature are contradictory.13,44 Taking into account the general trend of increasing nanofluid thermal conductivity with decreasing particle size,45 the present enhancement can be elaborated. The TEM images (Fig. 7) depict the needle-shaped pristine CuO nanoparticles, with an average particle size of ~15 nm, whereas the doped nanomaterial particles appear as bigger aggregates of ~5 nm particles. Nanofluids with smaller nanoparticle size have a higher surface area exposed for heat conduction.46 Owing to the high surface potential energy the smaller nanoparticles tend to easily agglomerate. The interfacial resistance acts as a barrier that impedes the particle-particle heat conduction from affecting the overall thermal conductivity enhancement. Lower interfacial resistance increases the heat conduction at the point of contact between the particles. At low volume fraction the decrease in the conduction contribution makes the impact of Kapitza resistance insignificant. Since the tendency for cluster formation is even greater at high concentrations, the observed anomalous increase in the thermal conductivity could be a result of the agglomeration of the particles at higher concentration (volume fraction >0.01%). The rise in agglomeration can also affect the sedimentation rate of the nanoparticles increasing the heat transfer.47 The data presented in Figures 2 and 4 indicates the increase in thermal conductivity as a function of particle concentration and temperature, which are crucial factors influencing the particle agglomeration. To provide evidence for the above hypothesis, DLS was used as a tool to probe the effect of aggregation in nanofluids.48 The average particle size is depicted in Figure 8 as a function of volume fraction and temperature. The increased interparticle distance at low volume fractions refrain the nanoparticles from forming aggregates, whereas at short interparticle distances (high concentrations) the increased van der Waals forces lead to stronger interparticle attraction. At elevated temperatures, the boost in the kinetic motion of the nanoparticles


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increases the collisions between the particles, which are responsible for clustering and agglomeration. Daviran et al. reported the effect of temperature to play role in the enhancement of effective thermal conductivity which not necessarily due to increased Brownian motion but may also result in formation of clusters due to collision.49 The dependence of both these phenomena (temperature and clustering) are depicted in Fig. 8, illustrating the key contribution of particle aggregation for the enhanced heat transfer of these nanofluids at high concentrations and temperatures. For a nanofluid with well-dispersed particles the effective thermal conductivity can be represented by the Maxwell’s equation:

=

( )ф( ) ( )ф( )

(eq1)

Where, , and are the thermal conductivities of the nanofluid, base liquid (water) and the particles, respectively; ф corresponds to the volume fraction of the particles in the nanofluid. Yu and Choi calculated the volume fraction of the particles taking into account the thermally bridging nanolayer:50,51 ф = ф(1 + )

(eq2)

Where, is the ratio of thickness of the nanolayer to the particle radius. For an aggregating system introducing the nanolayer effect, the equation (1) can be modified modeled as:

=

( ) ф ( )() ( )ф ( )()


(eq3)


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Where ! and ф! corresponds to the thermal conductivity and volume fraction of the aggregates respectively.

Conclusion We demonstrated the thermal conductivity variation of the nanofluids of pure metal oxides (ZnO, CuO) and Zn-doped CuO with different molar ratios of Zn2+ ions. The difference in specific heat capacity values of the synthesized nanomaterials were availed to affirm the role of thermal boundary resistance at the interface. Furthermore, the measured thermal conductivity values at two different ranges of concentrations were evaluated to explain the most probable mechanism for the enhancement in heat transfer. We conclude that, at lower concentrations, the increased heat transfer is a result of the combined effect of Brownian motion and thermophoresis, while at increased volume fractions the rise in thermal conductivity is attributed to the aggregation of the particles. The hypothesis was supported by particle size and agglomeration data, which was probed by DLS analysis. Acknowledgments: The authors are grateful to Prof. Pawel Keblinski (Rensselaer Polytechnic Institute, NY, USA) for his assistance in improving the manuscript by providing valuable suggestions.


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Table 1. Specific heat capacities determined from DSC measurements

Sample

Heat flow at

Weight (g)

Specific heat capacity (W ) at heating rate 5˚C min-1

260˚C (W/g)

(Jg-1K-1)

CuO

0.3254

0.00557

12.60

1Cu:1Zn

0.1083

0.00791

2.95

2Cu:1Zn

0.1781

0.0164

2.34

Cu0.89Zn0.11O

0.258

0.0279

1.99



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Figure 1. X-ray diffraction pattern of (a) ZnO (b) CuO (c) 1Cu:1Zn (d) 2Cu:1Zn and (3) Cu0.89Zn0.11O


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Figure 2. Temperature dependent thermal conductivity enhancement of the (a) ZnO (b) CuO (c) 1Cu:1Zn (d) 2Cu:1Zn (e) Cu0.89Zn0.11O at nanoparticle loadings of 0.05 % volume fraction.



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Figure 3. DSC curves of (a) CuO (b) 1Cu:1Zn (c) 2Cu:1Zn (d) Cu0.89Zn0.11O


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Figure 4. The variation of the effective thermal conductivity of (a) ZnO (b) CuO (c) 1Cu:1Zn (d) 2Cu:1Zn (e) Cu0.89Zn0.11O nanofluids as a function of the volume fraction of the nanoparticles in water. knf and kw are the thermal conductivities of the nanofluid and water, respectively.



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Figure 5. Cooling curves for the nanofluids of (a) ZnO (b) CuO (c) 1Cu:1Zn (d) 2Cu:1Zn (e) Cu0.89Zn0.11O nanofluids


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Figure 6. Variation of the effective thermal conductivity of (a) ZnO (b) CuO and (c) Cu0.89Zn0.11O nanofluids at low volume fraction of the nanoparticles. knf and kw are the thermal conductivities of nanofluid and water respectively.

(a)



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(a)

(b)

(c)

(d)

Figure 7. HR-TEM images of the synthesized nanoparticles revealing the morphology of (a) CuO, and zinc-doped CuO at Cu/Zn molar ratios of (b) 1Cu:1Zn (c) 2Cu:Zn (d) Cu0.89Zn0.11O. The particle sizes are depicted in red color.


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(a)

(b)

Figure 8. Variation of average particle size obtained from DLS measurements plotted as a function of (a) temperature and (b) volume fraction. The particle sizes measured 24 hours after the preparation of the nanofluids.



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Doping Effect on the Thermal Conductivity of Metal Oxide Nanofluids

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