Synthesis of Cu-Al layered double hydroxide

Applied Clay Science 107 (2015) 98–108

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Applied Clay Science journal homepage: www.elsevier.com/locate/clay

Research paper

Synthesis of Cu–Al layered double hydroxide nanofluid and characterization of its thermal properties Samarshi Chakraborty a, Ishita Sarkar a, Krishnayan Haldar a, Surjya Kanta Pal b, Sudipto Chakraborty a,⁎ a b

Department of Chemical Engineering, Indian Institute of Technology, Kharagpur, India Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, India

a r t i c l e

i n f o

Article history: Received 22 September 2014 Received in revised form 8 January 2015 Accepted 12 January 2015 Available online 31 January 2015 Keywords: Cu–Al LDH Nanofluid Thermal conductivity enhancement Stability analysis

a b s t r a c t Synthesis of pristine Cu–Al layered double hydroxide (LDH) nanofluid via one step method and study of its thermal properties are the core essence of the current work. Nitrate salts of Cu, Al and Na were mixed in a particular molar ratio at constant pH to produce desired Cu–Al LDH. Different dispersion techniques were utilized to uniformly disperse Cu-Al LDH in water to obtain Cu–Al LDH nanofluids. Broadly used characterization techniques were implemented to identify and characterize pristine Cu–Al LDH nanoparticle. These techniques were used to determine crystallite size, composition, morphology and characteristics vibration of interlayer anion present in the nanoparticle. The nanofluids were characterized for particle size, cluster size, surface tension and thermal conductivity. Particle size analysis was carried out to confirm the formation of nanofluid. Dynamic light scattering (DLS) method had been employed to measure the clustering tendency of nanofluid. Effect of nanofluid loading on thermal conductivity was studied in depth. Influence of particle size, shape and composition on thermal conductivity of nanofluid had also been selected as an essential topic of investigation. Zeta potential and visual phase separation study were carried out to measure the stability of concerned nanofluid. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Among the few selected positively charged layered compounds, layered double hydroxide, also known as hydrotalcite, had achieved a lot of attention. Layered Double Hydroxides (LDH) known as a brand of ionic layered material composed of positively charged brucite type sheets. It contains anionic counter ions and solvent molecules in the interlayer space. LDH is denoted by the common chemical formula: [M2+1 − x M3+x(OH)2]x+(Am−)x/m x yH2O, where, M2+ is a divalent cation (such as Ni2 +,Mg2 +,Cu2 +,Zn2 +), M3 + is a trivalent cation (such as Cr3+, Al3+), A is an interlayer anion with m− charge, x and y are fraction constants (Wang and O'Hare, 2012). LDH is also identified as anionic clay comprising of closely filled planes of hydroxyl anion that lies on top of triangular lattice. LDH could be prepared via many ways such as co-precipitation (Sahu and Pugazhenthi, 2011), anionic exchange (Kooli et al., 1997; Rocha et al., 1999), reconstruction method based on memory effect (Chibwe and Jones, 1989) and hydrothermal/ microwave treatment (Benito et al., 2006). Application of LDH includes Abbreviations: DLS, Dynamic Light Scattering; EDS, Energy Dispersive X-Ray Spectroscopy; FTIR, Fourier Transform Infrared Spectroscopy; JCPDS, Joint Committee on Powder Diffraction Standards; LDH, Layered Double Hydroxide; PDI, Polydispersity Index; SEM, Scanning Electron Microscope; TEM, Transmission Electron Microscope; XRD, X-Ray Diffraction. ⁎ Corresponding author at: Chemical Engineering Department, IIT Kharagpur, Kharagpur-721302, West Bengal, India. Tel.: +91 3222 283942 (office). E-mail address: [email protected] (S. Chakraborty).

http://dx.doi.org/10.1016/j.clay.2015.01.009 0169-1317/© 2015 Elsevier B.V. All rights reserved.

a much diversified spectrum due to its tuneable properties such as acid shielding effect, superior adsorption ability, anion exchange skill, thermal stability, flame retardant and gas barrier potential. Pharmaceutical and cosmetic application (Choy et al., 2007), and use of LDH for polymer nanocomposite synthesis (Chen and Qu, 2003; Krishna and Pugazhenthi, 2012) were owed to such properties. It was also used for several other purposes such as heterogeneous catalysts (Rives et al., 2003; Jinesh et al., 2010), drug delivery hosts (Rives et al., 2014). According to this literature survey, potential of LDH as a heat transfer fluid was unexplored. Thermal properties of LDH such as thermal conductivity, surface tension and stability were overlooked so far. Nanofluids are colloidal solutions of nanoparticles (with an average particle size of 100 nm or less in at least one dimension) suspended in base fluid to improve the thermal performance (Mahbubul et al., 2014). Use of nanofluids in heat transfer application was widely investigated by many researchers. Stephen U.S. Choi (Choi, 1995) first invented nanofluid as energy efficient heat transfer fluid via dispersing metallic nanoparticles in traditional base fluid. The resulting fluid had displayed excellent improvement in thermal conductivity when compared with common base fluid. Scientists working on the field of heat transfer used nanoparticles in traditional base fluids like water, ethylene glycol and engine oil due to their potential benefit and applications in microelectronics, energy supply, transportation and metallurgical applications. Thermal conductivity of nanofluids changes with shape, size and composition of nanoparticle. Wang and his co-workers (Wang et al., 1999) determined the thermal conductivity of CuO and Al2O3

S. Chakraborty et al. / Applied Clay Science 107 (2015) 98–108

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Fig. 1. Schematic diagram of Cu–Al LDH nanofluid preparation.

nanoparticle in different base fluids. They studied the influence of particle size and dispersion method on thermal conductivity. Similarly, Xie et al. (Xie et al., 2002) evaluated thermal conductivity of Al2O3 nanofluid having particle size in the range of 12–302 nm. Metal oxide itself does not display extraordinary thermal conductivity although it can improve the thermal conductivity of base fluids in nano suspension. Most of the work reported on the nanofluid synthesis had mainly employed two step synthesis techniques. In step by step techniques, nanoparticle synthesis was carried out first. Finally, nanoparticle was dispersed into the base fluid to prepare the nanofluid. This method had a major drawback because it leads to particle agglomeration. Particle agglomeration was

found to be detrimental for nanofluid stability and its thermal properties (Zhu et al., 2004). According to existing literature, no attention had been given on the potential of LDH as nanofluid. Thermal conductivities of copper (Cu) and aluminum (Al) are significantly high as compared to other cheap metals. Previously heat transfer studies had been carried out on CuO and Al2O3 nanoparticles separately. LDH possesses a dynamic chemical structure, which allows combining Cu and Al together, without any calcination. In this paper, Cu–Al LDH nanofluid was prepared via one-step method using the nitrate salts of Cu and Al in the molar ratio of 2:1, respectively. Different characterizations were carried out to determine crystal size, composition, dispersion, particle size, shape, stability, surface tension and thermal conductivity of Cu–Al LDH nanofluid and nanoparticle. 2. Experimental facility 2.1. Raw materials Copper nitrate (Cu(NO3)2.3H2O), Aluminum nitrate (Al(NO3)3.9H2O), Sodium nitrate (NaNO3) and Sodium hydroxide (NaOH) of analytical grade were procured from Merck, India. Distilled water had been used throughout the preparation period. 2.2. Synthesis of Cu–Al LDH nanofluid

Fig. 2. XRD analysis of pristine Cu–Al LDH nanoparticle.

One step method was implemented for nanofluid synthesis. Coprecipitation method was used to prepare pristine Cu–Al LDH solution. Fig. 1 shows Cu–Al LDH preparation technique. In this present work, two solutions were initially prepared. Solution 1 comprised of aqueous solution of Cu(NO3)2.3H2O, Al(NO3)3.9H2O and NaNO3 had been prepared by mixing the nitrate salts in the molar ratio of 2:1:2, respectively.

100


Table 1 Results of XRD analysis of Cu–Al LDH nanoparticle. d (h k l)

2θ°

Basal spacing (nm)

Crystal size (nm)

JCPDS file no

Average crystal size (nm)

(003) (006) (211) (015) (802) (113)

11.67 23.33 35.70 39.33 53.34 61.89

0.76 0.38 0.25 0.23 0.17 0.15

47.82 90.06 14.12 121.56 274.32 29.16

037-0630 037-0630 046-0099 037-0630 046-0099 037-0630

25.96 25.96 – 25.96 – 25.96

Solution 2 was made with 2 M NaOH solution which was supplied drop wise into Solution 1 until the pH level reached 10.7. This NaOH solution acted as a precipitating agent. Subsequent liquid was stirred for 16 h at room temperature. Resulting liquid was filtered to collect the precipitate. Final precipitate was washed several times to remove excess NaOH. To ensure this, precipitate was water washed until the pH level of filtrate has reached 7. This method of preparation is in line with previously reported work in the open literature (Sahu and Pugazhenthi, 2011; Chakraborty et al., 2014). Cu–Al LDH liquid was prepared by dispersing the final water washed precipitate into water and stirring the mixture for 12 h. Resultant liquid had been sonicated (40 kHz ultrasonic bath) for 30 min. Various quantities of final nanofluid was added and then mixed with distilled water to produce nanofluids of different concentration. NaNO3 played a critical role in this preparation method. It was used to provide additional nitrate ion for anionic intercalation between the cationic layers. This synthesis took place in an atmospheric condition which allowed atmospheric CO2 to also react with mixture. This reaction produced CO2− ion which can intercalate between the metal hy3 droxide layers which was not desired. The main reaction could be possible either in an inert environment or in presence of sufficient nitrate ion. Sodium nitrate present in the solution prevents such undesired CO2− intercalation. 3

2.4. Characterization of Cu–Al LDH nanoparticle and nanofluids X Ray diffraction (XRD) measurement was performed for LDH at room temperature using High Resolution Panalytical X-Ray Diffractometer (Philips) equipped with Ni-filtered Cu Kα radiation (λ = 1.5406 Å). The patterns were acquired over a 2θ range of 7°–70° with an increment of 0.02° and scan speed of 0.5 s. The FTIR spectra of pristine LDH was recorded using Perkin Elmer Fourier transform infrared spectroscope (Spectrometer100) that casts on potassium bromide (KBr). TEM analysis was executed in TECNAI G2 20S Twin. Morphological characteristics of LDH were investigated by scanning electron microscope (SEM EVO 60, Carl Zeiss). The cluster size and zeta potential

2.3. Preparation of Cu–Al LDH nanoparticle For Cu–Al LDH identification study, X-Ray diffraction (XRD), Scanning electron microscope (SEM) and Fourier transform infrared spectroscopy (FTIR) analysis of nanopowder were essential. To obtain the powder, final water washed precipitate was dried at 70 °C in a hot air oven for 5–6 h. Dried sample was grinded using mortar-pestle to form powder LDH.

Fig. 3. FTIR spectrum for pristine Cu–Al LDH nanoparticle.

Fig. 4. SEM images of pristine Cu–Al LDH; (a) 2 KX and (b) 5 KX magnification.


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Fig. 6. Histogram plot of Cu–Al LDH nanofluid TEM images for average particle length analysis; (a) Fig. 5(a) and (b) Fig. 5(b).

Fig. 5. TEM images of Cu–Al LDH nanofluids at two different locations; (a) location 1 and (b) location 2.

of LDH nanofluids were determined using Malvern Instrument, Zeta Sizer Nano-ZS. Surface tension evaluation was conducted by means of Surface Tensiometer of Testing Instrument, Test master. Thermal conductivity of nanofluid was evaluated by Decagon Devices KD2 Thermal Conductivity Meter. 3. Results and discussion Crystal size, basal spacing, vibration band identification related to different chemical bonds and surface morphology, were considered to be important features associated to Cu–Al LDH nanoparticle

Fig. 7. Cluster size vs. concentration curve for Cu–Al LDH nanofluid.

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Table 2 Polydispersity index of Cu–Al LDH nanofluids in various concentrations. Sample name

Concentration (vol.%)

PDI

Cu–Al LDH nanofluids

0.25 0.50 0.80 1 1.25

0.36 0.45 0.44 0.33 0.45

description. Average nanoparticle size and cluster size measurement of Cu–Al LDH nanofluids were key features which are discussed in this paper. Thermal properties were estimated via thermal conductivity measurement. The suspension stability was explained by the means of zeta potential analysis and settling tendency of the nanofluid sample. 3.1. Cu–Al LDH nanoparticle characterization 3.1.1. XRD analysis It could be observed from Fig. 2 that the pristine Cu–Al LDH was poor crystalline in nature. The basal spacing value of all peaks in pristine Cu– Al LDH was calculated using Bragg's equation and results are displayed in Table 1. The individual crystallite size was calculated using the Scherrer equation β = kλ/Lcosθ, where β is width of diffraction peak at half maximum height, λ is wavelength of X-Ray, θ is angle of incidence, k is Scherrer constant and L is crystal size (Monshi, 2012). To measure the average crystallite size basic Scherrer equation can be written by taking logarithm in both sides we get, lnβ ¼ ln

kλ kλ 1 ¼ ln þ ln : L: cosθ L cos θ

ð1Þ

Now by plotting lnβ vs ln(1/cosθ), we got a straight line with slope and intercept. From the value of intercept crystallite size can be obtained. Measurement error appeared in determining the average crystal size of Cu–Al LDH nanoparticle due to low adjusted R2 value obtained from linear fit (R2 value was 0.57). Complete XRD result is displayed in Table 1. These XRD results had been matched with the JCPDS file no. 037-0630 and 046-0099 (Song et al., 2013). Average crystal size obtained from JCPDS file no 037-0630 is 25.96 nm. As per the existing literature, nanoparticle crystallinity in solid state could influence its thermal conductivity (Zhu et al., 2006; Garg et al., 2008). However in this work, we propose to use Cu–Al LDH as a nanofluid and not in solid crystalline state. Therefore, it

Fig. 8. Surface tension vs. concentration curve of Cu–Al LDH nanofluid.

can be concluded that solid state crystallinity of nanoparticle will not influence the thermal conductivity adversely in liquid state. Our results also show that thermal conductivity of solution enhances with addition of Cu–Al LDH nanofluid. 3.1.2. FTIR analysis FTIR bands of pristine Cu–Al LDH are demonstrated in Fig. 3. Pristine Cu–Al LDH had three distinct vibration bands for NO− 3 anions. Most dis−1 tinct NO− , 3 anion vibration band layers in the wave region of 1385 cm − was credited to NO3 ion absorption band. Two other nitrate ion vibration bands could be seen in the region of 1463 and 841 cm− 1 (Sahu and Pugazhenthi, 2011; Chakraborty et al., 2014). The wide band in the wave region of 3200–3700 cm−1 (Sahu and Pugazhenthi, 2011) was due to the existence of hydroxyl (O–H) expanding vibration of hydrogen bonded metal hydroxide sheets and water molecules in the gallery space. The band formed in the range of 1600–1640 cm−1 was due to the twisting mode of water molecules δ (H–OH) present in the interlayer space. It points out the presence of water molecules. The bands reported in the wave-number range of 400–800 cm− 1 were due to the pulsation of metal oxygen bond in the brucite type frame. 3.1.3. SEM-EDS analysis SEM images of pristine Cu–Al LDH could be used to observe nanoparticles in the form of flakes and large agglomerates. The layered structures can be seen in the Fig. 4(a–b). The figure clearly indicates that large numbers of LDH nanoparticle were stacked together. It was understandable that LDHs were difficult to disperse in their initial dimensions. Hence, drying the precipitates had created the chance of agglomeration which would lead to larger particle diameter which could reduce the thermal conductivity of LDH. Extensive stirring and ultra-sonication were required to break the nanoparticle into smaller dimensions, making it useful for heat transfer application. These methods were considered to be energy inefficient; therefore nanofluids were used in colloidal form. This technique allowed us to control the agglomeration of nanoparticle and make the nanofluid in one step and more energy efficient way. (Fig. 4a–b) Cu–Al LDH nanoparticle was studied by Energy Dispersive X-Ray Spectroscopy (EDS). Quantitative EDS result confirmed the presence of Cu, Al and O in Cu–Al LDH nanoparticle. From the data of EDS analysis, Cu, Al, O present in nanoparticle were in the ratio of 7.5:1:13.4. 3.2. Cu–Al LDH nanofluid characterization 3.2.1. TEM analysis TEM analysis allowed us to precisely determine particle size, shape and nature of dispersion of nanoparticles in nanofluids. Needle shaped nanoparticle could be observed in TEM image in Fig. 5(a) and (b). The findings were in accordance with other researchers (Hu et al., 2007; Memon et al., 2014) working on different LDH. Size analysis was carried out using ImageJ software developed at National Institute of Health, Maryland, USA. To check that whether the resultant solution was nanofluid, the suspended particle in it had to be below 100 nm in at least one dimension. To determine the average particle size in the solution, TEM analysis was carried out. Fig. 5(a) and (b) had been captured from two different locations of TEM grid and analyzed using ImageJ software to measure both dimensions (length and diameter) of each nano needle. The length data of each particle had been used to plot both histogram (Fig. 6(a) and (b)) and weighted average value obtained from histogram plot was considered as the average particle length which was 46.4 nm and 68.3 nm. Analysis of Fig. 5(a) and (b) also showed that the particle diameter varied within 10 nm. Although the particle length showed two different values at two TEM grid locations but it is to be noted that in both the cases all the particle dimensions were well below 100 nm which confirmed the formation of nanofluid. The uniform distribution of nanoparticles could not be achieved by above mentioned preparation technique.


103

Fig. 9. Thermal conductivity and thermal conductivity ratio vs. concentration of Cu–Al LDH nanofluid graph.

This heterogeneous distribution could be eliminated by the addition of surfactant or other surface active agents. TEM images showed that Cu–Al LDH nanoparticles form either mesh or bundle like structure. This typical mesh structure could eventually lead to agglomeration beyond a certain concentration value. 3.3. Thermal property investigation Thermal property of nanofluids depends upon several factors such as cluster size, thermal conductivity and surface tension values of nanofluids. Detailed study on these factors is reported below: 3.3.1. Cu–Al LDH nanofluid cluster size analysis Weighted average of number% data for each solution was used to calculate average cluster size. Fig. 7 showed that cluster size of water based Cu–Al nanofluids varied in the range of 86–126 nm. In this case, cluster size in general increased with the increasing concentration except in the range of 0.8–1.0 vol.% where it reduced. Possible reason behind this trend was attributed to the fact that DLS measures size over a broad range from 0.4 nm to 8 μm; therefore taking the weighted average value of size might have incorporated such nature. Other researchers (Pang et al., 2012) also observed similar trend. The cluster size measurement of Cu–Al LDH nanofluids depends on the stability of the suspension with respect to time. DLS measurement immediately after preparation could give even smaller particle size. Measurement condition was slightly varied for each sample which leads to measurement uncertainty. However, value of polydispersity index was less than 0.7 for all nanofluid concentration which meant that all the suspension were mono dispersed in nature and suitable for DLS analysis (see Table 2). Table 3 Thermal conductivity results for different Cu–Al LDH nanofluid concentration. Sample name

Concentration (vol.%)

Thermal conductivity enhancement (Ek)

Maximum error of measurement

Pure water Cu–Al LDH

– 0.25 0.50 0.80 1.00 1.25

– 4.02 8.48 16.07 12.05 4.91

±3.06%

Comparative study between the particle size measured by TEM and cluster size analyzed in DLS clearly showed that the nanoparticles form cluster in nanofluids. Analysis of TEM image Fig. 5 displayed that particles are needle shaped. The cluster size obtained by DLS analysis was 2–3 times than that of actual particle length. (Fig. 7).

3.3.2. Surface tension analysis Scientists had found that reduced surface tension was beneficial for heat transfer application (Mohapatra et al., 2013; Ravikumar et al., 2014a,b,c). However, surface tension analysis of Cu–Al LDH nanofluids at higher concentration (beyond 0.25 vol.% loading) showed small increment in surface tension level whereas at lower concentration surface tension value reduces marginally as compared to the base fluid. Higher concentration of nanoparticle loading might lead to non-Newtonian behavior which might have affected the spreadability and increased the surface tension. Many contradicting opinions exist regarding the changes associated to surface tension with nanoparticle concentration. Different types of trends were observed by several other researchers working on different nanofluids. Murshed and fellow researchers (Sohel Murshed et al., 2008) determined the surface tension of TiO2/ water nanofluid by means of a surface tensiometer. They observed that the presence of TiO2 nanoparticles in water leads to decrease in surface tension value of the resultant nanofluid at normal temperature. Vafaei and co-workers (Vafaei et al., 2009) also found that the surface tension of Bi2Te3/water nanofluids decreased with the increased particle loading up to an optimum level then increased with the increase of particle concentration (Tanvir and Qiao, 2012). Opposite trend could also be observed in the work of Kumar et al. (Kumar and Milanova, 2009). They witnessed that surface tension value of carbon nanotubebased nanofluids was greater than that of water (base fluid). Most of the literature surveys related to surface tension variation with nanofluid concentration agree to the fact that high concentration of nanofluid leads to increased surface tension. Higher concentration could have resulted in reduced inter particle gap. This would lead to increase in Van der Waal force hence increase in surface tension value. However at low concentration mechanism was difficult to comprehend. At low concentration of nanofluid, electrostatic repulsive force in the liquid/ air interface dominates over Van der Waal force resulting in reduced surface tension. This mechanism was responsible for surface tension reduction in Cu–Al LDH nanofluid at 0.25 vol.% loading in contrast with its base fluid. On the other hand, surface tension value showed

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Table 4 Summary of experimental studies on thermal conductivity enhancement for different nanofluids. Sl. no.

Reference

Name of nanoparticle (volumetric loading%)

Name of base fluid

Thermal conductivity enhancement (%)

1

Masuda et. al (Masuda et al., 1993)

2

Lee et. al (Lee et al., 1999)

3

Murshed et al. (Murshed et al., 2008)

4

Turgut et al. (Turgut et al., 2009) Pang et al. (Pang et al., 2012) Present study

SiO2 (1.1–2.4%) γ-Al2O3 (4.3%) TiO2 (4.3%) CuO (3.5%) Al2O3 (4%) CuO (4%) Al2O3 (0.5%) Al2O3 (1%) TiO2 (0.2–3%)

Water Water Water Water Water Ethylene Glycol Ethylene glycol Ethylene glycol Water

1.1 32.0 11.0 12.0 10.0 20.0 9.0 12.0 7.4

Al2O3 (0.5%) SiO2 (0.5%) Cu–Al LDH (0.8%)

Methanol Methanol Water

10.74 14.29 16.07

5 6

small increment from 0.5 to 1.25 vol.% loading as compared to base fluid. The surface tension values of Cu–Al LDH nanofluids were shown in Fig. 8. Cu–Al LDH did not possess significant surface active nature as compared to surfactants like SDS, CTAB, and Tween 20. Surfactant possesses a unique feature; it can decrease the surface tension of water by its adsorption at the liquid–air interface. Cu–Al LDH nanofluids did not possess similar properties; hence the surface tension value only varies between small ranges with increasing Cu–Al LDH concentration. The maximum precision error during surface tension measurement was around ±1% which were evaluated according to ASME test code PTC 19.8-1983 (Abernethy et al., 1985). 3.3.3. Thermal conductivity Fig. 9 showed that thermal conductivity of Cu–Al LDH nanofluids increased with increasing concentration of Cu–Al LDH in water at 20 °C. The addition of Cu–Al LDH nanofluids in trace amount increased the thermal conductivity of water. Thermal conductivity showed nonlinear increment with increased nanofluid concentration up to an optimum concentration; then it decreased again. Thermal conductivity enhancement was measured by the following formula (Pang et al., 2012) Ek ¼

kn f −kb f 100 kb f

ð2Þ

Fig. 10. Zeta potential graph with different concentration of Cu–Al LDH nanofluids.

where Ek is the thermal conductivity enhancement, knf and kbf are the thermal conductivities of water based nanofluids and the base fluid (water), respectively. Thermal conductivity enhancement for 0.8 vol.% of Cu–Al LDH is 16.1% (see Table 3). The enhancement of thermal conductivity over the base fluids were attributed to many factors such as dispersion of the suspended particle, Brownian motion, liquid layering of nanoparticle, particle aggregation and shape of the particle. According to several scientists (Pak and Cho, 1998; Xuan and Li, 2000) the enhancement in thermal conductivity was due to uniform distribution of the nanoparticles. Other researchers (Koo and Kleinstreuer, 2005; Keblinski et al., 2008) had proposed that the mechanism responsible for thermal conductivity enhancement was the transmission of energy due to the impact between higher and lower temperature particles. The smaller size particles would have higher thermal conductivity which was owed to increased surface to volume ratio resulting in uniform particle dispersion. The effect of aggregation on thermal conductivity had been pointed out by many researchers (Prasher et al., 2006; Evans et al., 2008). They clearly demonstrated the consequence of aggregation on thermal conductivity. The thermal conductivity increment was strongly dependent on particle aggregation. There exists an optimum aggregation required to attain maximum possible thermal conductivity, which did not follow the prediction of uniform dispersion. Clustered nanoparticles lead to increase in heat transfer because heat conduction was higher and faster in case of solid particle (Evans et al., 2008). Our experimental findings were in partial accordance with this mechanism. Nanoparticle morphology could be altered due to such particle aggregation which indirectly improved the thermal conductivity (Wang and Fan, 2011). Maximum thermal conductivity enhancement could be observed up to an optimum concentration; beyond that concentration thermal conductivity started to decrease again. According to the studies conducted by other researchers, nanofluids with smaller sized particles (particle size less than 100 nm) were considered to be more prone to clustering. In this work, particle diameter was less than 10 nm and average particle lengths are 46.4 nm and 68.3 nm (weighted average value of particle size from TEM Fig. 5(a) and (b)). Smaller sized particle will have greater Van der Waal attractive force therefore enhances particle aggregation and clustering tendency. Excessive clustering might lead to sedimentation and lower thermal conductivity enhancement (Prasher et al., 2006; Feng et al., 2007). Reduced aspect ratio and undesired shape modification of nanoparticle could cause reduction in thermal conductivity with increasing volume fraction of nanofluid. The effect of geometrical shape of particle had an important role in thermal conductivity (Özerinç et al., 2009). The needle shaped particles showed a rise in thermal conductivity due to quick heat transfer in comparatively longer distances. Other reason could be correlated to the mesh made up of elongated particle that passes heat at a faster rate through the fluid (see TEM Fig. 5). According to Ghosh and Pabi (Ghosh et al., 2013), increased aspect ratio of suspended nanoparticle improves interaction area and induces increased heat transfer rate


105

Fig. 11. Images of Cu–Al LDH nanofluids at 0.25 vol.% concentration without stabilizer after; (a) 1 h, (b) 2 h, (c) 1 day and (d) 4 day.

through inter-particle collision. These were the possible reasons behind the superior thermal conductivity of elongated nanoparticle over spherical ones. Table 4 presents a summary of experimental studies of thermal conductivity enhancement using different nanofluids as compared to Cu–Al LDH nanofluid.

general instrument used for such purpose includes ultrasonic bath, magnetic stirrer. The ultra-sonication and stirring duration could influence the dispersion greatly. The main objective of ultra-sonication and stirring was to break the agglomerates into its initial size. Weakly attached agglomerates could be broken into their primary sizes.

3.4. Stability analysis

3.4.1. Zeta potential analysis The strong repulsive force also played vital role in the stability of nanofluids. Higher the repulsive force greater would be the stability. Zeta potential value beyond the range of +30 mV to −30 mV is an indication of good suspension stability. Prior to the analysis, all the nanofluid samples were kept undisturbed for 1 h to check whether

Stability was one of the biggest concerns in nanofluid preparation. Nanoparticle suspension showed a tendency to agglomerate and precipitate after its preparation. The process involved in nanofluid preparation played an important role in making stable nano-suspension. The

106


Fig. 12. Images of Cu–Al LDH nanofluids at 1.25 vol.% concentration without stabilizer after; (a) 1 h, (b) 2 h, (c) 1 day and (d) 4 day.

the stability (zeta potential value) reduces with time. Fig. 10 has revealed that the zeta potential value of Cu–Al LDH nanofluids was in the range of + 50 to + 57 mV. Such high zeta potential values could be interpreted as an indication of good suspension stability (Yu and Xie, 2012). In general, stabilizers like different surfactants and polymers were added during nanofluid preparation to reduce the attractive van der Waal force. In general, nanofluid without stabilizer tends to lose its stability with time; therefore it had to be used within 1 h of preparation. All Cu–Al LDH nanofluids showed good stability without any stabilizer up to 1 h of preparation.

(Figs. 11–12). Cu–Al LDH nanofluids at both lowest and highest volume percentages show no sedimentation or phase separation up to 2 h. Therefore these nanofluids could be used within 1 h of preparation without much degradation in their properties. Nanofluid started to display phase separation after 1 day for 1.25 vol.% loading. On the fourth day, phase separation was very much evident in this case. On the contrary, 0.25 vol.% nanofluid solution displayed no phase separation throughout the time period.

3.4.2. Visual phase separation study Images were captured at four different time periods to check the stability of LDH with time (Fedele et al., 2011; Ghadimi et al., 2011)

In this work, the water based nanofluids containing Cu–Al LDH nanoparticles were synthesized via co-precipitation method. Particle size, cluster size, stability, thermal conductivity and surface tension of

4. Conclusion


water based nanofluids were measured. Obtained conclusions are listed below: (1) Particle size analysis revealed that authors had successfully prepared nano-sized (at least one dimension less than 100 nm) particle suspension. Analysis of TEM images using Image J software showed that the particle diameter was less than 10 nm and average particle length was well below 100 nm. (2) Extent of particle clustering had been investigated using dynamic light scattering technique. Cluster size range varied from 86 to 126 nm which was 2–3 times larger than the actual particle length. (3) Thermal conductivity value increased with increase in nanofluid vol.% up to an optimum point then it started to decline again. The maximum thermal conductivity enhancement was reported as, 16.1% for 0.80 vol.% of Cu–Al LDH nanofluids. (4) The surface tension results clearly showed the surface inactive nature of Cu–Al LDH nanofluid as compared to any surfactant. Surface tension value of Cu–Al LDH nanofluid in different vol.% varied in the range of 69.7–73.6 mN/m. (5) Nanofluid stability analysis clearly demonstrated enhanced stability of nanofluid suspension. At maximum Cu–Al LDH vol.% loading no phase separation was observed for 2 h. Zeta potential values differed in the range of +50 to +57 mV for all Cu–Al LDH vol.% loading. This was a clear indication that authors had successfully prepared stable nanofluid suspension.

Since Cu–Al LDH showed an appreciable increase in thermal conductivity it could be used for different heat transfer enhancement applications such as jet/ spray cooling in steel/metal industries. Nomenclature d Basal spacing k Scherrer constant knf Thermal conductivity of nanofluid kbf Thermal conductivity of base fluid L Crystal size vol.% Volume percentage

Greek letters β Width of diffraction peak at half maximum height λ Wavelength θ Angle of incidence

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