41

CHAPTER-3

PREPARATION AND STABILITY OF NANOFLUID

Nanofluids are prepared using various equipments such as magnetic stirrer, Ultra

sonic bath sonicator and pH meter. Further, to check the stability of nanofluid

Nephleometer is used. These techniques are discussed briefly in this chapter. The

Response Surface Methodology (RSM) is adopted in identify the process parameters such

as surfactant quantity and pH value for each volume fraction of nanofluids which are

stable for 30 days.

3.1. PREPARATION OF NANOFLUIDS

The preparation of nanofluid must ensure proper dispersion of nanoparticles in the

liquid and proper mechanism is needed to attain the stability of the suspension against

sedimentation. Alumininum oxide (Al2O3) nanoparticles are procured from M/s. United

Nanotech products Ltd., Howrah, India (XRD of Al2O3 nanoparticles is shown in A1).

The size of nanoparticles is 50-100 nm. Under atmospheric temperature condition these

particles form loose agglomerates, which are of the order of micrometers. However they

can be dispersed in the fluid quite successfully which results in breaking of the

agglomerates by sonication.

Fig.3.1 Digital pH meter (Make: GLOBAL, India)

42

The clustering of nanoparticles is avoided by inducing surface charge on to the

particles by adjusting the pH value of the base fluid. The pH value of the base fluid is

adjusted with the addition of small amount of hydrochloric acid or sodium hydroxide.

The digital pH meter used in the present study is shown in fig.3.1.

Al2O3 nanoparticles are dispersed in deionized water using magnetic stirrer

(Fig.3.2.). The dispersion of the particles is achieved by first mixing the required volume

of the powder in the chemical measuring flask with de- ionized water. The unknown

weight of the nano powder is estimated based on the known percentage of volume

fraction, density of the particle and density of the water by the law of mixtures. After

preparing the proper mix of the nanoparticles and water, the flask is placed on the

dimmer controlled magnetic base and another different pole magnetic strip is placed

inside the flask. By supplying the voltage to the base magnet the strip present in the

nanofluid starts rotating. During the process Sodium Dodecyl Sulphate (SDS) surfactant

is added to the solution in proper proportions to ensure stability of nanofluid. Mingzheng

Zhou et al. [142] conducted experiments on different kinds of surfactant solutions and

reported that SDS surfactant had better properties over other types of surfactants. Hence,

SDS is selected as a surfactant for the present study.

Fig.3.2 Magnetic Stirrer (Make: REMI, India)

After stirring the sample is allowed to place in an Ultrasonic bath sonicator

(Fig.3.3.) for a period of 30 minutes.

43

Fig.3.3 Ultrasonic Bath Sonicator (Make: REMI, India)

3.2. STABILITY EVALUATION OF NANOFLUID

The quality of nanofluid dispersion is monitored by measuring turbidity using

Nephelometer (Fig.3.4.) with a tungsten filament light source. Turbidity indicates the

stable suspension of particles in the fluid and is measured by the reflection of light from

the fluid sample and designated in terms of Nephelometric Turbidity Units (NTU).

Initially deionized water is filled in the cuvvete of Nephelometer and placed in the test

chamber. The NTU value is set to zero. Then the prepared nanofluid is filled in the

cuvette of Nephelometer and placed in the test chamber and left undisturbed till the stable

reading is obtained.

Fig.3.4 Nephelometer (ELJCO, India)

44

A calibration curve for different volume concentrations of Al2O3 nanoparticles

with NTU is drawn. The measurements are made without surfactant and immediately

after the sample preparation to study the effect of particulate. Calibration curve of the

volume fraction of nanofluid with corresponding NTU values is shown in Fig.3.5. The

NTU value appeared to decrease with increase in volume fraction of the nanoparticles.

Roge et al. [143] studied optical properties of Al2O3 and found that when incident light

energy reaches beyond 12eV reflectivity decreases. Thus, turbidity decreases as Al2O3

concentration increases in the fluid. Hence, lower NTU value indicates larger

concentration of dispersed Al2O3 nanoparticles in the fluid.

Fig.3.5 Calibration curve of the volume fraction of nanofluid against turbidity

3.3. PREDICTION OF OPTIMUM PROCESS PARAMETERS FOR 30 DAYS

STABILITY OF NANOFLUID

The stability of nanofluid depends on surfactant (SDS) and pH value of base fluid

apart from sonication time. In the present work sonication time is taken as 30 min as

reported by Liu et al. [51]. Response Surface Methodology (RSM) is adopted to predict

the optimum surfactant and pH values for the specified volume fraction of nanoparticles

to attain 30 days of nanofluid stability. To achieve the desired aim, the investigations are

planned in the following sequence:

45

a. Identifying the predominant factors, which influence dispersion stability and

finding the upper and lower limits of chosen factors .

b. Developing the experiment’s design matrix

c. Conducting the experiments as per the design matrix

d. Developing the model, calculating the coefficients

e. Checking the adequacy of the developed model

f. Analyzing the effect of different parameters on dispersion stability

3.3.1. Identification of predominant factors which influence dispersion stability and

finding the upper and lower limits of chosen factors

Dispersion stability is an important characteristic of nanofluid quality. Three

techniques can be applied to prevent the settling of nanoparticles in the base fluids viz

one is to use electrostatic repulsion forces between particles, second one is to use

surfactants and the third is to apply ultra sonic waves.

The electrostatic repulsion force between particles is directly dependent on zeta

potential (ζ) of the particle that represents potential difference between the surface of the

grains and the external plane of Helmholtz. The zeta potential of nanoparticles is related

to the pH value of the base fluid. The pH value limits are chosen from 3 to 10 for this

study, since beyond these limits the nature of base fluid drastically changes and results in

abnormal properties.

The main cause of the sedimentation of nanoparticles is due to their high specific

surface area and surface activity, Brownian motion [39] and Vanderwaals forces [2],

which directly result in the clustering of nanoparticles. However, addition of appropriate

amount of surfactant, an interfacial film surrounding nanoparticles will be formed as a

result of adsorption of dispersant on the interface. When the intensity and thickness of

interfacial film is adequate, the nanoparticles can be protected from agglomeration. SDS

is used as surfactant for the present study. The lower and upper limits of surfactant are

estimated based on Electric Double Layer (EDL) adsorption model [144] with respect to

the volume fraction of nanoparticles. The Volume fraction of nanoparticle is varied

between 0.2%to 1.0% since nanofluids exhibits much enhancement in heat transfer at

lower concentrations. For the chosen limits of nanoparticles the surfactant quantity is

found to be 0.2% and 1.4% volume fractions respectively form EDL theory.

46

3.3.2. Developing the experimental design matrix

An experiment is a series of trials or tests, which produces quantifiable

outcomes due to slightly wide ranges of the factors. In the present study a five level

central composite rotatable design matrix to optimize the experimental conditions is

proposed. Center Composite rotatable Design (CCD) of second order found to be the

most efficient tool in (RSM) to establish mathematical relation of the response surface

using smallest possible number of experiments without losing accuracy [145].

The identified factors and their lower and upper limits, units and their levels chosen

are summarized in Table 3.1.

Table 3.1: Factors and their levels

Serial Number Parameter Notation Unit

Levels 2) ( 1) (0) (+1) (+2) -2 -1 0 1 2

1. Nanoparticles A(X1) % 0.2 0.4 0.6 0.8 1.0 2. Surfactant B(X2) % 0.2 0.5 0.8 1.1 1.4 3 pH value C(X3) 3 4.75 6.5 8.25 10

A central composite rotatable design for three factors at five level comprises of

twenty number of experiments out of which first eight experiments indicates full

factorial, the next six experiments indicate axial points and the last six experiments are

central points. The axial points are chosen by α=1.682 which make this a rotatable

design. All chosen variables at inter mediate level (0) constitute the centre points and

combinations of each of the variables at either it’s lowest (-2) or highest (+2) with the

other three variables of intermediate level constitute the star points. Thus the twenty

experimental runs allowed the estimation of the linear, quadratic and two ways

interactive effects of variables on the output response (NTU). The method of designing

such a matrix is dealt by Cochran et al. [145].

For the convenience of recording and processing the experimental data, upper and

lower levels of parameters are coded as +2 and -2. The coded value of intermediate levels

can be calculated using the following expression.

( )max mini

max min

2x x x X

x x2

− + =−

(3.1)

47

where, Xmax is the upper level of the parameter, Xmin is the lower level of the

parameter and Xi is the required coded values of the parameter of any value of X from

Xmin to Xmax.

3.3.3. Conducting the experiments

The objective of the present work is to prepare the nanofluid and perform tests to

analyze individual effects of surfactant quantity and pH value on dispersion stability.

Nanofluid is prepared as described in 3.1. Each fluid sample is let to stand about 30 days

and then the turbidity in terms of NTU is measured using Nephleometer. The nanofluids

have been prepared according to the levels specified in the design matrix and the

corresponding value of NTU is measured. The layout of central composite rotatable

design along with results is shown in Table 3.2.

Table 3.2: The layout of central composite rotatable design with results

EXPERIMENT NUMBER

NANOPARTICLES % VOLUME FRACTION

SURFACTANT % VOLUME FRACTION

pH VALUE NTU

1 0.36 0.44 4.42 262 2 0.84 0.44 4.42 250 3 0.36 1.16 4.42 296 4 0.84 1.16 4.42 240 5 0.36 0.44 8.58 260 6 0.84 0.44 8.58 265 7 0.36 1.16 8.58 240 8 0.84 1.16 8.58 198 9 0.20 0.80 6.50 280 10 1.00 0.80 6.50 230 11 0.60 0.20 6.50 272 12 0.60 1.40 6.50 265 13 0.60 0.80 3.00 298 14 0.60 0.80 10.00 240 15 0.60 0.80 6.50 246 16 0.60 0.80 6.50 214 17 0.60 0.80 6.50 250 18 0.60 0.80 6.50 220 19 0.60 0.80 6.50 240 20 0.60 0.80 6.50 210

48

3.3.4. Developing the model, calculating the coefficients

Representing the turbidity of the nanofluid “NTU”, the response function can be

expressed as

NTU=f (A, B, C) (3.2)

The model chosen is a second degree response to check the nonlinearity and is

expressed as:

NTU=β0+ β1 (A) + β2 (B) + β3 (C) +β4 (A2) + β5 (B2) +β6 (C2) + β7 (AB) +β8 (AC) +β9

(BC) (3.3)

Using MINITAB 14 statistical software package, the significant coefficients are

determined and the final models are developed using significant coefficients to estimate

NTU values. Details about estimated regression coefficients for NTU are presented in

Table 3.3.

The model is developed in coded values and is given as follows: Turbidity,

NTU=230.480–13.846*A-5.475*B-13.366*C+10.475*B2+10.352*C2-13.875*B*C (3.4)

Table 3.3: Estimated regression coefficients for NTU

Term Coef SE Coef T P Remarks

Constant 230.480 6.430 35.847 0.000 SIGNIFICANT

NANOPARTICLES -13.846 4.266 -3.246 0.009 SIGNIFICANT

SURFACTANT -5.475 4.266 -1.283 0.228 SIGNIFICANT

pH VALUE -13.366 4.266 -3.133 0.011 SIGNIFICANT

NANOPARTICLES* NANOPARTICLES 5.702 4.153 1.373 0.200 INSIGNIFICANT

SURFACTANT* SURFACTANT 10.475 4.153 2.522 0.030 SIGNIFICANT

pH VALUE*pH VALUE 10.652 4.153 2.565 0.028 SIGNIFICANT

NANOPARTICLES* SURFACTANT -11.375 5.574 -2.041 0.069 INSIGNIFICANT

NANOPARTICLES* pH VALUE 3.875 5.574 0.695 0.503 INSIGNIFICANT

SURFACTANT* pH VALUE -13.875 5.574 -2.489 0.032 SIGNIFICANT

S = 15.76 R-Sq = 82.0% R-Sq(adj) = 65.7%

49

3.3.5. Checking the adequacy of the developed model

The adequacy of the model is tested using the Analysis of Variance technique

(ANOVA). As per this technique, if the calculated value of Fratio of the developed model

is less than the standard Fratio (from F-table) value at a desired level of confidence (say

95%), then the model is said to be adequate with in confidence limit. ANOVA test results

presented in Table 3.4 are found to be adequate at 95%confidence level. Fig.3.6 indicates

scatter plots for NTU and reveals that the actual and predicted values are close to each

other within the specified limits.

Fig.3.6 Scatter plot for NTU

Table 3.4: ANOVA test results for NTU

NTU Source DF Seq SS Adj SS Adj MS F P Regression 9 11300.0 11300.0 1255.6 5.05 0.009 Linear 3 5467.4 5467.4 1822.5 7.33 0.007 Square 3 3137.2 3137.2 1045.7 4.21 0.036 Interaction 3 2695.4 2695.4 898.5 3.62 0.053 Residual Error 10 2485.2 2485.2 248.5 Lack-of-fit 5 973.2 973.2 194.6 Pure Error 5 1512.0 1512.0 302.4 0.64 0.680 Total 19 13785.2

Where DF- Degree of Freedom, SS- Sum of Squares, MS- Mean Square, F- Fishers ratio

50

3.3.6. Effect of different parameters on dispersion stability

Based on the developed model the effects of parameters such as surfactant and pH

value on stability for the specified volume fraction of nanofluids are analyzed. The

optimum quantities of surfactant and pH for each volume fraction of nanofluid are

identified from the surface plots drawn.

3.3.6.1 Influence of pH value and SDS on nanofluid stability

Fig. 3.7 demonstrates the variation of NTU of each nanofluid with respect to the

pH value at different volume fractions of surfactant. It can be seen that the NTU of each

kind of nanofluid decreases with increase in pH value of the base fluid. Suspension

stability is directly dependent on zeta potential (ζ) of particle that represents the potential

difference between surface of grains and the external plane of Helmholtz. The zeta

potential is defined as the electrical potential developed at the solid – liquid interface in

response to the relative movement of solid particles and liquid or as strength of the

particle electrical barrier. Higher this potential with the same polarity more superior is the

electrostatic repulsion between particles. On the other hand, when the suspension is close

to the iso-electric point (ζ=0), the particles tend to flocculate. The zeta potential of

nanofluid is related to pH value of the base fluid. The dispersion stability of Al2O3

nanofluid with distilled water is poor because the iso-electric point of Al2O3 is close to 7.

With the increase in pH value of base fluid, the zeta potential of nanofluid is far away

from the iso-electric point and a higher zeta potential maintains high repulsion which can

keep the nanoparticles dispersion more stable and uniform. Hence, with higher pH value

in the base fluid more amount of charge exist on the surface of nanoparticle which

suppresses cluster formation thus enhanced stability. Since the stability of the nanofluid

depends on the pH of base fluid, hence the trends in Fig.3.7 are similar for all particulate

volume fractions.

It can be observed that the NTU of each nanofluid first decreases and then

increases to a maximum with surfactant volume fraction (fig.3.7). The optimum

surfactant quantity varies proportionally with the volume fraction of nanoparticles. An

approximate analysis for the influence of fraction of surfactants on the stability of

nanofluids might be as follows: The electrostatic stabilization mechanism is based on the

51

adsorption–desorption of ions onto/from the particles surface. The attractive and

repulsive particle-particle interactions are based on DLVO (Derjaguin- Landau-Verywey-

Overbeek) theory. The electric double layer surrounding nanoparticles will be formed as

a result of the adsorption of dispersant on the interface when appropriate amount of

surfactant was added. The forming of electric double layer (EDL) directly resulted in the

electrostatic repulsion between surfactant coated nanoparticles, which strongly reduces

the particle agglomeration due to vanderwaals forces of attraction [2].

Fig.3.7 Turbidity of Al2O3 nanofluid with different volume fractions of surfactant

and pH values

(a) 0.2% Volume fraction of nanofluid (b) 0.4% Volume fraction of nanofluid (c) 0.6% Volume fraction of nanofluid (d) 0.8% Volume fraction of nanofluid

(e) 1.0% Volume fraction of nanofluid

52

However, when too much surfactant (more than saturated adsorption) was added,

there was oversaturated adsorption on the surface of nanoparticles. Moreover, the

positive ions in high concentration get into the electric double layer, which can eliminate

negative charges of electric double layer (EDL) and lead to the weakness of stability of

nanofluids. Therefore, too much surfactant will favor reunion of nanoparticles. Though

the trends are similar in Fig.3.7 (a) to (e), the optimum values are varying with

nanoparticle volume fraction due to the enhancement of adsorption surface area.

3.3.6.2. Interaction effects of surfactant quantity and pH value on NTU

The interaction effect between the process parameters and response can be clearly

analyzed by the contour plots. By generating contour plots using software (MINITAB

Ver 14) for response surface analysis, the optimum is located by characterizing the shape

of the surface. Fig. 3.8. (a) to (c) represent the contour plots for NTU.

(a) (b)

(c)

Fig.3.8. Contour plot of NTU for different volume fractions nanoparticles

(a) 0.2% Volume fraction of nanofluid (b) 0.6% Volume fraction of nanofluid

(c) 1.0% Volume fraction of nanofluid

From the contour plots of NTU, it is understood that surfactant quantity and pH value

of base fluid plays a major role in the stability of nanofluid. The lower NTU value (i.e.

SURFACTANT(% )

pH V

ALUE

260

240

220

200180

1.351.201.050.900.750.600.450.30

9.0

7.5

6.0

4.5

3.0

Hold ValuesNANOPARTICLES(%) 0.8378

NTU

260

180200220240

Contour Plot of NTU vs pH VALUE, SURFACTANT(%)

SURFACTANT(% )

pH V

ALUE

280

260

240

220

200

180

1.351.201.050.900.750.600.450.30

9.0

7.5

6.0

4.5

3.0

Hold ValuesNANOPARTICLES(%) 0.3622

NTU

260280

180200220240

Contour Plot of NTU vs pH VALUE, SURFACTANT(%)

SURFACTANT(% )

pH V

ALUE

260

240

220

200

180

1.351.201.050.900.750.600.450.30

9.0

7.5

6.0

4.5

3.0

Hold ValuesNANOPARTICLES(%) 0.6

NTU

260

180200220240

Contour Plot of NTU vs pH VALUE, SURFACTANT(%)

53

more stability) is observed at higher pH level for all volume fractions of nanofluids.

Development of more charges on the surface of nanoparticles is responsible for lower

NTU values. However the influence of surfactant is varying with respect to the

nanoparticle volume fraction. The lower NTU values are obtained at different volume

fractions of surfactant for various nanoparticle concentrations (Fig’s. 3.8 (a), 3 (b) and

(c)). The increase in the adsorption surface area with the nanoparticles concentration

needs more surfactant quantity for the formation of electric double layer (EDL). Hence,

the optimum surfactant quantity is varying with the particulate concentration.

3.3.6.3. Response surface of NTU The response of NTU of each nanofluid with different volume fractions of surfactant and

pH values is shown in fig.3.9.

Fig.3.9 Response surface of NTU (a) 0.2% Volume fraction of nanofluid (b) 0.4% Volume fraction of nanofluid(c)

0.6% Volume fraction of nanofluid (d) 0.8% Volume fraction of nanofluid (e) 1.0% Volume fraction of nanofluid

54

The optimal quantities of surfactant and pH value for each kind of nanofluid

based on longer stability (the point at which NTU is minimum) are identified from the

response plot drawn between surfactant volume fraction, pH value and NTU. The

optimum surfactant quantity and pH value for each volume fraction of nanofluid is

obtained from the response surface plots and are shown in Table 3.5.

Table 3.5. Optimum surfactant and pH values for various nanoparticle volume fractions

Nanoparticle volume

fraction, %

Surfactant Volume

fraction, %

pH value

0.2 0.28 9.75

0.4 0.51 9.75

0.6 0.73 9.75

0.8 0.95 9.75

1.0 1.55 9.75

3.4. STABILITY TESTING OF NANOFLUID

Stability of nanofluid is tested by conducting an experiment using the setup as

shown in fig. 3.10. Nanofluid is circulated at 500 liters per hour with an electric pump.

The test is conducted for a continuous period of ten hours.

Fig.3.10. Setup used for testing nanofluid stability

Nanofluid Electric

pump

55

Fig.3.11 shows the pictures of nanofluid (0.2 % volume fraction) samples

collected before and after continuous recirculation over a period of 10 hrs. From visual

observation of the figures 3.11 (a) to (d), no sedimentation of the particles is noted in the

samples which indicate that nanofluids retains stability even after the continuous working

also. However the collected nanofluid samples are tested for NTU values and are shown

in Table 3.6. The tabulated values are also indicating insignificant change in the NTU and

hence nanofluids are said to be stable.

(a) (b)

(c) (d) Fig. 3.11 Nanofluid samples collected for stability analysis

1 day (b) 3 day (c) 4 day (d) 5 day.

Table 3.6. NTU values of nanofluid specimen samples

Duration day

Sample before recirculation NTU

Sample after recirculation NTU

1 402 410 3 405 411 4 403 415 5 409 416

56

3.5. SUMMARY

Nanofluids samples are prepared by dispersing Al2O3 nanoparticles in de- ionized

water with various process parameters as per design of experiments. The samples are

analyzed for optimum process parameters using Response Surface Methodology.

Nanofluid is prepared with the optimum process parameters obtained from surface plots

and testing is carried out for stability. Experimentation is carried out to measure the

properties such as Thermal conductivity, Viscosity, Specific heat and Density for the

prepared nanofluids.