A review of influence of nanoparticle synthesis and geometrical parameters on thermophysical properties and stability of nanofluids

Thermal Science and Engineering Progress 11 (2019) 334–364

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Thermal Science and Engineering Progress journal homepage: www.elsevier.com/locate/tsep

A review of influence of nanoparticle synthesis and geometrical parameters on thermophysical properties and stability of nanofluids

T

A. Idrish Khan , A. Valan Arasu ⁎

Department of Mechanical Engineering, Thiagarajar College of Engineering, Madurai 625015, India

ARTICLE INFO

ABSTRACT

Keywords: Synthesis Properties Nanoparticle size Nanoparticle shape Sonication Stability

After the advent of nanofluids, researchers across the world are actively engaged in carrying out experimental research works on nanofluids for heat transfer applications such as heat exchangers, electronic cooling, etc. Nanofluids are also used as working fluid in the renewable energy systems such as solar collectors, solar still, solar cells and solar ponds to harvest the solar energy to the maximum extent. Application of nanofluid in heat transfer systems is proved to enhance the heat transfer characteristics of base working fluid, which led to the reduction of area, materials and size of heat transfer systems. Existence of nanoparticle in nanofluid enhances the thermo-physical properties of the base fluid and hence higher rate of heat transfer as compared to base fluid. This research review article provides as much as comparative researches done on preparation, the effect of size and shape on thermo-physical properties, and the characterization techniques and stability of nanofluids. From the extensive review of comparative studies, first of its kind, it is found that among the several parameters, the size and shape of nanoparticle and the sonication process highly influence the enhancement in both thermophysical properties and stability of the nanofluid. But the size and shape of nanoparticle depends mainly on synthesis of the nanoparticle. Further, the stability of the nanofluid is highly influenced by the surface charge of the nanoparticle, which depends on sonication process. Hence, the synthesis of nanoparticle and the sonication process are the key parameters to enhance thermophysical properties and stability of the nanofluid. This review would enable the researchers to understand the key parameters and their effects on the thermophysical properties and stability of nanofluids and to prepare effective and stable nanofluids for maximizing the heat transfer rate and effectiveness of thermal energy conversion systems such as heat exchangers, solar thermal collectors, etc.

1. Introduction In the middle of the 20th century, the engineered machines started to function very fast; leading to the heat dissipation at the faster rate which is quite essential for the continuous working. Meanwhile the size has been reduced and become portable because of the technological advancement. Since, the quantity of heat to be dissipated is large and to be done in compact space due to miniature of the devices; another heat transfer fluid becomes necessary other than the conventional one such as water, ethylene glycol, propylene glycol, engine oil and transformer oil. Choi and Eastman of the Argonne National Laboratory introduced a novel method to improve the thermal conductivity of conventional heat transfer fluids by dispersing particles of size smaller than 100 nm and coined the fluid name as nanofluids [1]. The researchers found that the nanoparticles have superior thermal properties. The enhancement of the thermal conductivity of the base fluid is the prime motto of the

⁎

nanofluid. Several mechanisms had been proposed for the enhancement of the thermal conductivity of the nanofluid. Prasher et al. [2] discussed the promising mechanisms for thermal energy transfer in nanofluids: (i) translational Brownian motion; (ii) the existence of an inter-particle potential; (iii) convection in the liquid due to the Brownian movement and stated that the key reason for enhancement was due to the Brownian movement of the nanoparticles. Since then, the concept of nanofluids has been a great attraction to many researchers due to its high performance and compactness. The thermophysical properties studies on nanofluid have been done by researchers and gave theoretical and empirical models to predict the properties of the nanofluid. The models reported by various researchers for the effective thermal conductivity and viscosity of nanofluids had different approaches to approximate the properties. However, some gets closer to the experimental values, while others have a great difference. Since then, a proper and accurate approximation of properties has not been furnished by any models.

Corresponding author. E-mail addresses: [email protected] (A.I. Khan), [email protected] (A. Valan Arasu).

https://doi.org/10.1016/j.tsep.2019.04.010 Received 11 August 2018; Received in revised form 11 January 2019; Accepted 14 April 2019 2451-9049/ © 2019 Elsevier Ltd. All rights reserved.

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Nomenclature CNT AFM BET BRIJ 97 CLS CTAB CTAC DLS EFTEM FESEM FT-IR HRTEM IPA

NP-10 PAO PCS PVA PVP SDBS SDS SEM Span-80 TBT TEM TEOS TGA XRD β-CD

carbon nanotube atomic force microscopy Brunauer–Emmett–Teller poly(10)-oxyethylene oleyl ether centrifugal liquid sedimentation cetyl tri-methyl ammonium bromide hexadecyl tri-methyl ammonium chloride dynamic light scattering energy filtering transmission electron microscope field emission scanning electron microscope Fourier transform infrared spectroscope high resolution transmission electron microscope isopropanol

nonylphenoxypoly ethanol poly-alpha-olefin photon correlation spectroscopy poly vinyl alcohol polyvinylpyrrolidone sodium dodecyl benzene sulfonate sodium dodecyl sulfate Scanning electron microscope sorbitan mono-oleate tetra-butyl titanate transmission electron microscope tetra-ethyl ortho silicate thermo gravimetric analysis X-ray diffraction β-cyclodextrin

only a few comparative experimental studies carried out so far to understand the influence on nanofluid thermophysical properties by varying a specific parameter of nanoparticle or base fluid (synthesis method, operating conditions and raw materials used in synthesis, nanoparticle shape and size and sonication) and to ascertain the optimum parameter. Such comparative studies are collected and reviewed for the first time in this article. Also this article presents an extensive review on factors affecting the stability of nanofluid, which is an important challenge hindering the practical utilization of nanofluid. Consolidation of comparative research articles may give different light on the field of nanofluid and it is easy to understand the effects of variation done to the nanofluid. Positively, this article will provide the scientific community with enough information about comparative studies on nanofluids, so as to choose the right parameters, influencing the performance of nanofluids such as the geometry of the nanoparticles, preparation of nanofluid and properties of nanofluid and its stability.

Theoretical and experimental studies are needed to explain the differences in the results and in proper understanding of heat transfer enhancement characteristics of nanofluids [3]. The nanofluids have properties based on the nanoparticle material, which interests the researchers to develop nanoparticles of different materials. Advent of the MWCNT, Graphene, Graphene oxide, Exfoliated Graphene increased the attraction towards nanofluid research, since they have high thermal properties. Then many application oriented researches were on to find the suitable nanofluid for their particular application. Recent advancement in the nanomaterial is the graphene quantum dots. The zero-dimensional graphene units of lateral size < 100 nm are termed as graphene quantum dots (GQDs). Due to the prominent edge effect in addition to the quantum confinement effects, GQDs are applied in the research focusing on photovoltaic devices, LED, photo-catalysts, batteries, chemical sensors, biosensors, bioimaging devices and so on [4]. Hussein [5] listed out the drawbacks and advantages of the nanofluids and showed more advantages than drawbacks. The main drawback that hinders the commercial application of nanofluid is the sedimentation of nanoparticles in the suspension. The researchers have been studied the nanofluids on different aspects such as synthesis, properties, application, and stability. Each researcher delivered their experimental or theoretical results to scientific community for the development of the technology. Many review articles were published in the last decade [6–11]; almost all were focused on the experimental and theoretical research on the synthesis, characterization, thermophysical properties, heat transfer mechanism, convective heat transfer and optical properties of the nanofluid. These review articles gave an insight into the research on nanofluid, discrepancies prevailing on the research and the need of further more extensive experimental or theoretical research for understanding the science behind the flow and thermal characteristics of the nanofluid. Many researches were done independently with minor or major difference in many aspects such as nanoparticle synthesis, size and shape of nanoparticle and dispersion of the nanofluid, for the enhancement of the properties and stability. The scientific community started to compare these researches on the varying parameter aspect of the nanofluid and to conclude their effect. But it is not appropriate to arrive at a decision on comparing different research articles done by different researchers, as the research condition, laboratory environment, equipment, chemical reagents and their purity, size and shape of nanoparticles, etc. may differ between them. Thus, the comparative studies done by the same researcher at same timeline in the same environment can neglect these discrepancies on their results and could be considered to be more realistic than comparing two different individual research works. There are many laboratory based experimental research works for different nanofluids reporting only the effect of nanoparticle concentrations on thermophysical properties enhancement. But there are

2. Synthesis of nanofluid and nanoparticles The synthesis of nanoparticles is the prime task for the preparation of the nanofluid. The synthesis of nanoparticles is done through various methods. Primarily, major classification of synthesis is one-step method and two-step method. Wei Yu and Huaqing Xie [12] explained about the classification on synthesis and their advantages and disadvantages. Though various methods are available, two-step method is suitable for the preparation of nanoparticle, as it is easy and economical. Synthesis of nanoparticles/nanofluid consists of reactants and its concentration, method of preparation and operating condition are shown in Fig. 1. Good control of operating parameters such as temperature and mixing intensity can affect the desired particle characteristics. The formation of small particles with narrow size distribution is governed by nucleation/ growth kinetics and residence time distribution of the processing mixture [13].

Fig. 1. Sub-class of synthesis. 335

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2.1. Method of preparation

level and mode (continuous and rectangular pulse) of applied radiofrequency power. Chen et al. [16] synthesized gold (Au) nanoparticles with different nanoparticle sizes (25, 33, and 40 nm) using a seed mediated method for the study of photo-thermal conversion. They prepared three types of seed solution; a growth solution and its process are showed in Fig. 2. The surfactant hexadecyl tri-methyl ammonium chloride (CTAC) was added to both seed and growth solution. Then the prepared seed solution was aged for 30 min at 35 °C to decompose the excess of NaBH4 in the seed solution. After the completion of reaction, the final solution was centrifuged at 8000 rpm for 10 min to extract the Au nanoparticle. They observed that the bigger size of Au nanoparticle was produced for seed solution of lower concentration of seed particle. They explained the reason for the different size was that for same growth solution, seed solution of lower concentration of seed particle adsorbed more Au atoms. They reported that the three different seed solutions of varying particle concentration gave three different average sizes of Au nanoparticles which were stable in the aqueous solution. Pastoriza-Gallegoa et al. [17] analyzed copper oxide (CuO)/water nanofluid by two step method by two different sources of nanoparticles. They purchased the CuO nanoparticle from Nanoarch and synthesized CuO nanoparticles from copper acetate solution by quick-precipitation method in laboratory. They observed that the size distribution of synthesized nanoparticles was narrower than the purchased nanoparticles; i.e. the synthesized CuO nanoparticle has uniform size of diameter 11 ± 3 nm than purchased one (diameter 23–37 nm).

Karimi-Nazarabad et al. [14] synthesized tungsten oxide (WO3) nanoparticle by two different methods namely hydrothermal and microwave method. The two methods were processed with and without sodium chloride (NaCl) additive. They observed from the XRD that the crystalline nature was higher in hydrothermal method than microwave method, as the XRD peaks of tungsten oxide were sharper for hydrothermal method. But the crystallite size was greater for microwave method than hydrothermal method. All the synthesized WO3 nanoparticle samples were in spherical, polygonal and blade shapes and they coexist. The researcher showed that with the addition of sodium chloride as additive, increased the percentage of polygonal structure of nanoparticle, which became dominant among other structures. They concluded that the method of preparation and addition of additive NaCl influenced the morphology, shape and other properties of WO3 nanoparticles. Acsente et al. [15] synthesized the tungsten (W) nanoparticle by magnetron sputtering - gas aggregation (MS-GA) technique. They applied the radio frequency waves (13.6 MHz) of two different modes namely: continuous and rectangular pulse to the magnetron sputtering (MS) discharge, which formed the concave hexapods (80–200 nm) shaped and faceted cube-octahedral (40–80 nm) shaped nanoparticle, respectively. The XRD results taken immediately after the preparation showed that the nanoparticle contains both α and β phases of tungsten (α-W, β-W). In hexapod shaped nanoparticle, β phase (β-W) was the most dominant and had traces of α phase (α-W). The XRD results of W nanoparticles after two years of storage showed that in faceted cubeoctahedral shaped nanoparticle, there was a conversion of β-W (typically metastable) into more stable form α-W. But, the hexapod shaped particle remained in same phase (β-W), which showed stability over more than two years. They reported that tungsten nanoparticle could be prepared with good control of their shape and crystallinity, by tuning

2.2. Operating condition Callegari et al. [18] studied the growth of colloidal solution of spherical silver nanoparticle into different shapes using light. They synthesized spherical shaped silver particles by boro-hydrate reduction of silver nitrate (AgNO3) and exposed to the conventional fluorescent

Fig. 2. Synthetic procedure for Au NPs with different sizes [16]. 336

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ageing method without using any surfactant. They studied the effect of temperature, oxidation time and ageing time on the preparation of Fe3O4 nanoparticle. Ageing is the operation often used in the synthesis of nanoparticles. It sometimes causes macroscopic or microscopic changes in the solid phase, especially modification of crystal type and size, specific surface changes or changing in chemical composition of solid phase and it is commonly a slow process [22]. The ageing temperature is the temperature during the ageing process. The increase in ambient temperature from 10° to 60 °C, the average size reduced from 85–48 nm and increased size distribution width, showing that nanoparticle having non-uniform size. They studied the influence of ageing temperature from 5° to 90 °C, at fixed ambient temperature 22 °C. For the ageing temperature less than 15 °C and more than 50 °C, had wide size distribution width 47 ± 18 and 40 ± 22 nm respectively. They noticed that the optimum ageing temperature lie in the range of 25°–40 °C, as narrow size distribution is formed at that temperature range. They varied the time of addition of oxidant NaNO3 and kept the ambient temperature 10 °C and the ageing temperature 40 °C. When the oxidation time was more than 15 min, along with magnetite phase, acicular goethite phase (γ-FeOOH) was noticed. As the oxidation time increased, the acicular goethite phase (γ-FeOOH) also increased in the iron oxide nanoparticle shown in Fig. 3. They explained that the complete iron hydroxide crystallization decreased the iron (II) concentration and limit the formation of magnetite phase due to the crystal structure similarity, acicular goethite phase formed directly from iron hydroxide (II). They varied the ageing time from 5 min to 24 h at fixed ambient temperature (10 °C) and the ageing temperature (40 °C). They found that the nanoparticle size increased from 42 to 81 nm, on increasing the ageing time and observed that on increasing the ageing time, the particle size increased up to 24 h, after that ageing was insignificant. They explained the increase in size was due to the Ostwald ripening and concluded that the preparation of Fe3O4 nanoparticle depended on the temperature, the oxidation time and the ageing time and the pure Fe3O4 nanoparticle can be obtained using above said optimum conditions. Xie et al. [23] prepared graphene quantum dots (GQDs) through hydrothermal approach. They downsized graphite flakes hydrothermally at four different temperatures (90, 105, 120 and 150 °C). It was noticed that the size decreased from the 2.76 nm to 1.58 nm as the temperature increased from the 90° to 150 °C. The Photoluminescence (PL) intensity also increased with increased temperature and could be due to the reduction of defective sites in the GQDs. Table 1 shows the summary of related studies on effect of operating conditions on synthesis of nanoparticles. From Table 1, it is found that researchers varied the reaction temperature, annealing temperature, calcination temperature, heat treatment temperature, sintering temperature, Bath temperature, stirring speed, oxidation time, ageing time, sonication time and milling time based on the method of preparation.

Fig. 3. XRD patterns of nanoparticles samples obtained during oxidant addition with different time [20].

tube for 1 h and then assigned to different illumination using orange filter, the broadest blue filter and the narrowest blue filter for several hours. The orange filter yielded triangular plates with large aspect ratio. Triangular shape with small size was dominant for the broadest blue filter (354 nm < λ < 589 nm). For the narrowest blue filter (349 nm < λ < 467 nm), only few triangular plates were observed with along with some cubic and octahedral particles and large abundance of tetrahedral particles. They concluded that the photochemical growth of metallic silver nanoparticles could be controlled by choosing the color of light of preferred wavelength to achieve different shapes of silver nanoparticle. Yu et al. [19] prepared the gold nanoparticle by laser ablation in water about a broad range from 120 nm to 4 nm. They achieved it by keeping the pulse energy fixed at 5 μJ and changing the number of subpulses in multiple pulse laser during the ablation process. They prepared nanoparticle at sub-pulse number 3, 4, 6 with the ablation time 100 min, 75 min, and 50 min respectively. They reported that for higher sub-pulse number, size of particles was smaller. The UV results for all three sub-pulse number were slightly blue shifted, when the sub-pulse number was increased. They concluded that nanoparticle size was in good dependence with the sub-pulse number and can be fine-tuned up to 4 nm of gold nanoparticle. Devaraj et al. [20] synthesized zinc oxide (ZnO) nanoparticle from zinc acetate through chemical co-precipitation method. During preparation of ZnO nanoparticle, the researchers varied the stirring speed (900 & 1500 rev/min) and the reaction temperature (30 °C & 70 °C). From the XRD results of prepared ZnO nanoparticle, they observed that the size of the nanoparticle did not differ much with the variation in reaction temperature. But the shape was altered from spherical to hexagonal as reaction temperature changed from 30° to 70 °C. From the EFTEM images of ZnO nanoparticle, they inferred that, at reaction temperature 30 °C, the size of the nanoparticles decreased from 135.61 nm at 900 rev min−1 to 107.833 nm at 1500 rev min−1. While the reaction temperature at 70 °C, the size of the nanoparticle decreased from 133.80 to 92.53 nm as the stirring speed was varied from 900 to 1500 rev min−1. Thus, the size of the nanoparticle was decreased, as the stirring speed was increased. They explained that at the higher stirring speed, there was rapid nucleation and the growth occurred and it prevented the condensation of new nuclei on pre-existing ones. They also noticed that on annealing for 1 h at 600 °C, it desorbed all the impurities and the nanoparticle size was increased to 102.02 nm due to the Ostwald ripening mechanism. They reported that the reaction temperature, stirring speed and the annealing treatment influenced the morphology structure of zinc oxide nanoparticle during its synthesis. Muradova et al. [21] prepared iron oxide (Fe3O4) nanoparticle by

2.3. Reactants and its concentration Supattarasakda et al. [36] synthesized Hematite (α-Fe2O3) nanoparticles from the ferrihydrite precursor through chemical precipitation method with trace amounts of Fe(II) as the catalyst under nitrogen atmosphere. During the synthesis of Hematite nanoparticles, they varied the amount of the precursor concentration, the solution pH and the amount of Fe (II). They observed that the particle size decreased from 139.5 nm to 49.3 nm with decreasing precursor concentration from 0.5 M to 0.1 M. For the precursor concentration 0.3 M, as the pH increased from 5 to 7, the average particle size increased and on further increased pH, the particle size was decreased. They explained that it was due to the formation of different species of Fe (II) ions at the different pH values. They noticed that the particle size of hematite decreased from 134.5 nm to 83.1 nm with decreased molar ratio of Fe (II) to Fe (III) from 0.05 to 0.01. The particle size was decreased because the Fe (II) acts as a catalyst to induce the phase transformation from 2337

338

Magnetite nanoparticles coated with hexanoic acid and oleic acid Chemical co-precipitation method

Calcium phosphate Nanoparticles Co-precipitation microwave technique

Zinc oxide (ZnO) Deposition on mild steel- sono chemical process

Petcharoena and Sirivat [27]

Ahmed et . [28]

Sharifalhoseini et al. [29]

ZrO2 Reactive magnetron sputtering technique

Verma et al. [25]

Silica (SiO2) Sono-chemical process

Ageing temperature Oxidation time Ageing time

Iron oxide (Fe3O4) nanoparticle Ageing method

Muradova et al. [21]

Sankar et al. [26]

Heat Treatment temperature

Tungsten (W) chemical reduction method

Graeve et al. [24]

Ultrasonic wave Propagation at 60 °C for 20 min

Horn system (20 kHz), ultrasonic bath (40 kHz) and cup horn system (20 kHz)

800, 1000, 1100 and 1200 °C 5, 6, 7, 9 and 11.

0 °C to 90 °C

Reaction temperature

Annealing temperature pH

0, 10, 30, and 50 min

40, 60, 80 and 100 W

5°–90 °C Up to 4 h 5 min to 24 h

773 K, 923 K, and 1073 K

30 °C & 70 °C 900 & 1500 rev/min

Sonication time (ultrasonic Power −240 W at 35 kHz)

Sputtering Power

Reaction temperature Stirring speed

Zinc Oxide (ZnO) chemical co-precipitation method

Devaraj et al. [20]

Different Operating Conditions

Nanoparticles & Synthesis method

Researchers

Table 1 Effect of operating conditions on synthesis of nanoparticles.

−1

• • • • •

2

•

• • •

(continued on next page)

4.11 respectively. The synthesized nanoparticles were in spherical shape of size 10–40 nm. The lowest size was observed at 0 °C The bare magnetite nanoparticle size increased with increasing temperature because, the temperature accelerated the reaction. The size of hexanoic acid coated magnetite increases with temperature but size of the oleic acid coated magnetite decreases, due to the steric hindrance of the acid molecule. The synthesized nanoparticle varied from crystallite size 23 nm–106 nm and wide range of porosity (%) was achieved. At 800–1100 °C, the brushite phase transformed to β-dicalcium pyrophosphate (β-CPPA: β-Ca2P2O7) in single phase. On further treated at 1200 °C, it converted into (α-CPPA: αCa2P2O7) with low crystallinity. Depending on the pH and annealing temperature, the crystal structure varied as Monoclinic, Tetragonal, Hexagonal, Rhombohedral or their combination. The morphology of the nanoparticles was observed as sphere, fibrous network and rod. The higher amount of nanoparticles were deposited on mild steel in direct sonication horn system about 1.7 mg compared with indirect sonication bath and cup horn system about 0.8, and 0.5 mg, respectively. Morphology of the ZnO: (i) Horn system – Nanorods (ii) Bath – undefined (iii) Cup horn – network structure

sputtering power.

FESEM and TEM results also indicated the particle size increases from 3 nm to 8 nm • The with increasing sputtering power. synthesized particles were in tetragonal phase. • The confirmed the spherical morphology of nanoparticle • FESEM measurement confirmed the size increased from 5 to 40 nm, as sonication time • DLS increased from 0 to 50 min respectively. nanoparticle size became homogenous, as the time is increased. Sample of 50 min • The showed high distribution of uniform size. specific surface area and pore diameter of nanoparticle synthesized by 0, 10, 30, and • The 50 min sonication time was 201.45, 233.83, 242.67, 271.22 m /g and 4.97, 4.92, 4.6 and

FeOOH) also increased.

time increased from 5 min to 24 h, the size increased from 42 to 81 nm, after • Asthatageing ageing was insignificant. XRD, the crystallite size was determined, and noticed that that the crystallinity • From and the crystallite size increase continuously from 2 nm to 8 nm with increasing

crystallite sizes of heat treated tungsten nanoparticle at 773 K, 923 K, and 1073 K • The were 12, 16, and 20 nm from XRD respectively. measurements showed the average particle size of 260, 450, and 750 nm, depending • DLS on the heat-treatment at 773 K, 923 K, and 1073 K respectively. nanoparticle had mixture of α and β phases when heat treated at 773 K. But at • Tungsten 923 K and 1073 K, there was only α-phase peaks were found. shape of the nanoparticle was mostly spherical at all operating condition. • The temperature < 15 °C ∼ 47 ± 18 nm • Ageing temperature > 50 °C ∼ 40 ± 22 nm. At both condition nanoparticles had wide • Ageing size distribution width. the 15 min of oxidation time, along with magnetite phase, acicular goethite phase (γ• After FeOOH) was noticed. As the oxidation time increased, the acicular goethite phase (γ-

(ii) at 70 °C, the nanoparticle size decreased from 133.80 to 92.53 nm

−1

shape was changed from spherical to hexagonal as reaction temperature changed • The from 30° to 70 °C. speed changed from 900 rev min to 1500 rev min , • As(i)stirring at 30 °C, the nanoparticle size decreased from 135.61 to 107.833 nm

Comparative results

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339

Nano- Ti polymer Ball milling

Wang et al [33]

Methyl silica Sol–gel

Zinc oxide (ZnO) Electrochemical deposition (ECD) method

Zhang et al [32]

Sutha et al. [35]

Cobalt ferrite (CoFe2O4) Co-precipitation

Prabhakaran et al. [31]

Silica Alkaline extraction followed by acid precipitation

Cobalt ferrite (CoFe2O4) Co-precipitation

Prabhakaran et al. [30]

Saravanan et al. [34]

Nanoparticles & Synthesis method

Researchers

Table 1 (continued)

Heat treatment

Sintering temperature pH

573 and 873 K

600, 800 and 1100 °C 1, 3 and 7

0–300 min

50, 60, 70, 80, 85 and 90 °C

Bath temperature

Milling time

60, 70 and 80 °C

40 and 85 °C 500 and 800 °C

Reaction temperature

Reaction temperature Calcination temperature

Different Operating Conditions

• •

• • • • • •

•

gets thinner and thinner (v) 120–180 min – Smaller pieces (vi) 240 min – lamella (vii) 300 min – cluster Due to grinding, chemical bonds were broken. This led to formation of free radicals, plenty of dislocations and defects. The TEM results proved that the Ti and grinding agents had been grafted each other successfully by ball milling. Irrespective of pH, particles sintered at 600 °C were amorphous in nature confirmed by the XRD due to absence of peaks. XRD of sintered at 800 and 1100 °C, showed the crystalline nature and structure was cristobalite irrespective of pH. The peaks increased as the sintering temperature increased. The size of the particle observed varied from 42.5 to 58.16 nm. The pH and sintering temperature was directly proportional to the size. Increasing of sintering temperature and pH, increased transmittance property of the particle. The particles were amorphous in nature even though after the heat treatment, showed the high thermal stability. The size was reduced to 105 and 90 nm but the specific surface area increased to 504.32 and 575.86 at 573 and 873 K respectively. TEM image confirmed the spherical shape of particle and there was no morphological change. From EDX spectrum, it was observed that the percentage of carbon decreased with the heat treatment due to the removal of solvent and unreacted compounds. FT-IR showed that the heat treatment reduced/removed the surface methyl group, which was responsible for the hydrophobic nature and replaced by the hydroxyl group in methyl silica.

grown in the water bath temperature at 85 °C had the best morphology • ZnO and size varied as the milling time increased. • Morphology size decreased slowly upto 90 min (102.9 nm), and on further milling decreased • The rapidly until 240 min (28.1 nm), afterwards the particle size led to increase. of particle: • Shape (i) 0 min – irregular bulk (ii) 10 min – granular (iii) 60 min – sheet (iv) 60–120 min – sheet

of the bath temperatures upto 85 °C.

(ii) (ii) at 70–90 °C, hexagonal shaped nanorods

of the nanorod formed decreased with increase of bath temperature. • Diameter XRD, it was observed that the nanoparticle grew at preferred orientation (0 0 2) on • From reaching 85 °C i.e. perpendicular to the ITO glass substrate. were measured at room temperature by using a 350 nm excitation and observed • PLthatspectra the UV emission peaks had not shifted but the peak's intensity increased with increase

increased.

size of the particles was 9, 12 and 15 nm, when synthesized at 60, 70 and 80 °C • The respectively. magnetic properties are also varied with the reaction temperature. • The of the particle formed observed in FESEM images: • Shape (i) At 50 and 60 °C- flower bud-like nanorods with stripes on their surface

crystallite size was slightly higher for the particle at 85 °C (9.13 nm) than 40 °C • The (8.24 nm) due to the better nucleation rate at high temperature. increased from 500 to 800 °C the size got increased from 8.24 to • As29.28thenmcalcination and 9.13 to 26.75 nm, for particle synthesized at 40 and 85 °C respectively. calcinated particles showed nearly spherical shape. However, the particles synthesized • The at 85 °C showed better spherical shape than 45 °C calcinated at 800 °C. the XRD, it was observed that the intensity of peaks increased with increased • From reaction temperature showing that the crystalline nature and crystallite structure was

Comparative results

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A.I. Khan and A. Valan Arasu

line ferrihydrite into hematite. Thus, lower the amount Fe (II) was used to synthesize the smaller hematite particle size. They observed the change in physical morphology of α-Fe2O3 such as spherical, cubic, and ellipsoidal shape with the different molar ratios of Fe (II) to Fe (III). They concluded that the hematite nanoparticles of different morphology and size could be obtained by varying the precursor concentration, the solution pH and the amount of Fe (II). Zhang et al. [37] synthesized iron oxide (Fe3O4) by using KNO3 – ageing ferrous hydroxide gel method, in which amorphous ferrous hydroxide was first precipitated and then the aqueous gel was aged at 90 °C for various periods in the presence of nitrate ion [38]. They varied the concentration of potassium hydroxide (KOH) and ferrous sulphate (FeSO4) and noticed that the nanoparticle sizes were formed in the range 35–1500 nm. They reported that on the excess of OH−, the nanoparticles were in cubic shape; on small excess of Fe2+, the particle shape sharply changed to the spherical; on large excess of Fe2+, the particle exhibited the hexagonal shape. Shin et al. [39] prepared the silver nanoparticle from silver nitrate using polyvinyl pyrrolidone (PVP) as a stabilizer and γ–irradiation. They investigated the growth mechanism of the silver particle using PVP of various concentrations (0.2 wt%, 1.0 wt% and 5.0 wt%) and molecular weights (10,000, 55,000 and 1,300,000). The γ–irradiation from the source (60Co-γ-ray source with 30 kGy doses) produced the hydrated electron which reduced the silver ions into silver. The TEM images of the silver nanoparticle showed that the nanoparticle was in smaller size and surrounded by PVP. They observed that on increasing wt% of PVP, the average size of the silver nanoparticle was reduced. But on increasing the molecular weight of PVP, the size of the nanoparticles was increased. They explained that the concentration of silver nitrate remained fixed even though the wt% of PVP increased. When the PVP in the solution was increased, the number of silver ions reacted with the each molecule of PVP was decreased. Thus, the average size of the silver nanoparticle was decreased. The PVP of molecular weight (MW) 1,300,000 had more repeating units per molecule than PVP of MW 10,000. The PVP with a higher molecular weight could react with more silver ions per molecule resulted in silver nanoparticles of larger size. They deduced the three step mechanisms for growth of silver nanoparticle (i) silver ions interacted with PVP; (ii) nearby silver atoms that have been reduced by γ-irradiation aggregate at close range and (iii) nearby primary nanoparticles coalesce with other primary nanoparticles or interact with PVP molecules to form larger aggregates. They concluded that uniform shape of stabilized silver nanoparticle of smaller size with narrow size distribution could be prepared by γ–irradiation method with high wt% and low molecular weight PVP. Darvanjooghi and Esfahany [40] synthesized silica nanoparticle by stober’s method from the reactants Tetra-ethyl ortho silicate (TEOS) and ethanol and the ammonia solution (25 wt%) was used as a catalyst. They varied the amount of ethanol and ammonia and investigated the silica nanoparticle. The synthesized silica nanoparticles from the different amount of reactant and catalyst were in different sizes shown in Table 2. Al-Thabaiti et al. [41] used oriental plane leaf extracts as reducing, stabilizing- and capping-agent for the preparation of silver nanoparticles (AgNPs). They synthesized the silver nanoparticle with and without CTAB. In the presence of extract, the silver nanoparticles produced were in large numbers of quantum dots, spheres and triangular nanoplates. But in the presence of CTAB, spherical and mostly irregular morphologies were formed. They explained the kinetics and the mechanism of AgNPs synthesis. In the presence of CTAB, the interactions and solubility of the reducing molecules occur with the positive head group of CTAB. The AgNPs were also adsorbed and/or bound with the CTAB due to the electrostatic and Vander Waals forces, thus the morphology was transformed and the number of spherically shaped silver nanoparticle was increased. The formation of anisotropic morphologies also explained by them that the CTAB controls only the shape of particle but not the crystal growth during process. They reported that the AgNPs

could be synthesized by the green method and their morphology, shape and stability are depended on the concentration of AgNO3, leaf extracts, CTAB, pH, and the reaction–time. Athawale et al. [42] synthesized copper nanoparticles by the reduction of cupric nitrate (Cu(NO3)2·2.5H2O) in the presence of catalyst cetyl-trimethyl ammonium bromide (CTAB) and isopropanol (IPA). They investigated the synthesis of Cu nanoparticle at different Cu2+: CTAB molar ratios (1:0.25, 1:1, 1:15 and 1:30), which was simplified by adding a fixed concentration of Cu2+ ions (3 × 10−3 M, diluted in IPA). The TEM results for the two extreme concentrations 1:0.25 and 1:30 showed that the size of the particle formed was 5–20 nm and 2–10 nm respectively. The FT-IR spectrum of the copper Nano-sol prepared was the same to that of CTAB–IPA mixture before the addition of Cu2+ ions but with a minimal rise in the transmittance. This showed that CTAB–IPA mixture had good reduction capacity as the Cu2+ ions reduced to Cu0. They explained the mechanism of reduction of the precursor and the formation of Cu nanoparticle briefly. The alcohols act as reducing agents at raised temperatures during the synthesis of nanoparticles, but in this case, the reduction took place under ambient conditions only after the introduction of CTAB–IPA in the reaction mixture. They carried out the experiment with the addition of Cu2+-IPA solution to CTAB–IPA mixture and then it was reversed. They observed the difference in optical spectra for the two sets of experiments and explained that for the first case, the Cu2+-IPA solution was introduced in an excess of CTAB enabled instantaneous reduction of copper ions to Cu nanoparticle and the Cu nanoparticle was stabilized. While on the addition of CTAB to Cu2+-IPA solution; brought reduction of copper ions coming into contact with the incoming CTAB molecules. There was a competitive interaction occurring between them and it could result in incomplete reduction of copper ions. They reported that the solvent IPA used along with the sequence in which the freshly prepared Cu2+ and CTAB solutions in IPA were mixed play a crucial role in effective reduction of copper. Thus, the fine Cu nanoparticle with narrow size distribution can be prepared with high molar concentration of CTAB. Wu and Chen [43] synthesized pure metallic copper nanoparticles from cupric chloride (CuCl2). They used hydrazine as reducing agent in the aqueous CTAB solution maintained at pH 10, by the addition of ammonia solution. They varied the concentration of CTAB from 0.3 mM to 0.1 M and noticed that it did not affect the size of the prepared Cu nanoparticle. They explained that the micellar concentration of CTAB was 0.94 mM, so they act as capping agent instead of forming micelles to prohibit the growth of nanoparticles. They increased the reducing agent Hydrazine concentration for the constant CuCl2 concentration (1.0 mM), which led to decrease in diameter of the nanoparticle. The decrease in diameter was felt up to Hydrazine concentration 0.04 M and above that size remained almost constant. They explained that the size remained constant above certain concentration was due to the unavailability of the reaction sites for the reduction in the solution. They reported that the pure metallic copper nanoparticle can be achieved by the reduction of cupric chloride with hydrazine in the aqueous CTAB solution without any need of extra inert gases. Khanna et al. [44] prepared oleic acid capped copper nanoparticle using sodium formaldehyde sulfoxylate (SFS) in the aqueous medium. Table 2 Volume of reactants used for synthesis of silica nanoparticles [40]. Sample No. Volume of reactants TEOS (ml) Ethanol (ml) Ammonia solution (25 wt%) (ml) 1 2 3 4

340

6.2 6.2 6.2 6.2

17.9 17.9 22.4 22.4

1 2 2.7 3.1

Diameter of nanoparticles from DLS (nm) 10.6 20.0 38.6 62.0

Copper oxide (CuO) Wet chemical method

Hematite (α-Fe2O3) Chemical precipitation method Iron oxide (Fe3O4) by using KNO3 – ageing ferrous hydroxide gel method

Silver

Agarwal et al. [45]

Supattarasakda et al. [36]

Sikorska et al. [46]

Silver γ–irradiation method

Titania (TiO2) Modified version of a Schlenk technique

Copper (Cu)

Shin et al. [39]

Masih Darbandi and James H. Dickerson [47]

Shankar and Rhim [48]

Zhang et al. [37]

Nanoparticles & Method

Researchers

341 Copper Acetate, Copper Chloride, and Copper Sulphate Reducing agents- Sodium hydroxide (NaOH) and ascorbic acid

Titanium tetrachloride (TiCl4) Tetra-butyl titanate (TBT) Oleic acid Oleyelamine Acetic acid Cyclohexane

Silver nitrate using polyvinyl pyrrolidone (PVP) as a stabilizer

silver p-toluene sulfonate (CH3C6H4SO3 Ag/ AgTOS), Silver tetrafluoroborate (AgBF4) and Silver perchlorate (AgClO4) Lignosulfonates (LS)DP 839 DP 840 DP 841 DP 842 DP 843 DP 844 DP 845 DP 847 DP 849 LS SA

Ferrous Sulphate (FeSO4)

Potassium Hydroxide (KOH)

Ferrihydrite precursor

Copper Sulphate Copper Acetate

Precursors/Reactants

Table 3 Effect of reactants and its concentration on synthesis of nanoparticles.

Not Reported

Not Reported

0, 5 and 15 ml 0 and 15 ml 0, 35 and 40 ml 0,20, 23 and 25 ml

Not Reported

PVP – (0.2 wt%, 1.0 wt% and 5.0 wt%) Molecular weights- (10,000, 55,000 and 1,300,000)

Not Reported

Not Reported

1.5 × 10−1 (mol/l) 5 × 10−2 (mol/l) (2.5, 2.6, 2.8, 3, 3.5) × 10−2 (mol/l)

0.5 M–0.1 M

200 ml of 1 M solution

Concentration

2+

2+

−

•

•

• • •

4

(continued on next page)

(5.2 nm), tetragons (9.8 nm) and irregular (5.8 nm) with different sizes. Acetic acid act as both surfactant and dispersant. Size and shape of the nanoparticle could be controlled by varying the preparative condition without usage of highly toxic reagents. Reducing agent NaOH: fibrous shape; The length of the fibrous were in order of nanoparticle from copper acetate > copper chloride > copper sulphate Reducing agent ascorbic acid: rod, triangular and spherical shapes for copper acetate, copper chloride and copper sulphate respectively. FT-IR showed the presence of hydroxyl group on the surface of particle from ascorbic acid, which is responsible for high dispersion of nanoparticle.

4

- small and mono-dispersed • AgClO - more dispersed particle sizes • AgBF - mono-dispersed size distribution and formed hexagonal • AgTOS lattices. increasing wt% of PVP, the average size of the silver • On nanoparticle was reduced. increasing the molecular weight of PVP, the size was • On increased. size varied from 8.5 to 24 nm. • The method resulted in small particles, a very narrow • γsize–irradiation distribution with uniform spherical shape. can be done without any highly toxic/corrosive • Synthesis reagents in this method. were mono dispersed and were smaller than 50 nm in • Particles all conditions. nanoparticle were of various shapes such as • Synthesized asparagus (3.3 nm), wire (2.3 nm), cube (6.7, 22.4 nm), eggplant

842, DP 844 and DP 847.

nano-hexagons.

−

strongest Plasmon bands were observed in UV–Vis • The Spectroscopy for the samples containing the ligno-sulfonates DP

2+

spherical shape

nanoparticle from copper sulphate precursor was smaller in • CuO size and more uniform in shape than copper acetate size decreased from 139.5 nm to 49.3 nm with • Particle decreasing precursor concentration. were formed in the range 35 to 1500 nm. • Sizes size and shape were determined by changing the excess • Particle and OH ions concentration. of Fe the excess of OH – cubic shape • On small excess of Fe – spherical shape • On large excess of Fe – hexagonal shape • On of the synthesized particle varied from 2 to 33 nm. • Size images showed Ag NPs were in spherical shape, but a • TEM certain number of particles coexisting with nano-triangles and

obtained 14.56 g and 14.9 g of CuO nanoparticles. • They size and shape of nanoparticles: (i) copper acetate – • Average 67 nm of irregular in shape (ii) copper sulphate – 54 nm of

Comparative results

A.I. Khan and A. Valan Arasu

Thermal Science and Engineering Progress 11 (2019) 334–364

Nanoparticles & Method

Silver

Copper (Cu) One step synthesis

Copper (Cu) Chemical reduction method

Copper (Cu) Chemical reduction method

Copper (Cu) Chemical reduction method

Copper (Cu) Gamma radiolysis method

Researchers

Al-Thabaiti et al. [41]

Shikha et al. [49]

Liu et al. [50]

Athawale et al. [42]

Wu and Chen [43]

Joshi et al. [51]

Table 3 (continued)

342 PVA PVP CTAB BRIJ 97 SDS

Copper sulphate, Copper acetate and Copper chloride

Cupric chloride Hydrazine CTAB

Cupric nitrate CTAB

Copper acetate Hydrazine Water

Copper chloride di-hydrate L-ascorbic acid

AgNO3 Oriental plane leaf extract CTAB

Precursors/Reactants −4

−4

−4

−3

)

1 × 10−1 M, 1 × 10−2 M, 5 × 10−3 M, 1 × 10−3 M and 1 × 10−4 M (0.79, 1.5, 2.3, and 3.4) × 10−2 M 3.4 × 10−2 M Not Reported Not Reported Not Reported

1 mM–0.2 M 5 mM–3 M 0.3 mM–0.1 M

Cu2+:CTAB molar ratios (1:0.25, 1:1, 1:15 and 1:30) at fixed concentration of Cu2+ ions (3 × 10−3 M diluted in Isopropanol (IPA)

ratio of 1, 2, and 4 ratio of 1, 2.5, and 5 mass ratios of water are 1to 3

0.02 mol/L 0.08, 0.09 and 0.10 mol/L

[Ag ] = 4 × 10 , 8 × 10 , 16 × 10 (mol dm 5, 10 and 15 cm3 0, 4 × 10−4, 8 × 10−4, 12 × 10−4 (mol dm−3)

+

Concentration

• •

•

• • • •

(continued on next page)

unchanged after several months, revealing they were quite stable in the aqueous CTAB solution. Particle size decrease in diameter up to Hydrazine concentration 0.04 M and above that size remained almost constant. Mean diameter of Cu nanoparticles were 5–15 nm CTAB did not affect the size of the prepared Cu nanoparticle in this study but they act as capping agent PVA - Optical plasmon resonance absorption (OPRA) peak at 575 nm was obtained for highest amount 3.48x10-2 M. This peak was shifted towards higher wavelength with decreasing amount of concentration. The XRD results showed that the PVA gave Cu particles of more stable and fully reduced, while the PVP form oxides of copper and not suitable for synthesis. BRIJ 97 yielded lowest particle size but the effect of surfactant CTAB and SDS was not reported. Concentration of Cu (II): The UV–vis absorption peak shifted towards the higher side (800 nm), indicating the possibility of oxide formation. Concentration range of 10-2 M to 10-3 M, the absorption becomes sharper.

and 2 to10nm respectively

1:0.25 and 1:1 – yellow sol along with a higher degree of • Atsizeratio distribution ratio 1:15 - after the complete addition some cupric ions • Atremain unreacted in the solution fine size copper nanoparticles were formed • AtMoleratioratio1:30of -1:30 is ideal to obtain fine copper nanoparticles. • Intensities of the absorption in UV/vis spectra increased • with increasing cupric chloridebandconcentration and remained

nanoparticle synthesized was in uniform size distribution of size • Curanging 50 to100nm diameter with spherical and square shapes. explained the SEM images with specimen number, but did not • They give the details of reactants’ concentration with the specimens. results for the two extreme concentrations 1:0.25 and • TEM 1:30 showed that the size of the particle formed was 5 to 20 nm

0.10 mol/L of L-ascorbic acid.

12 nm respectively.

size of nanoparticles with increased concentration of • Decreased reducing agent was due to the difference in catalytic activity. presence poly hydroxyl structure on the surface nanoparticle • The gives an excellent dispersion effect. was no need of inert gas environment for the Cu • There nanoparticle preparation and the optimal concentration

triangular nanoplates.

the presence of CTAB, nanoparticles were spherical and mostly • Inirregular morphologies. morphologies were formed in the presence of CTAB. • Anisotropic leaf extracts and CTAB, CTAB was found to be a better • Between capping agent. dispersed copper nanoparticles was observed at all • Mono conditions of concentration of L-ascorbic acid 0.08, 0.09 and • Usage 0.10 mol/L gave the spherical Cu nanoparticle size of 28, 16,

the presence of extract alone, the silver nanoparticles • Inproduced were in large numbers of quantum dots, spheres and

Comparative results

A.I. Khan and A. Valan Arasu

Thermal Science and Engineering Progress 11 (2019) 334–364

Nanoparticles & Method

Oleic acid capped copper nanoparticle Single-stage method

Zinc sulphide (ZnS) Aqueous chemical reaction method

Bismuth

Copper Green reduction method

Researchers

Khanna et al.[44]

Florence and Can [52]

Petsom et al. [53]

Nagar and Devra [54]

Table 3 (continued)

Copper chloride

Ascorbic acid Dioctyl sulfosuccinate sodium salt (AOT) Ethanol indistilled water

Zinc chloride Oxalic acid (OA)

Copper chloride Water/acetone Water/EG Water/Acetone/ PVA

Precursors/Reactants

6 × 10−3–10 × 10−3 M

100–500 μl of 0.1 mM 100–500 μl of 0.1 mM ratio of 0:10, 2:8, 5:5, 8:2

Not Reported Molar ratios of 0.1, 0.2 and 0.3

1.7 g in 50 ml distilled water Not Reported Not Reported Not Reported

Concentration

343

•

•

•

•

•

•

diameter. Without ascorbic acid addition, more irregular shapes were observed. AOT: Hydrodynamic diameter decreased from 195.3 nm to 107.4 nm, as the concentration increased to 500 μl. Addition of AOT increased the amount of synthesized particles and changed the particle size. Ethanol: Ethanol increased the particle size from 98.5 nm to 162.9 nm when the ratio of ethanol and water increased. Synthesized amount of particle was decreased 10.3 mg at higher concentration. According to UV–Vis spectra, the intensity of the peak increased upto the 20% of leaf broth, indicating the increased rate of reaction and 20% was confirmed as optimum one. Particle size decreased to 48.01 nm from 63 nm upto 7.5 × 10−3 M and increased (78.5 nm) on further increase in concentration of precursor Bio-molecules terpenoids, nimbaflavone and polyphenols acts reducing, capping and stabilizing agent, whose particles are stable for 2 months. Particle shapes were cubical.

medium did not offer effective surface capping. • Water/EG particle size increased from 8 to 25 nm with the • Average increase of Oxalic Acid concentration of ZnS changed from wurtzite to cubic structure, as the • Structure OA concentration increased. acid: Increasing concentration led to a bigger • Ascorbic nanocrystal. However, it had not potentially changed the

shifted by about 30 nm.

pure particles were formed.

being hydrophilic in nature, the product from it can be re• PVA dispersed in aromatic solvents as well. added, the Cu nanoparticle formed was coated • AswiththethePVAPVAwaswhose Surface Plasmon resonance (SPR) was blue-

water/ethylene glycol medium oxide impurities were • For formed, while for oleic acid/acetone with and without PVA,

Comparative results

A.I. Khan and A. Valan Arasu

Thermal Science and Engineering Progress 11 (2019) 334–364

Thermal Science and Engineering Progress 11 (2019) 334–364

A.I. Khan and A. Valan Arasu

They prepared nanoparticle in two different medium (water/acetone and water/ethylene glycol) with and without PVA. The XRD results revealed that the oxide impurities were formed for water/ethylene glycol medium, while for oleic acid/acetone with and without PVA, pure particles were formed during the process. They explained that the effective capping was first done by oleic acid and then followed by PVA, thus it separated each particles and eliminate the oxidation of copper nanoparticle. The UV–vis spectroscopy results showed that the weak peaks are obtained for reaction mixture without PVA. As the PVA was added, the Cu nanoparticle formed was coated with the PVA whose Surface Plasmon resonance (SPR) was blue-shifted by about 30 nm. Xie et al. [23] prepared graphene quantum dots (GQDs) through hydrothermal approach from different precursors including carbon nanotube (CNT), graphene oxide (GO), and carbon black (Super P, SP). They confirmed that the GQDs from the CNT precursors were of lowest particle size (~1.56 nm) and have higher photoluminescence (PL) intensity. Table 3 shows the summary of related studies on effect of reactants and its concentration on synthesis of nanoparticles. Depending on the nature of the reactants and its concentration, a large number of investigation proved their effect on the nanoparticles. Best reactants and their optimum concentration were provided by the researchers are discussed in Table 3. More number of research works on oxides based nanoparticles is found in literature under the topic operating conditions. This shows that for oxide nanoparticles, there is less investigation on the reactants. Since, parameters such as reaction temperature, annealing temperature, calcination temperature, etc. is involved in the crystal growth, phase and to improve crystallinity, they have been extensively studied. Similarly, more number of research works on metal based nanoparticles is found in literature under the topic reactants and its concentration. Since, oxidation of metal nanoparticles is to be avoided; the reduction of metals from precursor should be done carefully. Thus, the reactants and its concentration dictate the synthesis of metal nanoparticles and studied extensively. The comparative study of the synthesis of nanoparticle based on the method of preparation, operating condition and reactants and its concentration is studied in detail. The variables such as method of preparation, operating condition and reactants and its concentration showed well defined effect on the synthesized nanoparticles. Desired character of the nanoparticle can be synthesized in the laboratory on performing the continuous research on above said variables. Each variable had its own effect on the nanoparticles, which are consolidated as inferred from the literature are shown in Table 4.

CNT/water using transient short hot wire (SHW) technique. They used cylindrical (CNT) and spherical shapes of the nanoparticles Gold (Au), TiO2, Al2O3, CuO having average diameter of 1.65, 20, 40 and 33 nm, respectively with various mass fraction. They compared the experimental results with the theoretical model proposed by Hamilton and Crosser for the spherical nanoparticle and Yamada and Ota for the carbon nanofibers. They observed that the thermal conductivity and thermal diffusivity did not alter for the period more than 48 h for the lower volume fraction nanofluids. They concluded that for lower concentration, the results agreed with the Hamilton -crosser model for the spherical shape and the unit-cell model equation of Yamada and Ota for the carbon Nano-fibers. Thus, at lower concentration, the models predict the thermal conductivity of nanofluid, while at higher concentration the prediction deviates from the experimental results. Murshed et al. [56] investigated the theoretical model of thermal conductivity of nanofluids by combined theoretical and experimental methods. They measured the thermal conductivity for the spherical nanoparticles with EG as base fluid experimentally and found that the Hamilton –Crosser and other models such as Maxwell and Prasher didn’t estimate the value of thermal conductivity accurately. The reason for non-agreement result was because of the Hamilton –Crosser model didn’t take up the parameters such as particle size and interfacial layer in the nanofluid (shown in Fig. 4). They proposed the model for the prediction of thermal conductivity with the parameters including the particle size and the interfacial layer and found good agreement with the experimental results.

Fig. 4. Sketch of a particle with interfacial layer in a fluid medium [56].

The proposed the model for thermal conductivity:

K eff = ((kp ×

klr )

3 1 (k p

3

[2

3 3 (k lr 1 [ p

+ kf ])(

3. Effect of nanoparticles shape on thermo-physical properties

p klr

3

1

+ 1] + (kp + 2klr )

kf ) + 2klr )

(k p

klr )

3 p[ 1

3

+

1

1])

For spherical nanoparticles

The researches around influence of shape of the nanoparticles in the nanofluid are discussed in detail in this chapter. Very limited works were found to be published under such topic. The influence of morphology study is very low due to the availability of nanoparticles with various shapes and its cost. The tailoring of morphology of nanoparticles is not as easy as the modification of size of the nanoparticles. It requires high physical and chemical science knowledge and also wellequipped instruments for the morphology modification. Zhang et al. [55] measured the thermal conductivity and thermal diffusivity of Au/toluene, Alumina/water, TiO2/water, CuO/water and

K eff = ((kp ×

1

2[

klr )

p klr

2 (k lr p

+ kf ])(

1

2 (k

p

[

2

1

2

+ 1] + (kp + klr )

kf ) + klr )

(k p

klr )

p[ 1

2

+

2

1])

1

For cylindrical nanoparticleswhere the particle (p) with radius, a and thermal conductivity, kp, the interfacial layer (lr) between particle/ fluid medium with thickness, h and thermal conductivity, klr, the fluid medium (f) with thermal conductivity, kf.

Table 4 Consolidated results from study on synthesis. Variables

Reported effect on nanoparticle

Method of preparation Operating condition Reactants and its concentration

Crystallite size, Crystal phase Nanoparticle shape, Crystallite size, Crystal phase and properties of nanoparticles Nanoparticle shape, Nanoparticle size and size distribution of nanoparticle

344

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A.I. Khan and A. Valan Arasu

Table 5 The thermal conductivity of the interfacial layer from investigation. Nanofluid

Relation

TiO2 (15 nm)/ethylene glycol. Al (80 nm)/ethylene glycol Carbon nanotubes/engine oil

klr = 1.25kf klr = 3kf klr = 60kf

Table 6 The percentage of each shape in tungsten oxide nanoparticle [14]. Samples

HT MW HN MN

= 1 + h/ a 1 = 1 + h /2a

Method

Hydrothermal Microwave Hydrothermal Microwave

NaCl

Absent Absent Present Present

% Shape Spherical

Polygonal

Blade

5.01 18.5 14.3 13.8

34.9 57.1 65.1 79.8

15.0 24.4 20.6 6.4

nanofluid by two-step method of concentration 0.5–2.5 wt.%. They dispersed the nanoparticle of different shapes such as spherical (23 nm), cubic (87.21 nm) and rod (92.47 nm X 8.27 nm) in the base fluid. The UV–vis spectra of all the three different nanofluids did not show any change in absorbance during the period of study of 3 months, showing that the nanofluid was stable. They measured the thermal conductivity for titania/water having different shape and found that the thermal conductivity was higher for cubic shape than other shapes. They explained the higher enhancement of thermal conductivity of cubic shape was due to the large surface area of nanoparticle. They found that viscosity was higher for the cubic shaped nanoparticle than other shapes. They reported from titania/water nanofluid, that the shape contributed about 5.62% in thermal conductivity enhancement of nanofluid and the cubic shaped nanoparticles gave higher thermal conductivity and viscosity to the nanofluid. Karimi-Nazarabad et al. [14] synthesized the tungsten oxide/glycerol nanofluid in four ways, which is discussed in detail in Chapter 2.1. The synthesized nanoparticle has spherical, polygonal, blade shape in different percentage shown in Table 6. They measured the thermal conductivity and viscosity using KD2 Pro and Brookfield viscometer respectively. They noticed that the nanoparticle reduced the viscosity of the base fluid glycerol and indicated the shear thinning behavior for HT, MW, MN, while HN undergone shear thickening. The viscosity of tungsten oxide/glycerol nanofluid whose particle shapes having larger percentage of polygonal-shape was higher than those that of the spherical-shape. The thermal conductivity of nanofluid follows the order of HT > MW > HN > MN, which showed that samples having higher percentage of spherical particles gave higher thermal conductivity. In general, non-spherical shape particles containing nanofluid showed higher viscosity than the spherical nanoparticles. The larger surface area of the non-spherical nanoparticle was reasoned by researchers for the higher viscosity of the nanofluid. On the whole researchers [59–61] concluded that non-spherical shape particles had higher enhancement in thermal conductivity. But others [14,57] reported that the spherical particles had higher enhancement. Thus, the ambiguity arises between the readers on the effect of nanoparticle shape on thermo-physical properties. Though the nanoparticle shapes are different, they may have different size, surface area and porosity. The investigation on these may disclose the confusion, and conclusion can be attained.

h = 2 ; h ∼ 1 nm for spherical and 2 nm for cylinderical; σ – diffuseness of interfacial boundary and its typical value is within 0.2–0.8 nm. The thermal conductivity of the interfacial layer determined by them is given in Table 5. Murshed et al. [57] prepared TiO2/water nanofluid with two different nanoparticle shape using CTAB surfactant. They dispersed rodshape (10 nm × 40 nm) and spherical particle (15 nm) for their investigation. They measured the thermal conductivity by transient hotwire method and found maximum thermal conductivity enhancement for 5 vol.% nanofluid was 29.70% and 32.8% for rod and spherical shaped nanoparticles respectively. They concluded that the effective thermal conductivity depended on the nanoparticle’s size and shape and can’t be predicted by the existing Hamilton and Crosser, Wasp and Bruggeman models. Kim et al. [58] investigated the thermal conductivity and stability of water based bohemite alumina nanofluid of various shapes. They prepared 0.3 vol.%–7.0 vol.% of nanofluid using two step method (without surfactant) with different alumina nanoparticle shape namely brick, platelet and blade. They measured the thermal conductivity for brick, platelet, and blade-shaped alumina particles dispersed in water by transient hot wire method. They observed that the nanofluids had enhanced thermal conductivity maximum up to 28%, 23%, and 16% at 7 vol.% for brick, platelet and blade-shaped alumina nanoparticles respectively. They concluded that the brick shaped alumina nanoparticle showed the highest thermal conductivity enhancement, stability and pointed out that shape of nanoparticle had significant effect on the stability and thermal conductivity. Ferrouillat et al. [59] investigated water-based nanofluid of different nanoparticles shapes. They measured the thermal conductivity and viscosity of SiO2 (spherical and banana) and ZnO (polygonal and rod) nanofluid using transient hot wire method and Brookfield rotational type viscometer, respectively. For SiO2/water nanofluid, the thermal conductivity of non-spherical nanoparticle found higher than spherical particle and viscosity of the nanofluid for both nanoparticle shapes were found to be close to each other. For ZnO/water nanofluid, the thermal conductivity was slightly greater than water and viscosity of ZnO/water nanofluid was slightly higher for the spherical than polygonal shaped nanoparticle. They reported that the shape had effect on thermo-physical properties of the nanofluid. Jisun Jeong et al. [60] prepared the ZnO /water nanofluid of 0.05 to 5.0 vol.% concentration with surfactant ammonium polymethacrylate. They studied the nanoparticle shape of ZnO having spherical (20–40 nm) and nearly rectangular (90–210 nm) shape in water based nanofluid. They measured the thermal conductivity and viscosity of the nanofluid by transient hot wire method and Ubbelohde viscometer respectively. They observed that the viscosity and thermal conductivity of nanofluid with nearly rectangular shape nanoparticle were larger than that of the nanofluid with sphere shape nanoparticle by 7.7% and 5.9% respectively for the 0.05–5.0 vol.% of nanofluid. They explained that the higher viscosity for nearly rectangular was due to the rotation of the particles, which was hard on comparing sphere, thus it increased the flow resistance. They concluded that the difference in thermo-physical properties may be due to the difference in effective aggregation radius between two different nanoparticle shapes taken under study. Maheshwary et al. [61] prepared the titanium dioxide/water

4. Effect of nanoparticles size on thermo-physical properties The researches on the influence of size of the nanoparticle on thermo-physical properties proved their dependency. Yang and Du [62] suggested that the particle size effect was due to the Van der Waals forces and the micro-motions of the particles. These are stronger for lower sized nanoparticles. The effect of particle size on the thermal conductivity and viscosity of the nanofluids, studied and reported by various researchers are reviewed in this section. Pastoriza-Gallegoa et al. [17] experimentally determined the density of the copper oxide (CuO)/water nanofluid (1–5 wt%) prepared from two different average diameter 33 ± 13 nm and 11 ± 3 nm. The density of the nanofluid was measured by them using an Anton 345

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PaarDMA512 P/60 vibrating tube densimeter with pressure range from atmospheric to 45 MPa, in the temperatures range of 283.15–323.15 K. They plotted the density of nanofluid as the function of pressure and temperature and observed slight variation in density of the nanofluid. They measured the viscosity of nanofluid with an Anton Paar AMV 200 viscometer, which measures by falling time of a rolling steel ball inside a glass capillary filled with the sample. They tabulated the results of viscometer measurements and noticed that the viscosity of nanofluid increased as the nanoparticle size decreased. They concluded that the influence of the nanoparticle size and size distribution on density of nanofluid was very negligible. In two step process, sizes of nanoparticles in the nanofluids are greater than the size specified by the supplier of the nanoparticles. This is due to agglomeration of the nanoparticles in base fluid during the preparation of the nanofluid. It shows that there is a requirement of the determining the size of nanoparticles after the preparation of the nanofluid [57,63]. Farbod et al. [64] prepared multi walled carbon nanotubes (MWCNTs)/water nanofluid of different length in different concentrations of 0.1, 0.25 and 0.5 vol.%. They refluxed the MWCNT for 1, 2, 4 h, where the length of the nanoparticle was reduced to 203, 171 and 134 nm respectively. The FT-IR and TGA of the nanoparticles confirmed that the CNTs refluxed for 4 h had higher percentage of hydrophilic functional groups. They investigated the stability and thermal conductivity in the temperature range of 20°–50 °C of prepared nanofluid. They observed that the thermal conductivity of nanofluid was higher for the 1 h refluxed CNT nanoparticles than pure CNT and pristine CNT nanoparticles. Though all the sample had higher stability (no sedimentation about 80 days), 0.1 vol.% with refluxed 4 h has the minimum slope in UV spectra showing that it had better stability than other samples. They concluded that highly stable CNT/water nanofluid could be prepared using functionalized CNTs and the thermal conductivity of nanofluids increased with the length reduction of the CNTs nanoparticle, and with increasing the temperature of nanofluid up to 50 °C. Maheshwary et al. [61] prepared the titanium dioxide/water nanofluid by two-step method of concentration 2.5 wt%. They dispersed the nanoparticle size of 34.03 nm and reduced the nanoparticle size by the probe sonication for period of 0–60 min, which was analyzed using the XRD. For the XRD analysis TiO2 nanoparticles were separated from nanofluid using slow-evaporation method. The XRD result showed that the particle size has been reduced up to 16.312 nm. The SEM and UV–vis spectra also confirmed the reduction of particle size. The thermal conductivity investigation of TiO2/water based nanofluid is

Table 7 Properties of alumina nanoparticles [65]. Sample symbol

αA-5 αA-25 αA-58 γA-58 αA-101 γA-122 γA-124 2

Specific area, (m /g) Particle size, (nm) Crystalline phase

5 302 α

25 60.4 α

58 26.0 α

58 26.0 γ

101 15.0 α

122 12.4 γ

124 12.2 γ

shown in Fig. 5. They experimentally found from titania/water, that nanoparticle size contributed about 24.5% in thermal conductivity enhancement. Xie et al. [65] prepared alumina nanofluid in two step method with water and ethylene glycol as base fluid. They used seven different alumina nanoparticles having different specific area, diameter and crystalline phase shown in Table 7. They investigated the thermal conductivity of alumina nanofluid was investigated with different specific area and pH of the nanofluid. They noticed that the thermal conductivity ratio increased, reached maxima at the specific surface area (SSA) 25 m2/g and then decreased with increasing specific surface area. They explained that the enhancement occurs due to two factors, i) as the size decreases, the specific area of nanoparticle increases. It enhances the thermal conductivity and on further increasing SSA; the Kapitza resistance also increased, which leads to reduction in enhancement of thermal conductivity ii) The phonon mean free path in polycrystalline alumina is estimated to be around 35 nm, which is comparable to the size of the nanoparticle 25 nm they used. The thermal conductivity of the particle may be reduced due to the scattering of phonon at the particle boundary. They reported that the nanoparticle size differed much from the mean free path; the thermal conductivity enhancement can be explained by the first factor. But if the nanoparticle size closer to the mean free path, the thermal conductivity enhancement can be explained by the second factor. They observed the thermal conductivity of nanofluid containing alumina nanoparticles having same size and same specific area with different phase (αA-58, γA-58) and stated that there was a very slight difference between results. They also noticed that the crystalline phase of alumina had no effect on thermal conductivity enhancement. They observed that the thermal conductivity enhancement decreased as the pH of the nanofluid increase for alumina nanofluid (0–5 vol.%). They reasoned that, the Isoelectric point (IEP) of alumina nanofluid is 9.2, thus on reaching nearer to it, the thermal conductivity decreased. They concluded that the alumina nanofluid with augmented thermal conductivity depended on the size of the nanoparticles and the interaction between the nanoparticle and the base fluid. Darvanjooghi and Esfahany [40] synthesized silica with particle size diameters of 10.6, 20, 38.6 and 62.0 nm and the preparation details are briefed in Chapter 2.3. The FT-IR spectrum of dried silica nanoparticles showed the presence of SieOeSi bond (1092 cm−1), hydroxyl group (eOH) (3430 cm−1) and silanol groups (SieOH, 950 cm−1). They prepared nanofluid with different volume fractions of 0.15%, 0.44%, 0.74% and 1.17%. They measured the thermal conductivity using transient hot wire method for the silica/ethanol nanofluid containing particle sizes of 10.6, 20.0, 38.6 and 62.0 nm. They observed that the thermal conductivity rapidly decreased below 20 nm while increased slightly above particle diameter of 20 nm. They stated that the silanol groups (^SieOH) on the surface of silica gave hydrophilicity, which reduces the interfacial thermal resistance. As the nanoparticle size decreased, the average number of silanol groups per nm2 decreased, which causes an increase in thermal resistance with decrease in size. The sensitivity analysis of the thermal conductivity showed that it was more sensitive to temperature than particle size and volume fraction. They concluded that the nanofluid exhibited higher thermal conductivity ratio at higher particle size due to reduction in interfacial thermal resistance.

Fig. 5. Thermal conductivity of TiO2/water nanofluid with the effects of sonication time and temperature [61]. 346

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A.I. Khan and A. Valan Arasu

Table 8 Values of critical temperature Tcr (°C) of nanofluid [66]. Vol.%

Al2O3

1 ∼4 7 ∼9

a (d )

where

CuO

47 nm

36 nm

29 nm

No hysteresis 69 63.8 58.7

No hysteresis 65.3 61.6 54

66.2 63.2 57.2 51

ηrel – Relative viscosity Da – Average diameter of aggregates, d – diameter of nanoparticle, φ – Volume fraction, φm – Crowding factor Zhao et al. [69] prepared the silicon dioxide/water nanofluid with roughly spherical nanoparticle with different diameter of 7 nm, 12 nm, 16 nm, 20 nm and 40 nm in seven nanoparticle volume fractions (0.1%, 0.2%, 0.4%, 0.8%, 1.2%, 1.6% and 2%). The viscosity of silicon dioxide nanofluid was measured with an Ubbelohde-type capillary viscometer at 20°, 25° and 30 °C. The relative viscosity (µr) increased rapidly when the nanoparticle size decreased from 20 nm. They observed that for the particles of small diameter, the relative viscosity was strong function of volume fraction. Still, with an increase of nanoparticle diameter, relative viscosity was negatively correlated with volume fraction. They explained the viscosity trend was due the magnitude of ratio of aggregate diameter to particle diameter (da/d). As the individual nanoparticle size increased, the magnitude of da/d decreased quickly, this showed that the aggregation of particles decreased with increasing nanoparticle size. They varied the pH of nanofluid by the addition of HCl and NH4OH. They observed that the viscosity of nanoparticle size lager than 20 nm didn’t dependent on the pH of the nanofluid, but a sight fluctuation was seen between pH 5 and 7. The value of da/d was different at different pH of the nanofluid. They explained that the change in the da/d and the electrical double layer of particles might lead to these results. They briefed about the viscosity of the silicon dioxide/water nanofluid at different size, pH of nanofluid and different concentration.

Nguyen CT et al. [66] prepared alumina/water for volume concentration ranging from 1 to 13% of two different size of alumina nanoparticle 36 and 47 nm. They determined the viscosity of alumina at temperature ranging from ambient to 75 °C by the ‘piston-type’ viscometer. They found the dynamic viscosity of two nanoparticle size was closer to each other for lower concentration. They observed that the viscosity results corresponding to 36 nm nanoparticle size were lower than viscosity results of 47 nm nanoparticle size. This difference in viscosity visibly became more noticeable for a particle fraction higher than 5%. They observed that above certain temperature there was an erratic damage made to the suspension and the temperature limit is called as critical temperature (Tcr). On heating the nanofluid beyond the critical temperature, the hysteresis effect was noticed and the viscous properties were altered. They observed that the hysteresis effect was not seen in lower concentration (about 1%) for both the sizes, but the hysteresis effect pronounced more as the volume concentration increases. The Tcr found to decrease with an increase of the vol% for a given nanoparticle size. On the other hand, for a given particle concentration, Tcr decreases for a smaller nanoparticle size. The values of Tcr (°C) of nanofluid under their study are given in Table 8. They concluded that the effect of nanoparticle size was dominant for higher concentration, while for lower concentration it has no significance and tabulated the critical temperature of alumina nanoparticle. Timofeeva EV et al. [67] investigated heat transfer characteristics of α-SiC/water nanofluid. They dispersed SiC nanoparticle of four different sizes 16, 29, 66, and 90 nm calculated from the specific surface area of nanoparticles in water and EG/water mixture (50:50). They maintained pH at 9.5 ± 0.3 to engage the electrostatic stabilization of the nanofluids. The thermal conductivity and viscosity of the nanofluids were measured by a KD2Pro and viscosity of the nanofluids in the temperature range of 15°–85 °C measured with a Brookfield DV-II rotational type viscometer. The observed that the 4–5% higher thermal conductivity enhancement for EG/water mixture than water at the same particle concentrations and size. The viscosity decreased on increasing the particle size irrespective of the base fluid due to the decrease in the surface area of nanoparticle. On increasing the temperature from 10° to 40 °C, the viscosity was increased and decreased above 40°–60 °C. They stated that increase in temperature may alter the isoelectric point or alter the particle to particle interaction. Chevalier, Tillement, and Ayela [68] synthesized ethanol based nanofluids by a direct precipitation of silica in alcohol at room temperature to obtain a colloidal solution, whose SiO2 nanoparticle sizes were 35, 94, and 190 nm. They measured the viscosity of nanofluid of solid volume fractions from 1.4% to 7% by capillary micro viscometer. The Newtonian behavior was observed for the shear rate values ranging from 5 × 103 to 5 × 104 s−1. The linear regression for the relative viscosity was applicable for 190 nm while for 35, 94 nm followed nonlinear and had good agreement with Krieger-Dougherty-type formula which is given below. The ratio of φa/φ was 3.4, 2.4 and 2.1for diameter 35, 94, and 190 nm respectively. They concluded that the Newtonian behavior exhibited in the nanofluid for broad range of shear rates. rel

= (1

( a (d )/

m)

= (Da /d)1.2

5. Effect of base fluid on thermo-physical properties The introduction of the nanoparticles in the base fluid is done to enhance the thermal conductivity of the base fluid. The enhancement of the thermal conductivity of the base fluid is not similar for all base fluid. The difference in enhancement is according to the base fluid that is used. For different application, different base fluids are used, and it is necessary to select of proper nanoparticles for the perfect base fluid. The effect of base fluid on thermo-physical properties as reported in literatures is well discussed in this section. Agarwal et al. [45] synthesized CuO particles; explained in Chapter 2.2 and added to three different base fluids (water, ethylene glycol and engine oil). The increase in the thermal conductivity was higher for water, on comparing with other base fluid. They observed 2 vol.% CuO nanoparticles based nanofluids at 70 °C gave 40% for distilled water, 19% for engine oil, 27% for ethylene glycol, increase in thermal conductivity. They explained the reason for such large deviation in thermal conductivity enhancement was that different base fluids have different thermal conductivity and heat carrying capacity and the rate of enhancement depends on strength of the interfacial layer between nanoparticles and base fluid. They concluded that different base fluid gave the dissimilar and different rate of increase in thermal conductivity enhancement. Sonawane et al. [70] synthesized anatase TiO2 nanoparticle through sol–gel method using ultra-sonication. They calculated nanoparticles size using the Scherrer formula and found to be 5 nm. The TiO2 nanoparticle was mixed with water, ethylene glycol and paraffin oil with the help of magnetic stirring and homogenized with the ultrasonic processor and prepared about 1–6 vol.% by two step method without any surfactant. They measured the thermal conductivity of the nanofluid using KD2 Pro. They observed that water based TiO2 nanofluid have thermal conductivity better than other nanofluid from different base fluids. The enhancement of thermal conductivity for water based TiO2 about 2.95%–22.131%; ethylene glycol based TiO2 about

2)

347

348

Water

Water

Water/SDS EG/SDS

[64]

[84]

[85]

[61]

Water

Water

[60]

[17]

Water/ Ammonium polymethacrylate

[59]

50/50 EG/H2O

Water

[58]

[83]

Water

[57]

Engine oil (SAE 20W50)

Toluene Water/ CTAB

[82]

Titania CuO CNT Gold (Au) TiO2

ZnO TiO2

SiO2 Alumina Titania Zirconia

MWCNT

Copper oxide (CuO)

boehmite alumina

CuO

Titanium dioxide

ZnO

ZnO

SiO2

Bohemite alumina

Alumina

Water

[55]

Nanoparticle

Basefluid/ Surfactant

Ref

1–3 vol.%

2 vol.%

0.1, 0.25 and 0.5 vol.%.

1–5 wt.%

1.0–8.4 vol.%

0.2–6 wt.%

0.5–2.5 wt%

1.08 vol.% 2.28 vol.% 0.82 vol.% 0.93 vol.% 0.05–5.0 vol.%

0.3 vol.%–7 vol.%

Mass fractions 0%, 10%, 20% and 40%. 0.5–2.5 vol.% 1–5 vol.% 0.1–0.9 vol.% 0.003 vol.% 0.005–0.05 vol fraction

Concentration

Elongated Nearly spherical

Not reported

Nanotube

Platelets Blades Cylinders Bricks Sphere

Nano-rhombics nanorods Nanoparticle

nearly rectangular Sphere spherical cubic Rod

Polygonal Rod

Spherical Banana

Brick Platelet Blade

Cylindrical Spherical spherical Rod

Spherical

Shape

Table 9 Effect of basefluid and geometric parameters of nanoparticles on thermo-physical properties.

10, 30 and 60 nm 10, 34 and 70 nm

9 nm 60 nm × 10 nm 80 nm × 10 nm 40 nm 33 ± 13 nm and 11 ± 3 nm Length of 203, 171 and 134 nm 10, 16, 25 and 100 nm 50, 75, 100 and 150 nm 71, 100, and 150 nm 44 and 105 nm

91 nm thick 78 nm thick 61 nm

20–40 nm 23 nm 87.21 nm 92.47 nm × 8.27 nm

90–210 nm

–

–

40 × 40 × 20 nm 15 × 5 nm 15 × 8 × 5 nm

20 33 10 μm × 150 nm 1.65 15 nm 10 × 40 nm

40

Size (nm)

2

bf

bf

nf

nf

2

2

2

2

nf

2

3

2

(continued on next page)

bf

of nanofluid increased as the nanoparticle size decreased • Viscosity of size and size distribution on density of nanofluid was very negligible. • Influence conductivity of nanofluid was higher for 1 h refluxed CNT nanoparticles (203 nm) than pure • Thermal CNT and pristine CNT nanoparticles conductivity enhancement ratio (k /k ) increased with increase in size of the particle, • Thermal irrespective of the nanoparticle under study degree of enhancement of thermal conductivity enhancement ratio (k /k ) was very different for • The different nanoparticle conductivity enhancement ratio (k /k ): SiO < TiO < ZrO < Al O • Thermal conductivity enhancement was higher for 10 nm for both the base fluid. At 1 vol.% (i) TiO / • Thermal water – 1.033 (ii) TiO /EG – 1.051 (ii) ZnO /water – 1.049 conductivity of ZnO nanofluids was higher than TiO nanofluids. • Thermal conductivity enhancement ratio was higher for EG than water. • Thermal conductivity depended on the nanoparticle, and it was inversely proportional to the mean • Thermal diameter of nanoparticle.

nanofluid

thermal conductivity at 303 K (W/mK) (i) spherical – 1.737 (ii) cubic – 2.043 (iii) Rod – • Effective 1.822 conductivity and viscosity was higher for cubic shape than other shapes. It is due to cubic • Thermal shape have the large surface area of nanoparticle than others contributed about 5.62% in thermal conductivity enhancement • Shape conductivity increased with increase in the nanoadditives. • Thermal conductivity was maximum for 6 wt% of nanorods nanofluid. The enhancement was about • Thermal 8.3% than base fluid. are Newtonian in nature • Nanofluids all concentration of nanorhombics nanofluid had lower viscosities compared to base fluid and other • For nanofluids. conductivity Enhancement Platelets < Blades < Bricks < Cylinders • Thermal • Viscosity Blades < Bricks < Cylinders < Platelets

conductivity and thermal diffusivity did not alter for the period more than 48 h for the lower • Thermal volume fraction nanofluids results agreed with the theoretical model proposed by various researchers • Experimental concentration, the results agreed with the Hamilton -crosser model for the spherical shape and • Atthelower unit-cell model equation of Yamada and Ota for the carbon Nano-fibers. conductivity enhancement – 2% for the volume fraction of particles 0.003% • Thermal conductivity enhancement was maximum for 5 vol.% nanofluid, 29.70% and 32.8% for rod • Thermal and spherical shaped nanoparticles respectively conductivity showed a nonlinear relationship with particle volume fraction at lower volume • Thermal fraction (0.005–0.02) and a linear relationship at higher volume fraction (0.02–0.05). thermal conductivity depended on the nanoparticle’s size and shape and can’t be predicted by • Effective the existing Hamilton and Crosser, Wasp and Bruggeman models thermal conductivity enhancement was at 7 vol.% i.e. brick – 28%, platelet – 23% and • Maximum blade – 16% Hamilton Crosser model was not perfect model to predict the thermal conductivity for • Conventional various shapes. conductivity of banana shape found higher than spherical particle. • Thermal of the nanofluid for both nanoparticle shapes were found to be close to each other. • Viscosities conductivity was slightly greater than water • Thermal was slightly higher for the spherical than polygonal shaped nanoparticle • Viscosity and thermal conductivity of nanofluid with nearly rectangular shape nanoparticle were • Viscosity larger than with sphere shaped nanoparticle by 7.7% and 5.9% respectively for the 0.05–5.0 vol.% of

Results

A.I. Khan and A. Valan Arasu

Thermal Science and Engineering Progress 11 (2019) 334–364

Basefluid/ Surfactant

Water

Water

Water EG

Ethanol

Kerosene/Oleic Acid

Water

Water

Ethanol

Water

Distilled water EG Engine Oil

Water EG Paraffin Oil

Ref

[86]

[87]

[88]

[40]

[89]

[90]

[66]

[68]

[69]

[45]

[70]

Table 9 (continued)

349

Titania

CuO

Silicon dioxide

Silica

Alumina

Alumina

Alumina

Silica

Alumina

Alumina

Alumina

Nanoparticle

1–6 vol.%

0–2 vol.%

0.1, 0.2, 0.4, 0.8, 1.2, 1.6 and 2 vol.%

1.4–7 vol.%

1–13 vol.%

1, 2, 4 and 6 wt.%

0.05–0.5 vol.%

0.15, 0.44, 0.74 and 1.17 vol.%.

2, 3 and 4 vol.% 2 & 3 vol.%

0.5, 1.0, 1.5 and 2 wt.%

1 vol.%

Concentration

Spherical

Spherical

Roughly spherical

–

–

–

–

spherical

spherical or ellipsoidal

irregular

spherical

Shape

5 nm

54 nm

7, 12, 16, 20 and 40 nm

35, 94, and 190 nm

36 and 47 nm

45 and150nm

13 and 50 nm

10.6, 20, 38.6 and 62 nm

8, 12, 16, 46, 71, 245 and 282 nm

20, 50, and 100 nm

11, 47 and 150 nm

Size (nm)

rel

knf kbf

gm

k

kl

36

k kl

k

47

kp kl

max

r

7

0.025d

3.461

max

1.645

p

0.006

max

4 −1

(continued on next page)

3

conductivity was higher for the particle size of lower diameter. • Thermal velocity was the most dominant function of temperature, which influenced the thermal • Brownian conductivity. in temperature and decrease in nanoparticle size enhanced the thermal conductivity ratio of • Increase nanofluid conductivity enhancement decreased as the particle size decreased from 50 nm, irrespective • Thermal of base fluid conductivity enhancement Correlation •= Thermal = (1 e ) Water – = 5.527( ) = 4.4134( ) , EG – mean correlation • =Geometric = 1 Thermal conductivity for large nanoparticles was higher than the result obtained from • Maxwell equation andenhancement predicted well by the volume fraction weighted geometric mean relation. conductivity rapidly decreased below 20 nm but increased slightly above diameter of 20 nm. • Thermal sensitivity analysis of the thermal conductivity showed that it was more sensitive to temperature • The than particle size and concentration. • =Correlation: 19.804 + 1.731 × 10 (T ) + 0.126( ) + 20.402(D ) Enhancement in thermal conductivity: 13 nm – 7.5% (0.05 vol.%), 22% (0.5 vol.%) 50 nm −5.3% • (0.05 vol.%), 17% (0.5 vol.%) increase in dynamic viscosity: 13 nm – 1.5% (0.05 vol.%), 10.3% (0.5 vol.%) 50 nm – 0.75% • Percentage (0.05 vol.%), 8.2% (0.5 vol.%) of smaller nanoparticle increased because of increase in surface area of nanoparticles causing • Viscosity higher interface resistance. conductivity increased with decrease in the nanoparticle size. Thermal conductivity ratio • Thermal 45 nm – 1.02–1.055 (1 t 6 wt%) 150 nm – 1.015–1.05 (1 t 6 wt%) viscosity ws moreover same for both size upto 4 wt% and predicted by the Einstein equation. • The Beyond 4 wt%, the 45 nm size particle had higher viscosity. µ < µ , for all concentration • Beyond critical temperature, the hysteresis effect was noticed and their properties were altered. • Hysteresistheeffect not seen in lower concentration (< 1 vol.%). • Viscosity stronglywas governed by both temperature and concentration. The size effect becomes significant • only for high concentration. behavior was observed for the shear rate values ranging from 5 × 10 to 5 × 10 s • Newtonian was a significant deviation is observed from Einstein equation. • There regression for the relative viscosity was applicable for 190 nm • =Linear 1 + 8.2 × and had good agreement with Krieger-Dougherty-type formula for 35, 94 nm • Non-linear viscosity (μ ) increased rapidly when the nanoparticle size decreased from 20 nm. • Relative viscosity was strong function of volume fraction for particles of small diameter. • Relative closely associated with pH value for the particles less than 20 nm. Particle larger than 20 nm • Viscosity has the viscosity remains almost unaffected with the pH value. increase in thermal conductivity (0–2 vol.%) (i) Distilled water – 13–19 (ii) EG – 5–10 (iii) • Percent Engine oil – 5–8 conductivity enhancement was different for different base fluids • Thermal of enhancement depends on strength of the interfacial layer between nanoparticles and base fluid • Rate the same concentration and temperature, thermal conductivity of CuO/distilled water was more • Atsensitive to change in concentration than CuO/EG and CuO/Engine oil. increase in thermal conductivity (1–6 vol.%) (i) Water – 2.95–22.131 (ii) EG – 2.788–19.521 • Percent (iii) Engine oil – 2.962–20.74 conductivity enhancement predicted by the Maxwell and Bruggeman models was linear and • Thermal lower than the experimental values. • The Bruggeman model was found to be in good agreement with paraffin oil and EG

Results

A.I. Khan and A. Valan Arasu

Thermal Science and Engineering Progress 11 (2019) 334–364

Thermal Science and Engineering Progress 11 (2019) 334–364

13 nm

2.788%–19.521%, and paraffin oil based TiO2 about 2.962%–20.74% for 1 and 6 vol.%, respectively. The higher enhancement of TiO2/water nanofluid was explained that the TiO2 had hydrophilic nature of eOH group attached to it and the lower viscosity enabled more particle-toparticle interactions in a given period of time. The theoretical prediction of thermal conductivity was done using Maxwell and Bruggeman models from the literature. The thermal conductivity enhancement predicted by the Maxwell and Bruggeman models was linear with the increase in volume fractions of nanoparticles, and their values were lower than the experimental values. They explained the discrepancy in the prediction that these models didn’t account the nanoparticles-fluid interface and the size of the nanoparticles. They concluded that the Bruggeman model was found to be in good agreement with paraffin oil and ethylene glycol. Wang et al. [6] synthesized the nano graphite particle by chemical intercalation and microwave expansion to obtain exfoliated graphite flake. The natural graphite is lyophilic to ethylene glycol and polyalpha-olefin (PAO) but lyophobic to DI water. They introduced the surface functional groups, which changes the wettability of the graphite flake. They prepared stable suspensions using de-ionized water (DI water), ethylene glycol, and poly-alpha-olefin (PAO), and hexadecane and found that the polar groups attached to the nanoparticles increases the hydrophilic nature, which formed a stable suspension. They measured the thermal conductivity of graphite suspended in DI water, ethylene glycol, PAO for volume fraction ranges from 0 to 1% and reported that enhancement was 201% for PAO, 153% for ethylene glycol, 113% for water, which exceeded the value reported in literature. Yu et al. [71] dispersed Aluminum nitride nanoparticle of size about 50 nm in ethylene glycol (EG) and propylene glycol (PG). Thermal conductivity and viscosity were measured by transient short hot-wire and viscometer (LV DV-II + Brookfield) respectively. They observed that, at lower concentration up to 5 vol.%, the thermal conductivity enhancement was almost same for both base fluids, above that thermal conductivity enhancement of propylene glycol was slightly higher than EG. At 10 vol.%, the thermal conductivity enhancement ratios were 38.71% and 40.2% for EG and PG based nanofluids respectively. They reported that the below 5 vol.%, the viscosity was independent of shear rates in the range of 2–100 s−1, showing Newtonian behavior for both base fluids. Sedaghat and Yousefi [72] studied the thermophysical properties of GQDs nanoparticles dispersed in water, ethylene glycol and water-ethylene glycol mixture (60:40) as base fluid with concentration 0.05 vol.% to 0.5 vol.%. Thermal conductivity was measured at temperature 10° to 50 °C and found maximum enhancement occurs for 0.5 vol.% at all temperatures, irrespective of basefluid. Maximum enhancement is about 53%, 21%, 18% for GQD nanofluid having base fluid water, EG and water/EG mixture, respectively. The viscosity of GQD nanofluid was measured at temperature 10° to 40 °C and noticed that viscosity increased with the concentration and decreased with the temperature. In general, the thermal conductivity and viscosity increased on the addition of nanoparticles to the basefluid. The characteristic enhancement varies with interaction between the nanoparticles and basefluid. Table 9 shows the summary of related studies on effect of basefluid and geometric parameters of nanoparticles on thermo-physical properties. Among the various comparative researches on basefluid, it was found that most works were done for the water, EG and EG/water mixture. The thermal conductivity ratio of the water and EG based nanofluid were nearer to each other. But the thermal conductivity ratio of the EG/ water mixture showed higher than the water.

0.05%–0.1 vol.% Water EG [92]

Alumina

–

16, 28, 66 and 96 nm – 4 vol.% SiC Water EG/water mixture (50:50) [67]

25 nm 0.1, 0.3 and 0.5 vol.% [91]

Copper-zinc alloy (Cu-Zn, 50:50)

utmost spherical

– 0–1 vol.% Graphite [6]

Water EG poly-alpha-olefin (PAO) Vegetable Oil/SDS Paraffin Oil/SDS SAE Oil/SDS

Flakes

12 nm 0.05–0.4 vol.%. Silica [73]

Water EG

–

40–50 nm Roughly spherical 0.005–0.05 vol fraction Alumina Water EG [74]

Concentration Nanoparticle Basefluid/ Surfactant Ref

Table 9 (continued)

Shape

Size (nm)

Results

2

conductivity ratio (1–6 vol.%) (i) Water – 1.016–1.108 (ii) EG – 1.034–1.14 • Thermal conductivity of EG-based alumina nanofluid followed the trend predicted by effective medium • Thermal theory conductivity ratio (0.05–0.4 vol.%) (i) Water – 1.05–1.12 (ii) EG – 1.04–1.11 • Thermal fluid water gave better thermal conductivity enhancement than EG due to highly hydrophilic • Base nature of SiO increase in thermal conductivity (0.2–1 vol.%) (i) Water – 15.23–114 (ii) EG – 31–153 (iii) • Percent PAO – 55–200

conductivity ratio (0.1–0.5 vol.%) (i) Vegetable Oil – 1.12–1.68 (ii) Paraffin Oil – 1.09–1.2 • Thermal (iii) SAE Oil – 1.05–1.15 Newtonian – Vegetable Oil Non-Newtonian – Paraffin Oil and SAE Oil • Viscosity Viscosity (0.1–0.5 vol.%) (i) Vegetable Oil – 1.05–1.12 (ii) Paraffin Oil – 1.07–1.2 (iii) SAE Oil • Relative – 1.09–1.35 of nanoparticle in paraffin and SAE oil did not affect much on thermal conductivity as • Addition vegetable oil. This was due to the internal resistance of the fluid. conductivity increased with respect to increase in particle size, irrespective of the base fluid. • Thermal thermal conductivity enhancement for SiC- EG/water mixture was 4 to 5% higher than the water. • The increased with respect to decrease in particle size, irrespective of the base fluid and • Viscosity temperature. viscosity of SiC/(EG/water) mixture nanofluid was lower than SiC/water • The conductivity ratio (0.05–0.1 vol.%) (i) Water – 1.003–1.097 (ii) EG – 1.015–1.2 • Thermal viscosity (0.05–0.1 vol.%) (i) Water – 14.2–917 (ii) EG – 25–392 • Relative • The effective medium theory was applicable to alumina/EG, as it is Newtonian in nature

A.I. Khan and A. Valan Arasu

6. Effect of sonication on thermo-physical properties During the preparation of nanofluids in two step method, the nanoparticle gets agglomerated, as soon as the particles are mixed with base fluid. The magnetic stirring of the suspension makes the nanoparticles to disperse evenly, but it doesn’t break the agglomeration 350

Thermal Science and Engineering Progress 11 (2019) 334–364

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completely. The sonication is the sound energy that is used to break the agglomeration of the nanoparticles for the even suspension in nanofluid. The researchers showed that the sonication is one of the important procedures for the preparation, without that the suspension may not stable for even short term periods. The breaking of clusters has effect on thermo-physical properties, as the micro-movement is increased, thus sonication process used for it has direct relationship with the change in thermo-physical property of nanofluid. The comparative research studies on the effect of sonication on thermo-physical properties by several researchers are reviewed and discussed below.

nanofluid, as there was formation of cluster within an hour. The thermal conductivity may attain the maximum or not, the nanofluid should have stability and it is the prime motto of the sonication. Without stability of the nanofluid, Achievement of thermal conductivity enhancement doesn’t play role in heat transfer. Karthikeyan et al. [77] prepared CuO nanoparticle from the copper acetate of particle size 7–9 nm. They prepared nanofluid of volume fraction (0.01–1%) in both water and ethylene glycol and then sonicated for 30 min without surfactant. They examined the thermal conductivity of CuO nanofluid after sonication, up to elapsed time of 20 min, the thermal conductivity ratio was almost unity after an elapsed time of 14 min. Thus, describing that thermal conductivity was also time dependent one and explained that it was due to the formation of the clusters, which was observed in microscopic images of CuO/water for 20, 60 and 70 min after the sonication of nanofluid.

6.1. Thermal conductivity Kazemi et al. [73] prepared the SiO2/water nanofluid by varying sonication time up to 6 h using Bandelin Sonoplus-HD3200 (200 W). The thermal conductivity ratio increased and reached 1.098 at 2 h and then drop down to1.07 at 4 h, as the sonication time was increased. They explained that the breakage of clusters on sonication increased the thermal conductivity ratio; but they didn’t explain the reason for the fall in enhancement on prolonged sonication of nanofluid. Thus, the thermal conductivity of nanofluid increased with the increase in sonication time and reached a peak. On further sonication, it started to decrease from the peak. This shows that there is an optimum period for sonication that accounts maximum thermal conductivity. Gowda Raghu et al. [74] dispersed alumina and CuO nanoparticle in EG by three sonication techniques i.e. probe sonicator (450 W) at 80% magnitude for 2 min, probe sonicator (150 W) at 80% magnitude for 2 min, and bath sonicator (Model: 75D VWR) for 4.5 h (max.power: 90 W). They observed the thermal conductivity for both particles and they were in the order of probe sonicator (450 W), followed by bath sonicator and probe sonicator (150 W). They found that for the both probe sonicators operated for same time, one with higher power gave the higher advantage. The bath sonicator was operated for 4.5 h has the higher thermal conductivity than probe sonicator (150 W); this showed that the sonication time too had effect on dispersion. Hong, Yang, and Choi [75] synthesized iron (Fe) Nano-crystalline powder by a chemical vapor condensation process using iron carbonyl [Fe(CO)5] as a precursor under flowing helium atmosphere. The nanofluid was prepared with ethylene glycol as base fluid without any surfactant. The TEM images revealed that particles were in the coreshell structure, with metal oxide as shell surrounding metallic core. They studied the thermal conductivity of Fe/EG (0.55 vol.%) by transient hot wire method for various sonication time (10–70) minutes. The sonication was done using Jeiotech ULH-700S generating ultrasonic pulses of 700 W at 20 kHz. The thermal conductivity increased upto 18% as the sonication time increased up to 50 min and then it was saturated. They expected that the breakage of nanocluster into smaller particles due to the high power pulses; increased the thermal conductivity enhancement. They observed the similar trend of thermal conductivity enhancement with varying the sonication time in 0.2, 0.3, and 0.4 vol.% Fe nanofluids. They compared the results with copper (Cu) nanofluids from literature and found that Fe nanofluid has effective thermal transport property with little agglomeration. They stated that the highly thermally conductive material was not always the best material for the suspension in improving thermal property of fluid. Hong, Hong, and Yang [76] studied Fe/EG experimentally by transient hot wire method for sonication time 30 min for 0.2 vol.% using Jeiotech ULH-700S. It also exhibited the same trend and attained maximum of 18% enhancement, after that the thermal conductivity enhancement was saturated. They measured the size of the particle using light scattering method, after sonication upto an hour. They found that the nanoparticles started to agglomerate, thus cluster size increased which resulted in the reduction of thermal conductivity to 8.5% after an hour of sonication. The average cluster size of nanoparticle was larger than 1 μm after the sonication within few minutes. This showed that the sonication didn’t improve stability of the

6.2. Viscosity Paritosh Garg et al. [78] prepared 1 wt.% of MWCNT/water nanofluid with 0.25 wt.% of Gum Arabic (GA). The four samples of nanofluid were prepared by varying the sonication time from 20 to 80 min in the steps of 20 min. The samples were measured with a low viscosity rotational type viscometer (LVDV-I Prime, Brookfield) at different temperature. They observed that the viscosity of the prepared nanofluid increased and attained a peak and then decreased with increasing sonication time. They explained that as the agglomerations break, the viscosity was increased. The prolonged sonication broke the MWCNT, resulted in shorter nanotubes and reduced aspect ratio. The disruption of the three-dimensional network of nanotubes with decreased aspect ratios of nanotubes was confirmed by wet TEM images. The same trend was obtained for Mahbubul et al. [79] who prepared alumina/water nanofluid (0.5 vol.%) of particle size 13 nm, using an ultrasonic homogenizer machine (model 505, Fisher Scientific) and investigated by varying the sonication time from 0 to 180 min by rheometer (LVDVIII ultra, Brookfield) at the temperature range 15°–45 °C. The viscosity of nanofluid at 15 °C without sonication was found to be high due to the agglomeration and non-homogenous dispersion of nanoparticle. On increasing the temperature from 15° to 45 °C, the viscosity of the nanofluid greatly reduced because the rheometer took roughly 10 min to change the temperature of the bath, meanwhile the rotation spindle broke the clusters present in the nanofluid and as the temperature increased; the Brownian motion also increased which caused the movement of particles. They observed that the 60 min of sonication as the optimum level and has highest viscosity due to the increase in surface to volume ratio. Mahbubul et al. [80] extended the period of sonication from 0 to 5 h for the alumina/water nanofluid. They analyzed the particle size distribution and found that narrowest distribution of size (119–150 nm) occurred at 2 h. For the higher sonication period, the size of the nanoparticle was decreased, but the size distribution gone wider. Thus, there was a formation of non-uniform size of nanoparticles in the suspension for higher sonication period. The viscosity of the alumina/ water nanofluid showed Newtonian at lower temperatures and lower shear rates. While at higher temperatures, it showed Non-Newtonian behavior. The nanofluid underwent shear thickening trend for all sonication time, at the higher shear rates. The flow behavior is found to be similar for all the sonication period in their investigation. Manikandan et al. [81] synthesized magnesium oxide (MgO) from magnesium nitrate hexa-hydrate and ammonium carbonate. The MgO nanoparticle (30–38 nm) dispersed in propylene glycol without any surfactant for the preparation of 2 vol.% nanofluid. They varied the probe sonication time and found 25 h as optimum sonication time. The viscosity of the nanofluid was nearly independent of shear rate in the range 66–105.6 s−1 and decreased with the increase in nanoparticle concentration. They explained the reduction in viscosity was due to perturbation of intermolecular hydrogen bonds in propylene glycol 351

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the range of 5 to 70 nm. The Nitrogen sorption (BET) was not preferable for the particle with irregular shape with high surface roughness. They explained the higher value of DLS might be due to the hydrodynamic shell around the particle. The XRD can be used for only crystalline structure which gave the crystalline size but not the size of the aggregate. They inferred that BET and XRD determines the size alone but not the distribution width of the nanoparticles. The particle diameter and distribution width of nanoparticle from different techniques are shown in Fig. 6. They concluded that determination of size of nanoparticles from different methods depended on not only the type of material, but also its shape and the porosity. Timofeeva et al. [96] calculated the average size of α-SiC particles through various methods such as, Dynamic light scattering (Brookhaven 90Plus), Diffraction light scattering (Microtrac S3500), SEM and BET. They explained that the higher value of DLS was due to the cluster formation of particles and poly dispersion. They reported and used the results from BET, because the volume and contribution of the interfacial phase would always remain proportional to the total surface area of dry powders. Beck et al. [88] measured the alumina of different sizes and phases using BET results and compared with TEM micrograph. They used DLS technique to obtain the hydrodynamic radii of the particle aggregate. They used the BET results for their further study rather than other techniques. Braun et al. [97] investigated the two methods i.e. Dynamic light scattering (DLS) and Centrifugal liquid sedimentation (CLS) with silica nanoparticles. They observed that the both DLS and CLS method were robust and suitable for the particle size in range of 35–50 nm. Zhao et al. [69] measured the aggregate size of silicon dioxide/water nanofluid, whose nanoparticle diameter were 7, 12, 16, 20 and 40 nm using Malvern-Zetasizer Nano S90. They noticed that the ratio of aggregate diameter to particle diameter decreased from 16 to 5, as the particle diameter increased from 7 nm to 40 nm. Tertsinidou et al. [98] measured the thermal conductivity by constructed transient hot-wire instrument and hot-disk instrument for the ethylene glycol with added CuO, TiO2, Al2O3 nanoparticles and water with TiO2 or Al2O3 nanoparticles or multiwall carbon nanotubes (MWCNTs). They found that results obtained by both techniques agreed each other.

caused by nanoparticle-polar liquid interaction. The FT-IR results confirmed the interactions between hydroxyl groups on the MgO surface with propylene glycol. The viscosity reduction was dependent on both concentration and temperature, with maximum viscosity reduction of 32% observed for 2 vol.% at 28 °C. There was an irreversible formation of gel, as the temperature of 2% MgO/propylene glycol nanofluid exceeds 60 °C. They observed that increase in temperature, decreased the contribution of hydrogen bonding interactions, which resulted in increased relative viscosity of MgO/propylene glycol nanofluids with increased temperature. 7. Characterization techniques After the synthesis of the nanoparticles or preparation of nanofluid, characterization is done. The characterization gives us the details of the synthesized nanoparticles, and confirms the size, shape, morphology and distribution of the nanoparticles in nanofluid. The characterization techniques are done in various available methods. The general available techniques are SEM, FESEM, TEM, HRTEM, XRD, DLS, TGA, and FT-IR (Table 10). Oliveira, Ceragioli, and Assaf [93] explained the function, differences and use of SEM, FESEM, TEM, Raman, FTIR and AFM, as tools for spectroscopy and morphology for the samples. A Person gets confused with the SEM and FESEM; difference between SEM and FESEM is the source of electrons that are emitted. There are two types of emission source, namely the thermionic emitter and field emitter. Thermionic Emitters use electrical current to heat up a filament made up of either Tungsten or Lanthanum hexaboride. During operation thermionic sources used in SEM have relative low brightness, evaporation of cathode material and thermal drift. To avoid these problems, field emitter used in FESEM. Field emitter does not heat up the filament so it is known as cold cathode field emitter. Field Emitter source have the enhanced image quality i.e. resolution is on the order of ∼2 nm at 1 keV and ∼1 nm at 15 keV [93]. Dorofeev GA et al. [94] studied the X-ray diffraction line profile by various ways such as Scherrer, Williamson–Hall and Warren-Aver-bach method. Only with high intensity lines were taken for the analysis. But for the analysis of the nanoparticle, the diffraction lines were broadened and have low intensity. They found out that the sizes calculated in different approach were close to each other for volume weighted sizes. Thus, they concluded that Scherrer equation was preferable, as it was easier for calculation. Agarwal et al. [45] measured the size of the synthesized CuO nanoparticle by Dynamic Light Scattering (DLS) and transmission electron microscopy (TEM) image. The result matches very closer. However, DLS have the higher value of average size of the particle. The sizes of two oxide nanoparticles (silica and zirconia) i.e. amorphous and crystalline structures are determined by the various methods and are compared. They inferred that the size determined by the TEM and small-angle X-ray scattering (SAXS) are in agreement on

8. Stability The stability of the nanofluid can be defined as how long the nanoparticles are suspended in the base fluid without separating into two phases. The suspension of the nanoparticles made it as nanofluid, as it gets separated; term nanofluid gets invalid or is null. Though there is high advantage of nanofluid, poor stability made it challenge for the usage. Researches started towards the improvement of stability of the nanofluid, thus the stability is to be measured or compared with others. Yu and Xie [12] discussed researches around the stability evaluation methods, ways to enhance the stability and the stability mechanisms of nanofluid. The relationship between stability of nanofluid with other

Table 10 Characterization technique followed by many researchers. Characterization Technique used

To find

X-ray Diffraction

Chemical composition, Crystal structure, Average crystallite size Authentication of purchased or synthesized nanoparticle and to know the impurities. Morphology of nanoparticle Size and distribution of nanoparticles in the base fluid Measurement of the optical property such as Absorption and Extinction coefficient Investigation of stability and the sedimentation with time Tauc relation direct band gap (eV) Specific surface area of nanoparticle Weighted Average particle size Measurement of the suspension stability Degradation with temperature The nature of surface adsorbents present on the nanoparticles

SEM TEM UV–Vis Spectrophotometer Brunauer–Emmett–Teller (BET) Dynamic Light Scattering Zeta potential TGA FT-IR

352

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Fig. 6. Particle diameter and distribution width (standard deviation) from DLS, TEM, BET and XRD in dependence on the particle diameter obtained from SAXS (a) silica and (b) zirconia nanoparticles [95].

rapidly and then slowly. The zeta potential achieved was 58 mV for 180 min of ultra-sonication. The zeta potential was measured after 30 days of preparation and noticed that the value was about 15 and 49 mV for sonication period of 0 and 180 min respectively. Mahbubul et al. [101] sonicated the 0.5 vol.% TiO2/water nanofluid for 30, 60, 90, 120, 150, and 180 min through horn type ultrasonic homogenizer (model 505, Fisher Scientific). The TEM images of the nanofluid of various sonication periods was examined and observed that the agglomeration was decreased on sonication. The cluster size of nanoparticles decreased with the sonication period up to 150 min, where after that there was no significant change in cluster size. The pH value of the nanofluid was 6.73, 6.53, 6.47, 6.36, 6.23, 6.19, and 6.15 respectively for the above sonication periods and observed the pH level was decreased. The isoelectric point of TiO2/water nanofluid was 6.5, which was achieved at 60 min of sonication, thus showed good stability after 60 min. The zeta potential value of nanofluid was found to be increasing with sonication. The dispersion of nanoparticles was made homogenous, as the ultrasonic energy broke down the nanoparticle agglomerates. And further they reported that pH value of the nanofluid was inversely proportional to the sonication time. Thus, nanofluid can be kept stable, by moving away from the isoelectric point with increasing duration of sonication.

Fig. 7. Relationship of stability with other parameters.

parameters such as sonication method, sonication period, surfactant, surface charge, pH and average particle size is shown in Fig. 7. The dispersion of nanoparticles is done using sonication technique with different type of equipment shown in Fig. 8.

8.2. Effect of sonication method The sonication can be done by various methods namely, direct method and Indirect method. Gowda Raghu et al. [74] done sonication by probe having 450 W, 150 W for 2 min and bath sonicator (90 W) for 4.5 h for alumina and CuO in water and EG. The 450 W probe sonication produced the smallest size (140 nm) particles in both DI water and EG-based nanofluids and the 150 W probe gave larger particle size and found that sonication was better in EG than in water. Chung et al. [102] studied several ultrasonic agitation systems, including a single piezo-actuated bath (VWR International, Model 50 T, 40 kHz, 45 W), a solenoid-actuated bath (Fisher scientific, Model FS20, 42 kHz, 70 W), as well as a static bath with immersed horn (Misonix Incorporated, Model XL2010, 20 kHz, 550 W) with two ZnO nanoparticle suspension in water for 60 min. Their study revealed that cylindrical horn immersed in suspension was better than bath type of sonication in terms of processing time and the fine size (100 nm from 560 nm and 1900 nm) of the suspension in the base fluid. The stability test of the nanofluid was done for 1000hrs and found that alteration in volume fraction due to sedimentation, which was low for horn type. Though the investigation was done on different type of agitation system, power of the system under the study was not same, thus the

8.1. Effect of sonication period The sonication time is the important parameter for stability [99]. Lee et al. [100] prepared alumina/water nanofluid of particle size of 30 ± 5 nm (0.01–0.3 vol.%) without any surfactant. The nanofluid of 0.1 vol.% was sonicated for 0 h, 5 h, 20 h and 30 h and their zeta potential was measured. The zeta potential was increased and reached a maximum at 5 h and then decreased with increased sonication period. Thus, the optimum period of sonication was 5 h, which was also confirmed by the TEM image at 5 h, which showed least agglomeration. They hadn’t explained the reason for the rise and fall of the zeta potential on sonication period. Mahbubul et al. [79] prepared 0.5 vol.% alumina/water nanofluid and sonicated for 30, 60, 90, 120, 150, and 180 min using an ultrasonic homogenizer machine. The average size of aggregation was measured using the photon correlation spectroscopy (PCS) method and observed that the average cluster size decreased rapidly with the start of sonication and then decreasing rate was higher. After certain time, the cluster size approached a uniform size. The zeta (ζ) potential which quantifies the stability of the nanofluid was found to rise to a peak 353

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Fig. 8. Different type of dispersing equipment.

molecule contains two structurally distinct parts, one of which is hydrophilic while the other is hydrophobic. On the basis of the charge of the polar head group, the surfactants are classified as anionic surfactant, cationic surfactant, nonionic surfactant, and zwitterionic surfactant. The structures of different type of surfactants are shown in Fig. 9.

effect of power of the system may have impact on the sonication of the nanofluid. They measured the ultrasonic power density delivered where the horn type (0.7 W/cm3) was greater than bath type (0.07 W/cm3). The authors had not explained about the effect of equipment’s power on sonication. The input power ends up as the heat in the transducers or the bath, with only a small fraction applied to the mechanical work of fragmentation. They found that horn type sonication increased the temperature of the suspension about 10 °C/min. Though there was a cyclic time for the dissipation of heat through nanofluid, there was a chance of vaporization of the base fluid, causing chance of alteration in the volumetric fraction of nanofluid, if it was continued. So the nanofluid was kept in a cold bath by Mahbubul et al. [101] to eliminate the effect of increase in temperature. Ghadimi and Metselaar [103] prepared 0.1 wt% titania/water nanofluid with nanoparticle size of 25 nm. They investigated the effect of sonication on stability with and without surfactant sodium lauryl sulfate (SDS). They prepared six different samples by simple mixture, ultrasonic bath and ultrasonic horn, with and without addition of SDS surfactant. They observed the stability of six nanofluid samples by UV sedimentation method for elapsed time of 7 days. The rate of sedimentation of nanoparticle was different among these 6 samples from which the stability of nanofluid was estimated. The observations of researchers are given in Table 11. They reported that all the samples were moderately stable according to Vandsburger’s thesis. The sample sonicated for 3 h with surfactant was the most stable nanofluid among the studies. Table 12 gives the summary on the effect of sonication parameters on stability. Depending upon the Equipment, Power of equipment and Time of sonication, the reduction of aggregate size varies. In general, increase in sonication time, reduces the aggregate or cluster size and enhances the suspension of nanoparticles in the basefluid. On prolonged sonication, the nanoparticles tend to form cluster and then stability of nanofluid reduces. Frequencies of 20 kHz, 40 kHz were used by the researchers, but the effect of frequency of the sonicator is not discussed, which may have impact on dispersion.

8.3.1. Anionic surfactant Carboxylate, sulfate, sulfonate, and phosphate are the polar groups of the surfactant and the hydrophilic part carries negative charge. The counter ions most commonly used are sodium, potassium, ammonium, calcium, and various protonated alkyl amines. Sodium and potassium impart water solubility, whereas calcium and magnesium promote oil solubility. Amine/alkanol amine salts give products with both oil and water solubility [112]. Sulphates and sulphonates are anionic at all pHs. Carboxylates are anionic only at high pH [113]. 8.3.2. Nonionic surfactant Nonionic surfactants have either a polyether or a poly-hydroxyl unit as the polar group and the hydrophilic part is uncharged. 8.3.3. Cationic surfactant The hydrophilic part carries a positive charge. Nitrogen-based compounds constitute the vast majority of cationic surfactants and carry the charge. Cationic surfactants containing primary, secondary or tertiary nitrogen (fatty amines, amine oxides and amine ethoxylates) are weak bases. Quaternary ammonium salts are cationic at all pHs, while weak bases are cationic only at low pH [113]. 8.3.4. Zwitterionic surfactant Zwitterionic surfactants contain two charged groups of different sign. Whereas the positive charge is almost ammonium, the source of negative charge may vary, although carboxylate is by far the most common. Khairul et al. [114] investigated the effect of surfactant concentration on stability of the nanofluid. The surfactant sodium dodecyl benzene sulfonate (SDBS) was used to stabilize the water based CuO and alumina nanofluids. They observed that on increasing the nanoparticle concentration, the nanofluid was tending to move towards base. The increase of surfactant concentration also showed the similar trend. The zeta potential provides the information about stability in magnitude. The zeta potential of a colloidal suspension is highly dependent on the pH value of the solution. The zeta potential of the alumina/water and

8.3. Effect of surfactant Surfactant is a substance which in aqueous solution, tends to congregate at the bounding surfaces. The tendency for a surfactant to accumulate at a boundary depends on the surfactant structure and also on the nature of the two phases that meet at the interface. Surfactant 354

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Not reported Clustering and less agglomeration is seen

CuO/water nanofluids without surfactant was +30 to +40 mV and +14 to +28 mV at various concentrations (0.05–0.15 wt.%) respectively. The anionic surfactant SDBS was chosen, as the nanoparticles were positively charged. The 0.10 wt% of SDBS in alumina/water nanofluid and 0.15 wt.% of SDBS in CuO/water nanofluid at which the magnitude of zeta potential was maximum; i.e. −72.2 mV and −85.1 mV, respectively. At the optimum concentration of surfactant, the complete dispersion of nanoparticles was achieved without any agglomeration, thus the zeta potential of nanofluid was found be maximum. The size distribution in base fluid were measured by the dynamic light scattering method and observed that the size of the nanoparticle decreased up to the optimum concentration of surfactant and then increased on further increase in surfactant concentration shown in Figs. 10 and 11. Wei et al. [115] prepared titanium dioxide (TiO2)/diathermic oil (DO) nanofluid from 0.1 vol.% to 1 vol.%, whose nanoparticles were anatase structure. The oleic acid was added as surfactant at mass ratio (oleic acid /TiO2) 0.3. The visual observation did not show any sedimentation or stratification even after ten days. The zeta potential was measured and found that it was decreased with the standing time (days). The size distribution of nanofluid was narrow at the time of preparation and became wider after 10 days, with increasing average particle diameter. Li et al. [116] modified amino functionalized multiwalled carbon nanotubes (MWCHTs-NH2) using β-cyclodextrin (β-CD) into β-CD modified MWCNTs (CD-CNTs). The EG based CD-CNT and MWCHTsNH2 nanofluid prepared from 0 to 0.1 vol.% concentrations. The stability was analyzed through the sedimentation test and the zeta potential analysis. There was no visual sedimentation even after two months for CD-CNTs, but the MWCNTs-NH2 nanofluids samples showed clear sedimentation after 15 days. It was noticed that using of β-CD on MWCNT produced better results. The zeta potential value was measured up to 60 days at regular interval and was observed that the zeta potential of MWCNTs-NH2 nanofluids decreased much more rapidly because MWCNTs were hydrophobic in nature. While the zeta potential of CD-CNTs nanofluid too decreased with the time but the values were all above 50 mV, showing very good stability. They explained that the introduction of β-CD, introduced a large number of hydroxyl groups to increase the hydrophilic property of MWCNTs. and due to the van der Waals force, the hydrogen-bonding interactions and the steric hindrance between β-CD, the probability of aggregation between CD-CNTs was greatly reduced. Agarwal DK et al. [89] investigated dispersion of alumina nanoparticle of size 50 and 13 nm in base fluid kerosene for regenerative cooling channels of semi-cryogenic rocket engine. The sonication was done with different ultra-sonication method (Bath and Disruptor type) having different power and frequency. They noticed that in spite of sonication of nanofluid, dispersed nanoparticle settled down within minutes showing higher level of instability. This was because of the hydrophilic nature of alumina nanoparticle. They tried electrostatic stabilization by addition of few drops of hydrochloric acid (HCl) to bring down the pH of the nanofluid. They could not achieve the stable suspension for any value of pH 3–5.6 electrostatic stabilization. So they performed steric stabilization by the addition of oleic acid in order to make it stable suspension. The steric stabilization modifies the hydrophobic surfaces of nanoparticles into hydrophilic surfaces and viceversa. Thus, the oleic acid was added to the base fluid, as the kerosene is non-polar. They varied the surfactant to particle volume ratio from 0.1 to 1.0. They found the nanoparticle was settled instantly after preparation for surfactant to particle volume ratio of less than 0.2 and greater than 0.7. The alumina/kerosene nanofluid thus prepared was visually inspected and particle settling was noticed. They reported that the stable alumina/kerosene nanofluid could be prepared using steric stabilization and observed the optimum level of surfactant i.e. oleic acid to particle volume ratio is 0.3. Pritam Kumar et al. [117] prepared TiO2/water nanofluid by two

Not reported The slope of the sedimentation rate is steep and possible to have a clear nanofluid after 1 month. −33.3 −55

212 237.65

Black agglomerations imply that chemical reactions did not properly happen. The sample with addition of surfactant has large agglomeration. Clustering around the small agglomerations Clustering is more evident 250.4 276.75 188.2 176.9 Fastest sedimentation and has lowest rate of precipitation (17%) Fastest sedimentation and has lowest rate of precipitation. Has highest precipitation rate (56%) Not reported −33.1 −44 −31.1 −47.9

A simple mixture With 0.1 wt% SDS With 15 min ultrasonic horn With 0.1 wt% SDS and 15 min ultrasonic horn With 3 h ultrasonic bath With 0.1 wt% SDS − 3 h ultrasonic bath

Table 11 Investigation of Ghadimi and Metselaar [103].

UV-sedimentation method Zeta Potential (mv) Sample (0.1 wt%TiO2)

Particle size (nm)

TEM Result

A.I. Khan and A. Valan Arasu

355

356

Alumina

[74]

DI Water EG

Graphite

[108] LB2000 (vegetable-based oil) PriEco6000 (unsaturated polyol ester)

Alumina

[106] EG and water (60:40)

Fe2O3

ZnO

[105] EG

[107] Water/(SDBS, SDS, citric acid, CTAB, Gum Arabic, TRION)

Mg(OH)2

[104] Water (with and without CTAB)

Alumina

Titania

Water

[79]

Nanoparticle

[101] Water

Basefluid/Surfactant

Ref

Table 12 Effect of sonication parameters on stability.

–

Bath sonicator (20 kHz)

0.5 vol.% (water) 0.1 vol.% (EG)

Probe (150 W & 450 W) Bath sonicator (90 W)

0.05 and 0.25 vol.% Ultrasonic cleaner (100 W and 40 kHz)

0.5 vol.%

0.2–0.5 vol.%

Ultrasonicator(200 W)

Ultrasonic processor (400 W power and 20 kHz)

0.8 wt%

1 vol.%

Ultrasonic homogenizer machine

Ultrasonic homogenizer machine

Sonication method

0.5 vol.%

0.5 vol.%

Concentration

2 min 4.5 h

0.5 and 1 h

1 and 2 h

30 to 80 min

4–100 h

10, 30, 50, 80, and 160 min

30, 60, 90, 120, 150, and 180 min

30, 60, 90, 120, 150, and 180 min

Sonication period

of sonication

(continued on next page)

acid, SDS, CTAB Settles immediately after 1 h of sonication • Citric Arabic (i) 1h – Zeta potential (-27 mV), Size (285.8 nm) • Gum (i) 1h – Zeta potential (−28.4 ± 1.06 mV), Size (109.73 ± 3.7 nm) • TRION with SDBS sonicated for an hour showed better stability than others. • Nanofluid – Zeta Potential 37.9 mV (0.5 h), 27.2 mV (1 h) for 0.05 vol.% 50 mV (0.5 h), • LB2000 32.1 mV (1 h) for 0.25 vol.% – Zeta Potential −16.5 mV (0.5 h), −42.7 mV (1 h) for 0.05 vol.% −80.8 mV • PriEco6000 (0.5 h), −60.1 m V (1 h) for 0.25 vol.% in sonication time from 0.5 to 1 h broke down the agglomeration into smaller sizes • Increase cluster size and volume content of cluster size in PriEco6000 were smaller than in • Graphite LB2000. size (140 nm) was produced by 450 W probe sonicator in both base fluids. • Smallest size reduced from 155 nm to 140 nm, while in Alumina/EG size reduced • InfromAlumina/water 175 nm to 140 nm. This showed the sonication is more effective in EG. largest size was produced by 150 W probe sonicator in both base fluids. i.e. (∼175 nm • The in alumina/water and ∼155 nm in alumina/EG) • Bath sonicator yields ∼153 nm in alumina/water and ∼148 nm in alumina/EG

cluster size decreased rapidly with the start of sonication from 210 nm to 110 • Average for 180 min potential rise to a peak rapidly and then slowly • Zeta potential achieved was 58 mV for 180 min of ultra-sonication. • Zeta images of the nanofluid of various sonication periods showed that agglomeration • TEM was decreased on sonication. nanoparticle cluster size decreased with the sonication period up to 150 min, above • The that the change in size was insignificant pH level was decreased from 6.73 to 6.15 on increasing the sonication period • The had good stability after 60 min of sonication • Nanofluid increased with increase in sonication period. • Stability without surfactant showed stable upto 7 days • Nanofluid with surfactant sonicated for 30 min and 50 min showed stable upto 30 days. • Nanofluid potential for 30 min sonication remains higher: −53 mV on 7th day reduced to • Zeta −51 mV on 30th day sonication period was 30 min • Optimum size decreased quickly from 459 nm to 91 nm between 4 and 60 h. • Clusters 60 h clusters grew on increasing the sonication about 220 nm upto 100 h. • Above images too confirmed the sizes at various sonication times and 60 h was found as • TEM optimum. sonicated for 60 h was examined after 30 days and did not find any visible • Nanofluid sedimentation. value of zeta potential showed that negative charge distribution on surface of • Negative oxide material. potential increased with the sonication time upto 70 min and then decreased. • Zeta potential decreased with increase in concentration. • Zeta (i) 1h – Zeta potential (−32.33 ± 1.5 mV), Size (206.25 ± 5.9 nm) (ii) 2h – Zeta • SDBS potential (−11.01 ± 2.96 mV), Size (1130.25 ± 16.7 nm) Settles immediately after 2 h

Results

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Thermal Science and Engineering Progress 11 (2019) 334–364

Alumina and Silica

Alumina

[110] Water

[111] Water

La2O3

[109] Ethanol

Nanoparticle

ZnO (20 nm & 40–100 nm)

Basefluid/Surfactant

[102] Water

Ref

Table 12 (continued)

15 wt%

1 mg/ml

10 wt%

0.02 vol.%

Concentration

Dissolver

Ultrasonicator (400 W and 20 kHz) Pulsed and continuous mode

Ball milling Ultrasonic Ultrasonic followed by ball milling Ball milling followed by ultrasonic

Piezo-actuated bath (40 kHz, 45 W) Solenoid-actuated bath (42 kHz, 70 W) Immersed Horn (20 kHz, 550 w)

Sonication method

Not mentioned

0–180 s

30–90 min 5–40 min 90 and 25 min respectively 90 and 25 min respectively

0 to 60 min

Sonication period

357

• •

• • • •

• •

• •

• • • •

suspension dispersed by ball milling, while ball milling could make it after the suspension treated by ultrasonic. Effect of sonication is studied at different vibration amplitudes of 10%, 30%, 60% (pulse ratio 0.1/2.0 (s/s) Breakdown of cluster rate is different at vibration amplitudes 10% and 30%. i.e. lower breakage rate at lower vibration amplitude For vibration amplitude of 60%, the variations in the cluster sizes rose slightly after 300 s of ultrasonication. This result could be explained by the fact that, with the increase of vibration amplitude, bubbles may grow so large that the time available in the adjacent rarefaction cycle will not be sufficient for them to collapse In the continuous mode, the results at vibration amplitude 10% and 60% were almost identical, and less efficient than at 30% Increase in specific energy input using higher vibration amplitude or continued irradiation lead to insignificant size reduction and sometimes even the re-agglomeration of the nanoparticles. Disc mill and dissolver disc: High shear rates in the liquid were realized by a rotating machine element and resulting in coarser product fineness Kneader and a 3-roller mill: They were very efficient and suitable for high suspension viscosities or particle concentrations. Particle sizes can be achieved above 140 nm. On dispersing silica, smaller energy efficiency and product fineness than dispersing alumina was obtained. Stirred media mill and an ultrasonic homogenizer: Particle sizes could be achieved below 140 nm. Width of the particle size distribution in the 3-roller mill and the disc mill remains nearly constant over the whole dispersion process Width of the particle size distribution in kneader and the stirred media mill increases with increasing specific energy input Silica was dispersed in an ultrasonic homogenizer and studied with different specific energy. Particle size decreases and distribution became narrower with increasing specific energy or dispersion time. Strength of the silica aggregates must be higher than the strength of the alumina aggregates. Ultrasonic homogenizer was found to resulting in small aggregate sizes at low specific energy input.

agitation reduced the particle size to below 200 nm within the first 10 min for • Ultrasonic 20 nm size, and within 30 min for 40–100 nm size particles. reduction in size was in very slow rate on further agitation. • The bath: Size reduced to ∼150 nm at 60 min for particle size 20 nm, while for • Piezo-actuated particle size 40–100 nm within 10 min bath: The size reduced to ∼110 nm at 60 min for particle size 20 nm, • Solenoid-actuated while for particle size 40–100 nm size was ∼140 nm system: The size reduced to 100 nm within 60 min for particle size 20 nm, while for • Horn particle size 40–100 nm within 20 min. horn was found to be more effective in dispersion. • Immersed experiments were carried out and found that 90 min ball milling as • Orthogonal optimized milling time. test conducted for 1 h showed that dispersion rate of the composite • Sedimentation dispersion methods of ultrasonic and then ball milling were higher than other methods. potential of the nanofluid treated by ultrasonic followed by ball milling is the highest • Zeta (∼17 mV), showing higher stability. of the particles: (i) Ball milling (332 nm), Ultrasonic (420.4 nm), ball milling • Diameter followed by ultrasonic (304.6 nm) and ultrasonic followed by ball milling (20.9 nm) images showed that composite dispersion methods were better than single dispersion • TEM method. Though, ultrasonic cannot improve the dispersion capability greatly after the

Results

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A.I. Khan and A. Valan Arasu

Fig. 9. Structures of different type of surfactants.

Fig. 11. Effect of SDBS weight fraction on the zeta potential and particle size of CuO/water nanofluid [114].

They investigated the thermal conductivity of TiO2/water nanofluid with three different surfactants for various concentration vol.% up to 1.0%. They used SDS (anionic), CTAB (cationic) and nonionic surfactant sorbitan mono-oleate (Span-80) for their study. They found that SDS had greater thermal conductivity enhancement than other surfactants used. They reported the results arrived were in favour of proposal made by Hu et al. i.e. the molecular weights of surfactants may affect the heat transfer and the low density or low molecular weight gives higher enhancement. Wang et al. [119] investigated the heat transfer enhancement of two different water-based nanofluids. They chose alumina and copper (Cu) nanoparticles for their study. They varied the different concentration of surfactant sodium dodecyl benzene sulfonate (SDBS) and pH of nanofluid. They measured the thermal conductivity of the nanofluid by transient plane source (TPS) method and transient hot wire device method. They found that for both water based nanofluid, as the SDBS concentration increased, the zeta potential increased and attained a minima and then increased on further increase in concentration of surfactant shown in Fig. 13. They explained that the SDBS can partially ionized in water and gave anionic species, since alumina and copper

Fig. 10. Effect of SDBS weight fraction on the zeta potential and particle size of Alumina/water nanofluid [114].

step method. They investigated the nanofluid with various surfactants such as cetyl trimethyl ammonium bromide (CTAB), acetic acid (AA), oleic acid (OA) and sodium-dodecyl sulfate (SDS) and found that surfactant CTAB and AA gave stable suspension than other surfactant. The nanofluid with surfactant CTAB and AA were stable for more than 500 h. They studied thermal conductivity enhancement of suspension with AA but not with CTAB. The viscosity of TiO2/water nanofluid with CTAB was slightly higher than the nanofluid with surfactant AA for higher solid fraction and higher shear rates. Asadi et al. [104] used three different surfactants for the preparation of Mg(OH)2/water nanofluid, cetyl Tri-methyl Ammonium Bromide (CTAB), sodium Dodecyl Sulfate (SDS), and oleic Acid, with the volume fraction of 0.02%. They investigated the stability of the nanofluid of 0.8% solid concentration that was ultra sonicated for 30 min and kept for 30 days. They observed that the CTAB surfactant showed the best results in both visual study and the Zeta potential was 52 mV after 30 days. The visual study is showed in Fig. 12.The researchers reported that the surfactant CTAB was better than SDS and oleic acid for the stability of the Mg(OH)2/water nanofluid. Saleh et al. [118] synthesized titanium dioxide (TiO2) nanoparticles by co-precipitation method of size 33 nm. They prepared TiO2/water nanofluid by two step method using an ultrasonic processor under continuous pulse for 2 h.

Fig. 12. Stability of the Mg(OH)2-water nanofluid containing different surfactants after 30 days [104]. 358

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Fig. 13. Effect of amount of SDBS on stability.

nanoparticles were positively charged in a neutral aqueous medium, had a strong affinity for anionic groups. Thus, the SDBS adsorbed on the surfaces of positive charged nanoparticles and gave electrostatic stabilization. On the excess addition of SDBS, the concentration of Na+ increased and they enter into the adsorbed surfaces and reduced the net effect of electrostatic stabilization. They varied the pH of the both water based nanofluid and observed that, as the pH of nanofluid increased; the zeta potential of nanofluid was increased and reached a maximum. On further increase in pH of nanofluid, the zeta potential decreased due to the compression of the electrical double layer. They kept SDBS concentration (0.05 wt%.) as constant and varied the pH of nanofluid (0.1 wt%). They found the similar results for both alumina and Cu Nano suspensions that the thermal conductivity enhancement increased with increasing pH of the nanofluid (pHalumina < 7.5, pHcopper < 9) and reached a maximum and then decreased on further increase in pH of nanofluid (pHalumina > 8, pHcopper > 9.5). They kept pH of the nanofluid as constant (pHalumina = 8, pHcopper = 9.5) and varied the SDBS concentration. For Cu nanofluid, the thermal conductivity enhancement increased up to 0.03% and stayed constant up to 0.07%, then decreased on further increase in SDBS concentration. For alumina nanofluid, the thermal conductivity enhancement increased up to slightly before 0.1% and then decreased on further increase in SDBS concentration. They reported that combined effect of pH and surfactant needed for the stable suspension and thermal conductivity enhancement and the optimal level of surfactant SDBS for the alumina/water and Cu/water nanofluid are 0.1 wt% and 0.07 wt% respectively. Khairul et al. [114] investigated the water based nanofluid of alumina (10 nm) and CuO (30–50 nm) nanofluid with anionic surfactant SDBS (sodium dodecyl benzene sulfonate). They varied the amount of surfactant SDBS concentration up to 0.19 wt.%. They found that on increasing wt.% of surfactant increased the pH value of nanofluid and nanofluid tend to be basic. Although after attaining an optimum wt% of surfactant there was an only gradual rise in pH with increasing surfactant wt.%. As the wt% of surfactant increased, the viscosity of the alumina/water nanofluid decreased and attained minima and then increased, while for CuO/water nanofluid viscosity increased first and decreased then and again increased, thus followed a non-linear pattern. They measured the thermal conductivity of the nanofluid using KD2 Pro. They observed that when the wt.% of surfactant was increased, the thermal conductivity enhancement for both nanofluids increased and attained maxima and then dropped rapidly. They concluded that stable suspension of alumina and copper oxide water based nanofluid can be attained by using the optimum level of surfactant, which was 0.10 wt.% of SDBS in alumina/water nanofluid and 0.15 wt.% of SDBS in CuO/ water nanofluid.

Fig. 14. Effect of surfactant type and concentration on zeta potential of nanorefrigerant.

Peng et al. [120] dispersed anatase titanium dioxide (TiO2) in refrigerant R141b with surfactant. They used nanoparticle size of 60 nm having surface area of 250 m2/g with surfactant SDBS (anionic), CTAB (cationic) and Nonylphenoxypoly ethanol (NP-10, nonionic) of varying concentration (0, 100, 200, 300, 400 mg/L). They observed the steady state particle size distribution to be varying with surfactant, size and concentration. They observed by increasing the concentration of SDBS (0–400 mg/L), the zeta potential increased to 165.2%; For CTAB concentration (0–200 mg/L) the zeta potential increased to 222.3% and then decreased about 18.6% for concentration 200–400 mg/L and NP10 has only slight variation in the zeta potential of the nanofluid shown in Fig. 14. This study has been done for the period of 4 h from the preparation but the effect of surfactant on nanofluid on longer period (stability) and optimum level of surfactants were not discussed by the researchers. They concluded that anionic and cationic surfactant prevented the aggregation by controlling the zeta potential whereas the influence of nonionic aggregation was decided by steric hindrance effect. Amiri, Shanbedi and Dashti [121] studied thermophysical properties and stability of water based amine-treated GQD (A-GQD) of size ranging from 5 to 20 nm. It was observed that the maximum enhancement in thermal conductivity was 10.1% at 0.001 wt.% and 18.6% at 0.002 wt.%. They observed that the zeta-potential values were consistent (49.8 to 52.1 mV) for the concentration range 0.002 to 0.02 wt.% but lower (26.4 mV) for 0.001 wt.%. Though all the three authors [114,119,122] investigated the alumina/water nanofluid, two authors [119,114] concluded that the optimum concentration of SDBS as 0.10 wt.% , while [122] reported as 1 wt.%. Thus, there is a conflict arises but there is a slight difference is noted between their research. Wang et al. [119] used alumina nanoparticle of size 15–50 nm having surface area of 100 m2/g. Khairul et al. [114] used alumina nanoparticle of size 10 nm having surface area of 160 m2/g, where Zawrah et al. [122] used nanoparticle of rod shaped size ∼50 nm. This conflict gives a sight that the optimum level of surfactant may depend upon the size and shape of nanoparticles used.

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Similarly, the research works on the stability of water based silica and titania nanofluid without surfactants done by different researchers are collected and presented in Fig. 16 to understand the effect of pH value and nanoparticle on stability. The Zeta potential of silica/water nanofluid [126–128] and titania/water nanofluid [124,128,129] varying with pH are plotted in Fig. 16. The zeta potential is found to be negative in the acidic region and continue to move to still lower values in the negative region with increase in pH. Both the silica and titania water based nanofluids showed higher stability in base region i.e. pH between 9 and 10. The pH at which the nanofluid is found to be stable is considered as effective pH, meanwhile the pH at which the nanofluid is unstable (i.e. zeta potential is zero) is known as iso- electric point (IEP). The effective pH and IEP of the various nanofluids with different surfactants are listed in the Table 13 from the literature. The surfactant is able to alter the effective pH of the nanofluid, which is evident from the Table 13. Average particle size of the nanoparticle also found to be varying along the pH of the nanofluid. Based on the ionic nature of the surfactant, the zeta potential of the nanofluid lies in the positive or negative range. The maxima and minima of the zeta potential of the nanofluid with various surfactants occur at same pH level, irrespective of the ionic nature of the surfactant. But the value of zeta potential varies within them, based on the value and average particle size; we may choose the best surfactant for the nanofluid. Another important study revealed that the IEP didn’t vary much for the various concentrations of the nanofluid and different sonication time [123]. Thus, IEP was independent of the concentration and the sonication time. Most of the stability of the nanofluid was measured using Zeta potential and spectral absorbance method. The measurement of zeta potential depicts the stability of nanofluid at that time, which decreases as the time increases showing that nanofluid loses its stability. As the agglomeration starts, the average size of the nanoparticle increases and distribution of the size of nanoparticle varies largely with time. The rapid decrease in the zeta potential of the nanofluid with time shows the instability of nanofluid. The stability of the nanofluid is altered by various methods i.e. sonication, addition of surfactant, pH of the nanofluid and it is measured by zeta potential, spectral absorbance method, visual sedimentation, isoelectric point, and rate of sedimentation. Though there are several methods to enhance the stability, all are interlinked at the point i.e. average size of the nanoparticle in base fluid and the surface charge on the nanoparticles in the base fluid.

Fig. 15. Alumina/water nanofluid.

8.4. Effect of pH The pH is a measure of the hydrogen ion concentration of a solution. Solutions with a high concentration of hydrogen ions have a low pH and solutions with low concentrations of H+ ions have a high pH. The pH scale is used to measure solutions in terms of acidity or alkalinity. The pH scale range from 0 to 14; anything below 7.0 is acidic, and anything above 7.0 is alkaline, or basic. The zeta potential is a qualitative indicator of the stability of colloidal dispersions. The magnitude of the zeta potential specifies the degree of electrostatic repulsion between adjacent, similarly charged particles in dispersion. The research works on the stability of alumina/water nanofluid without and with surfactants done by different researchers are collected and presented in Fig. 15 to understand the effect of pH value and surfactants on stability. The Zeta potential of alumina/water nanofluid without surfactant [122,123] and with anionic surfactant SDS [124] and SDBS [125] varying with pH are plotted in Fig. 15. The Zeta potential of alumina/water nanofluid without surfactant decreased and moved towards the negative region with increase in pH. The Zeta potential of nanofluid with anionic surfactant lies in negative region at all pH values. The variation of zeta potential is not much significant (about 10 mV) at all pH values.

9. Conclusion Lots of researches have been conducted on the nanofluid till date and every research gives the valuable information to the research society. This paper collectively gives an overview on the comparative studies on the nanofluid by various researchers including the topics namely, synthesis of nanofluid/nanoparticles, effect of nanoparticle’s shape and size on thermo-physical properties, effect of base fluid and sonication on thermo-physical properties, characterization techniques and stability. Each chapter deals with at least one of the peculiar investigations done by the researchers. From the comparative study of the synthesis of nanoparticle, the results inferred are discussed. In synthesis of metal nanoparticles, reactants and its concentration are varied; whereas for metal oxide nanoparticles, process temperature is varied to obtain required shape, crystallite size, crystal phase and properties of nanoparticles. Thus, on varying the synthesis method or operating condition or reactants and its concentration, the nanoparticle of different size and shape can be prepared. The nanoparticle shape affects the thermal conductivity and viscosity of the nanofluid. Mostly, the shapes are classified as spherical and non-spherical nanoparticles. The researchers formulated the theoretical models for properties to account the particle shape; the Hamiltoncrosser model is the well-known model but it too has some setbacks in

Fig. 16. Water based silica and titania nanofluid. 360

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Table 13 Effective pH for stable nanofluid. Ref

Nanoparticles

Base fluid and Surfactant

pH range

Concentration

Effective pH

Iso- Electric Point

Evaluation methods

[130]

Fe2O3

Water Water with SDBS Ethylene glycol water Water with SDBS Ethylene glycol Water/SDS Water/SDS Water Water Water

2–12

0.4 vol.%

4.88 3.97 5.33 9.46 9.23 8.40 6 9.5 greater than7 9.5 12

6.8

Zeta Potential

NA NA 2 NA NA

CuO [124] [127] [126] [131] [132] [125]

Alumina Titania Silica Silica Controlled reduced graphene oxide (CRGO) Copper Alumina Copper

2–12

* *

5.9 * *

2, 7, 10 1.5–13 2.88–12

0.05 wt% 0.01 wt% 0.10 to 0.20 wt% 0.01 wt% 0.2 mg/ml

Water/SDBS Water/SDBS Water/SDBS

2–12 2–11.6

0.05 wt% 0.1 wt%

8.5–9.5 ∼8 ∼9.5

NA NA NA

[133]

Titania

Water/SDS

10–12

0.1 wt%

∼ 10

*

[134] [135]

MgO (20 & 100 nm) Copper

8–12 2–12

0.05 vol% 0.05 wt%

*

∼10 5.3 (SDBS)

[136] [129]

1–10 3–11

*

*

3 wt%

*

[137]

Gold (prepared through PLAL) Zirconium dioxide Titania Graphite

EG Water/TX-10, CATB, and SDBS Water/CATB Water Water Water/PVP

1–11

0.05 vol%

9.5

∼3 ∼6 ∼4.5–5.5 2

[123]

Alumina

Water

2–12

0.1, 0.5 and 0.8 vol%

Acidic region

8 to 9.4

4–10

0.1 wt%

*

8 6.8 & 6.2

2–13

0.01 wt%

Acidic region

[122]

Alumina

[138]

Pristine SWNTs and DWNTs

Not Reported –

*

Water Water/SDBS (0.3 and 0.6%) Water

9.5

*

0.1 wt%

*

*

7.1

Zeta Potential Spectral Absorbance Zeta Potential Zeta Potential Zeta Potential Sedimentation Zeta Potential Spectral Absorbance Spectral Absorbance with ANOVA Zeta Potential Zeta Potential Spectral Absorbance Zeta Potential Zeta Potential Average particle size Zeta Potential Sedimentation balance method Zeta Potential Sedimentation Zeta Potential Zeta Potential

Not Available – NA.

predicting the properties. The nanofluid with non-spherical particles has higher or moreover same viscosity as the nanofluid with spherical particles. It is difficult to compare the different shaped nanoparticles, as the shape is variable; it is difficult to use the nanoparticles with different shape having same average diameter or length. This variation in size should be taken into account while comparing the results of comparative studies on the shape. The nanoparticle size affects the thermal conductivity greatly, as it has the strong relationship. Almost many researches proved that the enhancement of thermal conductivity increased with the decrease in the size of the nanoparticle. But a few researchers, who studied with wide range of particle size, proclaimed that below certain nanoparticle size, the enhancement of thermal conductivity got reduced and it was due to the kapitza resistance and phonon scattering. Thus, there is the critical size of nanoparticle for the enhancement of thermal conductivity. All the research works proved that the viscosity of the nanofluid is increased when the particle size is reduced and it is due to the increase in the surface area of the nanoparticle. Experimental studies showed that the percentage of enhancement of thermal conductivity differed with the base fluid. The researchers reasoned it and explained that the surface phenomenon that happened between the base fluid and the nanoparticle; the functional group present on the nanoparticle or the nature of base fluid is the main reason which gives the kapitza resistance. This difference of kapitza resistance with different base fluid gives rise to different enhancement. More number of experimental studies on thermophysical properties of nanofluid is done. Each researcher compared their result with prevailing models and provided the regression model based on their study. Those regressions are found to fit for a certain narrow limit/cases, due to the influence of size distribution, polydispersity, aggregation of nanoparticles, variation in cluster size and functional group attached to

the nanoparticles. The properties of nanofluid also depend on uniform and stable dispersion of nanoparticles and period of measurement after its preparation. Though there are many characterization techniques available, each has its own advantage which is discussed in this paper. The stability is the main criterion of the nanofluid and is considered to be the one of the great challenges to the world scientific community. The UV–vis absorbance, hydrodynamic size, zeta potential, microscopy and visual observations are used to measure the stability of the nanofluid. The researchers use any one or two above methods for the stability test. It is not easy to compare the stability of the nanofluid between two research works, as there is no standard method universally. Thus, there are discrepancies among the research articles: some claim the stability in days, week or in months. The ways to increase the stability of the nanofluid are presented in detail and its effect is also discussed in this article. Among many nanoparticles, it is noticed that the small amount of GQDs dispersed in basefluid have higher enhancement in thermal conductivity and it has good stability as well. The nanoparticle’s size and shape, base fluid and the dispersion technique are the prime factors for the change in thermophysical properties and stability. On the whole, on concentrating the synthesis of nanoparticles and dispersion techniques, high thermal conductivity enhancement and low viscosity nanofluid with long-term stability can be achieved and commercial application of nanofluid for maximum heat transfer rate and effectiveness of energy conversion systems shall be realized. Conflict of interest There is no conflict of interest.

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Acknowledgment

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