Nanofluids have been receiving great attention in recent years due to their potential usage, not only as an enhanced thermophysical heat transfer fluid but also because of their great importance in applications such as drug delivery and oil recovery. Nevertheless, there are some challenges that need to be solved before nanofluids can become commercially acceptable. The main challenges of nanofluids are their stability and operational performance. Nanofluids stability is significantly important in order to maintain their thermophysical properties after fabrication for a long period of time. Therefore, enhancing nanofluids stability and understanding nanofluid behaviour are part of the chain needed to commercialise such type of advanced fluids. In this context, the aim of this article is to summarise the current progress on the study of nanofluids, such as the fabrication procedures, stability evaluation mechanism, stability enhancement procedures, nanofluids thermophysical properties, and current commercialisation challenges. Finally, the article identifies some possible opportunities for future research that can bridge the gap between in-lab research and commercialisation of nanofluids.
Fluids of different types are usually used as heat carriers in heat transfer applications. Such applications where heat transfer fluids (HTF) have an important role are heat exchanging systems in power stations [
Considerable efforts were made on heat transfer enhancement through geometrical modification up to now [
The idea of dispersing solids in fluids was first proposed by Maxwell via his theoretical work more than 120 years ago [
Thermal conductivity comparison of common polymers, liquids, and solids [
One of the problems that arises from using fluids containing
Parameters influencing nanofluids effective thermal conductivity.
Factors to be considered when selecting nanomaterials on preparing nanofluids for heat transfer applications are (i) chemical stability, (ii) thermophysical properties, (iii) toxicity, (iv) availability, (v) compatibility with the basefluid, and (vi) cost. The most commonly used nanoparticles for nanofluids formulation are aluminium (Al), copper (Cu), silver (Ag), iron (Fe), titanium (Ti), silicon (Si), zinc (Zn), magnesium (Mg), carbon nanotubes (CNTs), graphene, graphene oxide, and diamond. Commonly used basefluids for nanofluid formulation are water, ethylene glycol (EG), EG – H2O mixtures, and oils [
Several researchers have reported scale formation, also known as “fouling effect,” on the surfaces when using nanofluids in applications at elevated temperature such as the inside of the annulus of heat exchangers [
(a) Rough surface and (b) nanocoated surface or nanofouled surface [
Kang et al. demonstrated in their work how coating a riser surface with nanoparticles reduced the pumping power and improved the system efficiency by 25% [
Relation between surface contact angle and fluids [
Nanofluids fouling effect can also increase or decrease the nucleation boiling heat transfer depending on the surface/liquid contact angle as demonstrated by Phan et al., where they showed in their work that the highest heat transfer coefficient was obtained at a contact angle close to either 90° or 0° [
Besides to using nanofluid as a HTF in heat transfer applications, which was the main reason behind the development of such category of fluid, it is also used in, for example, sunscreen products [
Data obtained from the Scopus database from 1995 to 2018 showed an exponential increase in the number of documents with the word “nanofluids” as part of the title as seen in Figure
Number of documents with the word nanofluids in the title.
Percentage of available document types.
Nanofluid, which is a term used to describe fluids containing dispersed particles of nanoscale, can be formed from nanoparticles of single element (e.g., Cu, Fe, and Ag), single element oxide (e.g., CuO, Cu2O, Al2O3, and TiO2,), alloys (e.g., Cu-Zn, Fe-Ni, and Ag-Cu), multielement oxides (e.g., CuZnFe4O4, NiFe2O4, and ZnFe2O4), metal carbides (e.g., SiC, B4C, and ZrC), metal nitrides (e.g., SiN, TiN, and AlN), and carbon materials (e.g., graphite, carbon nanotubes, and diamond) suspended in water, ethanol, EG, oil, and refrigerants [
This category of nanofluid was first proposed by Choi, in 1995, and is considered as the conventional form of nanofluids used, where a single type of nanoparticles is used to produce the suspension via different preparation methods [
Hybrid nanofluids are an advanced category of nanofluids which are made of a combination of more than one type of nanoparticles suspended in a basefluid. This type of fluids was first studied experimentally by Jana et al., in 2007, in order to enhance the fluid thermal conductivity beyond that of a conventional single material type nanofluid [
Uniformity of the particle dispersion depends mainly on the preparation method used and can have a significant effect on the thermophysical properties of the nanofluid. Meaning that if two similar nanofluids were to be prepared using different preparation methods, their thermophysical properties and tendency to agglomeration are most likely to vary from each other. This is because nanofluids are not simply formed from a solid-liquid mixture but requires special conditions to be present in the suspension such as homogeneity, physical and chemical stability, durability, and dispersibility. There are mainly two techniques used to fabricate nanofluids, namely, the bottom-up approach known as the one-step method and the top-down approach identified as the two-step method [
The single-step approach relies on combining the production and dispersion processes of nanoparticles into the basefluid via a single step. There are some differences in this procedure. One of the commonly used methods for synthesising nanofluids, known as the direct evaporation one-step approach, depends on solidifying nanoparticles that are originally in gaseous phase inside the basefluid itself. The method was developed by Akoh et al. [
Preparation of nanofluid using one-step vapour deposition method [
In this approach, nanoparticles are initially produced or purchased in the form of dry powder and then dispersed in the basefluid. The commonly employed equipment for dispersing nanoparticles in the basefluid is magnetic stirrers, ultrasonic bath, homogenizers, high-shear mixers, and bead mills. Unlike the one-step approach, the two-step approach is more commonly used to fabricate nanofluids due to having a lower processing cost and a wide availability of commercially supplied nanoparticles by several companies. Figure
Schematic procedure of the two-step nanofluids preparation.
Eastman et al. [
Some researchers claim that the two-step process is preferable for forming nanofluids containing oxide nanoparticles, while it is less effective toward nanoparticles of metallic origin [
Part of the challenges that faces commercialising nanofluids is their poor stability due to the interaction between the particles themselves and between the particles and the surrounding liquid [
Repulsion mechanisms: (a) steric repulsion and (b) electrostatic repulsion [
As previously mentioned, stability of nanofluids has a vital role in extending its shelf-life and preserving the thermophysical properties of the fluid. Different evaluation methods for the stability of nanofluids were discussed by different researchers [
The zeta potential analysis evaluates the stability of nanofluids through the observation of electrophoretic behaviour of the fluid [
Zeta potential between the slip plane and stern layer of a nanoparticle [
In any nanofluid, the zeta potential can be ranged from positive, at low pH values, to negative, at high pH values. In terms of nanofluid stability, zeta potential value > ±60 mV has excellent stability, ± (40 to 60) mV has good stability, ± (30 to 40) mV is considered stable, and < ±30 mV is highly agglomerative [
Kim et al. [
Zeta potential value as a function of pH for different nanoparticles dispersed in water [
This method is considered to be one of the simplest approaches to measure the stability of nanofluids [
Three behaviours of sedimentation can be observed in any unstable nanofluid:
Types of sedimentation behaviours in nanofluids, where
Xian-Ju and Xin-Fang [
Instable Al2O3 nanofluid phase separation speed regions [
All of the aforementioned researchers have confirmed that the stability of nanofluid can be indicated using the sedimentation photograph capturing method. Despite the fact that this approach represents a high-performance analysis of nanofluid stability with low cost, very few papers were published using this method [
Nanofluid centrifugation is a much faster method for determining the stability of the prepared fluid compared to the sedimentation photograph capturing approach. It has been employed in a variety of stability studies, in which a visual examination of the nanofluid sedimentation is performed using a dispersion analyser centrifuge.
Singh and Raykar [
This method was firstly proposed, in 2003, by Jiang et al. [
Examples of nanofluids absorption wavelength peaks reported using an UV-Vis spectral analyser.
Investigators | Nanoparticle | Basefluid | Peak wavelength (nm) |
---|---|---|---|
Liu et al. [ | Aligned CNTs | DW | 210 |
Jiang et al. [ | CNTs | DW | 253 |
Chang et al. [ | Cu | DW | 270 |
Chang et al. [ | CuO | DW | 268 |
Sato et al. [ | Ag | DW | 410 |
Hwang et al. [ | Fullerene | Paraffin oil | 397 |
Evaluation of the thermal conductivity changes in nanofluids, caused by the sedimentation of nanoparticles, was also proposed as a stability measuring approach known as the 3
Experimental configuration of the 3
Particles size distribution can be measured to determine the nanofluid stability using a transmission electron microscopy (TEM) or scanning electron microscopy (SEM) devices. These very high-resolution microscopes tend to capture the digital image, known as the electron micrograph, of approximately 0.1 nm in size [
The usual practice reported for inspecting the sample stability using a TEM device is by placing a drop of the as-prepared nanofluid on a carbon coated copper grid and then monitoring the distribution of the nanoparticles on top of the copper grid when the basefluid is completely evaporated [
Electron micrograph of CuO nanoparticles using (a) TEM [
Das et al. [
In addition to the TEM and SEM devices used to characterise the nanofluids stability, cryogenic electron microscopy (Cryo-EM) can also analyse the stability of nanofluids, if the microstructure of the nanofluid is unchangeable throughout the examination process [
Several literatures have reported diverse ways of improving the stability of nanofluids, which are discussed in the following section.
Adding surfactants, also referred to as dispersant, is an effective stability enhancement method that prevents the agglomeration of nanoparticles within the nanofluid [
Based on the head composition, dispersant can be divided into four classes:
Commonly used surfactants are listed in Table
Commonly used surfactants and their structure formulas.
Surfactant | Structure formula |
---|---|
Polyvinylpyrrolidone | |
| |
Sodium dodecyl sulphate | |
| |
Oleic acid (OA) [ | |
| |
Hexadecyl trimethyl ammonium bromide (HCTAB) [ | |
| |
Poly(acrylic acid sodium salt) [ | |
| |
Sodium dodecyl benzene sulfonate | |
| |
Dodecyltrimethylammonium bromide | |
| |
Gum Arabic [ | |
| |
Sodium octanoate | |
The disadvantage of using dispersant as a nanofluid stabilizer is its sensitivity to hot temperature. This is because the rise in temperature causes the bounds between the nanoparticles and the surfactant to be damaged and in some cases, it can chemically react into producing foams [
Timofeeva et al. [
One of the methods used to achieve long-term stability of nanofluids, without the need of surfactants, is by modifying the nanoparticles surface via functionalization. This is done by introducing functionalized nanoparticles into the basefluid in order to obtain a self-stabilized nanofluid. Usually, suitable functional organic groups are selected as they tend to attach to the atoms surface, enabling the nanoparticles to self-organize and avoid agglomeration [
There are two approaches where functional groups can be introduced. The first method is by introducing all the functional ligand in one step, which requires bifunctional organic compounds. A single functionality (
The two functionalized nanoparticles approaches. Method 1 (top):
Kayhani et al. [
Sonication, which is a physical method that depends on employing ultrasonic waves through the fluid, can be used to enhance the stability of the nanofluid by rupturing the nanoparticles attractional force within the sediments [
Probe type and bath type ultrasonicators [
Many researchers have used ultrasonication in preparing and stabilizing their nanofluids. It was also reported that the probe type sonicator gave a better improvement to the nanofluid than the bath type [
Although sonication technique is widely used, particularly in the nanofluid two-step preparation method, the optimum sonication time, wave, and pulse mode are still unknown. It was also pointed out that increasing sonication time does not necessarily improve the reduction in particle size, as it can largen rather than reduce the particle size as illustrated by Kole and Dey [
Manipulating the pH value of nanofluids changes the nanoparticles surface and can strongly improve the stability of the dispersed nanoparticles [
Many studies were carried out to demonstrate the effect of pH level on the stability of nanofluids [
Modak et al. [
Our review of the available literature quoted above shows that the pH value of nanofluids has a strong effect on their stability and that the optimum pH value varies between samples. It also revealed that the pH value is influenced by the nanofluid temperature.
Table
Summary of available studies on water base nanofluids stability measurements and dispersion improvement.
Researchers | Basefluids | Particles material | Parameters | Surfactant | pH control | Stability evaluation methods | Observations |
---|---|---|---|---|---|---|---|
Li et al. [ | Water | Cu | | SDBS and CTAB | – | Sedimentation photographs and zeta size analyser | Nanofluids with CTAB lasted for 1 week without sedimentation. |
| |||||||
Kim et al. [ | Water | Au | | – | – | Zeta potential analyser | Good particle dispersion for 1 month. |
| |||||||
Paul et al. [ | Water | Au | | – | – | TEM, SEM, and DLS | No agglomeration or sedimentation even after 48 h. |
| |||||||
Qu et al. [ | Water | Al2O3 | | – | 4.9 | SEM | Nanoparticles suspended stably for 3 days. |
| |||||||
Anoop et al. [ | Water | Al2O3 | | – | 6.5 (1 wt%) | TEM | Several weeks of stability was achieved. |
| |||||||
Rohini Priya et al. [ | Water | CuO | | Tiron | – | Zeta potential analysis and visual observation | Stability was maintained throughout the experiment. |
| |||||||
Chang et al. [ | Water | CuO | | Sodium hexametaphosphate (NaHMP) | 6.64–6.70 (with surfactant), and >9.5 (without surfactant) | Zeta potential analysis | CuO content > 0.04 vol% showed very high instability and particles tended to settle within minutes. |
| |||||||
Liu et al. [ | Water | CuO | | – | – | TEM | The uniformity and stability of the suspensions were poor after a couple of days. |
| |||||||
Yang and Liu [ | Water | SiO2 | | Trimethoxysilane | – | SEM | Functionalized nanofluids kept good dispersion for 12 months; pure nanofluid developed sedimentation after several days. |
| |||||||
Qu and Wu [ | Water | SiO2; | | – | 9.7; | TEM | Both types of nanofluids maintained their stability for several days, but the alumina nanofluid had better particles dispersion. |
| |||||||
Suganthi and Rajan [ | Water | ZnO | | Sodium hexametaphosphate (SHMP) | – | Zeta potential analysis and SEM | All samples showed good stability, with highest stability at 2 vol%; sonication for 3 h reduced the aggregated size leading to a better improvement. |
| |||||||
Duangthongsuk and Wongwises [ | Water | TiO2 | | CTAB | – | TEM | Few agglomerations were observed after 3 h from sonication. |
| |||||||
Hari et al. [ | DIW | Ag | Basefluid = 20 ml | CTAB | – | UV-Vis spectroscopy | The suspensions were stable for one week. |
| |||||||
Kole and Dey [ | DIW | Cu | | – | – | DLS and TEM | No visible signs of sedimentation for more than 15 days. |
| |||||||
Kathiravan et al. [ | DIW | Cu | | SDS | – | TEM | Nanofluid maintained particles dispersion for more than 10 h. |
| |||||||
Yousefi et al. [ | DIW | MWCNTs | | Triton X-100 | 7.4 | TEM | Colloid was stable for 10 days; optimum sonication time was found to be 30 min. |
| |||||||
Garg et al. [ | DIW | MWCNTs | | Gum Arabic | – | TEM | Over 1 month suspension stability achieved with no visible sedimentation or settling. |
| |||||||
Ding et al. [ | DIW | MWCNTs | | Gum Arabic | 2, 6, 10.5, and 11 | SEM | Nanofluids showed good stability for months. |
| |||||||
Abareshi et al. [ | DIW | Fe3O4 | | Tetramethyl ammonium hydroxide | 12.8 | Zeta potential analysis | Suspensions showed good dispersion and stability. |
| |||||||
Phuoc et al. [ | DIW | Ag | | – | – | TEM | Nanofluids were stable for several months. |
| |||||||
Parametthanuwat et al. [ | DIW | Ag | | – | – | – | Samples stability lasted for 48 h. |
| |||||||
Yousefi et al. [ | DIW | Al2O3 | | Triton X-100 | – | Visual observation | Suspension stability lasted for about 3 days. |
| |||||||
Hung et al. [ | DIW | Al2O3 | | Chitosan | – | UV-vis spectroscopy | Nanofluid of 3.0 wt% showed a difference of 5% in its stability, compared to the 0.5 wt% sample. |
| |||||||
Heyhat et al. [ | DIW | | – | – | SEM and Zeta potential analysis | Suspensions were stable due to having a zeta potential value of 30 mV. |
Nanofluids are considered superior to their basefluid, because a new type of fluid has been formed with a completely different thermophysical properties such as density, specific heat capacity, thermal conductivity, convective heat transfer, thermal diffusivity, and viscosity [
Nanofluid thermophysical properties.
The effective density of a nanofluid can be theoretically calculated through its basefluid density (
The only constraint to the aforementioned equation (
There were few attempts undertaken to measure the density of nanofluids experimentally, as the majority of researchers tend to use the mixing theory in order to predict its value [
Comparison between theoretically calculated effective density (
There are limited number of correlations available, for the effective density of nanofluid, that takes into account the particle size and shape, nanofluid temperature, added surfactant, and the nanolayer between the particles and the basefluid effect [
The effective specific heat of a nanofluid (
Zhou and Ni [
Comparison between theoretically calculated effective specific heat (
From the previous studies, it can be noticed that there are few published works on effective specific heat of nanofluids; hence more work is needed to narrow the gap of knowledge in this area. In addition, nanoparticles size and concentration, nanofluid temperature, and basefluid type have been shown to strongly influence the effective specific heat of nanofluids which was also pointed out by Sekhar and Sharma [
One of the main driving forces behind the concept of nanofluids is the enhancement of the thermal conductivity compared to conventional fluids, which has a positive effect on the fluid convective heat transfer. Adding nanoparticles to a conventional fluid improves its thermal conductivity, if the added nanoparticles had a higher thermal conductivity than its basefluid. Some of the most common nanoparticles and basefluids thermal conductivities are shown in Tables
Commonly used nanoparticles thermal conductivities [
Material | Thermal conductivity (W/mK) |
---|---|
Al2O3 | 40 |
CuO | 76.5 |
Fe2O3 | 6 |
MgO | 54.9 |
SiO2 | 1.34–1.38 |
TiO2 | 8.4 |
ZnO | 29 |
Ag | 429 |
Al | 238–273 |
Au | 310 |
Cu | 401 |
Fe | 75–80 |
MWCNTs | 2000–3000 |
Commonly used basefluids thermal conductivities [
Fluid | Thermal conductivity (W/mK) |
---|---|
EG | 40 |
Ethylene oxide | 76.5 |
Ethanol | 6 |
Glycerol | 54.9 |
Kerosene | 1.34–1.38 |
Toluene | 8.4 |
Water | 29 |
This increase in effective thermal conductivity can be linked to different factors such as the Brownian motion (Figure
(a) Nanoparticles Brownian motion and (b) nanofluid structure containing bulk fluid, nanoparticles, and nanolayers at the liquid/solid interface [
Many experimental and theoretical work have been carried out to investigate the changes in nanofluids thermal conductivity. Maxwell model (
Equation (
Examples of different effective thermal conductivity correlations available in literatures.
Researchers | Model | Remarks |
---|---|---|
Hamilton and Crosser [ | | Modified Maxwell model that determines the effective thermal conductivity of nonspherical particles using a shape factor ( |
| ||
Wasp et al. [ | | Spherical case of the Hamilton and Crosser model (i.e., |
| ||
Yu and Choi [ | | Another modified Maxwell model where all volume fraction and the combination of nanolayer and nanoparticles thermal conductivity are taken into account. The thermal conductivity of the nanolayer ( |
| ||
Xuan et al. [ | | Modified Maxwell model that takes into consideration the Brownian motion effect and the aggregation structure of nanoparticles clusters. The model was found to yield incorrect units in the Brownian motion as described by different researchers [ |
| ||
Koo and Kleinstreuer [ | | This model considers the kinetic energy of the nanoparticles that is produced from the Brownian movement in addition to the effects of particle vol%, particle size, basefluid properties, and temperature dependence. The thermal conductivity of both Brownian motion ( |
| ||
Xue and Xu [ | | An implicit model that assumes the existing of nanoparticles shells which cover the solid particle and interact with the surrounding basefluid. The model was developed based on the data of effective thermal conductivity of CuO/H2O and CuO/EG, where |
| ||
Prasher et al. [ | | This model uses the effect of Brownian motion as a correction factor to the Maxwell correlation to predict the enhancement in thermal conductivity caused from the nanoparticles local convection mechanism, where the |
| ||
Jang and Choi [ | | This model takes into account the relation between the kinetic theory and Nusselt number for flow past a sphere. The symbol |
Experimental measurements of nanofluids effective thermal conductivity were performed by several researchers using transient hot-wire method (low cost and easy to use, where the measurements are based on Fourier’s law and the effective thermal conductivity reported to be of 5% uncertainty) [
Very few published papers have considered the effective thermal diffusivity of nanofluids (
Nanofluid viscosity is a measure of the tendency of the suspension to resist the flow. It can also be defined as the ratio of the shear stress to the shear rate. The benefit associated with nanofluids heat enhancement is counteracted by the rise in effective viscosity caused from the added nanoparticles in the basefluid. This increase in viscosity leads to higher pressure losses and hence elevates the pumping power demands. The main parameters that influence the effective viscosity are the basefluid viscosity, nanoparticles concentration, particle shape, particle diameter, particles type, temperature, pressure, pH value, and shear rate [
Up to now, the Einstein equation can be assumed to be the only available universal correlation that can predict the effective viscosity of nanofluids of low concentration [
Nanofluids have been shown to be superior, as a HTF, to conventional known fluids available in the market. In order to commercialise such type of advanced fluids, some factors are required to be improved and better understood by researchers. Examples of these factors are listed below [ Experimental investigations of nanofluids need to be optimised with respect to stability, preparation technique, temperature, particle size, particle shape, and particles type. The right ratio of nanoparticles to basefluid should be found to obtain the highest effective thermal conductivity as well as the lowest possible effective viscosity from the fabricated nanofluid. This is important to meet the applications of heat transfer and overcome the pressure drop in the system. Additional research inputs are needed to develop much precise correlations, which can predict the changes in nanofluids pH value caused by temperature, particle concentration, type of basefluid, and so forth, since this affects the stability and thermophysical properties of nanofluids. Several studies have considered the fouling effect of nanofluids in a thermal aspect but, to the best of our knowledge, have ignored their influence on the dynamics of the fluid. Though, if fully deposited on the inner pipe surface, it can provide similar wettability properties as nanocoating.
The aforementioned challenges need to be focused on and tackled by researches so that commercialisation of nanofluids can be possible.
The types of nanofluids, preparation approaches, fluid stability, and stability enhancement have been reviewed. The article also extends to the thermophysical properties of nanofluids, covering both the theoretical and experimental aspects. According to literature, several studies have discussed the potential of enhancing heat transfer using nanofluids and how the stability of a nanofluid affects its thermophysical properties. It was also pointed out that the stability of a nanofluid gets affected by a range of factors, such as preparation technique, pH value, nanoparticle concentration, particles type, particle shape, particle size, and fluid temperature.
To the best of our knowledge, in all the literature related to using nanofluids, no existing work related to controlling the temperature of the fluid while fabricating the nanofluid using an ultrasonicator has been reported. This preparation approach is very important as it can result in a completely different pH values, settling behaviour, particles agglomeration, and thermophysical properties. Additionally, using an ultrasonic device, for fabricating nanofluids, will increase the temperature of the fluid gradually but is strongly affected by the ambient temperature where the sample is prepared, meaning that various locations or different weather conditions will most likely result in producing a diverse nanofluid.
In addition, one can conclude from the literature that the major drawback of using such type of fluids is the rise in pressure losses in piping systems caused from the increase in viscosity of nanofluids. This increase in viscosity leads to a higher shear stress between the fluid and the surrounding surface. Moreover, the nanoparticles hosted by the fluid are most likely to deposit on the inner surface of the pipe when used in elevated temperature applications, causing what is known as the fouling effect. The deposited layer or foul would act similarly as inner pipe coating with nanoparticles (i.e., nanocoating) since the foul is formed from nanoparticles that were hosted by the carrier fluid itself. It was reported, by a number of authors, that nanocoating has the advantage of reducing the surface roughness which strongly influences the shear stress between the surface and the fluid [
The authors of this work declare that there are no conflicts of interest regarding the publication of this paper.
The authors would like to acknowledge the support provided by the Nanotechnology and Advanced Materials Program (NAM) at the Kuwait Institute for Scientific Research (KISR), Kuwait, and the School of Water, Energy and Environment (SWEE) at Cranfield University, UK. They are also grateful to Professor M. Sherif El-Eskandarany, the program manager of Nanotechnology and Advanced Materials Program at KISR, for providing his knowledge in the field.