Thermal conductivity and dynamic viscosity of mono and hybrid organic- and synthetic-based nanofluids: A critical review

6.2.1 Effect of a base fluid

Figure 2 shows the standard thermal conductivity data for water, W:EG mixtures, W:PG, EG, and PG chosen to be compared with experimental results. Standard thermal conductivity data for water were obtained from the thermophysical properties of fluids system webbook issued by the National Institute of Standards and Technology, U.S. Department of Commerce [143]. The thermal conductivity of water and EG mixture was derived from ANSI/ASHRAE Standards for Ventilation System Design and Acceptable Indoor Quality [144]. The results obtained from the experiment compared with the standard thermal conductivity data are essential to test the reliability of the experimental setup and the results [75,94,96,99,145]. For most investigations, water-based fluid is the preferred choice since it is neutral to most synthetic-based nanoparticles at temperatures below 100°C, is the most convenient fluid, and easy to handle in the laboratory. The EG-based fluid is the primary option for the researcher to add low-pressure coolant to the engine. W:EG mixture-based fluid is used in high-temperature applications.

Figure 2

Thermal conductivity of conventional base fluids.

A few studies have been conducted to identify base fluids that function well with selected synthetic-based nanoparticles. Cabaleiro et al. [146] reported the effect of EG and PG on TiO₂ nanoparticles dispersion with two different nanocrystalline structures. The result revealed that the thermal conductivity of TiO₂/PG with lower thermal conductivity of base fluid than EG has a superior enhancement at all concentrations from 5 to 25 wt% at the same temperature compared to TiO₂/EG. The thermal conductivity of EG and PG is 0.248 and 0.207 W m⁻¹ K⁻¹ at 30°C, respectively. These results are consistent with those of Moosavi et al. [147]. Moosavi et al. [147] found that the thermal conductivity enhancement of ZnO at a lower thermal conductivity of EG was higher, with 10.5% than glycerol (G) with 7.2% at 3 vol%. The thermal conductivity of EG and G is 0.250 and 0.280 W m⁻¹ K⁻¹, at the same temperature, respectively. However, these results are refuted by the findings of Akilu et al. [148]. Akilu et al. [148] compared the thermal conductivity of EG and PG-based β-SiC nanofluid. The maximum relative thermal conductivity of 1 vol% of β-SiC nanofluid at 25°C is 5.5% for EG-based nanofluid compared to PG-based nanofluid with 2.9%. The result presented does not infer that the thermal conductivity enhancement of nanofluid is increased linearly with the thermal conductivity of the base fluid.

On the other hand, Sonawane et al. [149] considered water, EG, and paraffin oil with TiO₂ dispersion and found that TiO₂ dispersed in water with the lowest dynamic viscosity (0.806 mPa s) had the highest percentage of increase in thermal conductivity compared to EG (16.5 mPa s) and paraffin oil (30 mPa s). They explained that the lower viscosity of the base fluid has led to an improvement in the thermal conductivity of nanofluid because low viscosity of base fluid allows particles to interact more easily with each other, leading to an improvement in the Brownian motion of nanoparticles in fluids. It corresponds to further particle-to-particle interactions over a given time. As a result, TiO₂ nanoparticles can transfer heat more effectively to water than EG and paraffin oil. Nevertheless, despite the mechanism described, the lower enhancement was provided by TiO₂/EG instead of TiO₂/paraffin oil, which is contradicted by the observation that EG does not have the lowest viscosity in the group. Also, the thermal conductivity of all base fluids was optimally increased after 60 min of ultrasonication. After 60 min, the thermal conductivity performance of both nanofluids was slowly reduced.

However, the effect of ultrasonication time gave the opposite result to the previous work of Codreanu et al. [150]. The effect of water and EG-based fluid on Fe–C nanoparticles dispersion before and after ultrasonication has been investigated. Thermal conductivity improved by 35.3% without ultrasonication for Fe–C/water at 1 vol%. Percentages of thermal conductivity enhancement were reduced linearly to 28.9% after 7 h of ultrasonication. On the other hand, Fe–C/EG trend line at the concentration of 0.25 vol% was not equivalent to Fe–C/water. Maximum thermal conductivity enhancement was 24.1% before ultrasonication decreased after an hour of ultrasonication. However, the thermal conductivity was restored as the duration of ultrasonication increased until it reached 40.1% after 7 h. The explanation for this trend is that nanoparticles formed a unique cluster of nanoparticles with specific base fluids. When the Fe–C nanoparticles were dispersed in deionized water, the nanoparticles formed a cluster of uniform downy mass where they were rapidly settled. These clusters may not significantly affect particle size, although the cluster of Fe–C/water nanoparticles has decreased from 474.8 to 3–6 nm. On the other hand, when dispersed in EG, nanoparticles are formed in a uniform, small, compact cluster. The magnitude of Fe–C/EG nanoparticle cluster decreased from 101.6 nm to less than 10–24 nm after ultrasonication had a substantial impact on the improvement of thermal conductivity.

In addition to selecting a single type of base fluid for nanofluid analysis, research was also performed for the optimum percentage of mixtures of two different types of the base fluid. Sundar et al. [151] analyzed Al₂O₃ in water and EG mixture at three different weight concentrations. The test was conducted at 40:60, 60:40, and 80:20% by weight of W:EG with a volume concentration of 0.3–1.5% and a temperature between 20 and 60°C. Maximum thermal conductivity increase is 32.26% at a concentration of 1.5 vol% with W:EG 80:20% at the temperature of 60°C. The thermal conductivity of nanofluid is higher as the weight concentration of EG is lower. This research discovery is similar to the work done by Chiam et al. [95] using the same nanoparticles of Al₂O₃. Al₂O₃ nanoparticles with an average size of 13 nm were dispersed in three different volume ratios of W:EG of 40:60, 50:50, and 60:40%. The authors found that the highest thermal conductivity value increased with an increase in the water ratio. Thermal conductivity peaked at 12.8% with 1 vol% of Al₂O₃ at 60:40% of W:EG. The higher the amount of EG added to the water, the lower the percentage of the water and lower the thermal conductivity of the mixture.

Some studies have opted to use W:EG mixture at various volume ratios for their applications. Vajjha et al. [152] used a mixture of W:EG 40:60% by weight to investigate the thermophysical properties of the three different types of nanoparticles, Al₂O₃, CuO, and SiO₂. The usage of this percentage of the W:EG mixture is because it is widely used as a heat transfer fluid in the cold region of the world in building heaters and in automotive radiators. Timofeeva et al. [153] used the W:EG 50:50% volume ratio to evaluate the thermal conductivity, viscosity, and heat transfer coefficient of SiC nanoparticles at four different sizes. This mixture was used for cooling in transport and power electronics. The thermal conductivity increased by 4–5% when dispersed in W:EG 50:50% at the same concentration and size. This effect cannot be explained simply by the lower thermal conductivity of W:EG-based fluid since the difference in improvement values predicted from the effective medium theory is less than 0.1%. It is most correlated with the lower value of the interfacial thermal resistance in W:EG nanofluids. Xie et al. [154] used W:EG 55:45% by volume percentage to examine four different nanoparticles with the same diameter size of 30 ± 5 nm for their heat transfer behavior. This mixture was used since it was widely used in engine cooling and solar energy systems. Among Al₂O₃, ZnO, TiO₂, and MgO nanoparticles, MgO nanoparticles have improved thermal conductivity by 4.5% at the concentration of 0.1 vol%. Al-Waeli et al. [155] found that W:EG and W:PG at the same volume ratio of 65:35% did not show substantial improvements in thermal conductivity at the same concentration and temperature even though W:EG and W:PG had a notable increase in viscosity and density relative to the water-based fluid. Nano-SiC with a diameter of 45–65 nm with the aid of a surfactant is used for its thermo-physical properties to determine the best base fluid to be used in the photovoltaic/thermal system. The result was that both SiC/W:EG and SiC/W:PG with 0.5 wt% increase the thermal conductivity by just 1.66% at 25°C relative to the conventional water-based fluid.

6.2.2 Effect of types of nanoparticles

Figure 3 compared various types of nanoparticles dispersed in selected base fluids for nanofluid investigations. Synthetic- and organic-based mono- and hybrid-based nanoparticles have been dispersed in the base fluid of water, EG, and W:EG 60:40% to increase the thermal conductivity of the respective base fluid. As shown in Figure 3(a), the comparisons of 15 different types of water-based nanofluids were selected to compare the efficacy of selected nanoparticles in improving thermal conductivity. This distinction was made for nanoparticle dispersion in the water-based fluid at temperatures ranging from 20 to 35°C with thermal conductivity of water reduced from 1.0016 to 0.7193 mPa s, respectively [143]. Other effects that influence thermal conductivity, such as the size of nanoparticles, are considered not to be affected by this relationship. Overall, the thermal conductivity ratio increased with an increase in the normalized weight or volume concentrations. The maximum thermal conductivity ratio with aqueous Cu–CNT hybrid nanofluid can be seen as thermal conductivity increased by 36.2% at a volume concentration of 0.3% [156]. However, Jha and Ramaprabhu [157] just needed 0.03 vol% of aqueous Cu–MWCNT nanofluids to increase thermal conductivity by a little less than Cu–CNT dispersion by 35.3%. Conversely, aqueous mango bark decreased the thermal conductivity performance of water by 46% at a weight concentration of 0.5% [38]. Other aqueous nanofluids have improved thermal conductivity by less than 20%. GNP hybrid nanofluids with clove [37], Ag [158], and platinum [159] were investigated at concentrations ranging from 0 to 0.1 wt% with maximum thermal conductivity at 17.8%. Amiri et al. [160] and Askari et al. [91] measured aqueous MWCNT–Ag and GNP–Fe₂O₃ with a maximum weight concentration of 1% and maximum thermal conductivity improved by 20%. The addition of other nanoparticles may or may not increase the thermal conductivity of water-based fluid. Kim et al. [161] investigated mono-nanofluid of aqueous TiO₂ at a volume concentration of 1–3%, where the maximum increase in thermal conductivity is 10.3%. Then, Megatif et al. [109] applied CNT in aqueous TiO₂ at a concentration range of 0.1–0.2 vol% to increase the thermal conductivity by up to 21.2% relative to the base fluid. Conversely, Madhesh et al. [162] lowered the aqueous TiO performance by 2.2% by adding Cu with 2 vol%. This comparison demonstrated that the proposed carbon-based nanofluid to aqueous metal or metal oxide nanofluid would increase the thermal conductivity of mono-based nanofluid.

Figure 3

Effect of types of nanoparticles: (a) water (b) EG and W:EG 60:40%.

On the other hand, a comparison of different types of mono and hybrid nanoparticle dispersion in EG and a mixture of W:EG at a constant volume ratio of 60:40% is shown in Figure 3(b). There is a significant volume ratio of a mixture of W:EG in the literature. Even so, as mentioned in the previous section, a constant ratio of W:EG 60:40% was selected for this comparison, as this ratio was considered to have the most excellent performance relative to other volume ratios [16,75,95,163]. Samylingam et al. [16] used this base fluid mixture to increase their thermal conductivity by dispersing cellulose nanocrystal. Thermal conductivity was increased by 10% from a weight concentration of 0.001–0.015%, with the maximum thermal conductivity ratio of 1.89 achieved by a weight concentration of 0.013%. The second highest increase in thermal conductivity of W:EG 60:40%-based nanofluids is the dispersion of palm kernel nanoparticles reported by Awua et al. [40]. The maximum increase of 51% was achieved at 0.5 vol%. Other nanofluids improved thermal conductivity by ±20%. The maximum thermal conductivity for synthetic-based nanofluids exceeded 23.2% with the dispersion of 5 vol% of CuO in EG by Liu et al. [164] and the minimum thermal conductivity was achieved by Chiam et al. [95] with an improvement of 4.6% with the concentration of Al₂O₃ of 1 vol% in W:EG 60:40%. With the same type of nanoparticle of Al₂O₃ at a very low concentration of 0.05 vol% in EG, Choi et al. [165], however, achieved higher thermal conductivity enhancement by 17%. Nevertheless, Al₂O₃ was dispersed at a larger nanoparticle size, which therefore had a higher thermal conductivity. The effect of nanoparticle size is discussed in Section 6.2.5. Eastman et al. [166] and Nabil et al. [75] obtained a significant improvement in thermal conductivity by 13% when dispersing their selected nanoparticles in the base fluid. Nabil et al. [75] dispersed TiO₂–SiO₂ hybrid nanofluid at a concentration of 3 vol% in W:EG 60:40% of the base fluid, and Eastman et al. [166] dispersed Cu at a lower volume concentration of 0.5% in EG.

Mostafizul et al. [167] have dispersed three different types of nanoparticles, Al₂O₃, SiO₂, and TiO₂ in methanol with a maximum study concentration of 0.15 vol%. He documented that Al₂O₃ had the most remarkable improvement and that SiO₂ had the lowest increase in thermal conductivity in methanol. The possible reason is that all nanoparticles have different individual thermal conductivity. These nanoparticles travel using the Brownian motion, where they translate, rotate, and shift to various degree based on their fundamental thermal properties.

6.2.3 Effect of concentrations

Figure 4 shows the effect of normalized weight or volume concentrations on the thermal conductivity ratio of four most favoured types of synthetic nanoparticles, Al₂O₃, Cu-based nanoparticles, Ti-based nanoparticles, and carbon-based nanoparticles dispersed in three different types of base fluids. Since these data given are normalized, the minimum and maximum examined concentrations for each reference may be found in Tables 1 and 2. These nanofluids were compared for the temperature range from 20 to 30°C where the increase in thermal conductivity of water, EG, and W:EG 60:40% is 2.8% [143], 0.2% [168], and 2% [144], respectively. The effect of nanoparticle size is neglected, and all selected nanofluids did not use any surfactant or pH adjustment for the stability in the sample. The pH value was retained as a neutral pH. In general, the thermal conductivity of all nanofluids was enhanced as the concentration increased. The figure shows that in all types of base fluids, CuO and Al₂O₃ nanofluids have a higher thermal conductivity ratio than other nanofluids. When the concentration increases, the distance between the nanoparticles decreases as the particles bang into each other more often, increasing the frequency of the lattice vibration that opens the possibility of heat transfer between the particles.

Figure 4

Effect of nanoparticle’s concentration on thermal conductivity: (a) Al₂O₃, (b) Cu and CuO, (c) TiO₂, and (d) carbon based.

In Figure 4(a), Al₂O₃ was dispersed in four different base fluids, including water, EG, W:EG 60:40%, and methanol as extracted from the literature. There are three different diameters of Al₂O₃ used in the compared literature: 12–13, 35–36, and 77 nm. Eastman et al. [29, 166] and Sundar et al. [151] dispersed 35–36 nm of Al₂O₃ in water, EG, and W:EG 60:40% at 20°C. The maximum thermal conductivity ratio for these references is 1.29, 1.18, and 1.11 at the volume concentration of 0.01–0.05%, 1–5%, and 0.3–1.5%, respectively. Mintsa et al. [169] tested the same sample as Choi et al. at 25°C and obtained a significantly lower thermal conductivity ratio by 3%. Nevertheless, the maximum thermal conductivity ratio was reached by a volume concentration of 18% of Al₂O₃. Mostafizul et al. [167], Beck et al. [170], and Chiam et al. [95] investigated Al₂O₃ with a diameter of 12–13 nm at a temperature of 20, 27, and 30°C, respectively. The nanoparticle was dispersed in methanol, EG, and W:EG 60:40%, where the maximum thermal conductivity ratio for these comparisons is 1.23, 1.17, and 1.05, respectively. It was investigated at the concentrations of 0.01–0.15, 1–4, and 0.2–1 vol%, respectively. Suresh et al. [171] investigated aqueous Al₂O₃ with a diameter of 77 nm at the temperature of 20°C. The thermal conductivity ratio improved from 1.005 to 1.075, with Al₂O₃ dispersion of 0.1–2 vol%.

On the other hand, a comparison of different Cu-based nanoparticles homogeneously dispersed in water or EG was shown in Figure 4(b). CuO was dispersed in water and EG at a temperature range of 20–25°C and a diameter of about 29–50 nm. Eastman et al. [29] and Choi et al. [165] dispersed CuO with a diameter of 35–36 nm at 20°C with a concentration range between 0.01 and 0.05 vol%. CuO raises the thermal conductivity of water by 59% with a concentration of 0.05%, and a lower thermal conductivity was reached by EG by 23% at a concentration of 0.04%. Liu et al. [164] and Mintsa et al. [169] studied CuO in EG and water at higher concentrations. Liu et al. [164] examined CuO with a diameter of 30–50 nm dispersed at 20°C in water. Thermal conductivity improved by 23% with a concentration of 5 vol%. The increment percentage is the same for CuO/water nanofluid investigated by Mintsa et al. [169] at 12 vol% with a slightly smaller CuO of 29 nm at 25°C. On the other hand, Eastman et al. [166] performed a mono-Cu dispersed experiment in EG where thermal conductivity increased by up to 14% at a concentration of 0.5 vol%. Jana et al. [156] and Jha et al. [157] mixed Cu with CNT and MWCNT dispersed in a water-based fluid. The thermal conductivity ratio of both hybrid nanofluids increased by about 36% as the normalized concentration increased.

In addition to Cu–CNT nanofluid, Jana et al. [156] also examined the efficacy of Au in CNT/water nanofluid. Ironically, the thermal conductivity of hybrid nanoparticles has decreased the thermal conductivity of mono-nanofluid rather than increased as desired for hybrid nanofluid. The normalized thermal conductivity of CNT was then plotted to the volume concentration. The result showed that the thermal conductivity is non-linear to the volume concentration. The justification for this is that CNT, which has a broad AR of geometrical anisotropy, contributed to the anisotropic physical movement of interfacial thermal resistance. As the concentration increased, the loading of nanofluids increased, increasing the agglomeration where the final shape and size of nanoparticles in nanofluids become uncertain. On the other hand, Jha et al. [157] reported that Cu performed well with MWCNT when dispersed in water. Functionalized MWCNT processed the MWCNT, and the Cu–MWCT was synthesized by chemical reduction, which led to the discovery of more hydrophilic MWCNT. The higher the concentrations, these nanoparticles strengthen the working synergy between them, where Cu is homogeneously decorated in MWCNT and enhances the heat conduction between nanoparticles. This mechanism is not surprising, as several factors may have contributed to this discrepancy, as described earlier in this study.

The dispersion of mono and hybrid TiO₂ nanoparticles in water is compared in Figure 4(c). The comparison was made at a temperature range between 20 and 30°C. The thermal conductivity of TiO₂ nanoparticles in water increased from 4.3 to 10.3% as the volume concentration increased from 1 to 3% [161]. A slight increase in thermal conductivity was observed when SiO₂ was added to the TiO₂ nanofluid in W:EG 60:40% by volume ratio at all normalized volume concentrations. The thermal conductivity of TiO₂–SiO₂/W:EG 60:40% was increased by 3% at 3 vol% compared to mono TiO₂/water by Kim et al. [161]. Maximum improvement was obtained by adding Cu and CNT to TiO₂/water by 25.7 and 21.2%, respectively. Madhesh et al. [162] and Megatif et al. [109] have dispersed the maximum concentration of 2 and 0.2 vol% of their selected hybrid nanoparticles in the water-based fluid. Madhesh et al. [162] explained that there is more than one factor to the increase in thermal conductivity as the volume concentration increased. In addition to the chaotic Brownian motion of nanoparticles and the morphology of nanoparticles, the exposure of highly crystalline and heat conductive Cu nanoparticles to TiO₂ has laid the foundation for a thermal interface network between their microstructures and the fluid layers. Megatif et al. [109] found that 0.1 wt% of TiO₂–CNT hybrid nanoparticles had lower thermal conductivity than mono-CNT nanoparticles in the water-based nanofluid. CNT/water at 0.1 wt% is marginally higher than the hybrid TIO₂–CNT/water at 0.15 wt%. Hybrid TiO₂–CNT nanofluid surpasses the thermal conductivity of mono-CNT/water when the hybrid nanoparticle is 0.2 wt%. The thermal conductivity of TiO₂–CNT/water nanofluid at 0.2 wt% was increased by 3% compared with CNT/water nanofluid at 0.1 wt%. The rationale is that it could be due to the nanoparticles’ agglomeration potential that led to the clustering effect between TiO₂ and CNT that contributes to the lower thermal conductivity of the hybrid nanofluid.

Carbon-based nanofluid required a technique involving non-covalent and covalent functionalization or mixed with metal or metal oxide to achieve high dispersion of carbon atoms which then increase the thermal properties of the base fluid. The covalent functionalization of carbon-based nanoparticles is related to the carbon-based molecular structure via the formation of covalent bonds [172]. New nanoparticle mixed in carbon-based nanofluid that shares at least one pair of electrons is claimed to have covalent functionalization with the carbon-based nanoparticles. Covalent functionalization helps improve the dispersibility, processability, and reactivity of carbon-based nanoparticles. Therefore, Figure 4(d) compared the thermal conductivity ratio of six different carbon-based hybrid nanoparticles to metal or metal oxide, GO, and FGO at 20–30°C. Like other types of synthetic-based nanofluid, the thermal conductivity ratio is increased as the carbon-based hybrid nanofluids increases. The GNT-clove/water [37], GNP–Ag/water [158], GNP-Pt/water [159], GO/water [173], and FGO/water [173] were investigated at the same highest concentration of 0.1 wt%. Comparing these five nanofluids, the sequence of highest thermal conductivity ratio is GNP–Ag/water > GNP-Pt/water > GNT-clove/water > FGO/water > GO/water. The GNT–Fe₂O₃ [91] and MWCNT–Ag [160] was investigated at the highest concentration of 1 wt% and MWCNT-Fe₂O₃ [174] by 0.3 wt%. The GNP–Ag/water with 0.1 wt% has the thermal conductivity ratio 3% lower than MWCNT/Ag/water with 1 wt%. This result shows with the same Ag/water nanofluid, the mixture of GNP in Ag–water gives better thermal conductivity enhancement than MWCNT.

Close attention needs to be paid to the hybrid nanofluid of the synthetic and organic mixture of GNT and cloves studied by Sadri et al. [37]. GNT-clove/water nanofluid increased thermal conductivity by 14.9% at a concentration of 0.1% by weight. Clove was selected as an effective organic nanoparticle to boost thermal conductivity as this source can enhance the functionality of GNPs in water. In addition, it has also contributed to environmental sustainability and cost-effectiveness, as it is one of the most grown spices in tropical climate regions. Although the dynamic viscosity of GNT-clove is like the base water, the thermal conductivity reached a maximum of 22.92% at 45°C for 0.1 wt% of nanoparticles.

6.2.4 Effect of bulk temperature

Although the nanofluid analysis is mostly about the effect of concentration, the relative change in thermal conductivity when nanofluids are dispersed in a base fluid is based on the experiment’s initial temperature state. Most of the paper stated that the investigation was conducted at room temperature. However, the room temperature varies depending on the location of the lab, the humidity within the laboratory, and the weather of the region or country. It is often more convenient to mention the value of room temperature. Figure 5 describes the effect of bulk temperature on the thermal conductivity ratio of various mono and hybrid nanoparticles dispersed in water or a 60:40% by volume ratio of W:EG. The comparison was performed at the same concentrations of 1 wt% or 1 vol% for metal and metal oxide nanofluids and 0.1 wt% or 0.1% for GE nanofluids, where no surfactant drives the stability. Just like the previous comparison, in this comparison, the effect of nanoparticle size is ignored. All nanofluids have improved their thermal conductivity above the thermal conductivity of the base fluid. As shown in both figures, thermal conductivity ratio is increased as temperature increases for Ag–water [160], Ag-MWCNT/water [160], Fe₂O₃–water [91], Fe₃O₄–GNP/water [91], TiO₂-Cu/water [162], GO/water [173], FGO/water [173], GQD/water [175], Al₂O₃/W:EG 60:40% [95,151], and TiO₂–SiO₂/W:EG 60:40% [75]. On the other hand, the thermal conductivity ratio for TiO₂/water [176] and TiO₂–Ag/water [176] decreases as the temperature increases from 18 to 40°C.

Figure 5

Effect of bulk temperature: (a) water and (b) W:EG 60:40%.

Figure 5(a) shows the maximum thermal conductivity ratio was increased by 30% for Ag-MWCNT/water hybrid nanoparticle compared to the base fluid and increased by double compared to Ag–water at 80°C [160]. In the same research, MWCNT hybrid nanofluids with cysteine (Cys) and gum Arabic (GA) was also investigated for a concentration of 0.5 wt% in the temperature range of 30–80°C. The result shows that the thermal conductivity increment sequence is MWCNT-Ag/water > MWCNT-Cys/water > MWCNT-GA/water > Ag/water. The MWCNT developed a liquid layer interface that results in a higher thermal conductivity than that of the base fluid. With the addition of synthetic nanoparticles mixed in aqueous MWCNT, the nature of the covalent functionalization by Cys and Ag ions attached to the MWCNT surface has increased thermal conductivity. In contrast, GA is a non-covalent functionalization water-based MWCNT. Since Ag has a higher thermal conductivity than cysteine, the thermal conductivity of MWCNT-Ag is higher than MWCNT-Cys/water.

In Figure 5(a), particular emphasis should be addressed to the temperature of 40°C, where most of the mentioned references tested their nanofluids. Fe₃O₄–GNP/water has the highest thermal conductivity ratio with 1 wt%, followed by GQD/water with 0.1 vol%, and so on. These data show that GQD/water has a thermal conductivity ratio difference of just 4% when the concentration is 90% less than Fe₃O₄–GNP/water. As the concentration of GQD/water increased to 0.5 vol%, the thermal conductivity ratio climbed to 1.40. With a 50% lower concentration than Fe₃O₄–GNP/water, the increment in thermal conductivity ratio is 17% higher than Fe₃O₄–GNP/water. The reason for this is that GQD was synthesized using a zero-dimension carbon material with a diameter of 20 nm made from GE and carbon dots. It is chemically stable and has a larger surface area than other nanoparticles, resulting in higher surface properties and an increase in thermal conductivity. Like GQD/water, the thermal conductivity ratios of GO and FGO/water at 0.1 wt% are higher than hybrid Ag-MWCNT/water at 1 wt%. GO and FGO are oxidized and doped GE, respectively, with a modified plane hybrid of two and three single-bond carbon atoms (sp2 and sp3) with added functional groups to improve physical and chemical properties. GO is dispersible in practically all polar and nonpolar liquids, which increases the Brownian motion between carbon atoms. Even though the GO and the FGO are equal in size, structure, and morphology, the FGO/water data are higher than the GO/water. The reason for this is that adding fluorine (F) to GO alters the local environment surrounding the carbon atoms due to the strong electronegative nature of F- by producing extra sp3 and expanding the bandgap between carbon atoms which contributes to thermal enhancement.

Furthermore, at 40°C, the maximum thermal conductivity ratio is Fe₃O₄–GNP/water at 1 wt%. Askari et al. [91] investigated the effect of Fe₃O₄–GNP/water hybrid nanofluid compared to Fe₃O₄/water. At 20°C, the thermal conductivity of Fe₃O₄–water increased by only 2.3%. Adding GNP increases the value by 4.3–6.6% at the same temperature. As the temperature rises to 40°C, the thermal conductivity ratio of hybrid nanofluid rises by 3.3% to 1.24 compared to mono Fe₃O₄–water at the same temperature. The thermal conductivity of GNP is higher than that of Fe₃O₄, which contributed to the increase in aqueous Fe₃O₄–GNP hybrid nanofluid. As the temperature increases, the inter-particles and inter-molecular adhesion forces between Fe₃O₄ and GNP are weaker, resulting in an increase in random and Brownian motion that has contributed to the improvement of thermal conductivity. In addition, the average size of Fe₃O₄–GNP/water was 327 nm compared to Fe₃O₄/water at around 60.4 nm. The formation of clustering of GNP sheets with a higher tendency to stick to each other was concluded to be one of the contributions to the thermal conductivity enhancement of hybrid nanofluid. Batmunkh et al. [176] experienced a slight increase in thermal conductivity from 1.5 to 3.2% when mono-TiO₂ and TiO₂–Ag were dispersed in water at 15°C, respectively. However, as the temperature increases, both nanofluids experience a decrease in thermal conductivity ratio. The thermal conductivity of TiO₂/water returns to the value of the water-based fluid at 40°C. The difference in thermal conductivity ratio of this nanofluid is only 0.04%. The conductance contact between nanoparticles has been improved when adding 0.5 wt% of Ag in TiO₂–water. Nevertheless, the thermal conductivity ratio of TiO₂–Ag/water decreases from an increase of 3.2–1.4% when the temperature increases from 15 to 40°C. Since Ag has high thermal conductivity, the interface of TiO₂–Ag hybrid nanoparticles facilitates phonon conduction by reducing the boundary dispersion failure and interfacial resistance between nanoparticles and the fluid.

Figure 5(b) shows the three different nanoparticles dispersed in W:EG 60:40% by volume ratio. The experimental outcome by Chiam et al. [95] and Nabil et al. [75] on the effect of different types of base fluid was discussed in Section 5.1.1. They also discussed the effect of temperature on thermal conductivity ratio for Al₂O₃ and TiO₂-SiO₂ dispersed in the same base fluid from 30 to 70°C. Like most of the relationship between temperature and thermal conductivity ratio for water-based fluid, the linear increase in thermal conductivity ratio was also achieved for W:EG 60:40%-based fluid with respect to temperature. The thermal conductivity ratio for the same concentration of 1 wt% increases by 4.6 and 5.6% at the temperature 30°C and increases to 7.8 and 9.4% as the temperature increases to 70°C for Chiam et al. [95] and Nabil et al. [75], respectively. For the same concentration and type of nanofluid as Chiam et al. [95], Sundar et al. [151] dispersed a slightly bigger size of Al₂O₃ nanoparticles, and the thermal conductivity ratio is higher than Chiam et al. [95]. The thermal conductivity ratio is 1.13 at 20°C and is increased to 1.28 at 60°C. As temperature increases, nanoparticles are given higher kinetic energy, which induces micro-convection in nanofluids and correlates with improved thermal conductivity. Increased thermal conductivity is also observed due to the substantial Brownian motion of the nanoparticles in the base fluid. High temperatures can adversely affect clustering and reduce the repulsive forces between nanoparticles, resulting in a less stable mixture with low thermal conductivity [80].

6.2.5 Effect of nanoparticle size

Figure 6 shows the effect of nanoparticles’ size on the thermal conductivity ratio of nanofluids. The graph is plotted from two references for aqueous Al₂O₃ [169], TiO₂ [169], and ZnO [161]. The result was recorded for the sample at the same temperature of 25°C at the same volume concentration of 1%. It is evident that, as the nanoparticle size increases, the thermal conductivity ratio decreases for all references. Mintsa et al. [169] and Kim et al. [161] investigated TiO₂ and ZnO at the same particle sizes of 10 and 30 nm, respectively. ZnO thermal conductivity ratio is higher than TiO₂ for the same particle size of 10 nm, with 1.049 for ZnO and 1.032 for TiO₂. For a larger size of particles, the thermal conductivity of ZnO and TiO₂ decreased to 1.032 and 1.027, respectively. Therefore, the change in thermal conductivity ratio on nanoparticles’ size is less than 2% as the size increases from 10 to 30 nm. Mintsa et al. [169] also investigated Al₂O₃ dispersed in water for two different nanoparticle sizes, 36 and 47 nm. The thermal conductivity ratio was 1.06, with Al₂O₃ nanoparticles’ size 36 nm reduced by 1.7% as the size of nanoparticles increased to 47 nm. Mintsa et al. [169] indicated that for the same volume concentration, a nanofluid with smaller nanoparticles yields an effective thermal conductivity because it has more significant contact with the surface of the fluid and increases the potential of Brownian motion.

Figure 6

Effect of nanoparticle size on thermal conductivity in a base fluid of water.

Conversely, Timofeeva et al. [153] revealed an opposite trend in the particle size’s thermal conductivity effect. Four different SiC sizes were tested for their thermal conductivity in water and W:EG 50:50% by volume percentages. The thermal conductivity increases as the average SiC size increases from 16, 28, 66, and 90 nm. Beck et al. [170] also previously found the same pattern for aqueous Al₂O₃ nanoparticles. The size of Al₂O₃ was investigated at the wide range of nanoparticle sizes ranging from 8 to 282 nm. For smaller particles sizes up to 50 nm, thermal conductivity has increased as the nanoparticle size increases. Nevertheless, the size of nanoparticles is inadequate for a larger size of nanoparticles as the thermal conductivity is constant. Recent work by Omrani et al. [177] has also shown the same pattern for MWCNT. The effect of the size of the MWCNT was determined by measuring the AR. They measured thermal conductivity and dynamic viscosity of MWCNT for six different ARs from the short type, AR ∼ 10–40, to the long type, AR ∼ 1,250–3,750. The results showed that the thermal conductivity increased linearly as the AR increased for the water-based fluid at the measured temperature.

Besides that, numerous pieces of literature have established the effect of nanoparticle size in a different mode of investigation. Eastman et al. [166] reported that for the same volume concentration of 0.5 vol% nanoparticles in EG, thermal conductivity ratio of pure Cu, Cu with an average diameter of less than 10 nm is 10% higher than that of CuO with an average diameter of 35 nm, approximately four times bigger than Cu. Kalantari et al. [178], which used citric acid and citrate at various amounts to stabilize aqueous Ag nanoparticles at room temperature, showed that the amount of citric acid in a fluid was influenced by the size of Ag. The size of nano-Ag is decreased as the volume of citric acid increases. In his study, the decrease in the size of Ag contributed to the rise in thermal conductivity. Nevertheless, the thermal conductivity of nano-Ag nanofluids increases by up to 223% compared to the base fluid of water at the same temperature of 50°C. Bouguerra et al. [102] indicated that when nanoparticles are monodispersed with the smallest particle size, maximum thermal conductivity and minimum dynamic viscosity were observed. Five different dispersion patterns and alteration of the structure of aqueous Al₂O₃ nanofluids were discovered for a pH range of between 4 and 8 for a volume concentration of between 0.2 and 2% at 25^oC. They stressed that the dispersion patterns of nanoparticles in nanofluid could determine the thermal conductivity and dynamic viscosity and their effect on the overall performance of the thermal energy plant.

Recent research by Essajai et al. [179] stated that the nanoparticles rod shape of Au nanoparticles provides better thermal conductivity than spherical shape nanoparticles. However, they demonstrated that the rod shape affected the nanofluid diffusion coefficient, which contradicted previous findings by Sarkar et al. [135] and Avsec [137]. Alawi et al. [142] also analyzed the influence of four different types of metal oxides in various shapes. The thermal conductivity of Al₂O₃, CuO, SiO₂, and ZnO in the shape of blades, bricks cylindrical, platelets, and spherical nanoparticles has been compared. The spherical-shaped Al₂O₃, CuO, and ZnO had the highest thermal conductivity at the highest concentration of 5 vol%. On the other hand, SiO₂ nanofluids obtained the highest thermal conductivity with the same concentration of cylindrical-shaped nanoparticles.

6.3.1 Effect of concentration

Figure 7 shows the standard dynamic viscosity data as a function of the bulk temperature of the commonly used base fluid for nanofluids investigations and applications. It shows that dynamic viscosity decreases exponentially as temperature increases from 20 to 80°C. The lowest dynamic viscosity of the base fluid is water with 1.0 mPa s at 20°C and drops to 0.3543 mPa s at 80°C [143]. EG’s highest dynamic viscosity is 19.486 mPa s at 20°C and decreases to 2.98 mPa s at 80°C [168]. The W:EG mixture at various volume ratios is actively investigated to apply convection heat transfer [95,151]. The dynamic viscosity of the W:EG mixture is increased as more EG is added to the water-based fluid [144]. Dynamic viscosity may change due to the presence of solid nanoparticles through concentrations and may lead to an increase in the pressure drop, thereby affecting the efficiency of energy systems. Low dynamic viscosity is desired to reduce pressure drop, increase fluid velocity, and increase energy efficiency for effective drag reduction or forced convection heat transfer applications.

Figure 7

Dynamic viscosity of various types of base fluids.

Thus, Figure 8 shows the relationship between dynamic viscosity ratio versus normalized weight or volume concentration for various types of nanoparticles dispersed in water, W:EG 60:40%, and EG. The range of the concentration for presented nanofluids can be referred in Tables 1 and 2. The comparison was made for nanofluid at temperatures between 20 and 30°C without any surfactant. Although the size of nanoparticles and pH value may impact the dynamic viscosity measurement of nanofluids, this effect is ignored in this comparison. The dynamic viscosity ratio is increasing linearly as the normalized concentration increases.

Figure 8

Effect of concentration on dynamic viscosity: (a) water and (b) EG and W:EG 60:40% mixture.

Figure 8(a) shows the lowest dynamic viscosity ratio was gained by mango bark nanofluids. The aqueous mango bark with an average size of 100 nm increases dynamic viscosity by only 2% when nanoparticle concentration has increased to 1 vol% at a constant temperature of 20°C [38]. Barki et al. [70] also synthesized the mango bark in the laboratory, and the size of the nanoparticle is 200 nm. This nanoparticle was dispersed at a concentration from 0.1 to 4 vol% in water at the same temperature of 20°C. Comparing the results of Sharifpur et al. [38] and Bakri et al. [70] at the same concentration of 1 vol%, the dynamic viscosity from Bakri et al. [70] significantly decreased by 1% compared to the water-based fluid. Since the dynamic viscosity was reduced with the existence of nanoparticles in the fluid, mango bark might be suitable to be used as a drag reduction agent. However, as the concentration rose to 4 vol%, the dynamic viscosity increased exponentially to 107% relative to the base fluid. On the other hand, the hybrid aqueous GO reached an undesired highest dynamic viscosity by 233%, with a weight concentration of 1 vol%. On the other hand, in Figure 8(b), the lowest dynamic viscosity was achieved by CNT-EG and TiO₂–SiO₂/W:EG 60:40% with a respective concentration of 0.4 wt% and 3 vol%. The result of hybrid Al₂O₃ and TiO₂ is consistent with the previous work of mono Al₂O₃ and TiO₂ in W:EG 80:20% by Yiamsawas et al. [182]. Mono Al₂O₃ and TiO₂ nanofluid were investigated at the volume concentration range between 1 and 4% and temperature range of 15–60°C. The result has shown that Al₂O₃ nanofluid has a higher dynamic viscosity than TiO₂ nanofluid. They clarified that the result was obtained because Al₂O₃ has a larger particle size than TiO₂. The effect of particle size on dynamic viscosity ratio will be discussed in Section 6.3.3.

To obtain a more distinct difference in dynamic viscosity ratio between nanofluids, the dynamic viscosity ratio at the same concentration is shown in Table 3. Water-based nanofluids for 0.1 and 1 wt% and EG or W:EG 60:40%-based nanofluids for 0.1 and 1 vol% are shown in Table 3(a) and (b), respectively. The comparison was made for the sample temperature between 20 and 30°C. It shows that in water-based fluid, mango bark produced in a laboratory by Barki et al. [70] with a concentration of 0.125 wt% reduces the dynamic viscosity by 5.7%. This result indicates that this organic-based nanofluid has the potential to be used as a drag reduction additive. The drag reduction phenomenon was initiated by Forrest back in 1931 by dispersing wood pulp in the pipe flow. The dispersion of a minimal amount of pulp or particles called a drag reduction additive reduces the viscous friction and pressure drop, thereby minimizing power consumption [183]. Consequently, for the same Reynolds number in the pipe flow, the reduction of dynamic viscosity will help to accelerate the flow velocity and increase power consumption efficiency. Nevertheless, as the concentration of aqueous mango bark increased to 1 wt%, the dynamic viscosity increased exponentially by 106.6%, and a drag reduction additive is no longer effective. Mango bark produced by Sharifpur et al. [38] may be used as a drag reduction additive as the dynamic viscosity ratio for both nanoparticles almost constant for 0.1–1 wt%. On the other hand, other nanoparticles show an increase in the dynamic viscosity ratio at all concentrations. Samylingam et al. [16] obtained the highest dynamic viscosity compared to other nanofluids with an exponential rise of up to 167.8% at 1 vol%. As the concentration increased to 1.5 vol%, the dynamic viscosity ratio increased to 5.826. Contradictory, they wanted a low viscous fluid to pump the fluid into the cutting interphase of the lathe machine operation. This nanofluid was investigated to improve the cutting speed, cutting depth, and feed rate of the lathe cutting machine, thus improving the heat transfer and tool life.

Askari et al. [91] said that by increasing the concentration of GE–Fe₃O₄ nanoparticles in water, the attractive force becomes greater than the repulsive force resulting in nanoparticles sticking together, leading to an increase in the dynamic viscosity of the nanofluid. Chiam et al. [95] dispersed Al₂O₃ nanoparticles in three different volume ratio mixture of W:EG and claimed an increase in dynamic viscosity as the concentration increased because more nanoparticles are added in the respective base fluid that causes an increase in friction and flow resistance of nanofluids. They added that the nanoparticles dispersed in the higher volume ratio of EG have higher dynamic viscosity. This inference is appropriate because, based on the standard data from ASHRAE, the dynamic viscosity of the higher volume ratio of EG has a higher dynamic viscosity than that of the higher volume ratio of water. Nevertheless, a dynamic viscosity percentage increase should be independent of the base fluid’s dynamic viscosity, but only the nanofluid’s concentration [153]. The observed anomalies could be correlated with the difference between the structure and thickness of the interfacial layer around the nanoparticles in the various base fluid, which affect the effective nanofluid concentration and, ultimately, the dynamic viscosity of nanofluids.

Suresh et al. [171] stated that the volume concentration from 0.001 to 0.02% of aqueous Al₂O₃–Cu did not change the Newtonian behavior of water. The Newtonian behavior was preserved at all concentrations as the shear rate increased from 0 to 750 s⁻¹. The dynamic viscosity of aqueous hybrid Al₂O₃–Cu nanofluid did not vary significantly from that of aqueous mono Al₂O₃ nanofluid at a lower concentration. However, the disparity is noticeable at higher volume concentrations. The rationale is that, as the nanofluids concentration increased, the clustering and adsorption increase the hydrodynamic diameter of nanoparticles, leading to an increase in the dynamic viscosity ratio. Nabil et al. [75] observed a Newtonian behavior in TiO₂–SiO₂/W:EG 60:40% nanofluids when a range of shear rate was applied from 25 to 180 s⁻¹. Newtonian behavior was observed before 3 vol% and temperature of between 30 and 50°C. Similar behavior was observed in Sundar et al. [151] for Al₂O₃/W:EG 60:40% for a concentration range of 0.3–1.5 vol% and a temperature range of 20–60°C. Yu et al. [184] also observed Newtonian fluid for ZnO/EG at a concentration below 2 vol% from 20 to 60°C since the viscosity is independent of shear rate from 20 to 100 s⁻¹. Nevertheless, a shear-thinning behavior was observed at a higher concentration of 3 vol% at a temperature below 40°C. As the concentration increases to 5 vol%, the shear-thinning was observed at all temperature under investigation from 20 to 60°C. The explanation for the shear-thinning was observed at this condition because the effective volume fraction of aggregates was much higher than the actual solid volume fraction. Chavan et al. [106] found the same pattern for Al₂O₃/water and SiO₂/EG at a shear rate of 0.1–500 s⁻¹ at 30°C. Both nanofluids are Newtonian with a concentration of less than 1 vol%, and the dynamic viscosity slope of Al₂O₃/water is steep than SiO₂/EG. These findings show that the dynamic viscosity of Al₂O₃/water is higher than that of SiO₂/EG. Shear-thinning was observed for concentrations above 4 vol%.

On the other hand, Garg et al. [180] studied the influence of ultrasonication on the dynamic viscosity of aqueous MWCNT at 1 wt% mixed with GA at 0.25 wt% nanofluid at 15 and 30°C. Ultrasonication was conducted for 20–80 min, with an interval of 20 min. They have shown that this nanofluid exhibits a shear-thinning or pseudoplastic type of non-Newtonian behavior without the presence of surfactant when a shear rate of up to 60 s⁻¹ at 15°C has been applied at all ultrasonication time. The shear-thinning effect was characterized by the potential de-agglomeration or realignment of the bundled nanotubes in the direction of the shearing force, resulting in less viscous drag. As the ultrasonication time increases, the nanotube spread, leading to the less pronounced shear-thinning behavior of the nanotube due to the shorter nanotube sizes.

6.3.2 Effect of bulk temperature

Figure 9(a) and (b) shows the effect of bulk temperature on the dynamic viscosity ratios of water, EG, and W:EG 60:40% by volume ratio nanofluids. The comparison is made for hybrid nanofluids with concentrations of less than 0.1 wt% or 1 vol%. Ten mono and hybrid nanofluids were plotted, and the trends differed for all nanofluids. At the respective temperature ranges, all nanofluids exhibit a greater dynamic viscosity ratio than the base fluid, except for mango bark at concentrations less than 0.5 vol%. When compared to the base fluid, all synthetic nanofluids increased dynamic viscosity by at least 5%. Fe₃O₄/water, GNP–Fe₃O₄/water, and CNT–TiO₂/water exhibit substantially similar dynamic viscosity ratios at all temperatures with the same concentration and the lowest increment when compared to other synthetic nanofluids. Al₂O₃/W:EG 60:40% from Chiam et al. [95] has a constant decrease, whereas Al₂O₃/W:EG 60:40% from Sundar et al. [151] fluctuates and exhibits the most significant rise in dynamic viscosity ratio as temperature increases. GNP–Ag/water and GQD/W:EG 60:40% show a consistent increase, while other nanofluids fluctuate as the temperature rises. According to Yarmand et al. [158], Barki et al. [70], Adewumi et al. [42], Aberoumand et al. [185], and Nguyen et al. [186], the increases in dynamic viscosity of any nanofluid can be attributed to the weakening of inter-particle and inter-molecular adhesive forces caused by an increase in heat convection between nanoparticles.

Figure 9

Effect of bulk temperature on normalized dynamic viscosity: (a) water and (b) W:EG 60:40%.

The dynamic viscosity of aqueous mango bark is almost identical to the base fluid relative to other nanofluids presented [113]. The dynamic viscosity ratio of aqueous mango bark decreases at low temperatures and concentrations. At the same volume concentration of 0.1%, the dynamic viscosity ratio of aqueous mango bark is lower than the base fluid. The lowest drop in dynamic viscosity ratio was reached at 60°C with a decrement of 21.8%. As the volume concentration increased to 0.5%, the dynamic viscosity increased by 3.2% at 10°C compared to the base fluid. As the temperature increases to 15°C, the dynamic viscosity decreases by 5.9% below the base fluid. As the temperature rises to 35°C, this value is retained below 5% of the base fluid. The dynamic viscosity ratio increased as the concentration rose to 1 vol%. The value increases to 8% at 10°C and decreases slightly below the dynamic viscosity of the base fluid with a tolerance of 1% to 35°C. These findings show an exciting feature of drag reduction for organic-based nanofluids at lower concentrations and temperatures as lower dynamic viscosity of nanofluids reduces the pumping power needed to drive the flow. The dynamic viscosity ratio then increases significantly as the temperature increases to a maximum value of 12.5% at 60°C. After which, the dynamic viscosity ratio of aqueous mango barks appeared to increase with temperature and concentrations dramatically. Nonetheless, the dynamic viscosity ratio of aqueous banana stem increased immediately following the 0.3 vol% nanoparticle concentration dispersed at 20°C [187]. With this concentration, the dynamic viscosity of nanofluid increased by 6%. The dynamic viscosity ratio increases to 1.14 as the temperature increases to 60°C. The maximum dynamic viscosity ratio of banana stem is 1.22, with a concentration of 1.5 vol% at 20°C.

6.3.3 Effect of nanoparticle size

The dynamic viscosity can change up to 40% by varying the size of particles [188]. Since the suggestion made by Forrest and Grierson on the effect of particle shape, size, and ARs may have an impact on the thermophysical properties of nanofluid, especially dynamic viscosity for drag reduction, several studies have been conducted on this relationship [189–191]. Figure 10 provides examples of the effect of the size of Al₂O₃ in EG on the dynamic viscosity ratio at 25°C by three different references. The effect of the size of Al₂O₃ is only noticeable at higher concentrations. The distinction was therefore provided with a volume concentration of 5% for all samples. The dynamic viscosity ratio from Kwek et al. [192] and the other two references are contradictory. The dynamic viscosity ratio of the sample from Kwek et al. [192] decreased, and the dynamic viscosity ratio of the sample from Adio et al. [193] and Selvam et al. [100] increased marginally as the particle size increased. The dynamic viscosity ratio decreases by 47.8% as particle size increases from 10 to 80 nm as the particle size increases for the sample from Kwek et al. [192]. Timofeeva et al. [153] agreed with Kwek et al. [192] for SiC/water and SiC/W:EG 50:50% of the same concentration, temperature, and pH value. The dynamic viscosity ratio is more profound for smaller particle size than for larger ones. Adio et al. [193] described that at the same volume concentration, the smaller size of nanoparticles provides a higher quantity of nanoparticles in the same volume concentration of nanofluid. The interaction between smaller nanoparticles will increase the Brownian velocity and the electro-viscous effect between them, contributing to the increment in viscosity. Therefore, the dynamic viscosity of nanofluid is higher with smaller nanoparticles. When the particle size is sufficiently small, starting from a specific critical size, it interacts actively with each other. At the same time, the fluid near the nanoparticles is formed to alter the rheological behavior, including the dynamic viscosity of the nanofluid [194].

Figure 10

Effect of nanoparticle size in EG on dynamic viscosity ratio.

On the other hand, Omrani et al. [177] demonstrated the decrement of dynamic viscosity as the CNT AR at the temperature of 25°C with a volume fraction of 0.05 vol%. The higher AR is more desirable as the dynamic viscosity ratio is the lowest at the highest AR at 2,500 compared to the lowest AR at 100. Alawi et al. [142] reveal that platelet-shaped nanoparticles for four different types of metal oxide have significantly reduced in dynamic viscosity compared to other shapes. However, he did not explain the rheological mechanism of the nanoparticles shape effect.