# Fewer Dimensions for Higher Thermal Performance: A Review on 2D Nanofluids
## 2 Types of 2D Nanofluids
### Carbon Nanotubes
Their performance in solar energy harvesting may be affected by various parameters, such as the type of CNTs, operating conditions, concentration, and involved systems. CNTs have various beneficial features, such as their very high aspect ratio and improved thermal, mechanical, and optical characteristics. The TC of CNTs reaches 6000 W/m\(\cdot\)K, which is a much higher value than that of the metals in bulk form. Liu et al. [70] observed that the suspension of CNTs at 1% vol. in ethylene glycol resulted in an enhancement of 12.4% in the TC. This increase was one of approximately 30% in the case of suspending CNTs in engine oil at 2% vol. Furthermore, Sadiri et al. [71] evaluated the influence of the type of the surfactant on the TC of MWCNTs dispersed in water at 0.5% wt. The investigation team observed a maximum enhancement in the TC in the case of the nanofluids with MWCNTs of 22.3% compared with water itself. Kumar et al. [72] numerically investigated MWCNTs dispersed in water flowing in a double helically coiled tube heat exchanger, which resulted in an increase of 30% in the Nusselt number when compared with that of water. Apart from their extremely high TC, CNTs also possess favorable optical properties. This makes them suitable for application in solar systems, as the use of colloidal suspensions in CNTs in will result in a spectral absorptivity alteration over the entire solar range. It has been previously demonstrated that incorporating CNTs and their hybrid structures into thermal base fluids can reduce transmittance, which will improve their competence in solar energy systems [73]. For instance, Hjerrild et al. [74] stated that using a nanofluid with CNTs instead of using only water led to a reduction in transmittance between 5% and 10% in the long wavelength and visible spectra. The transmittance reduction was found to depend on the concentration of CNTs in the base fluid. In the experimental work conducted by Gorji et al. [75], absorption spectroscopy was used to determine and compare the radiation absorption of water and functionalized CNTs dispersed in water. The results revealed a higher absorption of radiation when the functionalized CNTs were incorporated into the water. Moreover, the authors confirmed that the increase in the working temperature of the nanofluid promoted a decrease in radiation absorption. The higher absorption characteristics of nanofluids in CNTs makes them favorable for application in solar thermal energy. These nanofluids are suitable in solar energy systems such as collectors, solar ponds, and PV/T devices and systems. Applying nanofluids to these technologies has resulted in their efficiency enhancement and size reduction. Furthermore, the dynamic viscosity of CNT nanofluids deserves special attention from researchers. The rheological properties of water-based carbon nanofluids have been comprehensively studied for heat transfer enhancement and lubricant applications [76]. Studies on rheology included measurements with diverse types of SWCNTs [77] and MWCNTs [78]. Additionally, viscosity measurements on covalently functionalized CNTs have been also reported [79]. CNT suspensions in water are non-Newtonian fluids at comparatively high concentrations. Nonetheless, such nanofluids present a behavior close to the Newtonian one in the region dominated by low concentrations [80]. The CNTs possess extreme aspect ratios, considerably exceeding those of metal rods, cylinders, and discs. The viscosity of dilute CNT dispersions has been correlated with the length of the CNTs [81]. Anson-Casaos et al. [82] evaluated the viscosity of 1D diluted CNTs dispersed in water, performing viscosity measurements on the nanofluids of CNTs and functionalized CNTs. One type of functionalized CNT was directly suspended in water, and the other CNTs and functionalized CNTs were dispersed with the aid of surfactants. The authors evaluated the viscosity of the nanofluids based on the aspect ratio of the CNTs using prediction models.