# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 6 Backdraws and Future
The influence of the production should be further studied, namely the sonication time, volume fraction, and type of the NFs in order to avoid the sedimentation and agglomeration of the NPs in the BF and to achieve optimal performance;
* Lack of a common protocol for the manufacture and analysis of the thermal transport mechanisms in this type of fluids;
* Methods to scale-up production for commercialization are still in development;
* Some of the classical experimental fitting prediction models for thermal properties are not the most suitable for the estimation of the thermal conductivity, dynamic viscosity and density of the NFs;
* The classical modeling does not provide a fast prediction of the thermal properties, which may slow down the study and overall applicability of the NFs;
* Nowadays, it is clear the need to use the recent statistical data-driven machine learning models to obtain faster predictions of the thermal properties of the NFs. Those models are also more stable and sensitive than the empirical ones. However, for a in-depth knowledge on how the recent models work, the researchers must get proficient skills in machine learning methodology and in the most common data-driven models and algorithms;
* The proper machine learning model and algorithms should be chosen according to the dimension of the sample, the available computing resources, and the required prediction performance (modeling stability and sensibility). By way of guidelines that might be the path for future challenges and further studies and developments, the authors of the current work wish to emphasize the following:  
* The stability and durability of the nanofluids should be improved by optimizing the concentration of NPs and base fluid characteristics (e.g., chemical, viscosity). The stability of nanofluids should also be predicted by further analysis of the surface tension of the nanofluid vs. time;
* The general properties of the NFs should be improved by optimizing the preparation procedures (e.g., sonication time);
* The influence of the solvents should be further studied: the use of high polar solvents like the DMF (Dimethylformamide) and THF (Tetrahydrofuran) and non-polar solvents as hexane and heptane could be the right way to fully understand how polarity influences the alignment and thermal conductivity of the NFs;
* The influence of the polarity in the alignment of the NPS on the base fluid can also be assessed by the use of different surfactants with negative charge, such as for instance CATB (Cetyltrimethylamonium bromide);
* The impact of an applied magnetic field should be further studied, namely the value or range of values that make possible to reach the thermal conductivity maximum peak in less time;
* Additives for decreasing the NFs viscosity while maintaining the same level of thermal conductivity are of paramount importance to achieve the best performance of the NFs;
* The systems using nanofluids as working fluid should become more cost-effective, without the need of extra pumping power and expensive maintenance. This should be accomplished with the optimization of the microchannels configuration (e.g. number of channels, inlet/outlet positioning);
* Machine learning prediction models should be increasingly used in the future, since they provide faster and less expensive modeling of thermal properties of the NFs. Those data-driven models achieve more accurate and stable estimations than the classical ones. The path created by machine learning is beginning to clear up several doubts and backdraws, and it is a secure one for future studies and developments on the nanomaterials field of research.