Abstract
One or more nanoparticles (NP) combined with a base fluid (BF) yields nanofluid (NF). Compared to BFs, NFs have superior thermal conductivity (TC). Numerous studies' conclusions showed that various factors affect an NF's heat transfer (HT) capability, including pH, BF type, nanocomponents, temperature, solid volume fraction (SVF), and NP size. This article examines the impact of these factors by considering TC experiments. Techniques for preparing NFs and approaches for improving their stability are discussed too. Additionally, theoretically related works, artificial intelligence (AI), and experimental techniques are explored as ways to predict the characteristics of NFs. Researchers comprehend the effects of NPs on human health and the environment as they evaluate TC experimentally; this subject was also covered, and the impact of NPs on different aspects of mankind, including health, the environment, and the industries that use NFs, was discussed.. It was demonstrated that NPs can pose a risk to human health and cause permanent harm; furthermore, harmful to the environment, NPs can impact animals, soil, and water. It was shown that the main barriers in front of widespread application of NFs are the lack of laboratory methods standardization for reliable comparison, the NFs stability, environmental and health impacts of NPs, and suitable regulations.
| Original language | English |
|---|---|
| Journal | International Journal of Thermal Sciences |
| Volume | 220 |
| Issue number | 110317 |
| DOIs | |
| Publication status | Published - 22 Sept 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Artificial neural networks
- Environmental and health issues
- Machine learning
- Nanofluid
- Review
- Thermal conductivity
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