Dear Colleagues,

Several experimental data are being reported in the field of nanofluids that illustrate the direction for researchers to fabricate enhanced nanofluids with improved properties and lower toxicity level. These large amounts of data have established an opportunity to propose new applications for machine learning methods, artificial intelligence approaches, and Big Data analyses focused on nanaofluids and nanoparticles, namely, metal oxide nanoparticles, nanotubes, graphenes, quantum dots, protein nanoparticles, and topological materials. These materials could be widely used in different areas such as drug delivery, biomedical engineering, tissue engineering, heat transfer engineering, fuel engineering, computing, etc. However, there is a great amount of experimentally measured data which are not analyzed and evaluated with new analysis approaches. Recently, an array of interesting investigations have focused on new experimental methods and also on new data analysis models. We propose, therefore, a new Special Issue that covers both experimental methods and novel data analysis approaches for being employed to predict the properties and to evaluate the nanotoxicity of nanomaterials, etc. In this Special Issue, we cordially accept submission of all related studies in the fields of nanotechnology that investigate the problems through either experimental methods or data analysis approaches.

Dr. Mohammad Hossein Ahmadi
Prof. Dr. Giulio Lorenzini
Prof. Dr. Mikhail Sheremet
Guest Editors

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• Computational nanosciences
• Nanoparticles
• Nanotoxicity
• Nanomaterials
• Machine learning
• Artificial intelligence methods
• Deep learning
• Prediction
• Data preparation