Tooling Artificial Intelligence in Polymeric Nanofillers: A Review

Abstract

Background: Polymeric nanocomposites have gained significant attention due to their potential for enhanced properties and applications across diverse industries, including automotive, aerospace, electronics, biomedical, and environmental sectors. Objective: This review examines the advancements in polymeric nanocomposites made possible by nanofillers and highlights the transformative role of Artificial Intelligence (AI) in materials science. Methods: Using a detailed description of the methodology to select studies for inclusion and exclusion, key nanofillers such as carbon nanotubes, graphene, and metal-organic frameworks were examined for their ability to enhance the mechanical, thermal, electrical, and barrier properties of polymer matrices. Additionally, AI-based optimization approaches for synthesis and property prediction are discussed, along with a focus on green synthesis methods to align with sustainability goals. Results: Nanofillers significantly improve polymer properties, enabling their use in various industries. AI has revolutionized nanocomposite synthesis, facilitating optimized processes and reliable property predictions. Green synthesis methods offer sustainable alternatives; however, challenges persist, including achieving uniform filler dispersion, ensuring biocompatibility for biomedical applications, and reducing the costs associated with large-scale synthesis. Conclusion: This review highlights the potential of polymeric nanocomposites, emphasizing the importance of establishing standard synthesis and characterization procedures to enhance reliability and promote sustainable development in materials science. Further research is essential to address current limitations and fully realize the potential of nanocomposites in advancing industrial applications.