Synthesis, properties, applications, 3D printing and machine learning of graphene quantum dots in polymer nanocomposites

Abstract

This comprehensive review discusses the recent progress in synthesis, properties, applications, 3D printing and machine learning of graphene quantum dots (GQDs) in polymer composites. It explores various synthesis methods, highlighting the size control and surface functionalization of GQDs. The unique electronic structure, tunable bandgap, and optical properties of GQDs are examined. Strategies for incorporating GQDs into polymer matrices and their effects on mechanical, electrical, thermal, and optical properties are discussed. Applications of GQD-based polymer composites in optoelectronics, energy storage, sensors, and biomedical devices are also reviewed. The challenges and future prospects of GQD-based composites are also explored, aiming to provide researchers with a comprehensive understanding of further advancements that should be possible in related fields. Moreover, this article explores new developments in 3D printing technology that can benefit from the promise of composite materials loaded with graphene quantum dots, a promising class of materials with a wide range of potential applications. In addition to discussing the synthesis and properties of GQDs, this review delves into the emerging role of machine learning techniques in optimising GQD-polymer composite materials. Furthermore, it explores how artificial intelligence and data-driven approaches are revolutionising the design and characterisation of these nanocomposites, enabling researchers to navigate the vast parameter space efficiently to achieve the desired properties. The overall aim of this review is to build up a common platform connecting individual subsections of synthesis, properties, applications, 3D printing and machine learning of GQD in polymer nanocomposites together to generate a comprehensive review for the readers.