RESUMEN
Triboelectric nanogenerators (TENGs) as a revolutionary system for harvesting mechanical energy have demonstrated high vitality and great advantage, which open up great prospects for their application in various areas of the society of the future. The past few years have seen exponential growth in many new classes of self-healing polymers (SHPs) for TENGs. This review presents and evaluates the SHP range for TENGs, and also attempts to assess the impact of modern polymer chemistry on the development of advanced materials for TENGs. Among the most widely used SHPs for TENGs, the analysis of non-covalent (hydrogen bond, metal-ligand bond), covalent (imine bond, disulfide bond, borate bond) and multiple bond-based SHPs in TENGs has been performed. Particular attention is paid to the use of SHPs with shape memory as components of TENGs. Finally, the problems and prospects for the development of SHPs for TENGs are outlined.
RESUMEN
Metallopolymers (MPs) or metal-containing polymers have shown great potential as self-healing and shape memory materials due to their unique characteristics, including universal architectures, composition, properties and surface chemistry. Over the past few decades, the exponential growth of many new classes of MPs that deal with these issues has been demonstrated. This review presents and assesses the latest achievements and problems associated with the use of MPs as self-healing and shape memory materials. Among the most widely used MPs with self-healing properties, metal complexes based on polymers containing phenol, carboxylic acid, pyridine, azole, histidine and urethane donor fragments are identified. Particular attention is paid to the principles of action of the shape memory MPs. Of considerable interest is the use of MPs as functional materials for sensors, soft electronic devices, transistors, conductors, nanogenerators, bone tissue engineering, etc. Finally, the problems and future prospects of MPs with self-healing and shape memory properties are outlined. This review also analyzes articles published over the past five years.