Self-Lubricating Nano-Coatings for Tribological Applications: Materials, Mechanisms, and AI-Driven Design Approaches

Self-lubricating coatings with nanostructured structures are becoming crucial in order to decrease friction and wear in critical tribological systems like precision tools, biomedical implants, aerospace actuators, and engines. In this review, the most representative nano-engineered solid lubricants such as transition metal dichalcogenides, diamond-like carbon, hexagonal boron nitride, graphene, MXenes and hybrid multilayer architectures are discussed. The focus of the discussion is on the most important tribological mechanisms like lamellar sliding, oxidative adaptation, tribofilm formation, transfer-film development, and nano-asperity contact evolution. It also summarizes the most significant deposition techniques, such as cathodic arc physical vapor deposition, chemical vapor deposition, plasma-based deposition, and atomic layer deposition, as well as some of the most relevant performance parameters, like friction coefficient, wear rate, hardness and thermal stability. Although significant advances have been made, there remain a number of significant challenges in the field, including the absence of consistent test protocols, limited durability data for novel coatings like MXenes, and poor correlation of tribometer results from laboratory tests to service applications. The review focuses on the emerging importance of machine learning and physics informed modelling in tribological design, to bridge these gaps. The proposed TribNet-AI framework is specifically designed to combine experimental tribological data, outputs from molecular simulations and coating deposition parameters to facilitate the optimization of coating under multi-objective consideration.