MXenes are a rapidly expanding class of two-dimensional transition-metal carbides, nitrides, and carbonitrides with exceptional tribological properties.
Experimental studies have demonstrated that MXene coatings dramatically reduce friction and wear under dry sliding conditions through the formation of protective tribofilms, whose composition and mechanical properties depend critically on contact pressure, MXene composition, and monolayer thickness. However, a fundamental atomistic understanding of tribofilm formation mechanisms remains elusive, and the role of surface terminations - which strongly govern interlayer adhesion and stability - has so far proven difficult to experimentally investigate.
This project aims to bridge this gap by developing a chemically accurate machine learning potential (MLP), trained via an active learning strategy using ab initio DFT reference data, and deploying it in large-scale molecular dynamics simulations.
These sliding simulations will systematically explore five MXene compositions (Ti₂C, Ti₂N, Ti₃C₂, Ti₃N₂, Ti₃CN) with varying surface terminations (-O, -OH, -F) on iron substrates, elucidating the atomistic mechanisms behind friction reduction, tribofilm formation, and tribochemical degradation.
The project will deliver the first dynamically resolved, composition-dependent description of MXene lubrication, providing design principles for engineering coatings with enhanced durability in demanding aerospace, automotive, and manufacturing applications.
Principal Investigator, Company and Country
Maria Clelia Righi, University of Bologna, Italy