Chemifriction and Superlubricity: Friends or Foes?

TL;DR

This paper investigates how interfacial bonding affects friction in defected twisted graphene interfaces using atomistic machine-learning simulations, revealing mechanisms that can restore superlubricity and predicting velocity-dependent friction behavior.

Contribution

It uncovers a shear-induced atomic transfer healing mechanism and develops a phenomenological model to predict chemifriction effects in 2D material interfaces.

Findings

Discovered a shear-induced interlayer atomic transfer healing mechanism.

Identified negative differential friction coefficients under moderate loads.

Predicted a transition between logarithmic increase and decrease of friction with velocity.

Abstract

The mechanisms underlying chemifriction, i.e. the contribution of interfacial bonding to friction in defected twisted graphene interfaces are revealed using fully atomistic machine-learning molecular dynamics simulations. This involves stochastic events of consecutive bond formation and rupture, that are spatially separated but not necessarily independent. A unique shear-induced interlayer atomic transfer healing mechanism is discovered that can be harnessed to design a run-in procedure to restore superlubric sliding. This mechanism should be manifested as negative differential friction coefficients that are expected to emerge under moderate normal loads. A physically motivated phenomenological model is developed to predict the effects of chemifriction in experimentally relevant sliding velocity regimes. This allows us to identify a distinct transition between logarithmic increase and logarithmic decrease of frictional stress with increasing sliding velocity. While demonstrated for homogeneous graphene interfaces, a similar mechanism is expected to occur in other homogeneous or heterogeneous defected two-dimensional material interfaces.