Defect Dynamics in Two-Dimensional Black Phosphorus under Noble Gas Ions Irradiation

Fundamental understanding of the atomic-scale mechanisms underlying production, accumulation, and temporal evolution of defects in phosphorene during noble-gas ion irradiation is crucial to design efficient defect engineering routes to fabricate next-generation materials for energy technologies. Here, we employed classical molecular dynamics (CMD) simulations using a reactive force field to unravel the effect of defect dynamics on the structural changes in a monolayer of phosphorene induced by argon-ion irradiation, and its subsequent relaxation during post-radiation annealing treatment. Analysis of our CMD trajectories using unsupervised machine learning methods showed that radiation fluence strongly influences the type of defects that form, their dynamics, as well as their relaxation mechanisms during subsequent annealing. Low ion fluences yielded a largely crystalline sheet featuring isolated small voids (up to 2 nm), Stone-Wales defects, and mono-/di-vacancies; while large nanopores (~10 nm) can form beyond a critical fluence of ~10¹⁴ ions/cm². During post-radiation annealing, we found two distinct relaxation mechanisms, depending on the fluence level. The isolated small voids (1-2 nm) formed at low ion-fluences heal <i>via </i>local re-arrangement of rings, which is facilitated by a cooperative mechanism involving a series of atomic motions that include thermal rippling, bond formation, bond rotation, angle bending and dihedral twisting. On the other hand, damaged structures obtained at high fluences exhibit pronounced coalescence of nanopores mediated by a mechanism involving formation of 3D networks of P-centered tetrahedra. Additionally, we have studied defect evolution upon irradiating phosphorene with noble gas ions other than argon and effects of subsequent annealing. These findings provide new perspectives to use ion beams to precisely control the concentration and distribution of specific defect types in phosphorene for emerging applications in electronics, batteries, sensing, and neuromorphic computing.