Quantitative assessment of vacancy defect in self-ion irradiated molybdenum

Microstructural evolution–driven degradation governs material properties and is closely linked with defect behavior. Quantitatively characterizing defects and their evolution is essential for elucidating the underlying degradation mechanisms. In this study, the defects were introduced in Molybdenum at room temperature via self-ion irradiation, with damage levels ranging from 0.01 to 2 displacements-per-atom. The depth- dependent distribution of vacancies in the near-surface region was characterized using a variable-energy positron beam and compared with simulation results. Quantitative analyses of vacancy in the most damaged regions were carried out by combining Positron Annihilation Spectroscopy with a positron-trapping-model-based simulated annealing algorithm, supported by first-principles calculations. The defect size distribution -from single vacancy to vacancy clusters- was assessed at each damage levels, providing insights into the quantification of early-stage vacancy defects. At 0.01 dpa, vacacncy defect predomintaly exists as isolated single vacancies. With increasing damage, the proportion of isolate single vacancies gradually drops to ∼20% at a steady state (> 0.5 dpa), while clusters consisting of four or more single vacancies account for ∼12 % of the total vacancy defects. When the damage level exceeds 0.1 dpa, the formation of large clusters containing more than 15 vacancies. The observed vacancy accumulation and clustering trends are consistent with computational predictions and indicate that vacancy clustering in molybdenum is more pronounced than in tungsten during early-stage evolution. These finds delivery a quantitative experimental framework for understanding the irradiation-induced microstructural changes in molybdenum, provide novel experimental data to support and benchmark computational modeling approaches, implicating the design and evaluation of radiation-tolerant materials and can be extended to other materials where vacancy evolution is critical.