Features of primary damage by high energy displacement cascades in concentrated Ni-based alloys
Bibliographic record
Abstract
Alloying of Ni with Fe or Co has been shown to reduce primary damage production under ion irradiation. Similar results have been obtained from classical molecular dynamics simulations of 1, 10, 20, and 40 keV collision cascades in Ni, NiFe, and NiCo. In all cases, a mix of imperfect stacking fault tetrahedra, faulted loops with a 1/3⟨111⟩ Burgers vector, and glissile interstitial loops with a 1/2⟨110⟩ Burgers vector were formed, along with small sessile point defect complexes and clusters. Primary damage reduction occurs by three mechanisms. First, Ni-Co, Ni-Fe, Co-Co, and Fe-Fe short-distance repulsive interactions are stiffer than Ni-Ni interactions, which lead to a decrease in damage formation during the transition from the supersonic ballistic regime to the sonic regime. This largely controls final defect production. Second, alloying decreases thermal conductivity, leading to a longer thermal spike lifetime. The associated annealing reduces final damage production. These two mechanisms are especially important at cascades energies less than 40 keV. Third, at the higher energies, the production of large defect clusters by subcascades is inhibited in the alloys. A number of challenges and limitations pertaining to predictive atomistic modeling of alloys under high-energy particle irradiation are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".