Effect of Sequential Helium and Nickel Ion Implantation on the Nano-Indentation Hardness of X750 Alloy
Bibliographic record
Abstract
Abstract Sequential He+ and Ni+ implantations were performed to investigate their combined effect on the indentation hardness of heat-treated X750 alloy. The microstructure of the ion-implanted region was also characterized with transmission electron microscope (TEM). The X750 alloy displayed a pronounced softening with very low Ni+ implantation levels, ψ = 0.01–1.0 dpa, however it showed a clear increase in hardness when implanted with He+ up to CHe = 5000 appm. Samples subjected to sequential He+ and Ni+ implantations displayed hardness values between those presented by sole He+ or Ni+ implantation suggesting that the effects of ion-induced microstructural damage and helium accumulation on the hardness of this alloy can be considered as independent and additive over the range of conditions studied. This observation is in contradiction to previously reported TEM-based studies, which suggest that accumulated helium slows the dissolution/disordering of the γ′ hardening phase in this alloy. In our study, established theories were applied to assess the contribution of ion-induced defect clustering, γ′ precipitate disordering, and helium bubble accumulation to the hardness of the X750 alloy. It was observed that generation of ion-induced defect clusters and the formation of helium bubbles increased the indentation hardness slightly while the disordering of γ′ precipitates resulted in a dramatic decrease in the total hardness. Ni+ and He+ implantation also had different effects on the depth dependence of the indentation hardness indentation size effect (ISE). The ISE was pronounced in the samples subjected to only Ni+ implantation while it was almost absent in samples subjected to only He+ implantation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".