Micro mechanical testing of candidate structural alloys for Gen-IV nuclear reactors
Bibliographic record
Abstract
Ion irradiation is often used to simulate the effects of neutron irradiation due to reduced activation of materials and vastly increased dose rates. However, the low penetration depth of ions requires the development of small-scale mechanical testing techniques, such as nanoindentation and microcompression, in order to measure mechanical properties of the irradiated material. In this study, several candidate structural alloys for Gen-IV reactors (800H, T91, nanocrystalline T91 and 14YWT) were irradiated with 70 MeV Fe9+ ions at 452 °C to an average damage of 20.68 dpa. Both the nanoindentation and microcompression techniques revealed significant irradiation hardening and an increase in yield stress after irradiation in austenitic 800H and ferritic-martensitic T91 alloys. Ion irradiation was observed to have minimal effect on the mechanical properties of nanocrystalline T91 and oxide dispersion strengthened 14YWT. These observations are further supported by line broadening analysis of X-ray diffraction measurements, which show a significantly smaller increase in dislocation density in the 14YWT and nanocrystalline T91 alloys after irradiation. In addition, good agreement was observed between cross-sectional nanoindentation and the damage profile from SRIM calculations.
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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) | 0.001 | 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".