Improved radiation resistance in metals via adaptive martensitic transformation
Bibliographic record
Abstract
Materials typically experience serious microstructure and performance degradations under irradiation in nuclear reactors. To explore radiation-resistant metals with high design flexibility is urgently requested for the safe application of nuclear energy. In this work, we discover an anti-radiation mechanism for this purpose in a gradient nanostructured nuclear grade austenitic stainless steel prepared by a flexible surface nano-crystallization approach. A special 3-dimensional microstructure network, consisting of low-energy grain boundaries, stacking faults, and dislocation networks, is introduced in the nanostructure, so that a large-scale adaptive martensitic transformation mechanism is activated under irradiation even at extremely high radiation doses and high temperatures. Consequently, the radiation resistance is significantly enhanced, while a superior mechanical property is retained, in nanostructured samples compared to coarse-grained counterparts. Results presented in this work thus explore a strategy to prepare radiation-resistant metals in future. Zhang et al. design a nanostructure which activates an adaptive martensitic transformation mechanism in a nuclear grade austenitic stainless steel, achieving extraordinary radiation resistance with non-degraded mechanical properties.
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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) | 0.000 | 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 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".