Quantitative characterization of the microstructure of heat-treated Zr-Excel by neutron line profile analysis
Bibliographic record
Abstract
Neutron diffraction line profile analysis (DLPA) and transmission electron microscopy were used to characterize the components of the bimodal microstructure of Zr-Excel (Zr–3.5Sn–0.8Mo–0.8Nb), a nuclear structural material. The dual microstructure, consisting of equiaxed primary grains and martensitic domains both having hexagonal close-packed (h.c.p.) α crystal structure, forms when the as-received Zr-Excel alloy is heat treated at a high temperature and subsequently quenched, i.e . is solution treated. Because both microstructure components have the same crystal structure the reflections from the two components overlap significantly. The article presents how the multi-phase analysis capability of modern DLPA methods can be used to model the measured neutron diffraction patterns as the sum of two sub-patterns corresponding to the components of such a bimodal microstructure, which can be found in many hexagonal alloys relevant for industrial applications. The results show that the large equiaxed primary h.c.p. α grains have a highly correlated low-density dislocation structure and large sub-grains (∼300 nm), while the large martensitic domains have a randomly arranged very high density dislocation structure and sub-grains the size of ∼30 nm. The significantly different defect structures of the primary and martensitic phases manifest as large differences in the hardness and ductility of the individual components. As a result of this duality of the mechanical properties, solution-treated Zr-Excel materials can be considered as analogous to metal matrix composites where a softer ductile matrix contains a harder brittle reinforcing phase.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".