Solid-State Phase Transformation Explains the Mixed Crystallographic Character of Zr(Nb,Fe)<sub>2</sub> Nanoprecipitates in Zr-2.5Nb
Bibliographic record
Abstract
Zirconium alloys have widespread applications in nuclear energy, with Zr-2.5Nb commonly being used as pressure tube material in reactors. Their microstructure encompasses intermetallic nanoprecipitates (NPs) and solutes that significantly impact their behavior in corrosive environments and irradiation. Hence, we analyze the crystal structure of Zr((Zr,) Nb,Fe) 2 NPs using transmission electron microscopy (TEM), electronic density functional theory (DFT) calculations, and finite element analysis (FEA). Our findings unveil a mixed c14 and c15 Laves phase structure within the NPs and provide an explanation through the syncroshear mechanism. Through thermodynamic analysis, we evaluate the electronic, vibrational, and strain contributions to the free energy of the NPs. Our results indicate that the c15 structure is energetically favored at temperatures below 600 K, while the c14 structure prevails at higher temperatures. We provide an explanation for the observed coexistence of these structures in the NPs based on two key insights: (1) During annealing at high temperatures, the energetically favorable c14 NPs form, and (2) as the alloy cools, a partial phase transition to the c15 structure occurs, constrained by kinetic limitations. Furthermore, our study reveals that the NP/α-Zr interface is likely to be incoherent due to the considerable stresses involved. This finding is consistent with high-resolution transmission electron microscopy (HRTEM) micrographs, which demonstrate the presence of an incoherent interface.
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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".