Investigation of ZrN Non-Reactively Sputtered Diffusion Barrier Coating for U-Mo Dispersion Fuel
Bibliographic record
Abstract
Zirconium nitride (ZrN) coating as a diffusion barrier layer has been applied to a U-7wt.%Mo (U-7Mo)/Al dispersion fuel plate owing to its high melting point, high thermodynamic stability against U-Mo and Al, high hardness, and low absorption cross section for thermal neutrons.However, it has been experimentally revealed that a ZrN coating layer adopted in a U-Mo/Al dispersion fuel plate experiences a functional failure locally, and hence undesirably extensive fission-induced interaction layers (ILs) between U-7Mo fuel powders and the surrounding Al matrix reaction layer are locally produced when irradiated.It is believed that the local coating damage generated during the dispersionfuel-plate fabrication process accelerates the U-Mo/Al interdiffusion by acting as a fast diffusion path of solid materials.Unfortunately, there have been no studies scientifically identifying the causes of, or presenting solutions to, the problem of ZrN coating damage.Accordingly, based on comprehensive microstructural studies, the aim of this research is to experimentally and numerically investigate ZrN coating fracturing as a function of several variables at a high heat-treatment temperature during dispersion-fuel-plate fabrication.This research will help present appropriate solutions for preventing the occurrence of coating fracturing at the heat-treatment temperature, taking into account a realistic coating microstructure.ZrN coating was deposited onto U-7Mo powders using a direct-current magnetron non-reactive sputtering machine equipped with a turnable mixing drum.Microstructural studies on the as-fabricated ZrN coatings were conducted using a scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray diffractometer (XRD).This research is composed of the following three parts: v 5.3.2.2.Failure of coating thicker than critical thickness ................
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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".