Performance of Aluminide and Cr-Modified Aluminide Pack Cementation-Coated Stainless Steel 304 in Supercritical Water at 700 °C
Bibliographic record
Abstract
The choice of materials is of great concern in the construction of Gen IV supercritical water reactors (SCWR), particularly the fuel cladding, due to the harsh environment of elevated temperatures and pressures. A material's performance under simulated conditions must be evaluated to support proper material selection by designers. In this study, aluminide and Cr-modified aluminide coated 304, as well as bare stainless steel 304 as a reference material, were tested in supercritical water (SCW) at 700 °C and 25 MPa for 1000 h. The results showed that all three samples experienced weight loss. However, the aluminide coated 304 had 20 to 40 times less weight loss compared to Cr-modified aluminide coated and bare stainless steel 304 specimens, respectively. Based on scanning electron microscope/energy dispersive X-ray spectroscopy (SEM/EDS) and X-ray diffraction (XRD) analysis results, spinel and hematite Fe2O3 formed on bare 304 after 1000 h in SCW while alumina was observed on both coated specimens, i.e., aluminide and Cr-modified aluminide surfaces. Oxide spallation was observed on the bare 304 and Cr-modified aluminide surface, contributing to a larger weight loss. Based on the results from this study, pure aluminide coating with Al content of 10–11 wt % demonstrated superior performance than bare 304 and Cr-modified aluminide coated 304.
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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".