Thermal Aging Embrittlement of W Alloyed 9%Cr Ferritic Steels and Its Evaluation by Electrochemical Technique.
Bibliographic record
Abstract
Thermal aging embrittlement of newly developed W alloyed 9%Cr ferritic steel KA-STBA29/KA-STPA29 (ASME T92/P92) has been investigated. In order to clarify the controlling factor of the embrittlement, Charpy impact tests and determination of precipitates have been carried out using materials aged under various conditions. The decrease in the upper and lower shelf energies and the increase in the fracture appearance transition temperature (FATT) due to thermal aging were caused by the precipitation and coarsening of Laves phase. The decrease in the impact toughness was found to be uniquely correlated with the increase in the area fraction of Laves phase. In order to develop a non-destructive technique for detection of the thermal aging embrittlement, changes in electrochemical properties of KA-STBA29/KA STPA29 due to thermal aging have been investigated. The electrochemical polarization technique has been applied to thermally aged materials. Experimental results on electrochemical polarization measurements revealed that the peak current density “Ip” which appeared at a specific potential during potentiodynamic polarization measurements in 1N KOH solution linearly increased with the degree of embrittlement evaluated by the impact energy at 0°C. The increase in Ip corresponded to the increase in selective dissolution of Laves phase. As a consequence, the degree of thermal aging embrittlement can be estimated by the electrochemical technique.
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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".