pH estimation at corrosion fatigue crack tip in 13Cr-4Ni martensitic stainless steel
Bibliographic record
Abstract
The blades of hydraulic turbines experience repeated loading during operation, promoting initiation and propagation of fatigue cracks in a corrosive environment. To identify the environmental damage mechanism—anodic dissolution or hydrogen embrittlement—potential ( E ) and pH must be measured locally at the crack tip, as values for these parameters measured at the crack tip differ from those measured in the bulk electrolyte. Direct measurement of potential drop ( ∆E=E exterior - E interior ) and pH at the crack tip is, however, challenging. This study focuses on estimating local pH at the fatigue crack tip using thermodynamic analysis combined with ∆E measurements at the crack tip. The methodology was applied to the tip of cracks propagating in a martensitic stainless steel compact tension specimen (CT). ∆E was measured as the crack propagated in a simulated crack environment (deaerated synthetic river water). The potential dropped from 0.075 V SHE in the synthesized river water to -0.09 V SHE in the deaerated synthesized river water. XPS analysis of the corrosion product found on the fracture surface after testing revealed it consisted of Fe₂O₃ and FeCr₂O₄ . Based on a Pourbaix diagram, E = -0.09 V SHE coupled with presence of Fe₂O₃ and FeCr₂O₄ as corrosion products yields a thermodynamically stable solution with a pH ranging from 4.4 to 4.6.
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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".