The Influence of Hydrogen and Tensile Stress on Passivity of Type 304 Stainless Steel
Bibliographic record
Abstract
The effects of hydrogen and applied tensile stress on the passivity of type 304 stainless steel were investigated in borate buffer solutions containing chloride ions. Hydrogen was cathodically charged into specimens by applying a cathodic constant current density. Applied tensile stress is found to have no obvious effect on the breakdown potential of uncharged specimens unless the concentration of chloride ions in the borate buffer solution is high. Hydrogen significantly decreases the breakdown potential. For charged specimens at various tensile stress levels, breakdown potentials are well fitted as a function of charging current density using the second-order exponential decay equation. At a certain charging current density, the breakdown potential and applied tensile stress approximately follow a linear relationship. The slope of the straight line becomes more negative in the intermediate charging current density range than in both low and high current density ranges, indicating that tensile stress has a more obvious promoting effect on the breakdown of passive films formed on specimens charged in this current density range. It was observed that hydrogen increases the anodic current density in the passive range. Applied tensile stress affects the anodic current density in various degrees for the specimens charged at different charging current densities. © 2003 The Electrochemical Society. All rights reserved.
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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 | 0.001 |
| 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".