Investigation of Hydrogen-Promoted Pitting by the Electrochemical Noise Method and the Scanning Reference Electrode Technique
Bibliographic record
Abstract
Hydrogen-promoted metastable pitting of iron and its development into stable pitting were first investigated by the combination of the electrochemical noise method and the scanning reference electrode technique (SRET). Time records of current noise show that hydrogen significantly increases the number of current fluctuations, inferring that hydrogen might increase the initiation of pitting, and hydrogen also increases the magnitude of current fluctuations, suggesting that hydrogen might promote the growth of metastable pits. In addition, the duration of the time period for initiation of metastable pitting on the charged specimen is longer than that on the uncharged specimen, supporting that the active sites which induce pitting on the charged specimen might be much more numerous than on the uncharged specimen. An analysis of the power spectrum density shows that the level of the frequency-independent plateau of the charged specimen is higher than that of the uncharged specimen and the roll-off frequency tends to decrease with measurement time of the charged specimens, indicating that hydrogen increases pitting attack and decreases the repassivation rate of metastable pits on iron. Statistical analysis of pitting initiation events with various chloride ion concentrations in test solution shows that chloride ion increases pitting initiation significantly on the uncharged specimen. By contrast, the effect of chloride ions on the pitting initiation on the charged specimen is obscured by the significant increase in the number of detectable pitting initiations due to the presence of hydrogen in the specimen. Monitoring of pitting on uncharged and charged iron using in situ SRET shows clearly that the appearance of pits on the charged specimen is much earlier than that on the uncharged specimen and pitting sites on the charged specimen are more numerous than on the uncharged specimen. The results were discussed in terms of the effects of hydrogen on the passive film on iron and hydrogen-promoted anodic dissolution of iron.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".