Analysis of Hydrogen Permeation Through Pipeline Steel in Near-Neutral pH SCC Environments
Bibliographic record
Abstract
The Devanathan-cell technique has been used to determine the permeation flux of hydrogen through X-65 steel at the open-circuit potential in solutions associated with near-neutral pH stress corrosion cracking. The effects of organic material and microbial activity were also simulated by suitable additions to the solution. A model based on a constant-concentration boundary condition was found to fit the experimental data better than one based on a constant flux condition. The addition of 9,10-anthroquinone-2,6-disulphonic acid to the solution to simulate the effect of organic material lowered the hydrogen permeation flux. Sulfide additions to simulate the action of sulfate-reducing bacteria initially increased the rate of hydrogen permeation, but subsequently resulted in a decrease in permeation current. The hydrogen permeation rate measured on the original oxide-covered surface is lower and more variable than that on polished surfaces. Corrosion rate measurements were also made, from which the fraction of atomic hydrogen diffusing through the specimens was estimated. The results are discussed in terms of the mechanism of hydrogen evolution and absorption.
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 | 0.000 | 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".