Influence of Ru on structure and corrosion behavior of passive film on Ti-6Al-4V alloy in oil & gas exploration conditions
Bibliographic record
Abstract
Abstract In order to investigate the influence of minor Ru on electrochemical behavior and structural characteristics of passive film on the surface of Ti-6Al-4V alloy in various oil & gas exploration conditions, electrochemical techniques, XPS, SEM and corrosion simulation tests were carried out, and the results revealed that the oil & gas exploration conditions had a serious impact on the electrochemical behavior and corrosion resistance of tested alloys, the passivation film resistance and corrosion potential of tested titanium alloys were significantly reduced with the increasing of acidity and temperature. With the addition of minor ruthenium, the potential of passive film on the Ti-6Al-4V-0.11Ru alloy surface was been risen because of the high surface potential of ruthenium element, the content of metallic ruthenium and tetravalent titanium oxides TiO2 in the surface film of Ti-6Al-4V-0.11Ru alloy all increased with the increasing of temperature, which led to that the thickness, stability, corrosion resistance and repair ability of passive film on the surface of Ti-6Al-4V-0.11Ru alloy was all better than that of Ti-6Al-4V, these results were also confirmed by corrosion simulation tests.
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) | 0.001 | 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".