Effect of g-family incorporation on corrosion behavior of PEO-treated titanium alloys: a review
Bibliographic record
Abstract
By being exposed to air or moisture or by a chemical reaction, titanium (Ti) forms an oxide layer on its surface, which is stable and tightly adherent and provides it with protection from the environment, since titanium is a reactive material. Due to its extremely low thickness (∼10 nm), this oxide layer is easily destroyed under corrosion conditions. Through plasma electrolytic oxidation (PEO), titanium and titanium alloys can be equipped with thick and adhesive titanium dioxide (TiO2) coatings to enhance their surface characteristics. In the PEO process, titanium dioxide composite coatings can be formed by mixing proper additives with electrolytes, such as powders, particles, sheets or compounds. Graphene and its family derivatives (i.e. graphene oxide and reduced graphene oxide) are among the most popular additives used in PEO composite coatings due to their high stability in corrosive media. Graphene-family nanosheets can accumulate in PEO coatings because of their porous nature, changing the surface characteristics dramatically. The use of graphene-family nanosheets in electrolytes can be useful in reducing coating porosity and improving final corrosion properties by adjusting electrolyte conditions. Therefore, the diffusion pathways for corrosive ions in composite titanium dioxide coatings become considerably more tortuous than those for pure titanium dioxide.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".