Influence of different sodium-based additives on corrosion resistance of PEO coatings on pure Ti
Bibliographic record
Abstract
In the present study, the plasma electrolytic oxidation method was used to form ceramic coatings on pure Ti substrates. For this purpose, five different aluminate-based electrolytes containing various sodium-based additives (sodium hydroxide, sodium phosphate, sodium silicate, sodium tetraborate and sodium fluoride) were used. The role of additives in the growth, macrostructure, surface and cross-sectional microstructure, phase composition and corrosion resistance of the coatings was evaluated. An X-ray diffractometer and scanning electron microscope were used to study the microstructure and phase composition of the coatings, respectively. The corrosion resistance of the coatings was investigated by applying electrochemical impedance spectroscopy and potentiodynamic polarization tests in a 3.5 wt. % NaCl solution. The results revealed that use of a sodium phosphate additive led to formation of a coating (4.33 µm thick) with the most suitable microstructure and highest corrosion resistance (4.36 × 106 Ω cm2), which was 53 times higher than that of uncoated Ti.
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 | 0.001 |
| 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) | 0.000 | 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".