Structure–Property Correlation in EEMAO Fabricated TiO<sub>2</sub>–Al<sub>2</sub>O<sub>3</sub> Nanocomposite Coatings
Bibliographic record
Abstract
We grew TiO2-Al2O3 nanocomposite coatings on titanium substrates by electrophoretic enhanced microarc oxidation (EEMAO) technique under several voltages and established a correlation between microstructure, surface hardness, and corrosion resistance of the coatings in sulfuric acid and sodium chloride solutions. Structural analysis revealed that the coatings contained anatase, rutile, alumina, and tialite phases. Formation kinetics of tialite phase was studied. It was found that increasing the voltage gives rise to a coarser morphology, i.e., larger pore size, and incorporation of more alumina nanoparticles into the layers. It is shown that surface hardness of the titanium substrates increased by a factor of 4 following EEMAO treatment. Corrosion resistance of titanium was enhanced significantly. Resistance against pitting corrosion was improved as well. We proposed a formation mechanism for the TiO2-Al2O3 composite coatings at different voltages based on the chemical and electrochemical foundations.
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".