Characteristics of Colored Passive Layers on Titanium: Morphology, Optical Properties, and Corrosion Resistance
Bibliographic record
Abstract
Electrochemically formed colored passive layers on titanium and their optical, surface morphology, and corrosion properties are presented and discussed. With the application of progressively higher AC voltages (VAC) during preparation of these passive layers, they are found to become more protective of the underlying metal, as determined from corrosion resistance measurements employing electrochemical polarization curve and inductively coupled plasma mass spectrometry experiments. The passive layers on titanium were found to be uniform in their surface morphology with no apparent cracks or pits. Surface morphology, and its relation to optical properties, was also investigated using visible light microscopy, profilometry, and near-infrared ultraviolet visible reflectance spectroscopy measurements. A correlation between the light reflected from the entire sample surface and the coloration of surface grains was also observed through these measurements. The reflectance spectra showed a red-shift of wavelength maxima (λmax) values as AC voltages and, therefore, thicknesses were increased. Overall, these passive layers are protective of an already remarkable metal, and with greater knowledge of the properties of colored protective layers, their potential may be employed in a wide range of applications.
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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) | 0.000 | 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".