Spectral and Physical Properties of Electrochemically Formed Colored Layers on Titanium Covered with Clearcoats
Bibliographic record
Abstract
We present the application and characterization of two commercial polymer clearcoats to electrochemically formed colored passive layers on titanium with the aim of providing physical protection required in many of titanium's applications, while allowing the unique appearance of the colored layers to show through. Thin layers of an acrylic automotive clearcoat (∼3.5 μm thick) and an epoxy marine clearcoat (∼6.5 μm thick) are applied to the colored titanium surfaces using spin coating, and are found to slightly modify their visual properties, while maintaining their bright, well-defined sparkling colors. Both clearcoats are found to significantly reduce the surface roughness, thereby reducing potential wear from friction and the adhesion of fine dirt particles. They are also found to notably decrease the wetting properties of colored titanium, furthering its protection against damage from ambient and aqueous media. The clearcoats show the ability to protect colored titanium from physical and chemical damage, with the automotive clearcoat exhibiting superior adhesion. Our electrochemical coloring technique combined with the application of clearcoats creates a new and unique system that does not rely solely on a polymer coating for its colorful appearance and protection against corrosion.
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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".