Influence of Clearcoats on the Spectral and Physical Properties of Electrochemically Formed Colored Passive Layers on Zirconium
Bibliographic record
Abstract
We report on the application and characterization of two commercial polymer clearcoats to electrochemically formed colored passive layers on zirconium with the aim of providing effective physical and chemical protection while allowing the unique and colorful appearance of the colored passive layers to show through. Thin layers of an acrylic automotive clearcoat ( approximately 3.5 mum thick) and an epoxy marine clearcoat ( approximately 8.5 mum thick) were applied to the colored zirconium surfaces via spin coating and were found to only slightly modify their visual properties, maintaining their vibrant colors. As clearcoats were applied, the outer surface was found to be smoother than the surface of colored zirconium, thereby reducing potential wear from friction and the adhesion of fine dirt. Clearcoat-protected samples were found to wet less easily than colored zirconium alone, thus furthering its protection against damage in ambient (surface weathering) and aqueous media (aqueous corrosion). Light microscopy experiments at a 50-400x magnification revealed the absence of any structural defects in the clearcoats. The clearcoats show the ability to protect colored zirconium from physical and chemical damage, with the automotive clearcoat exhibiting superior adhesion. Our electrochemical coloring combined with the application of clearcoats creates a novel system that possesses unique esthetic properties while simultaneously offering protection against various forms of environmental damage such as weathering or 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 | 0.001 |
| 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) | 0.001 | 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".