Wear-Resistant Nanostructured Sol-Gel Coatings for Functional Applications
Bibliographic record
Abstract
Improvement of the wear resistance of functional surfaces is crucial in order to facilitate a variety of practical applications, such as self-cleaning or anti-fogging. This especially holds for functional surface nanostructures, whose tops can easily get worn off when exposed to even low abrasion forces. Thus, our work addresses the enhancement of the wear resistance of such fine-scale structures. We present an efficient manufacturing procedure for generating long-term durable surfaces with simultaneously tailored wetting behavior and high optical quality. Our approach is based on a sol-gel coating that consists of an alumina layer with specific nanoroughness yielding the function-relevant surface structure, and a protective thin smooth silica film providing the mechanical robustness without influencing that functional structure. The roughness of the alumina layer can be systematically adjusted, thus enabling us to achieve desired wetting effects all the way up to superhydrophilicity and, after application of an additional thin hydrophobic top coat, to superhydrophobicity. To demonstrate the enhanced robustness of these coatings we perform abrasive wear tests and investigate the impact of abrasion cycles on the wetting effects and optical properties of the coatings. Furthermore, the durability of the structures is directly revealed by advanced roughness characterization procedures based on Atomic Force Microscopy followed by power spectral density function (PSD) analysis.
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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 | 0.001 |
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".