Wear-Resistant Nanostructured Sol-Gel Coatings for Functional Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it