Characterization of Multilayer Anti-Fog Coatings
Bibliographic record
Abstract
Fog formation on transparent substrates constitutes a major challenge in several optical applications requiring excellent light transmission characteristics. Anti-fog coatings are hydrophilic, enabling water to spread uniformly on the surface rather than form dispersed droplets. Despite the development of several anti-fog coating strategies, the long-term stability, adherence to the underlying substrate, and resistance to cleaning procedures are not yet optimal. We report on a polymer-based anti-fog coating covalently grafted onto glass surfaces by means of a multistep process. Glass substrates were first activated by plasma functionalization to provide amino groups on the surface, resulting in the subsequent covalent bonding of the polymeric layers. The anti-fog coating was then created by the successive spin coating of (poly(ethylene-maleic anhydride) (PEMA) and poly(vinyl alcohol) (PVA) layers. PEMA acted as an interface by covalently reacting with both the glass surface amino functionalities and the PVA hydroxyl groups, while PVA added the necessary surface hydrophilicity to provide anti-fog properties. Each step of the procedure was monitored by XPS, which confirmed the successful grafting of the coating. Coating thickness was evaluated by profilometry, nanoindentation, and UV visible light transmission. The hydrophilic nature of the anti-fog coating was assessed by water contact angle (CA), and its anti-fog efficiency was determined visually and tested quantitatively for the first time using an ASTM standard protocol. Results show that the PEMA/PVA coating not only delayed the initial period required for fog formation but also decreased the rate of light transmission decay. Finally, following a 24 hour immersion in water, these PEMA/PVA coatings remained stable and preserved their anti-fog properties.
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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".