Atmospheric pressure Townsend discharges as a promising tool for the one‐step deposition of antifogging coatings from N<sub>2</sub>O/TMCTS mixtures
Bibliographic record
Abstract
Abstract The need to ensuring the “see‐through” property of transparent materials when exposed to sudden temperature changes or very humid conditions has encouraged the development of antifogging strategies, such as the deposition of (super)hydrophilic coatings. However, despite the effectiveness of these coatings in combating the effects of fogging, most of the coating techniques explored to date are typically time‐consuming and environment‐unfriendly. Bearing this in mind, we demonstrate that the application of dielectric barrier discharges operated at atmospheric pressure proves to be successful in preparing antifogging coatings on glass samples from 1,3,5,7‐tetramethylcyclotetrasiloxane (TMCTS) and nitrous oxide (N2O). The antifogging performance of the coatings was found to be governed by the [N2O]/[TMCTS] ratio and not by the [N2O] + [TMCTS] sum. Coatings prepared under a [N2O]/[TMCTS] = 30 were superhydrophilic (water contact angles ≈ 5°–10°) due to surface silanol groups and endowed glass samples with a superior antifogging property, as revealed by the ASTM F 659‐06 test. In contrast, because of the lesser hydrophilicity (water contact angles ≈ 60°), coatings prepared under a [N2O]/[TMCTS] = 10 did not endow glass samples with antifogging property. Regardless of the deposition conditions, the plasma‐deposited coatings displayed crack‐free smooth surfaces (Rrms = 2−4 nm).
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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".