Suppression of Hydrophobic Recovery in Photo-Initiated Chemical Vapor Deposition
Bibliographic record
Abstract
Photo-initiated chemical vapor deposition (PICVD) functionalizes carbon nanotube (CNT)-enhanced porous substrates with a highly polar polymeric nanometric film, rendering them super-hydrophilic. Despite its ability to generate fully wettable surfaces at low temperatures and atmospheric pressure, PICVD coatings normally undergo hydrophobic recovery. This is a process by which a percentage of oxygenated functional group diffuse/re-arrange from the top layer of the deposited film towards the bulk of the substrate, taking the induced hydrophilic property of the material with them. Thus, hydrophilicity decreases over time. To address this, a vertical chemical gradient (VCG) can be deposited onto the CNT-substrate. The VCG consists of a first, thicker highly cross-linked layer followed by a second, thinner highly functionalized layer. In this article, we show, through water contact angle and XPS measurements, that the increased cross-linking density of the first layer can reduce the mobility of polar functional groups, forcing them to remain at the topmost layer of the PICVD coating and to suppress hydrophobic recovery. We show that employing a bi-layer VCG suppresses hydrophobic recovery for five days and reduces its effect afterwards (contact angle stabilizes to 42 ± 1° instead of 125 ± 3°).
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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) | 0.000 | 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 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".