Ice‐Shedding Endurance Enhancement of Lubricated Polyurethane NP‐GLIDE Coating Through Dual Cross‐Linking with Covalent and Hydrogen Bonds
Bibliographic record
Abstract
Abstract An NP‐GLIDE coating, featuring a liquid‐like polymer brush layer on its surface and nanopools of a grafted liquid ingredient for dewetting enablement within its matrix, is normally covalently cross‐linked (CX). This paper introduces a dually cross‐linked (DX) NP‐GLIDE coating, employing urethane bonds and H─bonds for crosslinking. Both the DX and CX coatings, utilizing poly(dimethyl siloxane) (PDMS) as the dewetting enabler, exhibit low ice adhesion strengths (τ). Post‐lubrication with silicone oil (SO), ice is readily shed under gravity from the coatings. The DX coating maintains low τ over numerous icing/de‐icing cycles, outlasting the CX coating. This durability is likely due to the lubricant‐loss‐responsive nature of the DX matrix. The matrix increases its H─bond cross‐links responding to lubricant loss and the cross‐linking density increase triggers additional lubricant release into the PDMS brush layer. This cyclic process persists over time. Under optimal conditions after 31 icing/de‐icing cycles, the lubricated DX coating demonstrates an impressively low τ of 1.8 ± 0.5 kPa. In contrast, the lubricated CX counterpart has a τ of 32 ± 11 kPa, while plain glass records a τ of 325 ± 14 kPa. This study underscores the significant potential of dual cross‐linking for enhancing ice‐shedding endurance of coatings.
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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".