New insights into tailoring physicochemical properties for optimizing the anti‐icing behavior of polyurethane coatings
Bibliographic record
Abstract
Abstract The most popular topcoat in the coatings industry, polyurethane (PU), still lacks an understanding of ice‐related phenomena and hence achieving an icephobic PU‐based coating has remained an important unresolved issue. This work presents fresh perspectives on how factors including chemical characteristics, cross‐link density, and mechanical characterization impact the anti‐icing capabilities and ice‐adhesion of PU coatings. In this regard, the effects of the of aforementioned coatings having various crosslink densities on wettability, ice formation, and ice adhesion were assessed via multiple approaches. Two acrylic polyols with different hydroxyl content and two commonly used aliphatic polyisocyanates with different %NCOs were utilized to fabricate the PU coatings. In addition, coatings were designed with different stoichiometric ratios providing a route to various crosslink densities and mechanical properties. Wettability characteristics were investigated using water contact angle, contact angle hysteresis, and sliding angle. The roles of urethane linkages and hydrogen‐bond formation between water molecules and urethane composition of PU coatings on ice nucleation and ice adhesion were studied through DSC, push‐off, and centrifugal tests. Mechanical characteristics and surface roughness of the coatings were investigated by tensile test and optical profilometer, respectively.
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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 | 0.001 |
| 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".