Enhancing anti-icing efficacy in hybrid polyurethane coatings: Evaluating the significance of molecular weight, chemical structure, and content of PEG/PDMS
Bibliographic record
Abstract
• The potential of PEG/PDMS copolymers to significantly enhance the anti-icing properties of polyurethane coatings. • Evaluating the role of PEG/PDMS copolymers in enhancing anti-icing properties. • Identifying a QLL that inhibits ice nucleation and decreases ice adhesion melting in an unfrozen interfacial layer. • Assessing how molecular weight and chemical structure of PEG/PDMS copolymers influence the anti-icing efficacy. This study investigates the advantages of adding polydimethyl siloxane/polyethylene glycol (PEG/PDMS) copolymers to polyurethane coatings, with a particular focus on optimizing anti-icing efficacy. A range of characterization techniques are applied, including attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS) analysis, surface roughness measurements, wettability analysis, tensile testing, and ice adhesion measurements, to elucidate the intricate relationships between copolymer molecular weight, chemical structure, and content and their collective effect on the anti-icing properties of the developed coatings. Tailored PEG/PDMS copolymers significantly reduce ice nucleation temperatures and enhance the anti-icing properties of polyurethane coatings. Adding PEG/PDMS copolymers to polyurethane alters the surface roughness, wettability, and mechanical properties of the coatings to improve anti-icing performance. The presence of copolymers decreases ice adhesion strength (<50 kPa), attributed to the formation of a quasi-liquid layer that acts as a lubricant between the ice and the coatings, and delays ice formation. Furthermore, the enhanced durability of copolymer-containing coatings ensures a long-lasting anti-icing effect after multiple icing/de-icing cycles, although some degradation was observed over time. The tailored PEG/PDMS copolymers demonstrate potential for maximizing the anti-icing properties of polyurethane coatings and advancing anti-icing technologies.
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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".