Highly cross‐linked UV‐cured siloxane copolymer networks as icephobic coatings
Bibliographic record
Abstract
Abstract Preventing ice growth on infrastructure, vehicles, and appliances remains a significant engineering challenge. Damage caused by ice growth on these installations can be expensive to repair, and their failure can be dangerous. Materials such as cross‐linked polymer networks make effective anti‐ice coatings and can prevent ice growth: reducing the cost of infrastructure repairs and limiting downtime. A link between cross‐link density and ice adhesion has been demonstrated, such that lower cross‐link density materials tend toward lower ice adhesion. Here we describe a method of lowering cross‐link density by incorporating the covalently bound comonomers methyl methacrylate, lauryl methacrylate, and styrene into UV‐cured PDMS‐based polymer networks. Cross‐link density, hardness, surface roughness, and ice adhesion on these materials are tested, showing the influence of comonomer proportions on their properties. Durability is found to increase with the addition of 5, 10, and 25 wt% comonomer, with little to no effect on ice adhesion until 25 wt%, where increases in ice adhesion are observed. Coatings show promisingly low ice adhesion of ~50 kPa, maintaining this low adhesion for up to 50 deicing cycles.
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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".