Hard Epoxy Coating with Lasting Low Ice Adhesion Strength
Bibliographic record
Abstract
A high-hardness ( H ) coating typically has an elevated ice adhesion strength (τ), while a soft coating tends to shed ice easily. We recently reported a one-step process for a bilayer polyurethane coating that achieved both high H and low τ, effectively decoupling H from τ. However, these low τ values remained stable only through 12 icing/deicing cycles, beyond which τ rapidly increased. To maintain consistently low τ, another bilayer coating is prepared using a diamine, an epoxy compound, and poly(glycidyl methacrylate) with poly(dimethylsiloxane) (PDMS) side chains. This coating features a bulk hardness of 0.31 ± 0.03 GPa, PDMS nanopools in the matrix, and a liquid-like PDMS brush layer on the surface. A silicone oil mixture (SO m ) can be added to the formulation before coating formation. Increasing the SO m content enlarges PDMS/SO m nanopools and surface roughness. Lubricated coatings are produced by applying SO m or individual silicone oils (SOs) of varying viscosities to preformed nonporous and microporous coatings. This study compares ice-shedding properties of these coatings over 30 icing/deicing cycles. Results show that the smooth bilayer epoxy coating, lubricated with 1.55 g/m 2 SO m, maintains τ values <5 kPa, 100 times lower than glass, after 30 icing/deicing cycles. This marks the first polymer coating to simultaneously exhibit high H alongside such consistently low τ values over numerous icing/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) | 0.002 | 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".