Hard Epoxy Coating with Lasting Low Ice Adhesion Strength
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.002 |
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 it