Smart low interfacial toughness coatings for on-demand de-icing without melting
Bibliographic record
Abstract
Ice accretion causes problems in vital industries and has been addressed over the past decades with either passive or active de-icing systems. This work presents a smart, hybrid (passive and active) de-icing system through the combination of a low interfacial toughness coating, printed circuit board heaters, and an ice-detecting microwave sensor. The coating's interfacial toughness with ice is found to be temperature dependent and can be modulated using the embedded heaters. Accordingly, de-icing is realized without melting the interface. The synergistic combination of the low interfacial toughness coating and periodic heaters results in a greater de-icing power density than a full-coverage heater system. The hybrid de-icing system also shows durability towards repeated icing/de-icing, mechanical abrasion, outdoor exposure, and chemical contamination. A non-contact planar microwave resonator sensor is additionally designed and implemented to precisely detect the presence or absence of water or ice on the surface while operating beneath the coating, further enhancing the system's energy efficiency. Scalability of the smart coating is demonstrated using large (up to 1 m) iced interfaces. Overall, the smart hybrid system designed here offers a paradigm shift in de-icing that can efficiently render a surface ice-free without the need for energetically expensive interface melting.
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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 | 0.001 |
| 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".