Development of a hybrid anti-icing coating with superhydrophobic and electrothermal properties
Bibliographic record
Abstract
This work involves the development of a hybrid anti-icing coating. The multi-layered coating consists of an electrothermal heating film and superhydrophobic top coating. The heating film is composed of a thin layer of conductive copper-based epoxy, insulated between layers of a polydimethylsiloxane (PDMS) elastomer. The superhydrophobic coating is based on FAS-13 modified SiO₂ nanoparticles dispersed in a PDMS matrix. The composite coating was applied to a stainless steel substrate by consecutively depositing each layer by spray-coating. The SiO₂/PDMS coating exhibited a static contact angle of 164.3° and contact angle hysteresis of 2.8°, confirming its superhydrophobic properties. The anti-icing properties of the superhydrophobic coating were investigated experimentally inside a cold chamber at -20°C. The ice adhesion strength was reduced from 284 kPa on the untreated substrate to 50 kPa on the superhydrophobic coating, while the droplet freezing time was delayed from 8 seconds to 76 seconds. The heating film demonstrated a fast thermal response and excellent thermal stability. With the maximum surface power density of 3.46 W/cm², the surface temperature of the heating film could be raised to 10°C in just 45 seconds. In simulated spray-icing tests, the hybrid superhydrophobic-heating film achieved complete anti-icing with a minimum surface power density of 0.26 W/cm². Consequently, the superhydrophobic coating was found to reduce the energy required for anti-icing of the heating film by 41%. The hybrid coating was found to be durable and retained its superhydrophobicity after being subjected to repeated icing and de-icing cycles. The results demonstrate that the hybrid coating has potential for practical applications in the marine and offshore industries due to its simple, versatile fabrication process and energy-efficient anti-icing performance.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".