Advanced dual-responsive silicone-based nanocomposites: Enhancing the de-icing efficacy of power transmission lines by harnessing magnetic and solar energy
Bibliographic record
Abstract
In regions prone to severe weather, ice accumulation on power transmission lines creates significant challenges, including structural damage and power outages. Although various de-icing strategies exist, many are labor-intensive, costly, and provide only short-term solutions. This study introduces a novel nanocomposite coating composed of surface-modified iron oxide nanoparticles embedded in a silicone-based polymer. This coating harnesses thermomagnetic and photothermal properties to convert magnetic and solar energy into heat to produce effective de-icing of transmission lines. We evaluated the surface characteristics using contact and sliding angle measurements, profilometry, and differential scanning calorimetry, along with freezing delay and ice adhesion tests under nonimpact conditions. The 30 wt% nanoparticle coating achieved the highest contact angle (116°), whereas the 20 wt% coating (SFe20) demonstrated superior performance with the lowest sliding angle (12° ± 0.8°) and ice nucleation temperature (−25.1 °C). Under simulated sunlight, the SFe20 coating melted ice within 210 s, raising the surface temperature from −5 °C to 21 °C. Additionally, its thermomagnetic response facilitated ice detachment at low temperatures, with surface temperature changes twice that of the control samples lacking nanoparticles. These findings demonstrate that the SFe20 coating is a promising, energy-efficient alternative to conventional mechanical and thermal de-icing methods in the power industry.
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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".