Regenerative Superhydrophobic Coatings for Enhanced Performance and Durability of High-Voltage Electrical Insulators in Cold Climates
Bibliographic record
Abstract
Superhydrophobic coatings can be a suitable solution for protecting vulnerable electrical infrastructures in regions with severe meteorological conditions. Regenerative superhydrophobicity, the ability to regain superhydrophobicity after being compromised or degraded, could address the issue of the low durability of these coatings. In this study, we fabricated a superhydrophobic coating comprising hydrophobic aerogel microparticles and polydimethylsiloxane (PDMS)-modified silica nanoparticles within a PDMS matrix containing trifluoropropyl POSS (F-POSS) and XIAMETER PMX-series silicone oil as superhydrophobicity-regenerating agents. The fabricated coating exhibited a static contact angle of 169.5° and a contact angle hysteresis of 6°. This coating was capable of regaining its superhydrophobicity after various pH immersion and plasma deterioration tests. The developed coating demonstrated ice adhesion as low as 71.2 kPa, which remained relatively unchanged even after several icing/de-icing cycles. Furthermore, the coating exhibited a higher flashover voltage than the reference samples and maintained a minimal drop in flashover voltage after consecutive testing cycles. Given this performance, this developed coating can be an ideal choice for enhancing the lifespan of electrical insulators.
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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".