Self-Similar Superhydrophobic Whisker-Based Coatings with High Impact and Abrasion Resistance
Bibliographic record
Abstract
Spray-on fabrication of superhydrophobic (SH) coatings using nanoparticles (NPs) has broad applications, but their mechanical durability often falls short of practical requirements. In this work, we demonstrate that mixing NPs with well-dispersed SiC whiskers (SiCw) can significantly improve the SH stability and mechanical durability of sprayed coatings, even under complex and harsh impact/abrasion conditions (such as sandblasting), which is attributed to the formation of a fully connected, avian nest-like, self-similar SiCw framework via the random packing of whiskers during coating drying. The resulting coating exhibits exceptional abrasion resistance, enduring 1,000 dry and 900 wet cycles in Taber abrasion tests (CS-17 wheels), and shows negligible degradation after impact testing at 3.12 × 10 6 J·m –2 . It is further shown that achieving high dispersity of the whiskers is critical to the coating’s preservation of microstructural roughness against various environmental impacts. The whisker-based coatings demonstrate excellent contact time reduction, icing delay, and self-cleaning capabilities on a variety of substrates, including textiles and power line strands. At −15 °C and 67% RH, the well-dispersed coating delays icing by 1,442 s─over 300 s longer than its poorly dispersed counterpart. This work presents a green spray-on strategy to produce durable SH coatings without complicating the fabrication process, offering persistent superhydrophobicity for real-world applications.
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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 | 0.000 | 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) | 0.000 | 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".