Recent Progresses of Superhydrophobic Coatings in Different Application Fields: An Overview
Bibliographic record
Abstract
With the development of material engineering and coating industries, superhydrophobic coatings with exceptional water repellence have increasingly come into researchers’ horizons. The superhydrophobic coatings with corrosion resistance, self-cleaning, anti-fogging, drag-reduction, anti-icing properties, etc., meet the featured requirements from different application fields. In addition, endowing superhydrophobic coatings with essential performance conformities, such as transparency, UV resistance, anti-reflection, water-penetration resistance, thermal insulation, flame retardancy, etc. plays a remarkable role in broadening their application scope. Various superhydrophobic coatings were fabricated by diverse technologies resulting from the fundamental demands of different fields. Most past reviews, however, provided only limited information, and lacked detailed classification and presentation on the application of superhydrophobic coatings in different sectors. In the current review, we will highlight the recent progresses on superhydrophobic coatings in automobile, marine, aircraft, solar energy and architecture-buildings fields, and discuss the requirement of prominent functionalities and performance conformities in these vital fields. Poor durability of superhydrophobic coating remains a practical challenge that needs to be addressed through real-world application. This review serves as a good reference source and provides insight into the design and optimization of superhydrophobic coatings for different applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".