Lotus-Leaf-Inspired Biomimetic Coatings: Different Types, Key Properties, and Applications in Infrastructures
Bibliographic record
Abstract
A universal infrastructural issue is wetting of surfaces; millions of dollars are invested annually for rehabilitation and maintenance of infrastructures including roadways and buildings to fix the damages caused by moisture and frost. The biomimicry of the lotus leaf can provide superhydrophobic surfaces that can repel water droplets, thus reducing the penetration of moisture, which is linked with many deterioration mechanisms in infrastructures, such as steel corrosion, sulfate attack, alkali-aggregate reactions, and freezing and thawing. In cold-region countries, the extent of frost damage due to freezing of moisture in many components of infrastructures will be decreased significantly if water penetration can be minimized. Consequently, it will greatly reduce the maintenance and rehabilitation costs of infrastructures. The present study was conducted to explore any attempted biomimicry of the lotus leaf to produce biomimetic coatings. It focuses on anti-wetting characteristics (e.g., superhydrophobicity, sliding angle, contact angle), self-cleaning capability, durability, and some special properties (e.g., light absorbance and transmission, anti-icing capacity, anti-fouling ability) of lotus-leaf-inspired biomimetic coatings. This study also highlights the potential applications of such coatings, particularly in infrastructures. The most abundant research across coating materials showed superhydrophobicity as being well-tested while self-cleaning capacity and durability remain among the properties that require further research with existing promise. In addition, the special properties of many coating materials should be validated before practical 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) | 0.000 | 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 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".