NIR-responsive self-healing superhydrophobic coatings: Enhanced, corrosion resistance, and mechanical stability
Bibliographic record
Abstract
Superhydrophobic coatings with special wettable surfaces hold significant promise for applications in self-cleaning, anti-corrosion, and other industrial domains. However, their practical implementation is hindered by fragile mechanical durability and poor chemical stability. This study addresses these limitations by developing near-infrared (NIR)-responsive self-healing superhydrophobic coatings using a one-step spraying method. Epoxy resin serves as the shape memory matrix, while DTMS@PDA@SiO 2 @CNTs particles function as photothermal conversion agents and hydrophobic components. The optimal synthesis parameters—reaction pH of 8.5, a carbon nanotube (CNTs) to TEOS ratio of 1:3, and a dopamine (DA) to CNTs ratio of 1:1—were determined to maximize photothermal conversion and coating performance. The coatings demonstrated exceptional hydrophobicity, with a water contact angle of 167° and a scratch repair efficiency of up to 82 % under NIR irradiation. Even after structural damage, their superhydrophobic properties could be restored by activating the shape memory effect through photothermal heating. The coatings exhibited superior stability against mechanical wear, maintaining hydrophobicity after sandpaper abrasion and tape peeling. They also showed excellent resistance to chemical corrosion, effectively preventing ion penetration and maintaining performance in acidic, alkaline, and saline environments. These findings highlight the potential of NIR-responsive self-healing superhydrophobic coatings to overcome existing durability and stability challenges, extending their applicability in harsh industrial and environmental conditions.
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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 | 0.001 | 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 | 0.001 | 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".