Enhancing Icephobic Coatings: Exploring the Potential of Dopamine-Modified Epoxy Resin Inspired by Mussel Catechol Groups
Bibliographic record
Abstract
A nature-inspired approach was employed through the development of dopamine-modified epoxy coating for anti-icing applications. The strong affinity of dopamine's catechol groups for hydrogen bonding with water molecules at the ice/coating interface was utilized to induce an aqueous quasi-liquid layer (QLL) on the surface of the icephobic coatings, thereby reducing their ice adhesion strength. Epoxy resin modification was studied by attenuated total reflectance infrared spectroscopy (ATR-FTIR) and nuclear magnetic resonance spectroscopy (NMR). The surface and mechanical properties of the prepared coatings were studied by different characterization techniques. Low-temperature ATR-FTIR was employed to study the presence of QLL on the coating's surface. Moreover, the freezing delay time and temperature of water droplets on the coatings were evaluated along with push-off and centrifuge ice adhesion strength to evaluate their icephobic properties. The surface of dopamine-modified epoxy coating presented enhanced hydrophilicity and QLL formation, addressed as the main reason for its remarkable icephobicity. The results demonstrated the potential of dopamine-modified epoxy resin as an effective binder for icephobic coatings, offering notable ice nucleation delay time (1316 s) and temperature (-19.7 °C), reduced ice adhesion strength (less than 40 kPa), and an ice adhesion reduction factor of 7.2 compared to the unmodified coating.
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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".