Ice-responsive Coatings: Evaluating the effect of Hydrogen bond donors on Deep eutectic solvents/Ionic liquids Anti-icing efficiency
Bibliographic record
Abstract
• Study investigates HBDs’ role in DESs, focusing on hydrophilicity and hydrophobicity. • DESs as ice-responsive components boosted anti-icing due to superior HBD capacity. • Hydrophobic HBDs outperformed hydrophilic ones, maintaining mechanical properties. • SS-NMR and low-temperature ATR-FTIR confirm a thicker QLL on DES-based coatings. • Modified frost patterns improved DES coating resistance to frost buildup over cycles. Although there has been pioneering research on the anti-icing properties of Ionic liquids (ILs) and Deep Eutectic Solvents (DESs), coatings based on these materials are still in the early stages of development. Given the limited understanding of DESs in anti-icing applications, we investigated the role of hydrogen bond donors (HBDs) within DESs, focusing on their hydrophilicity and hydrophobicity. After conducting a comprehensive study using advanced characterization techniques, including ATR-FTIR, X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD) analysis, and wettability measurements, our findings demonstrate that DESs can be effectively introduced as ice-responsive components on a surface, significantly improving their anti-icing performance. Our comparative analysis showed that the introduction of hydrophobic HBDs into DES-based coatings reduced ice adhesion strength to 13 kPa, while maintaining an ice formation temperature of −35 °C. Despite plasticising characteristic of DESs, notably, the mechanical properties, including tensile strength, remained consistent with the neat coating, though with enhanced elongation at break. Solid-state NMR spectroscopy revealed the formation of a thicker quasi-liquid layer (QLL) on the surface of coatings containing hydrophobic HBDs, which was further confirmed by low-temperature ATR-FTIR analysis. Moreover, these coatings exhibited modified frost formation patterns, leading to increased resistance to frost buildup over multiple cycles. Ice adhesion strength of coatings was examined against accelerated weathering, icing/de-icing cycles, as well. This study presents a novel approach for designing sustainable, high-performance icephobic coatings, emphasizing the potential of DESs as effective ice-responsive components in achieving superior anti-icing and anti-frosting capabilities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".