To be or not to be a hydrophobic matrix? the role of coating hydrophobicity on anti-icing behavior and ions mobility of ionic liquids
Bibliographic record
Abstract
• Effect of matrix on anti-icing properties of coatings containing ILs was revealed. • Matrix hydrophilicity/hydrophobicity and crosslink density influence IL mobility. • Conductivity measurements at subzero temperatures effectively distinguished IL mobility. • The superior hydrogen-bond donor ability of the NTf 2 - anion lead to super low ice adhesion strength. • Solid-state NMR spectroscopy revealed the unique hydrogen-bond donor capabilities of ILs. The demand for anti-icing coatings to endure extremely low temperatures is substantial. Despite the innovative pioneering research on the anti-icing potential of ionic liquids ( ILs ), the development of such coatings is still in its infancy. Our study investigates how matrix hydrophobicity influences mobility of ILs at subzero temperatures and, consequently, their anti-icing behavior. Wettability results highlight the key role of IL anion hydrophobicity. Dielectric spectroscopy distinguishes ion mobility in the coatings at low temperatures. We also investigate how varying crosslink density affects ion mobility by measuring the water absorbency. Higher mobility of released ILs from the coatings at subzero was confirmed by their presence in water solutions, validated with UV–vis spectroscopy, and resulted in increased ionic conductivity. Differential scanning calorimetry and experimental setups were employed to assess ice formation temperature, time, and ice adhesion strength. Notably, surfaces containing IL exhibited a remarkable reduction in ice formation temperature to –23.5 ℃ and achieved an exceptionally low ice adhesion strength (∼15 kPa), attributed to the formation of a quasi-liquid layer (QLL). Solid-state NMR spectroscopy provided confirmation of the existence of QLL at the interface. Ice adhesion strength of coatings was examined against accelerated weathering, icing/de-icing cycles, as well as endurance against frost formation under freeze–thaw. Our findings underscore the significance of selecting the right matrix with regards to hydrophobicity and ILs when designing coatings for subfreezing applications.
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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 | 0.000 | 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 | 0.001 |
| 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".