Innovative ice mitigation: Exploring the potential of choline-based deep eutectic solvents and ionic liquids synergies
Bibliographic record
Abstract
• Choline-based IL harbouring hydrophobic anion in coating exhibited superior hydrogen-bond donor ability to water. • Switching from EG to GL in DESs increased hydrogen-bonding sites, boosting anti-icing properties. • Coating containing GL-based DES reduced ice formation temperature to −36 °C and adhesion strength to 10 kPa. • Solid-state NMR validated the presence of QLL, contributing to ultra-low ice adhesion. • DESs in coatings altered frost patterns, enhancing durability against frost formation. The development of anti-icing coatings for extremely low temperatures is still emerging. Deep eutectic solvents (DESs), as subset of ionic liquid (IL) analogues, have recently gained increasing attention for their unique and versatile applications. Given no investigation regarding anti-icing capabilities of DESs, our study focused on the exciting potential of choline-based DESs. The intriguing potential of hydrogen bonding through the synergistic combination of DESs and ILs offers significant promise for innovative solutions to ice-related challenges that remain largely unexplored. We conducted a comprehensive study on the anti-freezing properties of DESs by synthesizing choline-based ILs featuring both hydrophilic and hydrophobic anions. We aimed to explore how the diverse hydrogen-bond donors in DESs combined with the synthesized ILs to enhance the system’s ability to prevent ice formation. Substituting ethylene glycol (EG) with glycerol (GL) resulted in achieving an ice formation temperature of − 36 °C and an exceptionally low ice adhesion strength of 10 kPa, due to a thicker quasi-liquid layer on the coating surface, confirmed by solid-state NMR spectroscopy. The altered frost formation patterns of the DES-containing coatings demonstrated an enhance resistance against frost formation. This comprehensive study underscored the promising synergy between DESs and ILs for highly effective ice mitigation.
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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".