Navigating ice-free Horizons: A Review on the Role of Ionic liquids and Deep eutectic solvents in Anti-icing Technologies
Bibliographic record
Abstract
• Unveils ILs and DESs as cutting-edge materials for next-gen anti-icing coatings. • Explores IL-DES synergy for advanced ice mitigation and dynamic interface control. • Tackles the challenge of ice adhesion with innovative, sustainable material solutions. • Highlights DES potential as ice growth inhibitors, a largely unexplored frontier. • Paves the way for durable, adaptive coatings in energy, transport, and infrastructure. This review investigates the unexplored potential of ionic liquids (ILs) and deep eutectic solvents (DESs) as innovative solutions for advancing anti-icing technologies, particularly in harsh sub-freezing conditions, with a focus on coating applications. Despite limited exploration, ILs and DESs stand out due to their remarkable properties—low melting points, excellent hydrogen-bond donor capabilities, thermal stability, and the formation of quasi-liquid layers that drastically reduce ice adhesion. While research on IL-based coatings for ice mitigation is still in its infancy, the promising synergy between DESs and ILs paves the way for creating highly effective ice-resistant surfaces. DESs, recognized for their eco-friendly and straightforward preparation, have been primarily studied for anti-freezing resilience, leaving their potential as ice growth inhibitors largely unexplored. This review presents DESs as effective ice growth inhibitors and highlights their synergistic combination with ILs, functioning as dynamic interface melting agents, for enhanced ice mitigation performance. Furthermore, the review consolidates current studies, emphasizing the need for further investigation into ILs and DESs for combating ice formation. It also proposes future research directions, such as exploring diverse IL chemistries, enhancing the stability of ILs, and investigating novel matrix materials to improve mechanical durability. The review provides a broad perspective on integrating these materials into various industrial and environmental applications, offering fresh insights into their transformative potential in anti-icing systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".