Cyclic Voltammetry for Accurate Icing Detection on Simulated Aircraft Surfaces
Bibliographic record
Abstract
Icephobic coatings are considered sustainable and cost-efficient technologies for preventing hazardous ice buildup in the aerospace industry.However, icing behaviour during initial ice formation on coated surfaces is not well understood.This icing period was characterised using cyclic voltammetry to determine the exact temperature of ice formation on simulated aircraft surfaces with or without icephobic coatings.The temperature of a water droplet on the material surface was slowly lowered while continuously monitoring cyclic voltammograms as it froze to form ice.The resulting voltammograms exhibited increased faradaic current peaks during this phase change suggesting a switch from diffusion to a surface-confined mass transfer mechanism.This method was then extended to surfaces of different roughness as well as coatings doped with zinc oxide and neodymium oxide nanoparticles.These modifications lowered the surface freezing point, which provided further insight into the mechanism governing surface temperature shifts at freezing.Given its wide-spread applicability, this analytical method could facilitate icephobic coating development and ice monitoring, thereby reducing icing risks in aerospace and related industries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".