Cyclic Voltammetry for Accurate Icing Detection on Simulated Aircraft Surfaces
Bibliographic record
Abstract
Icing and ice accretion on aerodynamically critical surfaces of an aircraft increase drag, reduce lift, and raise stalling speed, which pose significant safety hazards to aircraft while in flight. Icephobic coatings have been intensively investigated by the Canadian and global aerospace industries for passive ice protection. Nevertheless, effective icephobic coatings suitable for aerospace applications are far from ideal. Ice protection of an aircraft still relies on active ice protection systems based on heating, mechanical expulsion, and deicing fluids, which are heavy-weight, power-intensive, and unfriendly to the environment. To address these challenges, rapid and accurate detection of icing is highly desirable to activate these ice protection systems only when needed. To this end, cyclic voltammetry was used for the first time to detect the initiation of icing on aircraft surfaces with or without icephobic coatings. In this study, a water droplet was sandwiched between a screen-printed electrode and a simulated aircraft surface. Cyclic voltammograms were then collected as the temperature was slowly decreased until the droplet froze to form ice. A sharp spike in faradaic current was recorded in the voltammograms during the phase transition, suggesting a switch in the mass transfer mechanism from diffusion to a surface-confined pathway. This electrochemical signal could then be used to precisely indicate the onset of icing. The developed sensing method shows potential in icing detection to manage active ice protections and ameliorate icing risks in the aerospace and aviation 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) | 0.000 | 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) | 0.001 | 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".