Comparative Evaluation of the Shear Adhesion Strength of Ice on PTFE Solid Lubricant
Bibliographic record
Abstract
The development of a durable and green icephobic coating plays a vital role in the aviation industry due to the adverse impact of ice formation on aircraft performance. The lack of study into how temperature and surface roughness impact icephobicity is the main problem with present icephobic coatings. This study aims to qualitatively evaluate the icephobicity performance of a polytetrafluoroethylene (PTFE) solid lubricant film, as an environmentally friendly solution, with a custom-built push-off test device in different icing conditions utilizing a wind tunnel. The ice-adhesion reduction factor (ARF) of the film has been assessed in comparison to a bare aluminium substrate (Al 6061). The impact of surface energy was investigated by comparing the water contact angle (WCA), the contact angle hysteresis (CAH), and the pull-off force of the PTFE solid lubricant and Al with an atomic force microscope (AFM). The results of ice shear adhesion on the PTFE solid lubricant film showed a significant reduction in the ice adhesion force at various substrate temperatures and surface roughness compared to the bare aluminium substrate. The difference in the ice adhesion between the solid lubricant and aluminium alloy was attributed to the differences in the detachment mechanism. For the PTFE-based solid lubricant, the interfacial detachment mechanism was based on the formation of interfacial blisters towards the centre of the ice. Consequently, upon continued application of the shear force, most of the energy injected would be distributed throughout the blisters, ultimately causing detachment. In the comparison of ice adhesion on PTFE solid lubricant and bare aluminium, the film showed minimal ice adhesion at −6 °C with an adhesion force of 40 N (ARF 3.41). For temperature ranges between −2 °C and −10 °C, the ice adhesion for bare aluminium was measured at roughly 150 N.
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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) | 0.000 | 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 | 0.000 |
| 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".