Effect of Different Aluminium Surface Treatments on Ice Adhesion Strength
Bibliographic record
Abstract
Excessive ice accumulation on power network equipment can affect their integrity and cause damage with serious socioeconomic consequences. To mitigate that, de-icing techniques (mechanical or thermal) have been developed, but these techniques are often limited in their application and are generally expensive and time consuming. Recently, companies and research groups have focused on the development and application of icephobic coatings such as superhydrophobic materials intended to drastically reduce ice adhesion force on exposed equipments. The aim of this paper is the examine the influence of aluminium surface treatments on ice adhesion. Preparation of new and various aluminium surface treatments as well as the need to improve the knowledge of the mechanisms involved in ice adhesion are part of this research. Depending of the type of materials, surface roughness can either promote the formation of air pockets within pores or between coating surface asperities (low adhesion strength), or it can create ice mechanical anchoring if water partially or totally penetrates the porosity. Aluminium anodization using phosphoric acid was studied. Surface morphology was evaluated using scanning electron microscopy and measurements of ice adhesion strength were performed using a centrifuge technique. Based on these results, several surface treatments of aluminium have been considered including aluminium anodizing with partial Al2O3 etching followed by different sealing steps using hydrophobic polymer compounds such as polytetrafluoroethylene.
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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 | 0.001 |
| 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".