Ice adhesion shear tests on ice-phobic surfaces
Bibliographic record
Abstract
Ice adhesion tests were conducted on five substrates with differing ice-phobic coatings. Rectangular-plate ice samples (25–62 cm2), freeze-bonded onto the surfaces, were pushed from one edge (at a nominal rate of 0.5 mm/s) until shear-detachment occurred. Freshwater and saline ice layers were investigated, where the intended test temperatures were -12 oC and -22 oC. For the freshwater ice cases it was found that spraying cold water onto the surfaces led to the formation of ice layers that never fully bonded/contacted the surfaces due to nonuniform freezing and lift-up/delamination. The mild bumpy texture of some of the surfaces and slight curvature of all of the substrates, where liquid could pool in ‘valleys’, contributed to this behavior. Hence, attempts to bond flat pre-shaped ice-plate specimens were unsuccessful. Furthermore, when spraying on smooth surfaces, ice-layer delamination occurred due to freezing of liquid and lift-up at the substrate edges. A thin layer (2–4 mm thickness) of saturated snow applied to the surfaces, however, did freeze and bond because it conformed to the non-flat features of the surfaces. Test results at -12 oC showed a wide variation of ice adhesive strength between the coatings (22–216 kPa). Degradation of the coatings with the number of tests was also noted (i.e. increasing adhesive strength), and was proportional to the adhesive strengths. At -22 oC the preparation method usually led to ice samples that were only partially bonded to the surfaces. In a few cases, video records enabled rough estimates of the contact areas so that approximate adhesive strengths were obtained. For saline ice generated by spraying at -12 oC and -22 oC, lift-off was not evident, however, no freeze-bonding occurred on any of the surfaces because a thin briny liquid layer was present at the interface.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".