Influence of Salinity on Surface Ice Adhesion Strength
Bibliographic record
Abstract
Abstract Ice accretion has detrimental effects on a wide range of sensitive engineering structures, especially those adjacent to or within the ocean. Here the effect of salinity on the adhesion of ice to different surfaces is investigated over a range of sub‐zero temperatures. The saline ice adhesion strength is found to decrease with the increasing salinity on all surfaces tested. The presence of thermodynamically stable brine at temperatures above −21.2 °C is found to drastically lower the saline ice adhesion strength of essentially all surfaces through a lubrication effect. At −25 °C, below the eutectic temperature between H2O ice and hydrohalite (NaCl 2H2O), high adhesion is observed. For smooth surfaces, hydrophobicity is effective at lowering the saline ice adhesion strength, and a hydrophobic silicon wafer exhibited a saline ice adhesion strength of 2.4 ± 1.8 kPa at −10 °C with 1 wt% NaCl. The adhesion of real frozen seawater differed from the NaCl solutions, where an adhesion strength ≈12 kPa is observed even at −25 °C. Given the strong adhesion of ice to marine infrastructure, the results here demonstrate how temperature, salinity, and surface characteristics must be considered when designing materials that can mitigate the icing of marine infrastructure and vessels.
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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) | 0.002 | 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".