Phase Equilibria of a Brush-Bearing Coating Swollen with a Lubricant and Regulation of Its Composition to Facilitate Ice Shedding
Bibliographic record
Abstract
Coatings that shed ice spontaneously reduce icy rain damage and improve aviation safety. A lubricant in equilibrium with a cross-linked polymer film bearing on its upper surface a covalently attached liquid polymer may serve as an outstanding ice-shedding coating. This paper reports a statistical thermodynamic treatment of such a system. First presented are the expressions for the chemical potentials of the lubricant in the coating matrix and in the grafted liquid polymer layer. The equality of the chemical potentials in these two phases is then used to compute the equilibrium volume fractions of lubricant in the phases in the lubricant-starved regime that does not feature an upper lubricant layer. Further derived are the equations for identifying the triple point when a top neat lubricant layer just begins to be secreted. Additionally presented are the equations for calculating the thickness of the upper lubricant layer in the lubricant-rich regime. Such phase transitions and system compositions at different temperatures and feed lubricant volume fractions can also be read from an ingeniously constructed phase diagram. For improved ice shedding in the lubricant-starved regime, the brush layer should be preferentially swollen by the lubricant over the matrix. This scenario can be achieved, according to the current theory, by enhancing the compatibility between the lubricant and the grafted polymer, decreasing the molecular weight of the lubricant, and increasing the cross-linking density of the coating matrix.
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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".