On the wetting translucency of hexagonal boron nitride
Bibliographic record
Abstract
When a liquid drop sits on an atomically thin layer of a 2D van der Waals (vdW) solid (like graphene) supported by a hydrophilic material, it is possible that the drop demonstrates an equilibrium contact angle that is influenced by this underlying hydrophilic material and hence is different from that observed on the bulk 2D material (e.g., graphite) surface. Such a behavior is known as the wetting translucency effect. While the wetting translucency effect of graphene has been extensively studied, the wetting translucency of hexagonal boron nitride (hBN) remains largely unexplored despite significant similarities in structural properties between these materials. In this study, we probe the wetting translucency of hBN. For this purpose, we conduct molecular dynamics simulations of water droplets and water films on hBN layers supported on a gold-like hydrophilic substrate. Our results show that for a substrate coated by monolayer hBN ("coated substrate"), depending on the contact distance between underlying substrate and hBN, an increase in the hydrophilicity of the underlying surface causes a monotonic increase in the overall adhesion energy between water and the coated substrate and a monotonic decrease in the contact angle of a drop on the coated substrate. For an increasing number of stacked hBN layers, the wettability of coated substrate becomes independent of the wettability of the underlying solid. Accordingly, our results confirm a distinct wetting translucency nature of hBN very similar to that observed in graphene.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".