Wettability of nanostructured hexagonal boron nitride surfaces: molecular dynamics insights on the effect of wetting anisotropy
Bibliographic record
Abstract
Nanostructured van der Waals (vdW) layered materials hold great potential for achieving smart surfaces with controllable wettability. Inspired by this possibility, we conduct Molecular Dynamics (MD) simulations of the wetting of nanostructured hexagonal boron nitride (hBN) surfaces. The nanostructure consists of periodically placed nanopillars made of hBN nanoribbons. We demonstrate that the polarity effect of the nanoribbon edges triggers wetting anisotropy of the nanoribbons: the vertical edges of the nanoribbons demonstrate a different wetting behavior as compared to the flat surfaces of the nanoribbons. Depending on the nature of the edge of the nanoribbon (armchair or zigzag), these vertical edges can be more hydrophilic for the zigzag edges or more hydrophobic for the armchair edges than the flat part. Such differences ensure that the nanostructured hBN surfaces become more hydrophilic (hydrophobic) as compared to the flat non-nanostructured hBN surfaces for cases where the edges of the nanoribbon are more hydrophilic (hydrophobic) than the flat part. Overall, the present study develops a most remarkable design space where by introducing nanopillars/nanoribbons on hBN and by merely changing the nature of the edges of these nanopillars, one can ensure atomistically thin coating of hBN with a wide range of wettability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".