Dynamics of Antimonene–Graphene Van Der Waals Growth
Bibliographic record
Abstract
Van der Waals (vdW) heterostructures have recently been introduced as versatile building blocks for a variety of novel nanoscale and quantum technologies. Harnessing the unique properties of these heterostructures requires a deep understanding of the involved interfacial interactions and a meticulous control of the growth of 2D materials on weakly interacting surfaces. Although several epitaxial vdW heterostructures have been achieved experimentally, the mechanisms governing their synthesis are still nebulous. With this perspective, herein, the growth dynamics of antimonene on graphene are investigated in real time. In situ low-energy electron microscopy reveals that nucleation predominantly occurs on 3D nuclei followed by a self-limiting lateral growth with morphology sensitive to the deposition rate. Large 2D layers are observed at high deposition rates, whereas lower growth rates trigger an increased multilayer nucleation at the edges as they become aligned with the Z2 orientation leading to atoll-like islands with thicker, well-defined bands. This complexity of the vdW growth is elucidated based on the interplay between the growth rate, surface diffusion, and edges orientation. This understanding lays the groundwork for a better control of the growth of vdW heterostructures, which is critical to their large-scale integration.
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 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 | 0.001 | 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".