(Invited) Growth of Van Der Waals Materials: New Insights from Real-Time Studies
Bibliographic record
Abstract
The existence of thousands of new 2D materials has been predicted and several of them have been fabricated, mostly by exfoliation from bulk crystals. In addition to their attractive properties, the availability of these 2D materials also creates a wealth of opportunity to engineer complex van der Waals (vdW) heterostructures, thus laying the groundwork to tailor manipulate the basic properties and device performances. However, despite its numerous advantages, this class of materials is yet to be integrated in real technological applications due to several challenges such as the lack of scalable fabrication processes. With this perspective, quasi-epitaxial growth methods have recently been explored to synthesize 2D materials and vdW heterostructures. Herein, by using group V 2D materials as a model system, this presentation will address the current understanding of vdW growth and the key mechanisms governing the nucleation, growth and stability on weakly interacting surfaces. New insights on the interplay between kinetics and thermodynamic driving forces will be described and discussed based on in situ low-energy electron microscopy and diffraction (LEEM/LEED) studies. The combination of LEEM and LEED with ex situ characterization methods allowed us to elucidate the growth mechanisms of stable and metastable 2D-Sb allotropes and 2D-AsSb alloys on conventional semiconductor substrates and graphene. These results lay the groundwork for the development of scalable vdW epitaxial growth methods for emerging 2D materials and vdW heterostructures. Acknowledgment: The authors acknowledge contributions from Robert Jacobberge, Hannes Zschiesche, Andrea Locatelli, Tevfik O. Menteş, Olga Waller, Charles-Antoine Deslauriers, Francesca Genuzio, Gianluigi Botton, Michael S. Arnold.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".