Scalable Characterization of 2D Gallium-Intercalated Epitaxial Graphene
Bibliographic record
Abstract
Scalable synthesis of two-dimensional gallium (2D-Ga) covered by graphene layers was recently realized through confinement heteroepitaxy using silicon carbide substrates. However, the thickness, uniformity, and area coverage of the 2D-Ga heterostructures have not previously been studied with high-spatial resolution techniques. In this work, we resolve and measure the 2D-Ga heterostructure thicknesses using scanning electron microscopy (SEM). Utilizing multiple correlative methods, we find that SEM image contrast is directly related to the presence of uniform bilayer Ga at the interface and a variation of the number of graphene layers. We also investigate the origin of SEM contrast using both experimental measurements and theoretical calculations of the surface potentials. We find that a carbon buffer layer is detached due to the gallium intercalation, which increases the surface potential as an indication of the 2D-Ga presence. We then scale up the heterostructure characterization over a few-square millimeter area by segmenting SEM images, each acquired with nanometer-scale in-plane resolution. This work leverages the spectroscopic imaging capabilities of SEM that allows high-spatial resolution imaging for tracking intercalants, identifying relative surface potentials, determining the number of 2D layers, and further characterizing scalability and uniformity of low-dimensional materials.
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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) | 0.000 | 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 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".