Electrostatic Deposition of Large-Surface Graphene
Bibliographic record
Abstract
This work describes a method for electrostatic deposition of graphene over a large area using controlled electrostatic exfoliation from a Highly Ordered Pyrolytic Graphite (HOPG) block. Deposition over 130 × 130 µm² with 96% coverage is achieved, which contrasts with sporadic micro-scale depositions of graphene with little control from previous works on electrostatic deposition. The deposition results are studied by Raman micro-spectroscopy and hyperspectral analysis using large fields of view to allow for the characterization of the whole deposition area. Results confirm that laser pre-patterning of the HOPG block prior to cleaving generates anchor points favoring a more homogeneous and defect-free HOPG surface, yielding larger and more uniform graphene depositions. We also demonstrate that a second patterning of the HOPG block just before exfoliation can yield features with precisely controlled geometries.
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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 | 0.000 | 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 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".