Direct growth of graphene on GaN via plasma-enhanced chemical vapor deposition under N<sub>2</sub> atmosphere
Bibliographic record
Abstract
Abstract One of the bottlenecks in the implementation of graphene as a transparent electrode in modern opto-electronic devices is the need for complicated and damaging transfer processes of high-quality graphene sheets onto the desired target substrates. Here, we study the direct, plasma-enhanced chemical vapor deposition (PECVD) growth of graphene on GaN-based light-emitting diodes (LEDs). By replacing the commonly used hydrogen (H 2 ) process gas with nitrogen (N 2 ), we were able to suppress GaN surface decomposition while simultaneously enabling graphene deposition at <800 °C in a single-step growth process. Optimizing the methane (CH 4 ) flow and varying the growth time between 0.5 h and 8 h, the electro-optical properties of the graphene layers could be tuned to sheet resistances as low as ∼1 kΩ/□ with a maximum transparency loss of ∼12%. The resulting high-quality graphene electrodes show an enhanced current spreading effect and an increase of the emission area by a factor of ∼8 in operating LEDs.
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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) | 0.000 | 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".