Alignment of semiconducting graphene nanoribbons on vicinal Ge(001)
Bibliographic record
Abstract
Chemical vapor deposition of CH4 on Ge(001) can enable anisotropic growth of narrow, semiconducting graphene nanoribbons with predominately smooth armchair edges and high-performance charge transport properties. However, such nanoribbons are not aligned in one direction but instead grow perpendicularly, which is not optimal for integration into high-performance electronics. Here, it is demonstrated that vicinal Ge(001) substrates can be used to synthesize armchair nanoribbons, of which ∼90% are aligned within ±1.5° perpendicular to the miscut. When the growth rate is slow, graphene crystals evolve as nanoribbons. However, as the growth rate increases, the uphill and downhill crystal edges evolve asymmetrically. This asymmetry is consistent with stronger binding between the downhill edge and the Ge surface, for example due to different edge termination as shown by density functional theory calculations. By tailoring growth rate and time, nanoribbons with sub-10 nm widths that exhibit excellent charge transport characteristics, including simultaneous high on-state conductance of 8.0 μS and a high on/off conductance ratio of 570 in field-effect transistors, are achieved. Large-area alignment of semiconducting ribbons with promising charge transport properties is an important step towards understanding the anisotropic nanoribbon growth and integrating these materials into scalable, future semiconductor technologies.
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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) | 0.001 | 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".